Oxiana Limited Mineral Resource Explanatory Notes As at June 30, 2007

Tables of Contents

Sepon Copper Resource Statement: June 30, 2007...... 2

Sepon Gold Resource Statement: June 30, 2007...... 27

Golden Grove Mineral Resources Statement: June 30, 2007 ...... 101

Prominent Hill Resource Statement: July 31, 2007...... 107

Martabe Mineral Resources Statement: December 31, 2007...... 112

Wiluna Nickel Laterite Statement: June 30, 2005...... 115

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Oxiana Limited 2007 Mineral Resource Explanatory Notes

Sepon Copper Resource Statement: June 30, 2007

Resource Statement

1.1 Scope

This June 2007 Sepon Cu Resource Statement includes the Supergene and Primary Copper Resources for the following deposits within the Sepon Project in Laos:

Khanong (KHN), Thengkham North (TKN), Thengkham South (TKS), Phabing (PHB)

Lane Xang Minerals Ltd (LXML) is the manager of, and 90% equity holder in the Sepon Project with the Government of the Lao People’s Democratic Republic holding the remaining 10% equity.

The estimates are derived from block models as follows:

KHN is an update of the 2006 ordinary kriged (OK) block model and includes data from additional resource and grade control drillholes.

The 2007 TKN ordinary kriged block model replaces the 2006 ordinary kriged (OK) block model following significant infill and extension resource drilling.

TKS is the 2007 ordinary kriged block model and replaces the 2004 multiple indicator kriged (MIK) block model after a phase of infill resource drilling. A further program of significant infill and extensional drilling is currently in progress at TKS and an updated estimate of this resource is planned for 2008.

The ordinary kriged resource estimate of mineralisation at Phabing is a new estimate for 2007 and represents additional Inferred supergene resources in the Sepon District.

Section 1 of this statement presents the copper resources categorised by the style of mineralisation, being either supergene or primary (tables 1-3). The competent persons’ statements for each of the estimates are included at Sections 2 to 5 where the total resources for each Copper Deposit are tabulated.

1.2 Mineral Resource Categories and Tabulation

Supergene and primary resource estimates are classified in the categories specified in the 2004 JORC Code and reported at several appropriate cut-off grades that approximate those employed at the operating Khanong mine. Resources are included in Tables 1-3.

1.3 Contributing Experts

The following identifies the key personnel involved in the estimation of the Sepon Copper Resources. A full listing of all involved can be seen in Sections 2 to 5 where details of the estimates are documented:

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KHN – All grade control data was sourced and validated by LXML geologists. Geological interpretations and geostatistical analyses were performed by Duncan Hackman of Hackman and Associates Pty Ltd (HA). Geostatistical analysis was conducted by Arnold van der Heyden of Hellman and Schofield Pty Ltd (HS) and grade interpolations were performed by HA.

TKN – Drillhole data was sourced, validated and the geological interpretation and domaining were undertaken by LXML geologists and Kerrin Allwood of Geomodelling Pty Ltd (GM). Geostatisitical analysis, resource modelling and grade interpolation were conducted by Mark Sweeney and Tracie Burrows of AMC Consultants (AMC).

TKS – Drillhole data was sourced and validated by LXML geologists and Kerrin Allwood of Geomodelling Pty Ltd (GM). Geological interpretation, domaining of the data and grade interpolation was performed by GM. Geostatisitical analysis was conducted by Steve Hyland of Ravensgate Pty Ltd (RG).

PHB – Drillhole data was sourced and validated by LXML geologists. Geological interpretation and domaining for grade estimation was conducted by LXML geologists and Jared Broome (Oxiana Limited). Grade interpolation was undertaken by Jared Broome.

Compliance with the JORC Code

This statement of mineral resources complies with the 2004 Australasian Code for the Reporting of Resources and Reserves (2004 JORC Code).

Sections 2 to 5 of this statement describes the factors considered in estimating and assigning resource categories under the code, following the format of Table 1 of the 2004 JORC Code. Comparisons between the 2007 and 2006 estimates for the deposits are also documented along with risks identified in the resource estimate.

The Competent Persons (as defined in the 2004 JORC Code) endorsing these statements of mineral resources are, for:

KHN – Duncan Hackman (HA), who has over 22 years experience as a geologist in mining and exploration, which includes fifteen years experience in resource estimation and six years experience in supergene copper deposits.

TKN & TKS - Paul Quigley (LXML) who has over 19 years experience as a geologist in mining and exploration, which includes 11 years experience in resource estimation and 7 years experience in sediment-hosted gold deposits. He also has substantial knowledge of the matters relating to supergene copper estimation, mine production, and reconciliation, and as such is responsible for data integrity, geological interpretation and resource classification.

PHB – Jared Broome and Antony Manini (Oxiana Limited). Jared Broome has over 15 years experience as a geologist in mining and exploration, which includes 12 years experience in resource estimation. Antony Manini has over 25 years experience as a geologist in mining and exploration, which includes over 10 years experience in supergene copper deposits.

Resource Tables

Tables 1 to 3 list the copper resources at Sepon as of the 30th June 2007.

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Table 1: June 2007 Sepon Copper Mineral Resource Estimate: KHN, TKN, TKS, PHB & Stockpiles at 0.5 % Cu cut-off grade

Greater than 0.5% Cu Cut Resource SUPERGENE MINERALISATION PRIMARY MINERALISATION Deposit Classification (JORC 2004) Mt Cu (%) Au (g/t) Ag (g/t) Cu (kt) Au (kOz) Ag (kOz) Mt Cu (%) Au (g/t) Ag (g/t) Cu (kt) Au (kOz) Ag (kOz) KHANONG Measured 16.6 3.31 0.20 20.5 547 108 10,931 2.0 1.73 0.19 6.7 35 13 435 Indicated 4.5 4.98 0.22 21.2 222 31 3,030 1.2 1.67 0.24 6.9 20 9 268 Inferred 4.4 2.84 0.25 10.0 124 35 1,404 7.2 1.18 0.06 5.6 85 13 1,311 Meas+Ind+Inf 25.4 3.52 0.21 18.8 893 174 15,366 10.4 1.34 0.11 6.0 140 35 2,014 THENGKHAM Measured ------NORTH Indicated 3.5 4.20 0.34 21.2 147 39 2,377 ------Inferred 6.9 1.14 0.30 8.1 79 68 1,812 1.2 1.06 0.23 8.7 13 9 347 Meas+Ind+Inf 10.4 2.17 0.32 12.5 226 106 4,190 1.2 1.06 0.23 8.7 13 9 347 THENGKHAM Measured ------SOUTH Indicated ------Inferred 10.7 1.39 0.19 - 149 66 - 4.5 0.81 0.16 - 36 23 - Meas+Ind+Inf 10.7 1.39 0.19 - 149 66 - 4.5 0.81 0.16 - 36 23 - PHABING Measured ------Indicated ------Inferred 2.0 3.37 0.16 2.0 68 10 129 ------Meas+Ind+Inf 2.0 3.37 0.16 2.0 68 10 129 ------STOCKPILES Measured 2.9 2.15 - - 61 ------Indicated ------Inferred ------Meas+Ind+Inf 2.9 2.15 - - 61 ------TOTAL Measured 19.4 3.13 0.20 20.5 608 108 10,931 2.0 1.73 0.19 6.7 35 13 435 Indicated 7.9 4.64 0.27 21.2 368 70 5,408 1.2 1.67 0.24 6.9 20 9 268 Inferred 24.0 1.75 0.23 7.8 421 179 3,345 12.9 1.04 0.11 6.1 134 46 1,658 Meas+Ind+Inf 51.4 2.72 0.23 16.2 1,398 356 19,684 16.1 1.17 0.13 6.3 189 68 2,361

Decimal Places do not imply precision.

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Table 2: June 2007 Sepon Copper Mineral Resource Estimate: KHN, TKN, TKS, PHB & Stockpiles at 1.0 % Cu cut-off grade

Greater than 1.0% Cu Cut Resource SUPERGENE MINERALISATION PRIMARY MINERALISATION Deposit Classification (JORC 2004) Mt Cu (%) Au (g/t) Ag (g/t) Cu (kt) Au (kOz) Ag (kOz) Mt Cu (%) Au (g/t) Ag (g/t) Cu (kt) Au (kOz) Ag (kOz) KHANONG Measured 11.6 4.43 0.19 25.0 512 71 9,278 1.4 2.20 0.22 6.6 30 10 294 Indicated 3.3 6.46 0.21 25.1 214 23 2,667 0.8 2.08 0.26 7.4 17 7 198 Inferred 3.1 3.67 0.25 12.0 115 25 1,214 2.9 1.89 0.11 7.8 55 10 735 Meas+Ind+Inf 18.0 4.67 0.21 22.7 841 119 13,159 5.1 2.00 0.16 7.4 103 27 1,227 THENGKHAM Measured ------NORTH Indicated 3.4 4.32 0.35 21.3 146 38 2,309 ------Inferred 1.8 2.49 0.32 13.0 45 19 763 0.6 1.48 0.26 10.2 9 5 190 Meas+Ind+Inf 5.2 3.68 0.34 18.4 191 56 3,071 0.6 1.48 0.26 10.2 9 5 190 THENGKHAM Measured ------SOUTH Indicated ------Inferred 4.0 2.67 0.18 - 108 24 - 0.7 1.39 0.17 - 10 4 - Meas+Ind+Inf 4.0 2.67 0.18 - 108 24 - 0.7 1.39 0.17 - 10 4 - PHABING Measured ------Indicated ------Inferred 1.9 3.54 0.16 2.0 67 10 123 ------Meas+Ind+Inf 1.9 3.54 0.16 2.0 67 10 123 ------STOCKPILES Measured 2.3 2.47 - - 58 ------Indicated ------Inferred ------Meas+Ind+Inf 2.3 2.47 - - 58 ------TOTAL Measured 11.6 4.43 0.19 25.0 512 71 9,278 1.4 2.20 0.22 6.6 30 10 294 Indicated 6.7 5.38 0.28 23.2 359 60 4,975 0.8 2.08 0.26 7.4 17 7 198 Inferred 10.9 3.08 0.22 6.0 336 77 2,100 4.2 1.75 0.14 6.8 74 19 924 Meas+Ind+Inf 29.1 4.14 0.22 17.5 1,208 209 16,354 6.4 1.89 0.17 6.8 122 36 1,417

Decimal Places do not imply precision.

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Table 3: June 2007 Sepon Copper Mineral Resource Estimate: KHN, TKN, TKS, PHB & Stockpiles at 1.5 % Cu cut-off grade

Greater than 1.5% Cu Cut Resource SUPERGENE MINERALISATION PRIMARY MINERALISATION Deposit Classification (JORC 2004) Mt Cu (%) Au (g/t) Ag (g/t) Cu (kt) Au (kOz) Ag (kOz) Mt Cu (%) Au (g/t) Ag (g/t) Cu (kt) Au (kOz) Ag (kOz) KHANONG Measured 9.3 5.19 0.19 27.7 485 57 8,327 0.9 2.63 0.24 6.9 25 7 210 Indicated 2.9 7.23 0.22 26.5 209 20 2,463 0.5 2.54 0.28 7.1 14 5 122 Inferred 2.5 4.34 0.24 13.8 107 19 1,091 1.6 2.41 0.13 9.1 39 7 474 Meas+Ind+Inf 14.7 5.45 0.20 25.1 800 97 11,881 3.1 2.50 0.19 8.1 78 19 807 THENGKHAM Measured ------NORTH Indicated 3.1 4.62 0.35 21.6 142 35 2,131 ------Inferred 1.0 3.52 0.35 17.8 36 11 586 0.2 2.00 0.31 13.6 4 2 87 Meas+Ind+Inf 4.1 4.35 0.35 20.7 178 46 2,717 0.2 2.00 0.30 13.1 4 2 87 THENGKHAM Measured ------SOUTH Indicated ------Inferred 3.0 3.19 0.18 - 94 17 - 0.2 2.07 0.17 - 3 1 - Meas+Ind+Inf 3.0 3.19 0.18 - 94 17 - 0.2 2.07 0.17 - 3 1 - PHABING Measured ------Indicated ------Inferred 1.7 3.87 0.17 2.0 64 9 108 ------Meas+Ind+Inf 1.7 3.87 0.17 2.0 64 9 108 ------STOCKPILES Measured 1.2 3.60 - - 43 ------Indicated ------Inferred ------Meas+Ind+Inf 1.2 3.60 - - 43 ------TOTAL Measured 9.3 5.19 0.19 27.7 485 57 8,327 0.9 2.63 0.24 6.9 25 7 210 Indicated 6.0 5.88 0.29 24.0 351 55 4,594 0.5 2.54 0.28 7.1 14 5 122 Inferred 8.1 3.72 0.22 6.9 301 56 1,784 2.0 2.34 0.15 8.8 46 10 561 Meas+Ind+Inf 23.4 4.86 0.22 19.5 1,137 169 14,706 3.5 2.45 0.20 8.0 85 22 893

Decimal Places do not imply precision.

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Resource Statement and Estimation Methodology – KHN

Resource Statement

The 2007 Khanong Resource Statement deals with the contiguous copper and gold Mineral Resources for the Khanong area of the Sepon Project in South Eastern Laos.

The 2007 Khanong Resource Estimate is an update of the 2006 Khanong Resource Estimate. It includes re-interpolated resources in the two areas where mining was conducted during the year which is where additional grade control data is available and where limited infill resource drilling is added. The 2007 Resources are depleted by mining conducted to 30th June 2007.

Copper Resources

The Resource Estimates are reported by 2004 JORC Code categories in Tables 1 and 2.

Contributing Experts

The Resource Estimate was undertaken by Duncan Hackman of Hackman & Associates Pty Ltd and supported by contributions from the experts listed in Table 3. The information supplied by the experts was used without alteration and in the context supplied.

Compliance with the JORC Code

This Resource Statement follows the guidelines of the 2004 Australasian Code for the Reporting of Resources and Reserves (the 2004 JORC Code). Section 2 of this Statement describes the factors considered in estimating and assigning Resource categories under the code. It follows the format of Table 1 of the 2004 JORC Code.

The competent person signing off on the Resource Statement is Duncan Hackman, who has over 22 years experience as a geologist in mining and exploration, which includes fifteen years experience in resource estimation and six years experience in supergene copper deposits.

DUNCAN HACKMAN. BAppSc, MSc: Economic Geology, MAIG Hackman & Associates Pty Ltd

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Table 1: 2007 Total Khanong Copper Resource Estimate

Table 2: 2007 Total Khanong Gold Resource Estimate

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Table 3: Expert Persons

Expert Person / Company Area of Expertise and Contribution Information Source

Duncan Hackman Geological and Resource Modeling and Estimation, database compilation Hackman & Associates Pty Ltd and QA/QC for 2003 and 2004 resource updates. Resource Modeling and Estimation for the 2005, 2006 and 2007 resource updates.

LXML Site Geologists QA/QC for 2005, 2006 and 2007 resource updates

Luke Burlet Assay, Geology and Geotechnical Database compilation and QA/QC pre Hellman & Schofield Pty Ltd 2003.

Dr. Ian Pringle Assay Database compilation and QA/QC pre 2003. Ian J Pringle and Associates Pty Ltd

Dr. Phillip Hellman Assay and Drilling QA/QC pre 2003. Hellman & Schofield Pty Ltd .

Arnold van der Heyden Geostatistics (variography, pre 2004 Resource Data and 2005 Grade Hellman & Schofield Pty Ltd Control Data) and Resource Estimate Audit (2002 estimate).

Page 9 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Resource Estimation Methodology

Table 4 summarizes the methodology in determining the Resource Estimate stated in Tables 1 and 2. It outlines the process followed for the 2002 estimate (refer to Khanong Copper DFS report, 2002, Section 3 for detail), the update resource estimates conducted in 2003 (western extension), the 2004 (northwest extension) and the 2005 to 2007 updates (southern extensions, northern infill drilling and grade control drilling areas) as all six estimates were generated using the same methodology. The stated statistics and observations reflect the status of data for the 2007 estimate. Information and data specific to the 2007 estimate is highlighted in italics.

Table 4: Resource Estimation Methodology

JORC CODE - Estimation and Reporting of Resources.

A total of 163 Diamond (13,853m), 412 Face Sampling Reverse Circulation (23,143m) predominantly vertical resource-delineation drill holes have been drilled in a 50m X 50m grid pattern at Khanong. Two areas were drilled at 25m spacing (cross-pattern) which confirmed geological and grade continuity at both 25m and 50m spacing. In addition 8282 vertical Reverse Circulation Grade Control drill holes were drilled predominantly in two areas (10m x 10m grid) where mining is being conducted.

The resource-delineation drilling grid now encompasses the main body of mineralization at Drilling Configuration Khanong, with the only area yet to be closed off being to the south between 608150E and 608450E (mid way along the deposit). The extent of the north plunging structurally controlled copper mineralization is yet to be determined. Satellite mineralization discovered to the south of the main body shows that there is potential in areas where supergene (mineralizing) processes are operating.

(The 2007 resources include an additional 65 Diamond Drill Holes (4,869m) and 4401 Face Sampling Reverse Circulation Grade Control drill holes (194,315m) to the 2006 resource estimate [these holes are included in the figures above].)

Correct drill hole location was verified using the topographic DTM and plans showing the tracks and road access. Drill hole orientation and depths were checked against site generated cross- sections.

15 Diamond to Reverse Circulation twin-holes and seven Reverse Circulation to Reverse Circulation twin-holes were drilled to assess the suitability of drilling techniques. The two techniques are comparable at all levels of analysis, with a suggested overall bias of 11% to RC samples, however this result is strongly influenced by a small number of high-grade wet samples, which when removed from the dataset the analysis returns a 6% bias in favour of the Diamond samples.

Diamond Drilling recoveries range between 85% (early Rio Tinto drilling) and 98% (feasibility drilling). There is no apparent correlation between Diamond Sample recoveries and copper Drilling QA/QC grade. There is a correlation between wet RC samples, RC-recovery less than <30% and positive RC copper grade bias. 4% of the database shows these characteristics and the estimation and classification procedures were adjusted to account for this characteristic (5.5% of the Measured Resource outside of the grade control drilled area has been is downgraded to Indicated and Inferred categories). The affect on the final estimate is considered of low risk.

(The 2007 Grade Control drilling is located peripheral to the high groundwater flow areas drilled in 2005. 3% of the grade control samples are noted as being wet, and 18% as being moist. 304 grade control samples from these intervals were removed from the resource estimate database as they are suspected to contain carry-over from previously drilled intervals (contamination). It appears that the likelihood of contamination or winnowing affecting the remainder of the dataset is low, however previous investigations (2002-2004) shows that the grades of the copper-oxide mineralization can be significantly affected by wet sampling and therefore the existing strategy of classifying this material as Inferred and Indicated was continued for the 2007 update area.)

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30,595 samples were analyzed for Cu, 25,356 for Ag, 31,054 for Au and 25,133 for Fe were analyzed from the resource delineation drill holes. An additional, 95,078 samples were analyzed for Cu, 79,986 for Au and 12,407 for Fe from the grade control drill holes. These

assays form the basis of the 2007 Resource Estimate. Sampling

(The Mn, As, Pb, Zn, sulphur and calculated-pyrite estimates were not updated in the 2007 estimate.)

Analytical protocols dictate that the Resource samples are initially assayed by Multi-Element- ICP (except Au - by 30g FA), with ore-grade determination being conducted on high-grade Cu, Ag, Au, Mn and Fe samples. 27% of Cu assays were determined by ore-grade analyses (nitric/aqua-regia acid digest ICPAES or AAS measurement). A single total-Cu analysis is appropriate for the Khanong deposit considering the vat-leach beneficiation process being Analysis employed.

Rigorous checks of the LAB results and data import procedures identified any spurious results for verification and/or re-assay.

(The 2005 to 2007 Grade Control samples were assayed at the Sepon Mine Laboratory for Cu by Tri acid digest (perchloric/hydrochloric/Nitric) digest AAS determination and Au by 30g Fire Assay, AAS determination.)

A thorough and rigorously implemented sampling protocol was adopted for the resource drilling at Khanong. QA/QC coarse-blanks, pulp-blanks, grade and matrix-matched certified reference- standards and duplicate samples were routinely inserted into the analytical stream at an interval of 1 in 25 samples.

Blanks show that cross contamination between consecutive samples is negligible (for pre 2004 data). The 2004 data shows contamination between samples when barren washes were omitted from the sample preparation. Reference standards are on average within 3% of the certified values and indicate that the assaying is of good standard and reliable. Field duplicate RC splits return an average precision of 6%, which is low for this form of drilling and confirms that the sample reduction protocols are appropriate and correctly implemented at Khanong.

Coarse blank assays within the 2005 QA/QC data shows minimal cross contamination between Assay QA/QC samples in both the resource and grade control drilling. The standards data show that the Cu, Au and Ag assays to be of acceptable quality and suitable for use in estimating resources of the deposit, with the resource drilling data being of better quality than the Grade control data.

Independent Laboratory checks (pre 2005) show good agreement between the primary and check laboratory assays. The results are unbiased with respect to each other and the overall relative precision is +/- ~6%.

QA/QC information in the earlier exploration data is incomplete. The available data suggests that the copper grade is underestimated by 3%, however this data is spatially non-clustered and now represents less than 6% of the Khanong resource database. Its inclusion into the resource database is considered of low risk and will not affect the resource classification criteria.

Pre 2005 the details of rock-type, alteration, mineralization and weathering were logged onto field sheets and appended to an access database, once passing stringent and rigorous validation tests. Careful planning of the logging process and codes has produced a fertile database with respect to domaining and modeling the economic geology features of the deposit.

Logging The AquireTM database and logging system were introduced in 2005. The GBISTM database then replaced this system in 2006 and the existing data transported. New descriptive data is logged manually and entered into the database. Analytical data is uploaded directly from laboratory SIF files.

(Key information used in interpreting and modeling mineralization at Khanong was omitted from the Grade Control logging, however coincident 50mx50m resource drilling in the grade control

Page 11 Oxiana Limited 2007 Mineral Resource Explanatory Notes

drill-grid was used to guide the interpretation through this area. The logged colour, hardness, oxidation and Metcode data is incomplete and of varying reliability, however was also used to determine the edits in domain interpretation.)

The bulk of copper and silver mineralization at Khanong exists in sub horizontal domains within a supergene blanket in the upper reaches of Khanong creek. The lower Cu-carbonate/oxide domain exists as thin blankets (5m – 10m thick) overlaying fresh dolomitic footwall lithologies. It is predominantly high-grade and contains approximately 32% of the 2007 copper resources (1%Cu cut). 47% of the 2007 copper resources are contained in two overlaying chalcocite clay horizons that are up to 50m thick in places. The remainder of the supergene zone consists of low-grade limonitic and “clean” clays and a surface gossan. Copper mineralization also exists Geology in three shallow dipping fault zones in the north of the deposit and in primary sulphides in the south of the deposit. All of these domains were modeled for resource estimation. A satellite chalcocite mineralized domain identified in 2005 exists to the south of the main deposit and is located within a narrow south-west striking depression in the supergene to fresh-rock contact.

(The 2007 Grade control drilling supports the original interpretation and has marginally extended the domains of chalcocite and Cu-carbonate/oxide mineralization.)

889 wax-immersion bulk density determinations were collected from diamond drill holes across the deposit (pre 2002 drilling) and form the basis for assigning bulk densities to the resource estimate. Average densities were applied to the copper-carbonate/oxide domain and the Tonnage Factors (in structurally controlled mineralization. Densities were assigned to the chalocite clay, limonitic situ bulk densities) clay, gossan and primary sulphide domains by regression with either clay percentage and/or Fe grade. Validation by way of averaging the modeled/assigned bulk densities and the raw data for each domain showed excellent correlation.

Thorough statistical analysis of the geology and assay data identified that the copper mineralization at Khanong required, and could be domained for grade estimation. Sharp contacts and differences in characteristic features such as copper grade ranges, observed mineral species, colour, clay content, alteration and secondary element abundance can be Resource Domain observed between the modeled domains. Mineralization continuity across the deposit lended to Modeling the application of surface modeling techniques (MinesightTM software) which were later combined to form solids used in the block modeling process.

(The 2006 upper and lower surfaces of modeled domains were edited to incorporate the additional 2007 data and create the solid models used in the 2007 Resource Estimate.)

Block model panel sizes reflected the drill and sample spacing, and domain morphology with parent blocks in the resource drilling grid being 25x25x5m in size (East,North,RL) and sub- blocking allowed to 5x5x1m in size. Parent blocks within the grade control drilling grid are 12.5x12.5x2.5m in size with sub-blocking allowed to 2.5x2.5x0.5m in size.

2m drill hole composites were selected following down-hole variography studies and grades were estimated into domains using Ordinary Kriging and the VulcanTM mining software.

Run 1: 70m x 70m x 6m ellipse (E, N, RL directions), octant search with four octants informed, minimum composite number of 10 and maximum of 20. Unless otherwise stated blocks informed with copper grades from this run are assigned the Measured category under the 1999 JORC code.

Run 2: 100m x 100m x 9m ellipse – octant search, with four octants informed, minimum sample number of 8 and maximum of 30 and a restriction on extreme grade influence is applied. Unless otherwise stated blocks informed with

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copper grades from this run are assigned the Indicated category under the 1999 JORC code.

Run 3: 120m x 120m x 16m ellipse – non-octant search, minimum sample number of 4 and maximum of 30 and a restriction on extreme grade influence is applied. Unless otherwise stated blocks informed with copper grades from this run are assigned the Inferred category under the 1999 JORC code.

The block model and grade estimates were validated using statistical and visual methods with good reconciliation established between the estimated grades and the composited drilling data. The 2002 resource estimate was audited by Arnold van der Heyden (Hellman and Schofield Pty. Ltd.) who conducted an alternative estimate that produced comparable results. Reconciliation between the 2002 and subsequent Resource models shows that later models differ only where additional data exists. 123Kt of copper metal have been added to the total Validation and Audit 2002 Resources Estimate over these years (0.5%Cu cut).

(Reconciliation between the 2007 and 2006 Resource models (before mining) shows that the later model differs only where additional data exists. 0.9Mt were added to the Resources and the average grade has dropped by 0.12%Cu (or 4% relative). 80Kt of copper metal has been added to the Measured Resources of which 15Kt are attributed to additional Resources and 65Kt are due to upgrading of Indicated and Inferred Resources.)

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Resource Statement and Estimation Methodology – TKN

Resource Statement

The 2007 Thengkham North Resource Statement documents the Resource Estimate for the near surface Thengkham North Copper Deposit located 9 km west of the Sepon Mine area in South Eastern Laos.

The 2007 Thengkham North Resource Estimate is an update of the October 2006 Resource Estimate. The update follows the inclusion of an additional 18,905 samples into the database for geological modelling and grade interpolation, an increase of 55% (This number reflects the increase in Thengkham North specific holes. Some additional data from neighbouring deposits was also used and had not been previously incorporated into the resource models.) The geology of the resource is similar to that at Khanong, a supergene copper deposit, presently being mined, and located within the Sepon Mine area. The Thengkham North deposit is largely drilled on 25x25m spacing.

The 2007 estimate shows a significant increase in the Cu Resources for the deposit. The additional drilling and subsequent geological modelling at Thengkham North has defined the limits of mineralization of the deposit, which were not clearly defined in the 2006 estimate. Infill drilling has also identified high grade variability, and an overall higher tenor of Cu mineralization than that reflected in the wider spaced earlier drilling.

Copper Resources

The Resource Estimates are reported by 2004 JORC Code categories in Table 1.

Contributing Experts

The Resource Estimate was coordinated by LXML employees and supported by contributions from the experts listed in Table 2. The information supplied by the experts was used without alteration and in the context supplied.

Compliance with the JORC Code

This Resource Statement follows the guidelines of the 2004 Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves (the 2004 JORC Code). Table 3 of this Statement describes the factors considered in estimating and assigning Resource categories under the code. It follows the format of Table 1 of the 2004 JORC Code.

The competent person signing off on the Resource Statement is Paul Quigley who has over 19 years experience as a geologist in mining and exploration, which includes 11 years experience in resource estimation and 7 years experience in sediment-hosted gold deposits. He also has substantial knowledge of the matters relating to supergene copper estimation, mine production, and reconciliation, and as such is responsible for data integrity, geological interpretation and resource classification.

Paul Quigley. BAppSc, AUSIMM Lane Xang Minerals Ltd.

Page 14 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Table 1: 2007 Thengkham North Copper Resource Estimate

Resource Category 0.5% Cu Cutoff (2004 JORC Code) Tonnes (Mt) Cu Grade Cu Au Grade Au Ag Grade Ag (%) (Kt) (g/t) (koz) (g/t) (koz) Measured ------Indicated 3.49 4.20 147 0.34 39 21.17 2,377 Inferred 8.18 1.13 93 0.29 77 8.21 2,159 Measured & Indicated 3.49 4.20 147 0.34 39 21.17 2,377 Measured, Inferred & Indicated 11.67 2.05 239 0.31 116 12.12 4,536

Resource Category 1.0% Cu Cutoff (2004 JORC Code) Tonnes (Mt) Cu Grade Cu Au Grade Au Ag Grade Ag (%) (Kt) (g/t) (koz) (g/t) (koz) Measured ------Indicated 3.37 4.32 146 0.35 38 21.29 2,309 Inferred 2.40 2.25 54 0.31 24 12.35 952 Measured & Indicated 3.37 4.32 146 0.35 38 21.29 2,309 Measured, Inferred & Indicated 5.77 3.46 200 0.33 61 17.62 3,261

Resource Category 1.5% Cu Cutoff (2004 JORC Code) Tonnes (Mt) Cu Grade Cu Au Grade Au Ag Grade Ag (%) (Kt) (g/t) (koz) (g/t) (koz) Measured ------Indicated 3.07 4.62 142 0.35 35 21.58 2,131 Inferred 1.22 3.27 40 0.34 13 17.12 673 Measured & Indicated 3.07 4.62 142 0.35 35 21.58 2,131 Measured, Inferred & Indicated 4.29 4.24 182 0.35 48 20.34 2,804

Decimal places do not imply precision. Cutoff grades approximate those employed at the nearby Khanong Copper Mine.

Table 2: Contributing Persons

Expert Person / Company Information Source Area of Expertise and Contribution

Jason McNamara Supervision of data compilation, data extraction, geological Superintendent Geology interpretation, wire-framing and classification by geologists. LXML

Elizabeth Zbinden QAQC for drill hole, assay, sampling, and SG data. LXML Senior Geologist – Resources

Duncan Hackman Review of SG data. Hackman & Associates Pty Ltd

James Cannell LXML Senior Exploration Geologist Geological Interpretation

Kerrin Allwood Resource database compilation, database validation and Geomodelling Ltd. geological interpretation

Variography and geostatistical analysis. Mark Sweeney

Page 15 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Expert Person / Company Information Source Area of Expertise and Contribution AMC Consultants Pty Ltd .

Tracie Burrows Resource estimation and block model validation. AMC Consultants Pty Ltd

Stuart Masters External Resource Estimation Project Coordinator CS-2 Pty. Ltd.

Resource Estimation Methodology

Table 3 summarizes the methodology in determining the Resource Estimate stated in Table 1.

Table 3: Resource Estimation Methodology

JORC CODE - Estimation and Reporting of Resources.

A total of 100 Diamond, 397 Face Sampling Reverse Circulation and 392 RC-Precollar with DD tails resource-delineation drill holes have been drilled in a predominantly 25mX25m grid pattern at Thengkham North (these numbers reflect the increase in Thengkham North specific holes. Some additional data from neighbouring deposits was also used and had not been previously incorporated into the resource models). To allow interpretation of the large scale east–west structure and small scale north–south structures, drilling was completed on both north–south sections and on east–west sections. To minimize ground disturbance the collar section spacing Drilling Configuration was nominally 50 m whilst the drill intersection spacing was nominally 25 m. 8% of the holes were drilled sub-vertically. The remaining holes were generally drilled at dip of -60O. Of the inclined holes, 9% were drilled towards 000O, 16% towards 090O, 54% towards 180O, and 14% towards 270O.

(This represents an increase compared to the 2006 resources of 44 Diamond, 148 Face Sampling Reverse Circulation and 298 RC-Precollar with DD tails resource-delineation drill holes)

Correct drill hole location was verified visually using the topographic DTM and plans showing the tracks and road access. Drill hole orientation and depths were checked against site generated cross-sections.

Four Diamond to Reverse Circulation twin-holes were drilled to assess the suitability of drilling techniques. Obvious contamination and smearing of grade is observed in the wet and moist intervals within some RC holes. Assays were assessed for inclusion-in/exclusion-from the resource dataset on a hole by hole basis resulting in the exclusion of 2 RC holes (see notes on recovery data, below).

71% of the assay data within mineralized domains is from diamond core and the remaining 29% from RC drilling Drilling QA/QC Diamond Drilling recoveries average 91%. There is no obvious relationship between Diamond Sample recoveries and Cu grade.

The average calculated recovery for RC drilling is 63% for all samples. Samples from mineralized domains average 65% calculated recovery while samples from non-mineralized domains average 63% calculated recovery. There is a small increase in Cu grade with decreasing recovery in some domains. There is a decrease in calculated RC recovery for moist samples and it is interpreted that material retention is an issue for these samples. Post November 2005, wet RC sampling was eliminated from the project with holes being converted to Diamond Drilling at the first encountered moist sample. RC drilling now comprises the minor proportion (29%) of data within the mineralised domains.

Page 16 Oxiana Limited 2007 Mineral Resource Explanatory Notes

The resource database comprises assays from 20,429 diamond ½ core samples (84% HQ) and 34,524 130-140mm diameter RC samples (split to a nominal 3-5kg sample wt).

Sampling (This represents an increase compared to the 2006 resource of 13,368 for assays from diamond ½ core samples and 11,609 RC samples. This is a threefold increase in higher quality diamond core.)

Copper grades have been determined by mixed acid digest ICP-AES determination for those samples with grades less than 0.5%Cu and by the ore-grade aqua-regia digest – IPC-AES detection method for those samples with grades above 0.5%Cu. Ag grades were determined Analysis by aqua regia digest ICP-AES determination. Au grades were routinely determined by 30g Fire Assay AAS analysis with high grade Au samples re-assayed by 30g Fire Assay, gravimetric analysis.

A thorough and rigorously implemented sampling protocol was adopted for the resource drilling at Thengkham North. Quality control coarse-blanks, pulp-blanks, grade and matrix-matched certified reference-standards and duplicate samples were routinely inserted into the analytical stream at rates of 1 in 25 samples.

Data quality appears uniform and acceptable over all drilling campaigns. Some minor indications of reduced quality in the most recent drilling with regard to sample swaps was detected however laboratory performance is acceptable

Assay QA/QC Blanks show that cross contamination between consecutive samples is negligible.

Reference Cu field standards and Laboratory standards show acceptable precision. Standards generally show a slight positive bias of 1-3% in both ICP analytical methods.

Details of rock-type, alteration, mineralization and weathering were logged onto field sheets and entered to an SQL database. Careful planning of the logging process and codes has produced Logging a fertile database with respect to domaining and modeling the economic geology features of the deposit.

The bulk of the copper and associated Ag, Au and Mo mineralization at Thengkham North exists in flat lying to moderately northerly dipping zones within the weathering profile. The mineralized zones occur over a strike length of 3 kilometres and typically show an along-strike Mineralization to across-strike ratio of 10:1. Chalcocite supergene mineralization is located adjacent to Geology and primary pyrite/chalcopyrite mineralization. Copper oxide and carbonate mineralization is best Resource Domaining developed over carbonate lithologies and the Cu in this mineralization has been re-mobilized during the weathering process. Domaining for resource modeling and estimation honours the oxidation/weathering state and metallurgical properties (a combination of Cu, S, Fe and Mn grades and logged mineralogy) of the mineralization.

3,729 wax-immersion bulk density determinations from across the deposit form the basis for Tonnage Factors (in assigning tonnage factors to the resource estimate. The amount of available density data has situ bulk densities) increased by 275% (over that available for the 2006 estiamte). Lithology weighted average dry bulk densities are applied to each of the mineralized domains. Average dry bulk densities of 1.97g/cc, 1.57g/cc and 2.79g/cc were determined for Chalcocite, Copper Oxide/Carbonate and

Page 17 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Primary mineralization within the deposit.

Block models were constructed in Datamine by AMC consultants using LXML generated flagged drill hole, composite data and wireframes. Geostatistical analysis was also performed by AMC and these parameters used in the grade estimation process. The following discussion details the Datamine block model process.

Grade was interpolated into a sub-blocked model with parent cell size of 25m x 25m x 5m (X, Y, Z). The model is coded into three different geological/metallurgical and statistical domain types used to interpolate grade. These domain types are; interpreted lithology, oxidation state and metallurgical mineralisation orientation.

Two metre composites were employed for grade interpolation. The geostatistical analysis was undertaken by AMC consultants (Brisbane office) using the Isatis geostatistical software package. Variography and estimation parameters were developed for copper, gold, silver, molybdenum sulphur, calculated pyrite, manganese, iron, calcium, magnesium, aluminium.

Coefficients of variations (‘CoV’) for the copper variable are relatively low. CoV’s are generally between 1.0 and 1.5. This is not as severe as usually observed in more nuggetty environments and should result in robust resource estimates, given that the ore domains have been carefully modelled.

Grade capping did not make a significant difference to the global statistics for each of the Grade Estimation modelled domains except for domains with low numbers of composites. Where groups of high grades were grouped together then generally they were not considered as outliers.

The relative nugget effect ranges from 10 percent to 25 percent. The average drillhole spacing is around 25m with the majority of the semivariogram variance consumed by ranges with values between 20m to 40m. The longest range structures generally vary between 100m and 250m. Shortest range structures are generally in the vertical direction which contains the densest composite data.

Copper grades were estimated into blocks by Ordinary Kriging (OK) using Datamine software. One search pass was required to fill the copper model with grades. The first search pass filled all of all cells with copper values. The second search pass, which was twice the size of the first pass, was required for some minor elements (displaying more sporadic data distribution than Cu). Additional quality estimators were informed by two passes; if uninformed they were left absent.

The minimum number of composites used to inform a cell with grade was 4 and the maximum was 10 for the first search pass. The second search pass used a minimum number of 3 samples and maximum number of 10 samples.

Both Absolute and Relative Kriging variances were calculated. All of the above information was stored in the models for use in the classification process.

Resource classification is based solely on confidence in copper grade and volume (tonnage) estimation.

Three dimensional contour surfaces (shells) were created at appropriate values for the following criteria:

Sample moisture Resource Sample recovery Classification Sample type Relative Kriging Variance Kriging slope of regression Number of samples used to inform the cell with grade Number of search passes required to fill a cell with a gold grade Distance to closest drillhole composite

These shells were then assessed on a domain by domain basis in conjunction with a subjective Page 18 Oxiana Limited 2007 Mineral Resource Explanatory Notes

assessment of the confidence in the interpretation of geological controls on mineralisation and the spatial configuration of drilling within the domain.

No resources are reported from the four waste domains as the geological and grade continuity of potentially economic intersections within these domains has not been demonstrated.

Only inferred resources are reported from the manganese clay and fresh domains as the geological and grade continuity of potentially economic intersections within these domains has not been demonstrated to a sufficient level of confidence within these two domains.

No measured resources have been reported from Thengkham North as the current 25 m by 25 m drilling pattern does not allow sufficient confidence in either geological interpretation or grade estimation.

Resource models have been validated both statistically and visually.

Validation and Audit AMC consultants have conducted a review of the Thengkham North resource models. No significant issues were identified with regard to the methodology.

Page 19 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Resource Statement and Estimation Methodology – TKS

Resource Statement

The 2007 Thengkham South Resource Statement documents the Resource Estimate for the near surface Thengkham South Copper Deposit located 7 km west of the Sepon Mine area in South Eastern Laos.

The 2007 Thengkham South Resource Estimate is an update of the August 2004 Resource Estimate. The update follows the inclusion of an additional 16,660 samples into the database for geological modelling and grade interpolation, an increase of 150% (This number reflects the increase in Thengkham South specific holes. Some additional data from neighbouring deposits was also used and had not been previously incorporated into the resource models). The geology of the resource is similar to that at Khanong, a supergene copper deposit, presently being mined, and located within the Sepon Mine area. The Thengkham South deposit is largely drilled on 50m x 50m spacing.

The 2007 estimate shows a significant decrease in the copper and gold resources for the deposit. This difference is due to a combination of more data and the use of a different modelling method. The August 2004 Resource Estimate was based on a block model in which copper grades were interpolated using multiple indicator kriging within a domain interpreted at a very low nominal grade (0.1% Cu). The 2007 model is based on a block model in which copper grades were interpolated using ordinary kriging within domains interpreted at varying, but higher nominal grades (0.2% Cu to 1.0% Cu). This resulted in a significant reduction in the influence of high grade data. The increase in data also further restricted the influence of high grades in previously sparsely drilled areas. Silver grades are low and have not been estimated.

Copper Resources

The Resource Estimates are reported by 2004 JORC Code categories in Table 1.

Contributing Experts

Compliance with the JORC Code

Paul Quigley. BAppSc, AUSIMM Lane Xang Minerals Ltd.

Page 20 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Table 1: 2007 Thengkham South Copper Resource Estimate

0.5% Cu Cutoff Resource Category (2004 JORC Code) Cu Grade Cu Au Grade Au Ag Grade Ag Tonnes (Mt) (%) (Kt) (g/t) (koz) (g/t) (koz) Measured ------Indicated ------Inferred 15.17 1.22 185 0.18 89 - - Measured & Indicated ------Measured, Inferred & Indicated 15.17 1.22 185 0.18 89 - -

1.0% Cu Cutoff Resource Category (2004 JORC Code) Cu Grade Cu Au Grade Au Ag Grade Ag Tonnes (Mt) (%) (Kt) (g/t) (koz) (g/t) (koz) Measured ------Indicated ------Inferred 4.78 2.47 118 0.18 28 - - Measured & Indicated ------Measured, Inferred & Indicated 4.78 2.47 118 0.18 28 - -

1.5% Cu Cutoff Resource Category (2004 JORC Code) Cu Grade Cu Au Grade Au Ag Grade Ag Tonnes (Mt) (%) (Kt) (g/t) (koz) (g/t) (koz) Measured ------Indicated ------Inferred 3.12 3.13 98 0.17 18 - - Measured & Indicated ------Measured, Inferred & Indicated 3.12 3.13 98 0.17 18 - -

Decimal places do not imply precision. Cutoff grades approximate those employed at the nearby Khanong Copper Mine.

Table 2: Contributing Persons

Expert Person / Company Area of Expertise and Contribution Information Source

Jason McNamara Supervision of data compilation, data extraction, geological interpretation, Superintendent Geology wire-framing and classification by geologists. LXML

Elizabeth Zbinden QAQC for drill hole, assay, sampling, and SG data. Senior Geologist – Resources LXML

Craig Michael Senior Resource Geologist Geological Interpretation LXML

Kerrin Allwood Resource database compilation, database validation, geological Geomodelling Ltd. interpretation, block modelling, model validation and resource classification.

Steve Hyland, Ravensgate Variography and geostatistical analysis.

Page 21 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Resource Estimation Methodology

Table 3 summarizes the methodology in determining the Resource Estimate stated in Table 1.

Table 3: Resource Estimation Methodology

JORC CODE - Estimation and Reporting of Resources.

A total of 44 Diamond, 160 Face Sampling Reverse Circulation and 147 RC-Precollar with DD tails resource-delineation drill holes have been drilled in a predominantly 50mX50m grid pattern at Thengkham South (these numbers reflect the increase in Thengkham South specific holes. Some additional data from neighbouring deposits was also used and had not been previously Drilling Configuration incorporated into the resource models.

(This represents an increase compared to the 2004 resources of 21 Diamond, 49 Face Sampling Reverse Circulation and 147 RC-Precollar with DD tails resource-delineation drill holes)

Seven Diamond to Reverse Circulation twin-holes were drilled to assess the suitability of drilling techniques. Obvious contamination and smearing of grade is observed in the wet and moist intervals within some RC holes.

44% of the assay data within mineralized domains is from diamond core and the remaining 56% from RC drilling

Drilling QA/QC Diamond Drilling recoveries average 92% in both mineralised and waste domains.

The average calculated recovery for RC drilling is 62% for all samples. Samples from mineralized domains average 55% calculated recovery while samples from non-mineralized domains average 66% calculated recovery. There is a small increase in Cu grade with decreasing recovery in some domains. 42% of the samples in mineralised domains are logged as moist or wet compared to 32% of the samples in waste domains. Post November 2005, wet RC sampling was eliminated from the project with holes being converted to Diamond Drilling at the first encountered moist sample.

The large proportion of RC data and the poor quality of some of the RC data was a contributing factor to the decision to report no measured or indicated resources for Thengkham South.

The resource database comprises assays from 10,603 diamond ½ core samples (95% HQ) and 16,945 130 -140mm diameter RC samples (split to a nominal 3-5kg sample wt). Of this database, 4,519 (44%) DD samples and 5,818 (65%) RC samples are within mineralised domains and so used for grade interpolation. Sampling (This represents an increase over the 2004 resource of 8,856 assays from diamond ½ core samples and 7,804 RC samples. This is a threefold increase in higher quality diamond core data.)

Page 22 Oxiana Limited 2007 Mineral Resource Explanatory Notes

A thorough and rigorously implemented sampling protocol was adopted for the resource drilling at Thengkham South. Quality control coarse-blanks, pulp-blanks, grade and matrix-matched certified reference-standards and duplicate samples were routinely inserted into the analytical stream at rates of 1 in 25 samples.

Data quality appears uniform and acceptable over all drilling campaigns.

Assay QA/QC Blanks show that cross contamination between consecutive samples is negligible.

Reference Cu field standards and Laboratory standards show acceptable precision. Standards generally show a slight positive bias.

Field duplicates (DD and RC) confirm that the sample reduction protocols are appropriate and correctly implemented in the Thengkham South dataset. There is good correlation between original and duplicate samples in both analysis methods.

Details of rock-type, alteration, mineralization and weathering were logged onto field sheets and entered into a GBIS (SQL) database. Careful planning of the logging process and codes has Logging produced a fertile database with respect to domaining and modeling the economic geology features of the deposit.

The bulk of the copper and associated Au and Mo mineralization at Thengkham South exists in moderately south dipping tabular to semi-tabular zones within the weathering profile. The mineralized zones occur over a strike length of 3 kilometres and typically show an along-strike to across-strike ratio of 10:1. Chalcocite supergene mineralization is located adjacent to primary pyrite/chalcopyrite mineralization. Copper oxide and carbonate mineralization is best developed further downslope and the Cu in this mineralization has been re-mobilized during the Mineralization weathering process. Geology and Resource Domaining Domaining for resource modeling and estimation honours the oxidation/weathering state and metallurgical properties of the mineralization (a combination of Cu, S, Fe and Mn grades and logged mineralogy).

The domain interpretations excluded zones of suspected downhole contamination. Further drilling to twin these holes for potential exclusion from future resource models is planned.

767 wax-immersion bulk density determinations from across the deposit form the basis for assigning tonnage factors to the resource estimate. Average dry bulk densities are applied to Tonnage Factors (in each of the mineralized domains. Average dry bulk densities of 2.0g/cc, 1.6g/cc and 2.8g/cc situ bulk densities) were determined for Chalcocite, Copper Oxide/Carbonate and Primary mineralization within the deposit.

A block model was constructed using Minesight software by Geomodelling Ltd. using wireframes to flag drill hole and composite data. Geostatistical analysis was performed by Ravensgate and these parameters were used in the grade estimation process. This discussion details the Minesight block model process.

Four grade/metallurgical and two oxidation domain wireframes were constructed from sectional interpretations supplied by site geologists. Assay and two metre composite data was coded using these domains. Grade Estimation Grade was interpolated into a regular block model with cell dimensions of 20m x 12m x 2.5m (X, Y, Z). The block model was coded by the geological/metallurgical and oxidation domains using majority block logic. Variographic models and estimation parameters were developed for copper, gold, molybdenum and sulphur.

Coefficients of variations (‘CV’) for the Cu, Mo and Au variables are relatively low, generally between 0.6 and 1.2. This is not as severe as usually observed in more nuggetty environments and should result in robust resource estimates, given that the ore domains have been carefully

Page 23 Oxiana Limited 2007 Mineral Resource Explanatory Notes

modelled. Two sulphur domains have CVs of 2.2 and 3.0.

Minimal grade capping did not make a significant difference to the global statistics for each of the modelled domains.

The relative nugget effect ranges from 10 percent to 30 percent for Cu, S and Mo. Gold has high relative nugget effect, ranging from 10 percent to 30 percent. The majority of the semivariogram variance occurs within ranges of 10m to 40m. The longest range structures generally vary between 60m and 120m along 070°. Shortest range structures are generally in the vertical direction which contains the densest composite data.

Copper, gold, sulphur and molybdenum grades were interpolated into blocks by Ordinary Kriging (OK). The minimum number of composites used to inform a block with grade was 5 and the maximum was 20, of which a maximum of 10 were allowed from any split quadrant of the search ellipsoid. Only composites with the same combination of geological / metallurgical and oxidation domain codes as the block being interpolated were used.

Resource classification is based solely on confidence in copper grade and volume (tonnage) estimation.

Only inferred resources are reported from Thengkham South as the current 50 m by 50 m drilling pattern does not allow sufficient confidence in either geological interpretation or grade Resource estimation. In addition, a significant proportion of the data used is from wet RC drilling samples Classification in which downhole smearing of grade is suspected.

Each domain was individually assessed for confidence in the interpretation of geological controls on mineralisation and the spatial configuration of drilling within the domain. A wireframe was used to exclude those blocks in the block model in which there was insufficient confidence to allow reporting of inferred resources.

The block model has been validated both statistically and visually. Validation and Audit The estimate was also checked by an alternative interpolation method (inverse distance squared within a nominal 0.2% Cu grade domain).

Page 24 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Phabing Resource Statement, October 2007

Introduction

The Phabing October 2007 Resource Statement deals with the copper Mineral Resources for the Phabing deposit located 9km west of the Sepon Mine area in South Eastern Laos.

The Resource model is based on the geological database as at 19th September, 2007, the geological interpretation by James Cannell of Oxiana Limited and solid modelling of the geology, block model construction and grade estimation were undertaken by Jared Broome of Oxiana Limited using VulcanTM software.

The geological interpretation was based on all drill holes up to hole PHB216. This includes data from 155 diamond drill holes (11,018.2m) and 133 reverse circulation drill holes ( 8554.3m) containing logged and assayed intervals of 1-metre average length . The geological model extends over an area of 1250m East- West and 930m North-South, which covers three main corridors of high-grade copper mineralisation defined within shallow westerly plunging lenses.

Sample data was composited to two (2) metre intervals and flagged by the domains defined in the geological interpretation. Ordinary Kriging was used to estimate grades within the geological domains. Resources were estimated for continuous high-grade copper carbonate mineralised lenses.

The resource estimate has been classified as Inferred on the basis of data density, data quality, confidence in the geological interpretation and confidence in the estimation.

Results

The October 2007 Inferred mineral resource of 2.03Mt at 3.4% Cu (above a 0.5% Cu cut-off) is reported from high- grade copper carbonate lenses that define three shallow westerly plunging domains within local grid coordinates of 596285 to 597500m East, 1874535 to 1875265m North and 192 to 300m RL.

Table 1 outlines the October 2007 Phabing mineral resource estimate.

Table 1: Inferred Copper Resource at Cu cut-off grades of 0.5, 1.0 and 1.5% Cu Block Tonnes Cu Au Ag Contained Metal Cut-off (,000s) (%) (g/t) (g/t) (Cu%) Cu (t) Au (oz) Ag (oz) 0.5 2,028,986 3.4 0.2 2.0 68,357 10,242 128,901 1.0 1,903,488 3.5 0.2 2.0 67,364 9,853 123,254 1.5 1,660,560 3.9 0.2 2.0 64,214 9,129 107,577

Tables 1 to 5 show rounded estimates. This rounding may cause apparent computational discrepancies. Significant figures do not imply precision. Nominal lower Cu grade cuts are applied.

Compliance with the JORC code assessment criteria This mineral resource statement has been compiled in accordance with the guidelines defined in the Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves (The JORC Code, 2004 Edition).

Jared Broome and Antony Manini are members of the Australian Institute of Geoscientists or Australasian Institute of Mining and Metallurgy. Both have sufficient experience relevant to the style of mineralisation and type of deposit under consideration and to the activity undertaken to qualify as Competent Persons as defined in the 2004 Edition of the Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves (The JORC Code, 2004 Edition).

Page 25 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Jared Broome Antony Manini B.App.Sc.(Hons), MSc, Grad. Dip. Business, MAIG BSc (Hons), Fellow AusIMM Senior Geologist – Resources and Mining Executive General Manager – Exploration and Resources Oxiana Limited Oxiana Limited

Key points relating to the Phabing October 2007 Resource Estimate:

1. The resource estimate applies to copper mineralisation within shallow westerly-plunging, high-grade copper carbonate lenses that constitute three corridors of mineralisation within the area 596285 to 597500m East, 1874535 to 1875265m North and 192 to 300m RL. Mineral resources range between 5m and 90m below surface.

2. The deposit is delineated by 155 diamond (mostly of HQ diameter with one recorded PQ diameter) drill holes (11,018.2m) and 133 reverse circulation drill holes (8554.3m) drilled on nominal north-south 100m sections with on section drill spacing of 25m. Some infill sections targeting high-grade copper mineralisation provides drilling on north-south 50m-sections with on section drill spacing of 25m. The majority of holes (85%) are angled at approximately 60 degrees to the south, 21 holes drilled approximately 60 degrees to the north and there are 22 sub-vertical holes.

3. HQ diameter diamond drill core sampling comprised selective cutting of the core longitudinally using a diamond saw enabling half-core sampling in nominal 1-metre lengths. Reverse circulation sampling method involved the collection and separation of the sample by way of cyclone and splitter producing an approximate 4kg sample split. Manual processing ensured the dispatch of maximum sample product through the splitter. Most reverse circulation samples are of 1-metre length.

Sample preparation was conducted by ALS in Vientiane following the process of crushing (boyd crusher, 70% passing 2mm), rotary split, pulverise (LM5, 85% passing 85 micron) with samples sent to ALS Chemex in Townsville, Australia for gold and multi- element analysis. Sample pulps are returned to Sepon.

Copper grades have been determined by mixed acid digest ICP-AES determination for those samples with grades less than 0.5% Cu and by the ore-grade aqua-regia digest – IPC-AES detection method for those samples with grades above 0.5%Cu. Ag grades were determined by aqua regia digest ICP-AES determination. Au grades were routinely determined by 30g Fire Assay AAS analysis with high-grade Au samples re-assayed by 30g Fire Assay, gravimetric analysis.

Samples applied to the Phabing resource estimate have been subject to the QA/QC protocols of the Sepon Operation including coarse blanks, certified reference standards and duplicate samples inserted into the analytical stream at a rate of 1 in 25 samples.

4. Copper, gold, silver, manganese and density estimation is conducted by Ordinary Kriging and guided by triangulations modelled on geological and mineralogical domains. Variogram analysis was conducted separately for variables within grouped copper carbonate and manganese oxide domains. 10,226 (645 within high-grade copper domains) two-metre composites inform the grade interpolation. Parent cell estimates (25mE x 10mN x 5mRL) were written to a sub-blocked model using two composite search parameter-sets to account for variable data distribution. The search neighbourhood applied an elliptical region (pass 1 of 45m x 20m x 10m, pass 2 of 100m x 50m x 30m) with a minimum of 10 composites for pass 1 and 5 composites for pass 2, and a maximum of 20 composites for both passes.

The Phabing deposit has been interpreted and triangulations constructed for the following domains: − Seven high-grade copper carbonate (CUO) lenses that define three distinct shallow westerly-plunging mineralised corridors. − Three clay-rich manganese oxide (MNO) lenses with shallow westerly plunges that envelope the high-grade carbonate lenses − Clay-saprolite domain used to assign density for non-estimated blocks.

6. The mineral resource is entirely classified as Inferred on the basis of data density and quality, and confidence levels in the geological interpretation and grade estimation.

8. The Inferred Cu mineral resource for Phabing of 2.03Mt at 3.4% Cu (above a 0.5% Cu cut-off) is entirely new mineralisation for the Sepon mineral district and has not been report in whole or part in any previous publication. The resource is entirely in situ without any mining of the defined mineralised lenses to date.

Page 26 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Sepon Gold Resource Statement: June 30, 2007

Resource Statement

1.1 Scope

This June 2007 Sepon Au Resource Statement includes the oxide, partial oxide and primary gold resources for the following deposits within the Sepon Project in Laos:

Discovery (DIS), Discovery Colluvial (DSC), Discovery West (DSW), Muang Luang (LOL), Khanong gold (KHN-Au), Nalou (NLU), Namkok East (NKE), Namkok West (NKW), Phavat North (PNV) Thengkham North (TKN), Thengkham South (TKS), Vang Ngang East (VNE), Houay Yeng (YNG), Dankoy (DKY)

Lane Xang Minerals Ltd (LXML) is the manager of, and 90% equity holder in the Sepon Project with the Government of the Lao People’s Democratic Republic holding the remaining 10% equity.

The estimates are derived from block models as follows:

DIS is an update of the 2006 ordinary kriged (OK) block model undertaken as part of the primary gold feasibility study and includes data from additional resource and grade control drillholes. The model area still includes the DSC area, although this area is reported separately. Additional oxide drilling in the eastern part of the model resulted in a further update of the area east of 27700E.

DSC is contained within the DIS resource model and is defined as the resources west of 26050E. DSC is an update of the 2006 ordinary kriged (OK) block model undertaken as part of the primary gold feasibility study and includes data from additional resource drillholes.

DSW is an update of the 2006 ordinary kriged (OK) block model and includes data from additional resource drillholes.

LOL is an update of the 2004 multiple indicator kriged (MIK) block model.

KHN-Au is an update of the 2006 ordinary kriged (OK) block model and includes data from additional grade control drillholes.

NLU is an update of the 2007 primary gold ordinary kriged (OK) block model and includes data from additional resource and grade control drillholes.

NKE is based on depletion of the 2005 ordinary kriged (OK) block model.

NKW is the 2005 ordinary kriged (OK) block model and remains unchanged from depletion in 2006.

PVN is an update of the 2006 ordinary kriged (OK) block model and includes data from additional resource drillholes. Solids were used to define resource limits between PVN and DKY due to their close proximity to one another.

TKN is an update of the 2006 ordinary kriged (OK) block model and includes data from additional resource drillholes.

TKS is an update of the 2004 multiple indicator kriged (MIK) block model and includes data from additional infill resource drillholes.

VNE is an ordinary kriged (OK) block model and remains unchanged from 2006.

YNG is a new ordinary kriged resource, reported for the first time in 2007.

Page 27 Oxiana Limited 2007 Mineral Resource Explanatory Notes

DKY is a new ordinary kriged resource, reported for the first time in 2007.

As a component of the Sepon Primary Gold Feasibility Study a significant program of resource drilling targeting primary gold resources has been completed since the reporting of the 2006 resources. The main areas where significant drilling has been undertaken are DIS, DSW and NLU. The primary components of PVN, TKN, TKS and LOL were re-estimated as data allowed. NKE, NKW, and VNE primary resource estimates remain unchanged (from June 2006).

Section 1 of this statement presents the gold resources categorised by oxidation state (tables 1-3). The competent persons’ statements for each of the estimates are included at Sections 7 to 14.

1.2 Mineral Resource Categories and Tabulation

Oxide, partial oxide, and primary resource estimates are classified in the categories specified in the 2004 JORC Code and reported at several appropriate cut-off grades in Tables 1-3.

June 2007 primary resource estimates are tabulated against the same cut-off values as oxide and partial oxide estimates.

1.3 Contributing Experts

The following identifies the key personnel involved in the estimation of the Sepon Gold Resources. A full listing of all involved can be seen in sections 2 to 7 where details of the estimates are documented:

DIS, DSC, DSW, NLU, NKW, NKE, VNE, PVN and LOL – All drillhole data was sourced and validated by LXML geologists. Geological interpretations and geostatistical analyses were performed either by LXML and Quantitative Geoscience Pty Ltd (QG), AMC consultants Pty Ltd, Ravensgate Pty Ltd (RG) or Geomodelling Pty Ltd (GM) geologists. All significant 2007 updated geostatistical analyses and grade interpolations underwent an external form of review and/or audit.

DIS, DSC, DSW and NLU primary gold updates – Tracie Burrows (AMC), with contributions from the experts listed in section 2. These estimates were based on the data, geological interpretations and other technical information supplied by LXML.

NKE (2005) and NKW (2005) – Mike Stewart (QG), with contributions from the experts listed in section 2. These estimates were based on the data, geological interpretations and other technical information supplied by LXML

VNE (2006) – Craig Michael (LXML). Drillhole data was sourced and validated by LXML geologists. Geostatistical analyses, resource modelling and estimation were conducted by Craig Michael under the guidance of Mike Stewart (QG).

PVN (2007) – Saut Parulian (LXML). Drillhole data was sourced and validated by LXML geologists. Geostatistical analysis was conducted by Mike Stewart (QG) in 2006. Resource modelling and estimation were conducted by Saut Parulian.

LOL (2007) – Stephen Hyland (RG). Drillhole data was sourced and validated by LXML geologists, resource domaining was conducted by Stephen Hyland (RG) under the direction of LXML geologists. Block modelling, geostatistical analyses and grade interpolation were conducted by Stephen Hyland (RG).

DKY and YNG – Chris Gerteisen (LXML). Drillhole data was sourced and validated by LXML geologists, resource domaining was conducted by Chris Gerteisen and LXML exploration geologists. Block modelling, geostatistical analyses and grade interpolation were conducted by Chris Gerteisen.

KHN-Au – Drillhole data was sourced and validated by and under the direction of LXML geologist and Duncan Hackman of Hackman and Associates Pty Ltd (HA). Geostatistical analyses were conducted by Arnold van der Heyden of Hellman and Schofield Pty Ltd (HS) and resource modelling and estimation conducted by Duncan Hackman (HA).

TKN – Drillhole data was sourced and validated by LXML geologists and Kerrin Allwood (GM) Resource modelling and domaining was conducted by GM under the direction of LXML geologists. Geostatistical analysis was conducted by Mark Sweeny (AMC) and grade interpolation conducted by Tracie Burrows (AMC).

TKS – Drillhole data was sourced and validated by LXML geologists and Kerrin Allwood (GM) under the direction of LXML geologists. Resource modelling and grade interpolation conducted by GM, geostatistical analysis was conducted by Stephen Hyland (RG).

Compliance with the JORC Code

This statement of mineral resources complies with the 2004 Australasian Code for the Reporting of Resources and Reserves (2004 JORC Code). Page 28 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Sections 7 to 14 of this statement describes the factors considered in estimating and assigning resource categories under the code, following the format of Table 1 of the 2004 JORC Code. Comparisons between the 2007 and 2006 estimates for the deposits are also documented along with risks identified in the resource estimate.

The Competent Persons (as defined in the 2004 JORC Code) endorsing these statements of mineral resources are, for:

DIS, DSC, DSW, NLU, TKN, TKS and LOL – Paul Quigley (LXML).

DKY and YNG – Chris Gerteisen (LXML).

NKW, NKE, VNE and PVN – Paul Quigley (LXML) and Michael Stewart (QG).

KHN-Au – Duncan Hackman (HA),

Paul Quigley has over 19 years experience as a geologist in mining and exploration, which includes 11 years experience in resource estimation and 7 years experience in sediment-hosted gold deposits. He also has substantial knowledge of the matters relating to supergene copper estimation, mine production, and reconciliation, and as such is responsible for data integrity, geological interpretation and resource classification.

Michael Stewart has over 19 years experience as a geologist in mining and exploration, which includes 11 years experience in resource estimation and 6 years experience in sediment-hosted gold deposits. He is responsible for geological interpretation and modelling, geostatistical analysis, and grade interpolation.

Chris Gerteisen (LXML), who has over 12 years experience as a geologist in mining and exploration, which includes 6 years experience in resource estimation and 7 years experience in sediment-hosted gold deposits.

Duncan Hackman has over 22 years experience as a geologist in mining and exploration, which includes fourteen years experience in resource estimation, seven years experience in estimating gold deposits, three of which are in estimating resources in sediment-hosted gold deposits.

Tables 1 to 3 list the gold resources at Sepon as of the 30th June 2007.

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Resource Tables

Table 1: June 2007 Sepon Gold Mineral Resource Estimate: DIS, DKY, DSC, DSW, KHN-Au, LOL, NKE, NKW, NLU, PVN, Stockpiles, TKN, TKS, VNE and YNG at 0.5 g/t Au cut-off grade.

Greater than 0.5g/ t Au Cut Resource OXIDE MINERALISATION PARTIAL OXIDE MINERALISATION PRIMARY MINERALISATION Deposit Classification Au Ag Au Ag Au Ag Au Ag Au Ag Au Ag Mt Mt Mt (JORC 2004) (g/t) (g/t) (kOz) (kOz) (g/t) (g/t) (kOz) (kOz) (g/t) (g/t) (kOz) (kOz) Measured 0.01 1.59 1.0 0 0 0.22 2.73 16.0 19 114 0.78 3.17 8.7 79 218 Indicated 0.86 1.55 3.7 43 103 1.04 2.45 13.3 82 443 3.82 2.68 8.2 329 1,007 DIS Inferred 0.07 1.18 1.8 3 4 0.06 1.16 10.7 2 22 1.56 1.60 6.7 80 337 Meas+Ind+Inf 0.94 1.52 3.6 46 108 1.33 2.44 13.6 104 580 6.15 2.47 7.9 488 1,563 Measured ------Indicated ------DKY Inferred 2.05 1.12 3.9 74 254 1.06 1.07 3.7 36 124 4.38 1.10 5.7 155 798 Meas+Ind+Inf 2.05 1.12 3.9 74 254 1.06 1.07 3.7 36 124 4.38 1.10 5.7 155 798 Measured - - - - - 0.15 4.42 5.7 21 27 - - - - - Indicated - - - - - 0.29 4.47 8.1 41 75 0.12 3.32 12.1 12 45 DSC Inferred - - - - - 0.01 2.20 9.2 0 1 0.01 0.89 8.1 0 3 Meas+Ind+Inf - - - - - 0.44 4.42 7.3 63 104 0.13 3.12 11.8 13 48 Measured 0.06 4.36 4.5 9 9 0.04 3.25 2.6 4 3 1.33 3.46 4.5 148 193 Indicated 0.04 3.03 6.1 4 8 0.63 3.05 5.1 62 104 3.11 2.45 5.9 245 587 DSW Inferred 0.01 0.65 0.2 0 0 0.10 2.88 5.3 10 18 0.30 1.50 5.0 15 49 Meas+Ind+Inf 0.11 3.61 4.8 13 17 0.78 3.04 5.0 76 125 4.75 2.67 5.4 408 829 Measured 2.71 1.01 3.2 88 276 ------Indicated 0.69 0.73 3.5 16 77 ------KHN-Au Inferred 0.37 0.66 1.5 8 17 ------Meas+Ind+Inf 3.77 0.93 3.1 112 370 ------Measured ------Indicated 0.08 2.05 6.3 5 16 0.07 2.02 8.0 4 18 0.75 1.95 7.1 47 172 LOL Inferred 0.00 0.75 2.8 0 0 0.00 0.55 2.4 0 0 0.03 2.05 5.2 2 5 Meas+Ind+Inf 0.08 2.04 6.3 5 16 0.07 2.00 7.9 4 18 0.78 1.95 7.1 49 177 NKE Measured 0.76 0.94 5.5 23 135 0.00 1.37 6.7 0 0 - - - - - Indicated 0.48 0.81 8.9 12 136 0.03 0.80 8.0 1 7 0.01 0.76 9.5 0 3

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Greater than 0.5g/ t Au Cut Resource OXIDE MINERALISATION PARTIAL OXIDE MINERALISATION PRIMARY MINERALISATION Deposit Classification Au Ag Au Ag Au Ag Au Ag Au Ag Au Ag Mt Mt Mt (JORC 2004) (g/t) (g/t) (kOz) (kOz) (g/t) (g/t) (kOz) (kOz) (g/t) (g/t) (kOz) (kOz) Inferred ------Meas+Ind+Inf 1.23 0.88 6.8 35 271 0.03 0.83 7.9 1 8 0.01 0.76 9.5 0 3 Measured 0.34 1.50 9.0 16 98 0.06 1.30 15.0 3 29 - - - - - Indicated 0.25 0.90 9.0 7 72 0.11 0.80 12.0 3 42 1.37 1.20 13.0 53 573 NKW Inferred 0.09 0.70 5.0 2 14 0.10 0.70 8.0 2 26 1.47 0.90 9.0 43 425 Meas+Ind+Inf 0.68 1.20 9.0 26 197 0.27 0.90 11.0 8 95 2.85 1.00 11.0 92 1,008 Measured 1.87 1.33 6.2 80 370 0.09 2.56 8.7 7 25 3.57 2.32 7.3 266 835 Indicated 0.44 0.78 4.9 11 69 1.19 2.42 8.4 93 324 7.15 2.19 7.8 504 1,801 NLU Inferred 0.16 0.75 4.7 4 25 0.58 0.99 6.2 19 116 2.67 1.26 6.5 108 555 Meas+Ind+Inf 2.47 1.19 5.8 94 464 1.87 1.98 7.8 119 466 13.39 2.04 7.4 878 3,192 Measured ------Indicated 1.28 1.32 3.0 54 123 0.60 1.41 2.7 27 51 0.62 1.48 3.2 29 63 PVN Inferred 0.29 1.27 3.9 12 36 0.13 1.03 2.5 4 11 0.64 1.66 2.7 34 56 Meas+Ind+Inf 1.57 1.31 3.2 66 160 0.73 1.34 2.6 31 62 1.26 1.57 2.9 64 119 Measured 2.72 1.16 - 102 ------0.01 2.31 - 1 - Indicated ------STOCKPILES Inferred ------Meas+Ind+Inf 2.72 1.16 - 102 ------0.01 2.31 - 1 - Measured ------Indicated ------TKN Inferred 0.69 0.80 7.0 18 154 ------Meas+Ind+Inf 0.69 0.80 7.0 18 154 ------Measured ------Indicated ------TKS Inferred 0.68 0.74 - 16 ------Meas+Ind+Inf 0.68 0.74 - 16 ------VNE Measured ------Indicated 0.26 1.90 5.0 16 42 0.06 3.20 20.0 6 39 - - - - - Inferred 0.20 0.90 3.0 6 19 0.16 0.90 3.0 5 15 1.31 1.40 5.0 59 211

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Table 2: June 2007 Sepon Gold Mineral Resource Estimate: DIS, DKY, DSC, DSW, KHN-Au, LOL, NKE, NKW, NLU, PVN, Stockpiles, TKN, TKS, VNE and YNG at 1.0 g/t Au cut-off grade.

Greater than 1.0g/ t Au Cut Resource OXIDE MINERALISATION PARTIAL OXIDE MINERALISATION PRIMARY MINERALISATION Deposit Classification Au Ag Au Ag Au Ag Au Ag Au Ag Au Ag Mt Mt Mt (JORC 2004) (g/t) (g/t) (kOz) (kOz) (g/t) (g/t) (kOz) (kOz) (g/t) (g/t) (kOz) (kOz) Measured 0.00 2.90 1.1 0 0 0.21 2.84 16.2 19 109 0.69 3.48 9.3 77 205 Indicated 0.51 2.12 4.8 35 79 0.95 2.61 13.5 79 411 3.12 3.11 9.3 312 930 DIS Inferred 0.02 2.26 2.7 2 2 0.03 1.77 10.4 1 8 1.11 1.97 6.8 70 243 Meas+Ind+Inf 0.54 2.13 4.7 37 82 1.18 2.63 13.9 100 528 4.92 2.90 8.7 459 1,378 Measured ------Indicated ------DKY Inferred 0.79 1.78 4.2 45 107 0.45 1.56 4.0 23 58 1.80 1.62 7.8 94 449 Meas+Ind+Inf 0.79 1.78 4.2 45 107 0.45 1.56 4.0 23 58 1.80 1.62 7.8 94 449 Measured - - - - - 0.14 4.68 6.0 21 27 - - - - - Indicated - - - - - 0.27 4.69 8.2 41 72 0.11 3.49 12.4 12 43 DSC Inferred - - - - - 0.01 2.20 9.2 0 1 0.00 1.19 7.2 0 1 Meas+Ind+Inf - - - - - 0.42 4.65 7.5 62 100 0.11 3.43 12.2 12 44 Measured 0.06 4.77 4.6 8 8 0.03 4.30 2.4 4 2 1.20 3.76 4.7 145 182 Indicated 0.04 3.15 6.2 4 8 0.52 3.56 5.1 59 85 2.33 3.01 5.9 226 442 DSW Inferred - - - - - 0.08 3.59 6.5 9 16 0.20 1.93 5.6 12 36 Meas+Ind+Inf 0.10 4.09 5.2 12 16 0.62 3.60 5.2 72 103 3.73 3.19 5.5 383 660 Measured 0.99 1.56 2.8 50 90 ------Indicated 0.08 1.35 7.3 3 19 ------KHN-Au Inferred 0.01 1.22 3.5 - 1 ------Meas+Ind+Inf 1.08 1.54 3.2 54 110 ------Measured ------Indicated 0.05 2.88 8.6 4 13 0.05 2.63 9.8 4 15 0.59 2.27 7.8 43 148 LOL Inferred 0.00 1.81 2.8 0 0 - - - - - 0.03 2.18 5.6 2 5 Meas+Ind+Inf 0.05 2.88 8.6 4 13 0.05 2.63 9.8 4 15 0.62 2.27 7.6 45 153 NKE Measured 0.25 1.24 5.9 10 48 ------Indicated 0.07 1.27 13.1 3 29 0.01 1.04 7.6 0 1 0.00 1.18 3.8 0 0 Inferred ------

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Greater than 1.0g/ t Au Cut Resource OXIDE MINERALISATION PARTIAL OXIDE MINERALISATION PRIMARY MINERALISATION Deposit Classification Au Ag Au Ag Au Ag Au Ag Au Ag Au Ag Mt Mt Mt (JORC 2004) (g/t) (g/t) (kOz) (kOz) (g/t) (g/t) (kOz) (kOz) (g/t) (g/t) (kOz) (kOz) Meas+Ind+Inf 0.32 1.25 7.4 13 77 0.01 1.04 7.6 0 1 0.00 1.18 3.8 0 0 Measured 0.19 2.20 11.0 13 67 0.02 2.20 16.0 1 10 - - - - - Indicated 0.06 1.50 14.0 3 27 0.01 2.00 14.0 1 5 0.54 2.10 18.0 36 313 NKW Inferred 0.01 1.20 5.0 0 2 0.01 1.00 9.0 0 3 0.26 2.10 14.0 18 117 Meas+Ind+Inf 0.26 2.00 11.0 17 92 0.04 2.00 15.0 3 19 0.80 2.10 17.0 54 437 Measured 0.96 1.88 5.3 58 162 0.08 2.71 8.4 7 22 3.24 2.47 7.6 257 796 Indicated 0.07 1.31 3.7 3 9 1.00 2.73 7.8 88 252 6.11 2.43 8.1 477 1,588 NLU Inferred 0.02 1.23 9.6 1 6 0.26 1.27 6.6 11 55 1.66 1.54 8.0 82 425 Meas+Ind+Inf 1.05 1.83 5.2 62 176 1.34 2.44 7.6 105 329 11.01 2.31 7.9 817 2,810 Measured ------Indicated 0.68 1.89 3.5 41 76 0.29 2.14 3.2 20 30 0.34 2.07 3.4 23 38 PVN Inferred 0.12 2.09 4.6 8 18 0.05 1.56 2.6 2 4 0.40 2.20 3.0 29 39 Meas+Ind+Inf 0.80 1.92 3.7 49 94 0.34 2.06 3.1 23 34 0.75 2.14 3.2 51 76 Measured 1.07 1.78 - 61 ------0.01 2.31 - 1 - Indicated ------STOCKPILES Inferred ------Meas+Ind+Inf 1.07 1.78 - 61 ------0.01 2.31 - 1 - Measured ------Indicated ------TKN Inferred 0.15 1.32 6.9 7 34 ------Meas+Ind+Inf 0.15 1.32 6.9 7 34 ------Measured ------Indicated ------TKS Inferred 0.10 1.10 - 3 ------Meas+Ind+Inf 0.10 1.10 - 3 ------Measured ------Indicated 0.18 2.50 7.0 14 41 0.06 3.50 22.0 7 42 - - - - - VNE Inferred 0.06 1.50 4.0 3 8 0.05 1.40 4.0 2 6 0.63 2.20 7.0 45 142 Meas+Ind+Inf 0.24 2.30 6.0 18 46 0.10 2.50 14.0 8 45 0.63 2.20 7.0 45 142

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Table 3: June 2007 Sepon Gold Mineral Resource Estimate: DIS, DKY, DSC, DSW, KHN-Au, LOL, NKE, NKW, NLU, PVN, Stockpiles, TKN, TKS, VNE and YNG at 1.5 g/t Au cut-off grade.

Greater than 1.5g/ t Au Cut Resource OXIDE MINERALISATION PARTIAL OXIDE MINERALISATION PRIMARY MINERALISATION Deposit Classification Au Ag Au Ag Au Ag Au Ag Au Ag Au Ag Mt Mt Mt (JORC 2004) (g/t) (g/t) (kOz) (kOz) (g/t) (g/t) (kOz) (kOz) (g/t) (g/t) (kOz) (kOz) Measured 0.00 2.90 1.1 0 0 0.18 3.13 16.6 18 94 0.61 3.78 10.1 74 197 Indicated 0.34 2.56 4.7 28 51 0.74 2.99 14.3 71 341 2.55 3.53 10.1 289 827 DIS Inferred 0.02 2.64 2.4 2 1 0.01 2.91 15.5 1 4 0.41 3.21 10.0 42 131 Meas+Ind+Inf 0.36 2.57 4.5 30 53 0.92 3.02 14.8 90 438 3.57 3.53 10.1 405 1,155 Measured ------Indicated ------DKY Inferred 0.32 2.64 4.2 27 44 0.18 2.07 4.0 12 23 0.79 2.15 9.7 55 247 Meas+Ind+Inf 0.32 2.64 4.2 27 44 0.18 2.07 4.0 12 23 0.79 2.15 9.7 55 247 Measured - - - - - 0.11 5.47 6.4 20 23 - - - - - Indicated - - - - - 0.24 5.06 8.3 40 66 0.09 3.88 13.0 12 39 DSC Inferred - - - - - 0.01 2.20 9.2 0 1 0.00 2.45 9.6 0 0 Meas+Ind+Inf - - - - - 0.36 5.15 7.7 60 90 0.09 3.88 13.0 12 39 Measured 0.05 4.91 4.7 8 8 0.02 5.24 2.5 4 2 1.09 4.01 4.8 140 169 Indicated 0.04 3.20 6.3 4 8 0.41 4.18 5.5 55 73 1.64 3.77 6.5 199 344 DSW Inferred - - - - - 0.06 4.31 7.2 8 13 0.08 2.99 6.7 8 17 Meas+Ind+Inf 0.09 4.19 5.3 12 16 0.49 4.24 5.6 67 88 2.81 3.84 5.9 347 530 Measured 0.40 2.08 3.4 27 43 ------Indicated 0.02 1.78 4.9 1 3 ------KHN-Au Inferred ------Meas+Ind+Inf 0.41 2.07 3.4 28 46 ------Measured ------Indicated 0.03 3.57 8.8 4 10 0.03 3.12 11.6 3 13 0.43 2.65 8.5 37 119 LOL Inferred 0.00 2.00 2.8 0 0 - - - - - 0.02 2.41 6.3 2 5 Meas+Ind+Inf 0.03 3.57 8.8 4 10 0.03 3.12 11.6 3 13 0.46 2.64 8.4 39 124 NKE Measured 0.03 1.91 7.5 2 8 0.00 1.85 6.5 0 0 - - - - - Indicated 0.01 1.85 10.2 1 4 ------Inferred 0.00 1.50 - 0 - 0.00 1.50 - 0 - 0.01 1.50 - 1 -

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Greater than 1.5g/ t Au Cut Resource OXIDE MINERALISATION PARTIAL OXIDE MINERALISATION PRIMARY MINERALISATION Deposit Classification Au Ag Au Ag Au Ag Au Ag Au Ag Au Ag Mt Mt Mt (JORC 2004) (g/t) (g/t) (kOz) (kOz) (g/t) (g/t) (kOz) (kOz) (g/t) (g/t) (kOz) (kOz) Meas+Ind+Inf 0.05 1.86 7.4 3 12 0.00 1.60 1.8 0 0 0.01 1.50 - 1 - Measured 0.13 2.60 12.0 11 50 0.02 2.40 16.0 2 10 - - - - - Indicated 0.02 2.00 18.0 1 12 0.01 2.70 11.0 1 4 0.32 2.40 15.0 25 154 NKW Inferred 0.01 1.60 8.0 1 3 - - - - - 0.13 2.20 11.0 9 46 Meas+Ind+Inf 0.16 2.50 13.0 13 67 0.03 2.50 14.0 2 14 0.45 2.30 14.0 33 203 Measured 0.52 2.50 4.7 42 78 0.06 3.20 8.1 6 16 2.51 2.82 8.1 228 657 Indicated 0.01 1.83 5.6 1 2 0.72 3.31 7.9 77 184 4.52 2.84 8.4 413 1,226 NLU Inferred 0.00 1.71 11.2 0 1 0.04 1.63 7.0 2 10 0.63 2.08 7.3 42 148 Meas+Ind+Inf 0.54 2.48 4.7 43 81 0.83 3.21 7.9 86 210 7.66 2.77 8.2 683 2,030 Measured ------Indicated 0.40 2.35 3.7 30 47 0.19 2.66 2.8 16 17 0.22 2.56 4.0 18 28 PVN Inferred 0.07 2.77 4.3 6 9 0.02 2.16 2.3 1 1 0.24 2.86 3.6 22 28 Meas+Ind+Inf 0.47 2.41 3.8 36 56 0.21 2.61 2.8 17 18 0.46 2.72 3.8 40 56 Measured 0.93 1.85 - 55 ------0.01 2.31 - 1 - Indicated ------STOCKPILES Inferred ------Meas+Ind+Inf 0.93 1.85 - 55 ------0.01 2.31 - 1 - Measured ------Indicated ------TKN Inferred 0.04 1.76 5.3 2 6 ------Meas+Ind+Inf 0.04 1.76 5.3 2 6 ------Measured ------Indicated ------TKS Inferred ------Meas+Ind+Inf ------Measured ------Indicated 0.13 2.90 8.0 12 33 0.05 3.60 21.0 6 34 - - - - - VNE Inferred 0.03 2.00 3.0 2 3 0.01 1.80 5.0 1 2 0.36 3.00 9.0 35 104 Meas+Ind+Inf 0.16 2.80 7.0 14 36 0.07 3.20 18.0 7 41 0.36 3.00 9.0 35 104

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Resource Estimation Methodology – DIS, DSC, DSW, NLU, NKE, NKW, VNE, PVN, LOL, TKN and TKS

Table 4: Experts Involved: DIS, DSC, DSW, NLU, NKE, NKW, Stockpiles, VNE, PVN, LOL, TKN and TKS.

Expert and Company Information Area of Responsibility/ Source Expertise

Paul Quigley Supervision of data compilation, data extraction, geological interpretation, Manager Geology wire-framing and ore control block models by LXML geologists, stockpiles, LXML and overall responsibility for classification of 2007 resource estimates.

Jason McNamara Supervision of data compilation, data extraction, geological interpretation, Superintendent Geology wire-framing and classification by LXML geologists of 2007 resource LXML estimates.

Mike Stewart Geostatistical resource estimations and preparation of 3D block models for QG NKW and NKE. Supervision of resource estimation for VNE.

Mark Sweeney Variography and geostatistical analysis for primary gold feasibility study AMC Consultants Pty Ltd models (DIS, DSW and NLU) and TKN. .

Tracie Burrows Resource estimation and block model validation for primary gold feasibility AMC Consultants Pty Ltd study models (DIS, DSW and NLU) and TKN.

Steve Hyland Geological interpretation, wire framing and resource estimation of the NLU Ravensgate and LOL oxide update. Geostatistical analysis and variography for TKS-Au

Bosta Pratama Geological interpretation, wire framing and resource estimation of DSW and Senior Resource Geologist DIS site validation models. LXML

Craig Michael Senior Resource Geologist Geological interpretation, wire framing and resource estimation of VNE. LXML Saut Parulian Senior Resource Geologist Geological interpretation, wire framing and resource estimation of PVN. LXML

Kerrin Allwood Database validation. Geological interpretation and wire framing of TKN and Geomodelling Ltd. TKS. Resource estimation and block validation for TKS

Tables 5, 6, 7 and 8 outline the key processes and features of the inputs used to create the July 2007 resource estimates, in the format of “Table 1” of the 2004 JORC Code (Estimation and Reporting of Resources).

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Table 5: Summary of Resource Estimation Methods and Key Inputs for these resources remained unchanged from 2006

The following tabulation summarises, by deposit, the drilling used in the 2006 Resource Estimates. No additional drilling or resource work has been undertaken in these areas during since the 2006 resource update:

# Au Drillhole # # Au Deposit metres # intervals LW* type holes assays assays DDH 18 1,139.5 809 784 NKE RC 233 7,166.5 7,154 7,122 696 DDH 32 1,766.0 1,338 1,315 NKW RC 426 20,920.7 20,811 20,450 1,409 GC-RC 2,918 49,170.0 39,287 39,193 6,401 VNE DDH 39 2,550.5 2134 1629 0 RC 236 17,266 17,205 16,735 889

* Leachwell™ accelerated bulk leach extractable gold. This is a subset of the Au assays tabulated (not additional).

NKE: The majority of NKE has been drilled on a 25x25m grid pattern of vertical RC holes. Diamond drillholes are generally inclined 60° to the south. The grid drilling encompasses two areas of supergene enrichment lying north and south of the surface outcrop of a rhyodacite porphyry dyke. Mining of NKE-A Drilling (the southern lode) commenced in June 2006. Configuration Although 75 new RC drillholes have been added to NKE (during 2006), to complete the 25x25m pattern around the margins of the orebody, these have not yet been incorporated into new estimates. The majority of these holes are outside the limits of interpreted ore. Similarly, 9,690m of grade control drilling have not been included as they have generally been confirmatory of the resource data.

NKW: NKW was originally drilled between 1995 and 1997 with south-dipping diamond drillholes. The main oxide ore zone was then RC drilled at 50x50m centres in 2000, and subsequently in-filled to 25x25m during 2001 and 2003, prior to production. The bulk of the oxide and partial oxide material in NKW has been RC grade control drilled on a grid pattern of 10mE by 5mN. This data is used for ore control grade estimation. Subsequent drilling during 2004 and 2005 was focused on sulphide resources. Additional drillholes also test the linking zones between NKE and NKW, and NKW and NLU and a further eighty holes were drilled in 2006 to test sulphide resource extensions. Drillholes within the Nalou resource limits were incorporated into the 2007 Nalou updates. NKW holes have not been incorporated as no updates of these resources were undertaken during 2007. There is currently no production from NKW.

VNE: Drillholes at VNE were drilled in a 25x25m grid pattern with minor areas closed to 25x12.5m. All 39 diamond holes (2,550.5m) were drilled between 1994 and 2003 and generally drilled at 60° to the north except for a few drilled at various azimuths and dips as exploratory holes. No grade control drilling or mining has been conducted at VNE. Difficulties in understanding orebody geometry required RC drillholes to be drilled dipping 60° both north and south to resolve the strike and dip of mineralisation. In 2006, 236 RC holes (17,266m) were drilled. Two main zones of mineralisation have been delineated, a shallow zone of surficial float and an outcropping sub-vertical source lode. The source lode has a small sulphide component which is open at depth.

The June 2007 Mineral Resources have been defined with RC drillhole data from both exploration/resource definition and grade control drill programs, and diamond drillhole data from exploration/resource drill programs. Although limited blast hole sampling was used for production grade control, the assay data is not used for resource estimation. No updates have have been performed in Drillhole these areas and the drilling datasets remain unchanged since 2005. QA/QC Holes greater than 50m were routinely downhole surveyed through the drill rods (chrome rods for RC holes). It is considered that sample location errors on shorter holes (ie. Less than 50-70m) are immaterial as holes are generally at high angles to mineralisation geometries.

During preparation of the VNE estimate, a number of drillhole collar location problems were identified, arising from the use of two coordinate systems. The issues identified are resolved and further Page 40 Oxiana Limited 2007 Mineral Resource Explanatory Notes

investigation shows that this problem does not affect holes used in other resource estimates within this statement.

All resource estimates are based on RC and diamond core samples. Diamond core varies between 7% and 15% of the total drilling. RC resource drilling is sampled at regular 1m intervals while diamond core is sampled on variable intervals dictated by geology, with a default sample length of 1m. Sampling of both drilling methods follows established procedures. A recent variability study documents the nature of the mineralisation and the response to sampling.

Grade control RC drillholes for the NKW deposit was sampled on 1.25m intervals (1/2 bench height). All RC samples were riffle split to a maximum of 5kg, with wet samples being air dried prior to splitting. RC sample quality is an area of some concern. There was commonly a large proportion of wet RC sampling present below the base of oxidation.

The proportion of wet sampling in oxide and partial oxide zones was relatively low. During interpretation, the presence of down-hole sample contamination below expected locations of high grade mineralisation into rocks that would generally not be expected to host significant mineralisation were noted, and the interpretations adjusted to exclude these tails. While it is difficult to arrive at statistically robust conclusions about the integrity of wet RC sampling, the data available suggests a potential positive grade Sampling bias from wet RC sampling.

The presence of wet RC sampling was considered during resource classification by reviewing the ratio of wet/moist to dry samples in determining block grade estimates. Areas of the estimate where wet samples have a high influence block grades were classified to reflect the associated introduced higher risk.

Low drill recovery samples tended to be of slightly lower grade than high recovery samples, however the net effect of this to the resource estimates is judged to be immaterial.

Analysis of field duplicates for samples collected during 2006 showed that sample splitting contributed a moderate-high imprecision (~60% absolute mean percentage difference (MAPD) on samples greater than 0.25g/t) to assay data. Analysis of sampling prior to 2006 (the bulk of assay data) showed a low- moderate imprecision (~12% MAPD). A review of sampling practices (with particular emphasis on grade control) was undertaken in 2006. Findings indicated a reduction in the quality of sampling being undertaken during RC drilling. Immediate actions were undertaken to rectify sampling practices. For resource drillholes all drilling through ore zones was undertaken using higher quality diamond drilling as of May 2006.

Analytical protocols require that all samples are assayed for Au by 30g FA (LXML lab standard). Samples with a FA grade greater than 0.4g/t are subsequently analysed via rapid cyanide leach (PM200, LW) for cyanide recoverable Au. Silver and copper are analysed as required using a 0.4g triple acid digest (BM102) and by C-S analyser. During 2004, the threshold for LW analysis was FA Au >1.0g/t. Analysis Few LW data exist for holes drilled prior to 2003.

Assay results and data transfers were routinely checked to identify potential errors.

Table 5 includes the number of samples FA and LW assayed.

Until December 2005, assays of certified and non-certified standard reference materials submitted to the Sepon laboratory indicated a reasonably consistent underestimation of gold grade of around 6% across all grade ranges. In 2006, this underestimation was more consistently around 3%.

Assay QA/QC Analysis of laboratory duplicates indicates a small analytical imprecision error (~4% absolute mean percentage difference), but this is unlikely to significantly affect the resource estimates.

Analysis of assays from barren samples submitted to the LXML lab indicates that there has been no significant grade contamination affecting the samples used to define resources.

Geology Gold and silver mineralization within NKE and NKW areas is closely associated with decalcification of Page 41 Oxiana Limited 2007 Mineral Resource Explanatory Notes

calcareous sediments, fractured/ sheared contacts with rhyodacite porphyry intrusions and irregularly distributed jasperoid (silica replacement) bodies.

Mineralisation is focused along lithological contacts and high-angle faults. There is a strong correlation between mineralisation and the dolomite-calcareous shale, rhyodacite porphyry- dolomite/calcareous shale and calcareous shale-chert contacts. Some degree of supergene enrichment probably occurs within oxidized zones, although in many instances the interpreted primary geological host apparently lies within the oxide zone due to erosional unroofing.

All of these domains were modelled for the June 2006 resource estimates and have remained unchanged for the 2007 reporting.

Wax-immersion dry bulk density determinations were performed on diamond drill core from all deposits and are the basis for assigning tonnage factors to resource estimates. Dry bulk density values were Tonnage applied based on deposit, oxidation state and lithology on the basis of majority lithology per block. There Factors are a total of 346 and 143 determinations (excluding spurious outliers) from VNE and NKE/NKW respectively.

Visual assessment of the relationship between grade distribution and underlying geology supports the use of grade-based domains for constraining the estimation of gold resources. This is confirmed by statistical analysis which shows a clear step in grade across mineralisation boundaries.

Estimation domains were interpreted for NKE and VNE deposits. These domains were developed using geological boundaries either as hard boundaries where appropriate, or as a guide to grade envelopes using a threshold of approximately 0.25g/t Au. In many cases mineralisation was localised by the presence of lithological contacts (e.g. dolomite/calc-shale), and while it may span the contact, is clearly related across it. Domains have been subdivided on the basis of orientation, which by proxy also separates areas of different mineralisation style. Mineralisation associated with shallow-dipping Resource lithological contacts is distinct from that associated with steeply dipping faults, and distinct from flat-lying Domain oxide hosted supergene mineralisation.

Modelling Estimation domains were created for VNE based almost entirely on grade distribution. This was due to the difficulty of creating coherent and continuous geology domains for lithologies (apart from the rhyodacite porphyry) and issues relating to consistency in geological logging are suspected as being the main reason for the difficulty.

The VNE rhyodacite porphyry bounds the southern edge of mineralisation in some areas and is likely to have been an important control in focussing mineralising fluids. This constitutes a hard boundary of the five estimation domains, which were interpreted as separate pods with individual grade characteristics.

The NKW model interpolation was constrained within lithological domains. The majority of oxide and partial oxide mineralisation is hosted within clay, jasperoid or decalcified shale lithologies which provided effective constraints to estimation. Within the primary zone, the interpreted mineralisation was discontinuous. Specific (non-lithological) estimation domains have not yet been developed.

For each deposit, grade was interpolated into blocks of 25mX x 6mY x 2.5mZ. The extents of all models are tabulated below. All models are aligned with an unrotated regular 5x3m ore control grid. 2m composite data within domains forms the basis of the grade interpolation.

Variography and search neighbourhood optimisation for each domain was performed using a combination of Minesight™ and Isatis geostatistical softwares. In general, the spatial continuity of gold grades shows a moderate nugget effect ratio, and short-moderate ranges of spatial continuity. Grade

Estimation Gold grades were estimated into blocks by Ordinary Kriging (OK) using MineSight™ software.

Estimation was performed in a single pass, as most domains were sampled on a regular grid pattern and samples are thus similarly located with respect to blocks. Gold grades were also estimated in a single pass into the “not ore” domain (i.e. blocks or portions of blocks falling outside the ore solids) for the NKE deposit.

The silver data set is incomplete and criteria for the selection of samples for Ag analysis have changed over time. Statistical analysis shows that Ag and Au are at best weakly correlated. The current value of contained silver (where Au >1.0g/t) is only 8% that of gold, and contributes a considerably lower Page 42 Oxiana Limited 2007 Mineral Resource Explanatory Notes

recovered value. The recovered Ag grade (through mining and milling) shows acceptable reconciliation with the global estimated grades, therefore the Ag estimates are considered to be acceptable.

Silver grades were estimated as follows: Deposit Domain Interpolation Ore OK (domains 1,2), assigned mean grade (domain 3) NKE Not Ore Not estimated NKW Unconstrained Inverse Distance Ore Inverse Distance VNE Not Ore Not Estimated

The recovery estimate was determined by averaging the LW:FA Au grades for each oxidation state domain. The interpretation of drillhole data to define the bases of oxide and partial oxide materials includes logging, assay, and mapping (where available) data and is strongly subjective. Given the recovery issues beneath the oxide zone (Au scavenging organic carbon); the local estimates of gold recovery at depth can be of low confidence. However, it is anticipated that on-the-whole the general recoveries estimated at depth are reasonable.

Drillhole logging is used to indicate the presence of organic carbon. However, some problems with the consistency and quality of carbon logging were identified, and there is doubt about the accuracy and significance the interpreted zones of organic carbon within the partial oxide and primary zones.

For NKW LW:FA ratio data and logging information were combined to create a “base of recovery” surface for reserve estimation. The modelled NKE and VNE oxidation state and “high carbon” zones were interpreted and modelled separately for reserve estimation.

Except for VNE, all deposits were grade control drilled with RC on 10x5m centres. This grade control data was included into the resource as follows:

NKW: Resource (QG) and ore control (LXML) models for NKW were combined for the purposes of mine planning and the December 2005 reserve estimate. The method of combining the models accounted for the use of lithological domains as estimation constraints. The reported resource estimates for NKW are derived from the resource data estimate only (although the reserve estimates are based on the combined models). The resource and reserve models agree within 5% on contained ounces.

NKE: The 2005 resource (QG) and 2007 ore control (LXML) models for NKE were combined for the purposes of mine planning and the June 2007 reserve estimate. The method of combining the models accounted for the use of mineralisation domains as estimation constraints. The reported resource estimates for NKE are derived from the resource data estimate only (although the reserve estimates are based on the combined models)..

VNE: Only resource development drillholes have been drilled, and no grade control or mining activity has occurred to date. It is unlikely that deleterious (preg-robbing) carbon is present as it has not been identified in drillholes, nor seen in the nearby Vang Ngang pit (mining completed).

Resource classification was based on the following factors:

⋅ Data density ⋅ Modelled spatial continuity ⋅ Estimation statistics (kriging variance, slope of regression, number of samples, etc) Resource ⋅ Data quality (including the proportion of wet RC samples contributing to block estimates) Classification Classification was applied to aggregate areas, rather than on a block-by-block basis, using explicit coding solids from sectional examination of the classification criteria above. Classifications were applied regardless of oxidation state (which affects recovery and treatment options). Similarly, presence of carbon is not taken into account in resource classification. It must be noted that the confidence on definition of oxidation state is poor.

Resources that have been modelled from RC grade control drilling (10mX x 5mY) are classified as Page 43 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Measured.

Resource models have been validated both statistically and visually. Validation and Audit CS-2 Pty Ltd has conducted and documented reviews/audits of previous resource estimates, and did not identify and significant issues with the methodology.

Page 44 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Table 6: Summary of Resource Estimation Methods and Key Inputs for DIS (includes DSC), DSW and NLU Gold Primary Gold Resource Models (note: NLU oxide gold resources are from a separate model, see table 7).

The following tabulation summarises, by deposit, the drilling used in the 2007 Primary Gold Project Resource Estimates:

# Au Assayed # Au Deposit Drillhole Type # Holes Metres LW* Metres Assays Assays DDH 109 9,226.60 8,543.24 8,543 172 DIS RC 899 44,018.60 41,151.40 41,151 6125 RC+DD “Tail” 58 9,946.50 9,817.85 9,818 1,280 GC-RC 7,430 58,605 58,360.8 35,912 9,929 DDH 95 9152.75 8,029.83 7,261 2,227 RC 588 44,818.00 42,677 42,677 2,276 DSW RC+DD “Tail” 44 8,044.78 5,173.28 5,180 1.796 GC-RC 33,896 48,888.80 48,858.80 36,940 4,549 DDH 154 11,040.25 10,174.10 9,009 8,476 RC 928 67,095.50 62,506.50 62,410 9,296 NLU RC+DD “Tail” 161 5,120.90 5071.60 5,086 0 GC-RC 3,464 47,947.5 47,616.3 34,435 9,195

* Leachwell™ accelerated bulk leach extractable gold. This is a subset of the Au assays tabulated (not additional).

DIS: The majority of drilling for the Discovery (DIS) Primary Gold resource definition program was drilled as extensions to the existing regular 25x25m centres that were used for earlier oxide resource definition drilling programs. The majority of the historic drilling carried out for the definition of oxide resources was drilled Drilling vertically to intersect mineralization that was seen to be generally shallow dipping at ~5 degrees towards the Configuration north. The deeper sulphide resources are generally covered also by a 25x25m drill pattern although towards the North-West of the main deposit area in a newly identified and high Au grade mineralized zone the drill density is predominantly at 50x50m. These deeper holes are predominantly diamond holes (with RC precollars) and are South oriented dipping at approximately -60 degrees in order to best intersect the main mineralized zones. To date a total of 109 DDH, 899 RC and 58 RC+DD drill-holes have been drilled in the DIS Area. A large amount (58,605m) of pre-existing RC grade control drilling had been carried out up to June 2007. All this drilling has been incorporated into the primary gold modelling estimate and was used directly to define near surface mineralization geometries that were subsequently extended into the deeper primary gold mineralization areas.

DSW: The majority of available resource drilling in the Discovery West (DSW) primary gold program resource definition area is drilled on regular 25x25m centres. Most of the drilling in this area was carried out for earlier phases of oxide Au resource definition. Additional primary gold resource definition drilling was designed and targeted towards partly in-filling the existing drill pattern as well as helping to define the known or expected primary gold mineralization extensions at depth. Nearly all drill-holes were orientated towards the South and were predominantly drilled at angles of -60 degrees. A total of 95 diamond holes, 588 RC holes and 44 RC+DD Tail” holes have been drilled at DSW and were used for the Primary Gold Resource modelling program. An extensive dataset from RC grade control holes (61,991.5m) exists in DSW and have been used in the definition of the mineralisation domains along with resource drill hole data.

NLU: Additional drilling specifically for targeting Primary Gold mineralization was carried out with the view to defining the extent of mineralization both laterally and at depth. The new drilling was an in-fill and extension of the pre-existing resource drill pattern initially used for targeting oxide resources. The original oxide drilling was generally a combination of 50x50m and 25x25m patterns. The new Primary Gold Project drilling closes the drilling pattern in most areas to a 25x25m grid. Some peripheral areas where low grade mineralisation is present is covered by a 50x50m drill pattern and in some areas this extends out to a 50x100m pattern. The bulk of the previously mined oxide resource in NLU was grade controlled on a 10mE by 5mN pattern using RC drill-holes in 2002. The majority of drill-holes at Nalou have been drilled vertically to target many of the flat lying or shallow dipping ore zones identified. The overall geological and mineralization modelling framework has been developed from the available 154 diamond holes, 928 RC holes and 161 RC+DD “Tail” holes Page 45 Oxiana Limited 2007 Mineral Resource Explanatory Notes

drilled and accumulated during the period. Grade control data (47,948m) has been used where available to refine the mineralisation domains.

Drillhole Drill collar locations have been validated through a process of database and spatial checking for both QA/QC historical and recent data. A number of holes were identified as having suspect locations. These issues were resolved prior to modelling of the data.

Holes greater than 50m (predominantly angled holes) were routinely downhole surveyed through the drill rods (chrome rods for RC holes). It is judged that sample location errors on shorter (ie. Less than 50-70m), predominantly vertical holes are immaterial. In effect, missing survey data are unlikely to significantly affect resource estimates.

All resource estimates are based on RC and diamond core samples. The proportion of diamond core varies between deposits but ranges from 5% and 20% of the total drilling. RC resource drilling is sampled at regular 1m intervals while diamond core is sampled on variable intervals dictated by geology, with a default sample length of 1m. Sampling of both drilling methods follows established procedures. A recent variability study documents the nature of the mineralisation and the response to sampling.

Historically grade control RC drillholes for DIS and NLU deposits were sampled on 1.25m intervals (1/2 bench height). DSE was sampled on either 1.25m or 2.5m intervals, with DSE-D and DSE-E completely on 2.5m intervals. Since December 2006 all grade control sampling has been undertaken on 1m intervals. All RC samples were riffle split to a maximum of 5kg, with wet samples being air dried prior to splitting.

Moist/wet RC sample quality has historically been an area of some concern and in 2006 new resource drilling protocols were introduced to reduce this risk. The proportion of moist/wet RC samples varies from deposit to deposit but there are on average 30-40% of “ore zone” samples flagged as wet or moist. Recent analysis has indicated that moist samples may in fact be of lowest quality. Further investigation into this Sampling observation is ongoing.

Samples from wet RC holes often showed intervals of mineralisation up to 20% longer than those from dry RC holes. During interpretation, the presence of downhole sample contamination below expected locations of high grade mineralisation into rocks that would generally not be expected to host significant mineralisation were noted, and the interpretations adjusted to exclude these grade-tails in drillhole data.

A twin hole study of 27 RC versus DD holes was undertaken during 2006. This study indicated that RC samples appear to have a positive bias in grade of 10-15% but also indicates that many of these twins showed moist/wet RC samples or low recovery (ie. poor quality RC holes, hence why they were twinned). Current procedure flags these holes for removal or alternatively for downgrading of the classification which mitigates the effect of and the risk associated with these samples.

In general, sample recovery in Primary (Pr) intervals is noticeably better than in oxidized (Ox) or transitional (Tr) intervals, and recovery for diamond core (90%) significantly better than RC (70%). The difference between ore and waste recovery is not significant.

Page 46 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Due to the focus on primary mineralisation new analytical protocols were established to ensure an adequate suite of elements was gathered for the Feasibility Study. To efficiently control costs separate waste/mineralisation protocols were devised. The following protocol was performed on the targeted mineralised zones:

Routine Analysis:

Fire Assay (Au) Multi-element ICP (Ag, As, Bi, Cd, Cu, Fe, Mn, Mo, Pb, S, Sb, Zn)

Ore Grade Analysis: Analysis +/- Au FA-Grav (if Au >10g/t Au) +/- Cu OG-46 (if Cu >0.5%Cu)

Metallurgical Specific Analysis:

Total Carbon; CAI (=C organic); CO3 by diff Total Sulphur; SCIS (=S sulphide); SO4 by diff AuCN; Cyanide soluble Au AuPR; preg-rob by calculation +/- Au Leachwell (if Au >10g/t Au) +/- Cu Leachwell (if Cu >0.5%Cu)

Assay results and data transfers were routinely checked to identify potential errors.

An extensive analysis of all assay QAQC data was undertaken as part of the Primary Gold Feasibility Study. The findings of this report can be summarised as:

Performance of LXML inserted standards generally shows a negative bias (wrt expected/certified grade) ranging from -4% to -8% depending on laboratories and the standard submitted. Because the bias varies from standard to standard it is possible that in some cases the bias may be related to matrix compositions. Assay Assays done with gravimetric finish are essentially unbiased. QA/QC Analysis of assays from barren samples (pulp and coarse blanks) submitted to the LXML lab indicates that whilst at times, there have been minor periods of reduced performance in the laboratories, there has been no significant grade contamination affecting the samples used to define resources.

Past analysis of laboratory duplicates indicated a small analytical imprecision error (~4% absolute mean percentage difference), but this is unlikely to significantly affect the resource estimates.

Gold and silver mineralization within NLU, DSW and DIS areas is closely associated with decalcification of calcareous sediments, fractured/ sheared contacts with rhyodacite porphyry intrusions and irregularly distributed jasperoid (silica replacement) bodies.

Mineralisation is focused along lithological contacts and high-angle faults. There is a strong correlation Geology between mineralisation and the dolomite-calcareous shale, rhyodacite porphyry- dolomite/ calcareous shale and calcareous shale-chert contacts. Some degree of supergene enrichment probably occurs within oxidized zones, although in many instances the interpreted primary geological host apparently lies within the oxide zone due to erosional unroofing.

All of these domains were updated for the June 2007 resource estimates. For NLU this represents the first model with defined grade domains as the 2005 resource was domained using geological solids only.

Wax-immersion dry bulk density determinations were performed on diamond drill core from all deposits and Tonnage are the basis for assigning tonnage factors to resource estimates. There are a total of 2935 determinations Factors (excluding spurious outliers) from these projects. Only measurements taken through the primary gold drilling program were used as this large dataset is considered to be of a higher quality than previous results. For DIS, NLU and DSW, deposit specific average dry bulk densities were calculated by rock unit, broken down

Page 47 Oxiana Limited 2007 Mineral Resource Explanatory Notes

by oxidation state, and applied to whole blocks based block coding. No significant changes were observed in the primary zone however reductions of around 20% were experienced in the transitional and oxide zones when compared to bulk density figures calculated for the 2006 oxide resource statement in DSW and DIS.

Estimation domains were interpreted for all deposits (NLU, DIS and DSW). These domains were developed Resource using geological boundaries as a guide to grade envelopes using a threshold of 0.20g/t Au, 0.25g/t Au and Domain 0.5g/t Au for DSW, DIS and NLU respectively. Different grade cut-offs were chosen based on apparent Modelling grade continuity.

In many cases mineralisation is focused along lithological contacts (e.g. dolomite/calc-shale), and while it may span the contact, is clearly related across it. Domains have been subdivided on the basis of orientation, which by proxy also separates areas of different mineralisation style. Mineralisation associated with shallow- dipping lithological contacts is distinct from that associated with steeply dipping faults, and distinct from flat- lying oxide hosted supergene mineralisation.

Block models were constructed in Datamine by AMC consultants using LXML flagged drill holes, composite data and wireframes. Geostatistical analysis was also performed by AMC and these parameters were used in the grade estimation process. Check models were run simultaneously by LXML as an additional means of validation. This discussion details the Datamine block model process.

For each deposit, grade was interpolated into blocks with a parent cell size of 25m x 24m x 2.5m (X, Y, Z).

The models are coded to include data in four different geological and statistical domains that are used to control estimation of grade into the models. These domains are; interpreted lithology, oxidation state, grade shell and mineralisation orientation.

Resource drilling (both RC and diamond) is dominantly sampled at 1m intervals. A 2m composite was selected for the Sepon primary gold statistical analysis and grade estimation. The geostatistical analysis was undertaken by AMC consultants (Brisbane office) using the Isatis geostatistical software package. From this work Variography and estimation parameters were developed for gold, carbon (organic and carbonate), sulphur (total and sulphidic), silver, iron and arsenic for all deposits. In addition to this global change of support work to quantify conditional bias issues was undertaken as required.

The average relative nugget effect was around 25 percent to 30 percent. The average drillhole spacing is Grade around 25m, and the longest range structures generally vary between 50m and 150m. Shortest range Estimation structures are generally in the vertical direction which contains the densest composite data. In summary, the spatial continuity of gold grades shows a moderate nugget effect and short-moderate ranges of spatial continuity.

Gold grades were estimated into blocks by Ordinary Kriging (OK) using Datamine software. Two search passes were required to fill the gold model with grades. The first search pass filled 95% of all cells with gold values. The second search pass, which was twice the size of the first pass, filled up to 99% of the cells with estimated gold values. Where uniformed cells remained the mean informed cell grade for that element (as calculated from the domain restraints for that element) was assigned and flagged in the model. Although isolated mineralisation exists outside of the grade domain no part of the estimation process has accounted for this material.

The silver data set is incomplete and the logic used to select samples for Ag assay has changed over time. Statistical analysis shows that Ag and Au are un-correlated (or at most weakly correlated). This suggests that it may be reasonable to assume that the available silver data is representative of the entire volumes to be estimated. However this assumption cannot be rigorously tested. The current value of contained silver (where Au >1.0g/t) is only 8% that of gold, and contributes a considerably lower recovered value. The recovered Ag grade (through mining and milling) shows acceptable reconciliation with the global estimated

Page 48 Oxiana Limited 2007 Mineral Resource Explanatory Notes

grades, therefore the Ag estimates are considered to be acceptable. Silver grade has been estimated also using Ordinary Kriging (OK) within the gold domain but has had unique Kriging parameters for each orientation domain.

Beneath the level of complete oxidation, two main factors affect gold recoveries: oxidation state (sulphide- bearing mineralisation is variably refractory) and the presence of organic carbon (pregnant solution robbing). The currently understood controls of these elements resulted in the following domaining: Sulphide Sulphur is domained on oxidation, lithology and gold grade domain whilst Organic carbon is modelled on oxidation and gold grade domain. Both elements were estimated using Ordinary Kriging (OK). Further studies and extensive metallurgical work are continuing to refine the understanding of these elements and their impact on processing.

All deposits have grade control data available and drilled with RC on 10x5m centres. Grade control drilling was used to interpret geology and grade domains on 10m spaced sections.

Analysis of the raw assay data for grade control and resource data was undertaken for each deposit (not separated on geology). The findings of the analysis were:

⋅ DIS – Paired GCAu and ResAu data show very similar Au distributions and mean grades. This suggests minimal risk in replacing a ResAu data based estimate with a GCAu data based estimate;

⋅ DSW – Paired GCAu and ResAu data show notably different Au distributions and mean grades; This suggests some risk in replacing a ResAu data based estimate with a GCAu data based estimate (in that it may be better or worse);

⋅ NLU – Paired GCAu and ResAu data show notably different Au distributions; This suggests some risk in replacing a ResAu data based estimate with a GCAu data based estimate (in that it may be better or worse); NLU also shows a significantly lower grade for paired data in the 20m separation set than for the 2m separation set.

The resource and grade control data sets were not mixed due to the potential difference in quality of the two data sets as stated above. Grade control data was then estimated separately within the gold grade domain and stored in the model and utilised as a comparison and check against the resource drillhole estimates.

Resource classification followed a two stage process and applies to the Au Resource Estimate only. Initially classification is applied on a block-by-block basis incorporating a number of quality and geostatistical criteria:

⋅ Sample moisture ⋅ Sample recovery ⋅ Relative Kriging Variance of a cell ⋅ Number of samples used to inform the cell with grade ⋅ Number of search passes required to fill a cell with a gold grade

Following this, a process of annealing was performed whereby classification solids were constructed around aggregate areas by examination of the following criteria on a section by section basis:

Resource Classification ⋅ Preliminary classification (as detailed above) ⋅ Drill hole density ⋅ Sample moisture ⋅ Geological and structural confidence

Classifications were applied regardless of oxidation state (which affects recovery and treatment options). Similarly, presence of carbon is not taken into account in resource classification. It must be noted that the confidence on definition of oxidation state is poor.

Areas in which RC grade control drilling exists (10mE x 5mN) are classified as Measured. Additional areas of measured have been defined in high confidence primary areas using the criteria as defined above however due to the variability experienced in the oxidation surfaces a “buffer” of lower confidence exists through the transitional zone.

Page 49 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Resource models have been validated both statistically and visually.

AMC consultants have conducted a review and partial audit of the primary gold resource models. No Validation significant issues with regard to the methodology were identified. Significant potential risk was identified for and Audit possible bias to be introduced in bulk density sample selection in poor recovery zones. Also significant risk was assigned to the incomplete understanding of carbon and sulphur with potential impacts on processing options. It is believed that these risks are being controlled by current procedures and the impact of carbon and sulphur confidence on reserve classification is well understood by all end users of these models.

Page 50 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Table 7: Summary of Resource Estimation Methods and Key Inputs for the Nalou Oxide Model

The following tabulation summarises the drilling used in the August 2007 Nalou Oxide Au Resource Estimates:

# Au # Assayed # Au Metres LW* Holes Metres Assays Deposit Drillhole Type Assays DDH 268 16,052.25 15,186.10 9,704.2 9,168 RC 931 67,228.50 62,639.50 62,543 9,296 NLU RC+DD “Tail” 161 5,120.90 5071.60 5,086 0 GC-RC 4,211 62,909.25 61,053.25 47,875 13,913

* Leachwell™ accelerated bulk leach extractable gold. This is a subset of the Au assays tabulated (not additional).

NLU: The drilling campaign conducted for the Primary Gold Development Program was carefully reviewed as part of the standard resource modelling processes. One of the results of this study was the increased understanding of the distribution of oxide Au Ore particularly in the vicinity of the area known as Pit Area A. To this end an additional program of resource definition drilling was undertaken to in-fill the pre-existing Drilling Primary Gold drilling pattern thereby improving the boundary resolution of the oxide Au resources. The Configuration density of this drilling was increased to approximately 50x50m and in some places 25x25m. In addition a close spaced (10x5m) grade control program was commenced to help preliminary planning of future mining within “Pit Area A”. Most of the Nalou Oxide Au study was carried out with the view to better defining the extent of mineralization laterally and the “transition zone” contact. The bulk of the previously mined oxide resource in NLU was grade controlled on a 10mE by 5mN pattern using RC drill-holes in 2002. This data was also used to help refine the near surface mineralization envelopes and then extend the known mineralization geometries for the Primary Gold Development Program. The majority of drill-holes at Nalou targeting the Nalou Oxide Au zones have been drilled vertically to target many of the flat lying or shallow dipping ore zones identified. The overall geological and mineralization modelling framework has been developed from the available 268 diamond holes, 931 RC holes and 161 RC+DD “Tail” holes as well as 4,211 RC Grade Control drilled during the period 1995 to February 2007. The newest phase of RC Grade Control Data as of July 28th, 2007 was used to help re-build and refine mineralization geometries for the recent oxide Au resource modelling. The new “Oxide” resource block model ultimately re-constructed is an up-date of the earlier generation Primary Gold Resource model.

(The 2007 resources include an additional 114 diamond (5,012m), 3 Reverse circulation (133m), 161 RC precollar with diamond tail (5,121m) and 717 Face Sampling Reverse Circulation Grade Control drill holes (14,724m) to the 2006 resource estimate [these holes are included in the figures above].)

The June 2007 Mineral Resources have been defined with RC drillhole data from both resource definition and grade control drill programs, and diamond drillhole data. Although limited blast hole sampling was used for production grade control, the assay data is not used for resource estimation.

All resource estimates for Nalou Oxide Au are based on RC and diamond core samples. Although most of the sample collected and assayed in the vicinity of Nalou Pit Area A” are of RC type. Diamond core samples comprise approximately 13% of the total drilling. RC resource drilling is generally sampled at

regular 1m intervals while diamond core is sampled on variable intervals dictated by geology and Sampling mineralisation, although a nominal sample length 1m is used. Sampling of both drilling methods follows

established procedures used at Sepon. Many previously documented studies have described the overall nature of Au mineralisation within various material types including oxidized material and also the typical observed response to sampling.

Page 51 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Most of the Grade Control RC drill-holes for the previously mined NLU deposits were sampled on 1.25m intervals (1/2 bench height). This series of RC samples were riffle split to a maximum of 5kg, with wet samples being air dried prior to splitting. New grade control procedure requires wet samples to be split by a process of quartering. The most recent RC Grade Control holes drilled in the vicinity of “Pit Area A” were sampled at 1 m intervals, thereby matching the majority of the standard RC Resource drill-holes.

The most recent phase of drilling for oxide resources were analysed at the Sepon Site laboratory. Analytical protocols require that all samples are assayed for Au by 30g FA (LXML lab standard). Samples with a FA grade greater than 0.4g/t are subsequently analysed via rapid cyanide leach (PM200, LW) for cyanide Analysis recoverable Au. Silver and copper are analysed as required using a 0.4g triple acid digest (BM102) and by C-S analyser. During 2004, the threshold for LW analysis was FA Au >1.0g/t. Few LW data exist for holes drilled prior to 2003.

Assay results and data transfers were routinely checked to identify potential errors

A thorough review of assay QAQC performance has been undertaken as part of the primary gold project. The findings of this analysis were:

Performance of LXML inserted standards generally shows a negative bias ranging from -4% to -8% depending on laboratories and the standard submitted. Because the bias varies from standard to standard it is possible that in some cases the bias may be related to matrix compositions. Assays done with gravimetric finish are essentially unbiased.

Analysis of assays from barren samples (pulp and coarse blanks) submitted to the LXML lab indicates that whilst at times, there have been minor periods of reduced performance in the laboratories, there has been no significant grade contamination affecting the samples used to define resources.

Past analysis of laboratory duplicates indicated a small analytical imprecision error (~4% absolute mean percentage difference), but this is unlikely to significantly affect the resource estimates Assay QA/QC A review of the QAQC data for the most recent drilling program found:

Blank performance was acceptable with rare evidence of cross sample contamination.

Standards indicate a negative bias of 3-5%. This means that routine assay results may be slightly underestimated.

Field duplicates were only collected on RC and show low to moderate performance. Due to current protocols RC was performed generally on low grade zones and high quality diamond drilling used to drill ore zones.

Check analysis was performed on 4,200 mineralised samples from a number of oxide projects, Nalou included. The samples were sent to ALS laboratories and findings show that results from both laboratories are comparable with a slight positive bias at ALS.

Nalou is a relatively flat lying and undulating deposit. The stratigraphy dips more steeply along the northern edge as it wraps into a roughly E-W striking fault which truncates mineralisation. The undulation is the result of small scale folding associated with generally NW-SE striking faults, which appear to have been the conduits for mineralising fluids. Gold and silver mineralisation lies along lithological contacts but is best developed in the hinges of folds. Karstic weathering is strongly developed in the Nalou deposit and as a result has created pods of high grade but generally discontinuous mineralisation. Geology The definition of the base of complete oxidation is critical in oxide resources as the introduction of partially oxidised material into the processing circuit can significantly reduce gold recoveries due to the presence of organic (“preg-robbing carbon”) and/or refractory gold.

Rapid cyanide leach (LW) is considered to be an adequate indicator of the gold recovery likely to be experienced by mineralised material when processed through the current gold processing plant.

Page 52 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Where the data was available the LW/FA ratio was used in conjunction with logging to help refine the placement of the interpreted oxide, transition and primary (fresh) surfaces.

The lithological domains from the recently completed the primary gold feasibility study were utilised with little to no modification required. These domains were used to code the appropriate material domains in the new Nalou Oxide Au model. The lithology domains in conjunction with the oxidation state designation in the resource block model were used to assign variable bulk densities within the block model.

The standard wax-immersion dry bulk density determination methods were continued throughout the most recent drilling programs carried out at Nalou. They were performed on diamond drill core from all deposits and are the main basis for assigning tonnage factors to resource estimates. There are a total of 2,935 determinations (excluding spurious outliers) from the primary gold deposits of which 891 were directly Tonnage associated with the Nalou deposit. This data-set was used for the Nalou Oxide Au modelling. As oxidation Factors state classification has a direct impact on dry bulk density allocation minor changes to tonnage have occurred compared to the 2007 primary gold model. The dry bulk density determinations once categorized in this way were applied to whole blocks in the block model based on a majority 50% block-in / block-out coding basis.

Visual assessment of the relationship between grade distribution and underlying geology supports the use of grade-based domains mineralization orientation and lithology type domains for constraining the estimation of Au and Ag distribution.

The mineralisation domain was defined using a “nominal” 0.3-0.5g Au/t lower cut-off. This domain was Resource defined using all available drill data including the RC Grade Control data available as at July 28th, 2007. Domain Modelling Rationalising of the existing primary gold orientation domains was undertaken which resulted in fewer orientation domains for the Nalou Oxide Au model. This was considered appropriate as some of the domains used previously were relatively small and as such did not contain a large number of samples.

The estimation orientation domains were developed in conjunction with the understanding of the existing geological boundaries and structures. Consideration was also given to the presence of the often observed flat-lying oxide hosted “supergene” mineralisation in some places.

The block size selected for use in the Nalou Oxide Au resource model was 15m(E) x 6m(N) x 2.5m(RL). This block size represents a significant reduction in block length when compared to the 25m(X) x 6m(Y) x 2.5m (Z) size block previously used for the Primary Gold Development block model and reflects the increase in data density in the Oxide Mineralisation.

The Resource drilling (both RC and diamond) and the recent RC Grade Control drilling are predominantly sampled at 1m intervals. These samples were composited to 2m lengths for grade estimation.

The Variography used for the new Nalou Oxide Au resource model relied heavily upon the variogram parameters determined by AMC consultants from the Primary Gold Modelling Project. The semivariograms generated and used for the Primary Gold Project modelling and also the New Nalou Oxide Au resource model were calculated and modelled using MineSight™ mining software. In general, the spatial Grade distribution of the gold and silver composites display moderate to high nugget to sill ratios (+33%) and Estimation usually short to moderate ranges with respect to spatial continuity. These observations were expected

when considering previous statistical analysis work carried out for this deposit and the underlying statistics observed for the Nalou are fairly typical for gold deposits.

Most domains were sampled on a reasonable regular grid pattern and as such it was decided that that quadrant searching or block discrimination was not necessary. Sample selection used during the interpolation process was constrained however and a maximum of 3 composites from any given drill-hole were allowed to be used to interpolate a block. The minimum and maximum number of composites Page 53 Oxiana Limited 2007 Mineral Resource Explanatory Notes

allowable to interpolate a block was set as 1 and 24 respectively.

There was a deliberate change in the search parameters used when interpolating blocks from the Resource Drilling data as distinct from the Grade Control drilling data. This was simply a response to the varying respective sample densities available for use in interpolation.

The silver sample data-set is not as extensive as the gold sampling data as the selection decisions related to Ag assaying has changed over time. Statistical analysis shows that Ag and Au are not closely correlated. As such silver was only interpolated into the “equivalent” blocks that were interpolated for Gold where Ag composite sample coverage was adequate. Silver grades were interpolated using the same method and parameters previously derived from the primary gold model.

Classification applies to the Au Resource Estimate only. Classification has been applied on a block-by- block basis incorporating a number of quality and geostatistical criteria:

⋅ Relative data density (ie. grade control drilling available versus only resource data) ⋅ Relative Kriging Variance of a cell ⋅ Number of composites used to inform the cell with grade ⋅ Distance of nearest composite to block centroid. Resource Classification These items were interrogated from the block model and plotted as histograms to review their overall distribution. From this analysis item thresholds are established which are then used to assign relative levels of interpolation confidence to each block. Any blocks within the main mineralized zones greater than 45m from the nearest composite was set as unclassified.

Unlike the primary gold models this classification did not undergo any process of annealing. Whilst the resulting “pattern” of classification seen is somewhat complex it does however give a clear indication of where there are areas of classification uncertainty within the block model.

The Nalou Oxide Au Resource models have been validated both statistically and visually and also by independent external review.

QG Consultants (QG) conducted a preliminary review on the new Nalou Oxide Au resource model in Validation and October 2007. A review document with respect to the data inputs to modelling and the overall block model Audit and resource estimates was produced. QG listed several issues that they considered initially to be

problematic with respect to the overall data quality and modelling methodologies. As a result further updates to the model were undertaken prior to reporting. Any remaining concerns are considered to have negligible effect on the Nalou Oxide Au resource estimate.

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Table 8. Summary of Resource Estimation Methods and Key Inputs for the DIS-F Model

The following tabulation summarises, by deposit, the drilling used in the 2007 Primary Gold Project Resource Estimates:

# Au Assayed # Au Deposit Drillhole Type # Holes Metres LW* Metres Assays Assays DDH 7 381.00 697 567.50 367 DIS-F RC 122 6172.00 0 6172.00 5536 RC+DD “Tail” 0 0 0 0 0 GC-RC 0 0 0 0 0 Drilling Configuration * Leachwell™ accelerated bulk leach extractable gold. This is a subset of the Au assays tabulated (not additional).

DSE-F: The majority of drilling for the Discovery (DSE-F) gold resource definition program was drilled to infill the existing regular 25x25m centres. The majority of the historic drilling carried out for the definition of oxide resources was drilled vertically to intersect mineralization that was seen to be generally shallow dipping at ~5 degrees towards the north. The DIS-F area is defined as being east of 27700E in the complete DIS model. A total of 7 DDH and 122 RC have been drilled for the DIS-F Area. A large amount of pre-existing RC grade control drilling had been carried out up to June 2006. All this drilling has been incorporated into this gold modelling estimate and was used directly to define near surfaced mineralization geometries.

(11 additional RC holes for 436m were drilled in the DSE-F area prior to this update)

RC resource drilling is sampled at regular 1m intervals while diamond core is sampled on variable intervals dictated by geology, with a default sample length of 1m. Sampling of both drilling methods follows established procedures.

Moist/wet RC sample quality has historically been an area of some concern and in 2006 new resource Sampling drilling protocols were introduced to reduce this risk.

During interpretation, the presence of downhole sample contamination below expected locations of high grade mineralisation into rocks that would generally not be expected to host significant mineralisation were noted, and the interpretations adjusted to exclude these tails which potentially represent downhole smearing of gold grade.

Samples from the recent program at DSE-F were analysed at the Sepon Site laboratory. Analytical protocols Analysis require that all samples are assayed for Au by 30g FA (LXML lab standard). Samples with a FA grade greater than 0.4g/t are subsequently analysed via rapid cyanide leach (PM200, LW) for cyanide recoverable Au. Silver and copper are analysed as required using a 0.4g triple acid digest (BM102) and by C-S analyser.

Assay results and data transfers were routinely checked to identify potential errors.

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An extensive analysis of all assay QAQC data was undertaken as part of the Primary Gold Feasibility Study. The DSE-F area is comprised predominantly of data reviewed during this analysis. The findings of this report can be summarised as:

Past analysis of laboratory duplicates indicated a small analytical imprecision error (~4% absolute mean percentage difference), but this is unlikely to significantly affect the resource estimates.

Gold and silver mineralization within DSE-F areas is closely associated with decalcification of calcareous sediments, fractured/ sheared contacts with rhyodacite porphyry intrusions and irregularly distributed jasperoid (silica replacement) bodies.

Mineralisation is focused along lithological contacts and high-angle faults. There is a strong correlation Geology between mineralisation and the dolomite-calcareous shale, rhyodacite porphyry-dolomite/calcareous shale and calcareous shale-chert contacts. Some degree of supergene enrichment probably occurs within oxidized zones, although in many instances the interpreted primary geological host apparently lies within the oxide zone due to erosional unroofing.

All of these domains were updated for the June 2007 resource estimates.

Wax-immersion dry bulk density determinations were performed on diamond drill core from all deposits and are the basis for assigning tonnage factors to resource estimates. There are a total of 2,935 determinations (excluding spurious outliers) from these projects. Only measurements taken through the primary gold drilling Tonnage program were used as this large dataset is considered to be of a higher quality than previous results. For Factors DSE-F deposit specific average dry bulk densities were calculated by rock unit, broken down by oxidation state, and applied to whole blocks based block coding. No significant changes were observed in the primary zone however reductions of around 20% were experienced in the transitional and oxide zones when compared to dry bulk density figures calculated for the 2006 oxide resource statement.

Resource Estimation domains were interpreted for the DSE-F deposit. These domains were developed using Domain geological boundaries as a guide to grade envelopes using a threshold of 0.25g/t Au. Modelling In many cases mineralisation was focused along lithological contacts (e.g. dolomite/calc-shale), and while it may span the contact, is clearly related across it. Domains have been subdivided on the basis of orientation, which by proxy also separates areas of different mineralisation style. Mineralisation associated with shallow- dipping lithological contacts is distinct from that associated with steeply dipping faults, and distinct from flat- lying oxide hosted supergene mineralisation.

Block models were constructed in MineSight™ by Ravensgate consultants using LXML wireframes, flagged drill hole and composite data. Geostatistical analysis was also performed by Ravensgate and these Grade parameters were used in the grade estimation process. Check models were run simultaneously by LXML as Estimation an additional means of validation. This discussion details the Minesight block model process.

For DSE-F deposit, grade was interpolated into blocks with cell size of 25m x 6m x 2.5m (X, Y, Z). The block size was selected based on drill spacing.

Page 56 Oxiana Limited 2007 Mineral Resource Explanatory Notes

A common grid origin and set of co-ordinates was used for DIS-F thereby allowing all models to be treated as a subset of a larger whole.

The models are coded to include data in geological and statistical domains used to control estimation of grade into the models. These domains include are: interpreted lithology, oxidation state, grade shell and mineralisation orientation. Resource drilling (both RC and diamond) is dominantly sampled at 1m intervals and composited to 2m for statistical analysis and grade estimation.

The geostatistical analysis was undertaken by Ravensgate consultants using MineSight™ MSCompass geostatistical software package. From this work Variography and estimation parameters were developed for gold and silver. In addition to this global change of support work to quantify conditional bias issues was undertaken as required.

The average relative nugget effect was around 25 percent to 30 percent. The average drillhole spacing is around 25m, and the longest range structures generally vary between 50m and 150m. Shortest range structures are generally in the vertical direction which contains the densest composite data. In summary, the spatial continuity of gold grades shows a moderate nugget effect and short-moderate ranges of spatial continuity.

Gold grades from the 2m down-hole composite data-set were interpolated into the resource model blocks by use of the Ordinary Kriging (OK) technique using MineSight™ software. Estimation was performed in a single pass for each orientation domains. A separate set of interpolation runs and Au grade items were interpolated depending on whether Resource Drilling data was used to interpolate a block. Most domains were sampled on a reasonable regular grid pattern and as such it was decided that that quadrant searching or block discrimination was not necessary. Sample selection used during the interpolation process was constrained however and a maximum of 3 composites from any given drill-hole were allowed to be used to interpolate a block. The minimum and maximum number of composites allowable to interpolate a block was set as 1 and 24 respectively.

The silver sample data-set is not as extensive as the gold sampling data as the selection decisions related to Ag assaying has changed over time. Statistical analysis shows that Ag and Au are not closely correlated. As such silver was only interpolated into the “equivalent” blocks that were interpolated for Gold where Ag composite sample coverage was adequate. Silver grades were interpolated using the same method and parameters previously derived from the primary gold model.

Classification applies to the Au Resource Estimate only. Classification has been applied on a block-by-block basis incorporating a number of quality and geostatistical criteria:

⋅ Relative data density ⋅ Relative Kriging Variance of a cell ⋅ Number of composites used to inform the cell with grade ⋅ Distance of nearest composite to block centroid. Resource Classification These items were interrogated from the block model and plotted as histograms to review their overall distribution. From this analysis item thresholds are established which are then used to assign relative levels of interpolation confidence to each block. Any blocks within the main mineralized zones greater than 45m from the nearest composite was set as unclassified.

Validation The DSE-F models have been validated both statistically and visually. The 2007 primary gold model upon and Audit which this model was based was the subject of an independent external review.

Page 57 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Table 9 compares the global resources by oxide state and resource category between the Jun 2006 and Jun 2007 estimates. As NKW and VNE remained unchanged, Dankoy and Houay Yeng are new resources, the comparison is limited to DIS, DSW, DSC, NLU, NKE and PVN

Table 9: Comparison of Jun 2006 and Jun 2007 Resource Estimates by Category and Oxidation State

JORC Oxidation >1.0 g/t Cut Resource Estimate Category State Mt Au g/t Ag g/t Au kOz Ag kOz Measured OX/POX 2.36 2.24 11.09 170 842 Indicated OX/POX 4.43 2.18 10.50 310 1495 Inferred OX/POX 3.66 1.91 6.58 224 776 Jun-06 Meas, Ind & Inf OX/POX 10.45 2.09 10.07 704 3383 Meas, Ind & Inf PRIMARY 21.02 2.38 9.00 1608 6079 Meas, Ind & Inf All 31.47 2.28 9.24 2309 9354 Measured OX/POX 1.70 2.31 6.79 126 370 Indicated OX/POX 4.41 2.63 7.46 374 1057 Inferred OX/POX 0.58 1.84 5.96 34 111 Jun-07 Meas, Ind & Inf OX/POX 6.69 2.48 7.16 534 1539 Meas, Ind & Inf PRIMARY 20.52 2.61 7.53 1722 4969 Meas, Ind & Inf All 27.21 2.58 7.44 2256 6507 Measured OX/POX -28% 3% -39% -26% -56% Indicated OX/POX 0% 21% -29% 21% -29% % difference 2006 - Inferred OX/POX -84% -3% -10% -85% -86% 2007 Meas, Ind & Inf OX/POX -36% 19% -29% -24% -55% Meas, Ind & Inf PRIMARY -2% 10% -16% 7% -18% Meas, Ind & Inf All -14% 13% -20% -2% -30%

The total oxide and partial oxide resources have reduced by 36% in tonnes and 24% in ounces with a 19% increase in Au grade. The total primary tonnes are reduced by 2%, but Au ounces increase by 7%, and Au grade has increased by 10%.

The reasons for these differences between the estimates (other than depletion through mining or addition through extension drilling) were qualitatively and, where possible, quantitatively assessed. These factors are ranked in order of decreasing importance:

Page 58 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Interpretation of mineralisation Highest

All June 2007 estimates are based on geological interpretations which constrain mineralisation within grade envelopes overlain on lithological models.

The June 2007 geological interpretation of DIS and NLU was significantly improved by dense grade control drilling and in- pit mapping. This resulted in a reduction in interpreted mineralisation continuity and consequent reduction to constrained tonnage and total resource.

Interpretation of OX/POX/PRIMARY boundaries Medium

The OX/POX boundary was re-interpreted for the June 2006 models as more data was available. Although there was no change in the criteria, local changes of interpretation (up and down) of these surfaces had resulted materially in the bulk densities applied to model blocks.

Estimation parameters Low-Medium (Search neighbourhood, variogram models etc)

A new variogram modelling has been applied for the area with additional data (DIS, DSW, DSC, and NLU).

Bulk Density factors Low-Medium

The method of assigning bulk density to OK models in June 2007 remains the same as used in June 2007.

In the 2007 estimation, average values were calculated across all Sepon gold deposits, differentiating between ore, intrusive lithologies and sediments, as well as by oxidation state.

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Resource Estimation Methodology – KHN-Au

The 2007 Khanong Resource Estimate deals with the contiguous copper and gold Mineral Resources for the Khanong area of the Sepon Project in South Eastern Laos.

The 2007 Khanong Resource Estimate is an update of the 2005 Khanong Resource Estimate. It includes re-interpolated resources in the two areas where mining was conducted during the year, and grade control data is available. The 2007 Resources are depleted by mining conducted to 30th June 2007.

Contributing Experts

The Resource Estimate was undertaken by Duncan Hackman of Hackman & Associates Pty Ltd and supported by contributions from the experts listed in Table 9. The information supplied by the experts was used without alteration and in the context supplied.

Table 9: Expert Persons

Expert Person / Company Area of Expertise and Contribution Information Source

Duncan Hackman Geological and Resource Modeling and Estimation, database compilation and Hackman & Associates Pty Ltd QA/QC for 2003 and 2004 resource updates. Resource Modeling and Estimation for the 2005 and 2006 resource updates.

LXML Site Geologists QA/QC for 2005, 2006 and 2007 resource updates and stockpile resource estimate

Luke Burlet Assay, Geology and Geotechnical Database compilation and QA/QC pre 2003. Hellman & Schofield Pty Ltd

Dr. Ian Pringle Assay Database compilation and QA/QC pre 2003. Ian J Pringle and Associates Pty Ltd

Dr. Phillip Hellman Assay and Drilling QA/QC pre 2003. Hellman & Schofield Pty Ltd .

Arnold van der Heyden Geostatistics (variography, pre 2004 Resource Data and 2005 Grade Control Hellman & Schofield Pty Ltd Data) and Resource Estimate Audit (2002 estimate).

Resource Estimation Methodology

Table 10 summarizes the methodology in determining the Resource Estimate stated in Tables 1 to 3. It outlines the process followed for the 2002 estimate (refer to Khanong Copper DFS report, 2002, Section 3 for detail), the update resource estimates conducted in 2003 (western extension), the 2004 (northwest extension) the 2005 to 2007 update (southern extensions, northern infill drilling and grade control drilling areas) as all six estimates were generated using the same methodology. Information and data specific to the 2006 estimate is highlighted in italics.

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Table 10: Resource Estimation Methodology

JORC CODE - Estimation and Reporting of Resources.

Correct drill hole location was verified using the topographic DTM and plans showing the tracks and road access. Drill hole orientation and depths were checked against site generated cross- sections.

Diamond Drilling recoveries range between 85% (early Rio Tinto drilling) and 98% (feasibility drilling). There is no apparent correlation between Diamond Sample recoveries and copper grade. There is a correlation between wet RC samples, RC-recovery less than <30% and Drilling QA/QC positive RC copper grade bias. 4% of the database shows these characteristics and the estimation and classification procedures were adjusted to account for this characteristic (5.5% of the Measured Resource outside of the grade control drilled area has been is downgraded to Indicated and Inferred categories). The affect on the final estimate is considered of low risk.

30,595 samples were analyzed for Cu, 25,356 for Ag, 31,054 for Au and 25,133 for Fe were analyzed from the resource delineation drill holes. An additional, 95,078 samples were

analyzed for Cu, 79,986 for Au and 12,407 for Fe from the grade control drill holes These Sampling assays form the basis of the 2007 Resource Estimate.

(The Mn, As, Pb, Zn, sulphur and calculated-pyrite estimates were not updated in the 2007 estimate.)

Page 61 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Rigorous checks of the LAB results and data import procedures identified any spurious results

for verification and/or re-assay.

The GBISTM database and logging system was introduced in 2006 and the existing data Logging transported. New descriptive data is logged manually and entered into the database. Analytical data is uploaded directly from laboratory SIF files. The GBIS database system was introduced in mid 2006 for the storage of resource drilling and assay data.

(Key information used in interpreting and modeling mineralization at Khanong was omitted from the Grade Control logging, however coincident 50mx50m resource drilling in the grade control drill-grid was used to guide the interpretation through this area. The logged colour, hardness, oxidation and Metcode data is incomplete and of varying reliability, however was also used to determine the edits in domain interpretation.)

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The bulk of copper and silver mineralization at Khanong exists in sub horizontal domains within a supergene blanket in the upper reaches of Khanong creek. The lower Cu-carbonate/oxide domain exists as thin blankets (5m – 10m thick) overlaying fresh dolomitic footwall lithologies. It is predominantly high-grade and contains approximately 32% of the 2007 copper resources (1%Cu cut). 47% of the 2007 copper resources are contained in two overlaying chalcocite clay horizons that are up to 50m thick in places. The remainder of the supergene zone consists of Geology low-grade limonitic and “clean” clays and a surface gossan. Copper mineralization also exists in three shallow dipping fault zones in the north of the deposit and in primary sulphides in the south of the deposit. All of these domains were modeled for resource estimation. A satellite chalcocite mineralized domain identified in 2005 exists to the south of the main deposit and is located within a narrow south-west striking depression in the supergene to fresh-rock contact.

(The 2007 Grade control drilling supports the original interpretation and has marginally extended the domains of chalcocite and Cu-carbonate/oxide mineralization.)

889 wax-immersion bulk density determinations were collected from diamond drill holes across the deposit and form the basis for assigning bulk densities to the resource estimate. Average densities were applied to the copper-carbonate/oxide domain and the structurally controlled Tonnage Factors (in mineralization. Densities were assigned to the chalocite clay, limonitic clay, gossan and primary situ bulk densities) sulphide domains by regression with either clay percentage and/or Fe grade. Validation by way of averaging the modeled/assigned bulk densities and the raw data for each domain showed excellent correlation.

(The 2006 upper and lower surfaces of modeled domains were edited to incorporate the additional 2007 data and create the solid models used in the 2007 Resource Estimate.)

2m drill hole composites were selected following down-hole variography studies and grades were estimated into domains using Ordinary Kriging and the VulcanTM mining software.

The deposit was divided into two search ellipsoid-orientation zones, reflecting the change in orientation of the supergene blanket, and variography and grade estimation was conducted for each of the mineralized domains in these zones. Three search ellipsoids and sample moisture criteria were used for composite selection and classifying the resource estimate. The following estimation runs were utilised: Grade Estimation Run 1: 70m x 70m x 6m ellipse (E, N, RL directions), octant search with four octants informed, minimum composite number of 10 and maximum of 20. Unless otherwise stated blocks informed with copper grades from this run are assigned the Measured category under the 1999 JORC code.

Run 3: 120m x 120m x 16m ellipse – non-octant search, minimum sample number of 4 and maximum of 30 and a restriction on extreme grade influence is Page 63 Oxiana Limited 2007 Mineral Resource Explanatory Notes

applied. Unless otherwise stated blocks informed with copper grades from this run are assigned the Inferred category under the 1999 JORC code.

(The 2007 update grade-control drilling based model (incl. contained resource drilling data was generated using the same parameters apart from block sizes (see above). Once generated the update areas were subset from the new 2007 models and spliced into a copy of the 2007 Resource Drilling model (with the update areas cooky-cut out) to generate the final 2007 Estimate The block model and grade estimates were validated using statistical and visual methods with good reconciliation established between the estimated grades and the composited drilling data. The 2002 resource estimate was audited by Arnold van der Heyden (Hellman and Schofield Pty. Ltd.) who conducted an alternative estimate that produced comparable results. Reconciliation between the 2002 and subsequent Resource models shows that later models differ only where additional data exists. 123Kt of copper metal have been added to the total Validation and Audit 2002 Resources Estimate over these years (0.5%Cu cut).

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Resource Statement and Estimation Methodology – TKN-Au

Resource Statement

The 2007 Thengkham North Resource Statement documents the Resource Estimate for the near surface Thengkham North Copper Deposit located 9 km west of the Sepon Mine area in South Eastern Laos.

The 2007 Thengkham North Resource Estimate is an update of the October 2006 Resource Estimate. The update follows the inclusion of an additional 18,905 samples into the database for geological modelling and grade interpolation, an increase of 55% (This number reflects the increase in Thengkham North specific holes. Some additional data from neighbouring deposits was also used and had not been previously incorporated into the resource models). The geology of the resource is similar to that at Khanong, a supergene copper deposit, presently being mined, and located within the Sepon Mine area. The Thengkham North deposit is largely drilled on 25x25m spacing.

The 2007 estimate shows a significant increase in the Cu Resources for the deposit. The additional drilling and subsequent geological modelling at Thengkham North has defined the limits of mineralization of the deposit, which were not clearly defined in the 2006 estimate. Infill drilling has also identified high grade variability, and an overall lower tenor of Cu mineralization than that reflected in the wider spaced earlier drilling.

Gold Resources

The Resource Estimates are reported by 2004 JORC Code categories in Table 1.

Contributing Experts

Compliance with the JORC Code

Paul Quigley. BAppSc, AUSIMM Lane Xang Minerals Ltd.

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Table 1: 2007 Thengkham North Gold Resource Estimate

0.5 g/t Au Cutoff – in Material containing <0.5% Cu Resource Category (2004 JORC Code) Au Grade Au Ag Grade Ag Cu Grade Cu Tonnes (Mt) (g/t) (koz) (g/t) (koz) (%) (Kt) Measured - Indicated ------Inferred 0.69 0.80 18 6.97 154 0.37 3 Measured & Indicated ------Measured, Inferred & Indicated 0.69 0.80 18 6.97 154 0.37 3

1.0 g/t Au Cutoff – in Material containing <0.5% Cu Resource Category (2004 JORC Code) Au Grade Au Ag Grade Ag Cu Grade Cu Tonnes (Mt) (g/t) (koz) (g/t) (koz) (%) (Kt) Measured ------Indicated ------Inferred 0.15 1.32 7 6.90 34 0.36 1 Measured & Indicated ------Measured, Inferred & Indicated 0.15 1.32 7 6.90 34 0.36 1

1.5 g/t Au Cutoff – in Material containing <0.5% Cu Resource Category (2004 JORC Code) Au Grade Au Ag Grade Ag Cu Grade Cu Tonnes (Mt) (g/t) (koz) (g/t) (koz) (%) (Kt) Measured ------Indicated ------Inferred 0.04 1.76 2 5.32 6 0.31 0 Measured & Indicated ------Measured, Inferred & Indicated 0.04 1.76 2 5.32 6 0.31 0

Decimal places do not imply precision. Cutoff grades approximate those employed at the nearby Khanong Copper Mine.

Table 2: Contributing Persons

Expert Person / Company Area of Expertise and Contribution Information Source

Jason McNamara Supervision of data compilation, data extraction, geological interpretation, Superintendent Geology wire-framing and classification by geologists. LXML

Elizabeth Zbinden QAQC for drill hole, assay, sampling, and SG data. LXML Senior Geologist – Resources

Duncan Hackman Review of SG data. Hackman & Associates Pty Ltd

James Cannell LXML Senior Exploration Geologist Geological Interpretation

Page 66 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Expert Person / Company Area of Expertise and Contribution Information Source

Kerrin Allwood Resource database compilation, database validation and geological Geomodelling Ltd. interpretation

Mark Sweeney Variography and geostatistical analysis. AMC Consultants Pty Ltd .

Tracie Burrows Resource estimation and block model validation. AMC Consultants Pty Ltd

Stuart Masters External Resource Estimation Project Coordinator CS-2 Pty. Ltd.

Resource Estimation Methodology

Table 3 summarizes the methodology in determining the Resource Estimate stated in Table 1.

Table 3: Resource Estimation Methodology

JORC CODE - Estimation and Reporting of Resources.

A total of 100 Diamond, 397 Face Sampling Reverse Circulation and 392 RC-Precollar with DD tails resource-delineation drill holes have been drilled in a predominantly 25mX25m grid pattern at Thengkham North (these numbers reflect the increase in Thengkham North specific holes. Some additional data from neighbouring deposits was also used and had not been previously incorporated into the resource models). To allow interpretation of the large scale east – west structure and small scale north – south structures, drilling was completed on both north – south sections and on east – west sections. To minimize ground disturbance the collar section Drilling Configuration spacing was nominally 50 m whilst the drill intersection spacing was nominally 25 m. 8% of the holes were drilled sub vertically. The remaining holes were generally drilled at dip of -60O. Of the inclined holes, 9% were drilled towards 000O, 16% towards 090O, 54% towards 180O, and 14% towards 270O.

(This represents an increase compared to the 2006 resources of 44 Diamond, 148 Face Sampling Reverse Circulation and 298 RC-Precollar with DD tails resource-delineation drill holes) Correct drill hole location was verified visually using the topographic DTM and plans showing the tracks and road access. Drill hole orientation and depths were checked against site generated cross-sections.

Drilling QA/QC 71% of the assay data within mineralized domains is from diamond core and the remaining 29% from RC drilling

Diamond Drilling recoveries average 91%. There is no obvious relationship between Diamond Sample recoveries and Cu grade.

The average calculated recovery for RC drilling is 63% for all samples. Samples from mineralized domains average 65% calculated recovery while samples from non-mineralized domains average 63% calculated recovery. There is a small increase in Cu grade with decreasing recovery in some domains. There is a decrease in calculated RC recovery for moist samples and it is interpreted that material retention is an issue for these samples. Post November 2005, wet RC sampling was eliminated from the project with holes being converted Page 67 Oxiana Limited 2007 Mineral Resource Explanatory Notes

to Diamond Drilling at the first encountered moist sample. RC drilling now only comprises a small proportion (29%) of the data within the mineralised domains.

To account for the factors associated with the quality of RC samples, the resource classification methodology (see below) takes into account drill type, drilling recovery and, for RC samples, sample moisture content. The resource database comprises assays from 20,429 diamond ½ core samples (84% HQ) and 34,524 130-140mm diameter RC samples (split to a nominal 3-5kg sample wt).

Sampling (This represents an increase compared to the 2006 resource of 13,368 for assays from

diamond ½ core samples and 11,609 RC samples. This is a threefold increase in higher quality diamond core.) Copper grades have been determined by mixed acid digest ICP-AES determination for those samples with grades less than 0.5%Cu and by the ore-grade aqua-regia digest – IPC-AES detection method for those samples with grades above 0.5%Cu. Ag grades were determined Analysis by aqua regia digest ICP-AES determination. Au grades were routinely determined by 30g Fire Assay AAS analysis with high grade Au samples re-assayed by 30g Fire Assay, gravimetric analysis. A thorough and rigorously implemented sampling protocol was adopted for the resource drilling at Thengkham North. Quality control coarse-blanks, pulp-blanks, grade and matrix-matched certified reference-standards and duplicate samples were routinely inserted into the analytical stream at rates of 1 in 25 samples.

Data quality appears uniform and acceptable over all drilling campaigns. Some minor indications of reduced quality in the most recent drilling with regard to sample swaps

Blanks show that cross contamination between consecutive samples is negligible. Assay QA/QC

Reference Cu field standards and Laboratory standards show acceptable precision. Standards generally show a slight positive bias of 1-3% in both ICP analytical methods.

Field duplicates (DD and RC) confirm that the sample reduction protocols are appropriate and correctly implemented in the Thengkham North dataset. AMPD values are typically below 10%. As anticipated there is slightly reduced performance in RC samples however a large portion of the mineralised zones have now been drilled with diamond drilling and this influence form RC is now considered negligible. Details of rock-type, alteration, mineralization and weathering were logged onto field sheets and entered to an SQL database. Careful planning of the logging process and codes has produced Logging a fertile database with respect to domaining and modelling the economic geology features of the deposit. The bulk of the copper and associated Ag, Au and Mo mineralization at Thengkham North exists in flat laying to moderately northerly dipping zones within the weathering profile. The mineralized zones occur over a strike length of 3 kilometres and typically show an along-strike Mineralization to cross-strike ratio of 10:1. Chalcocite supergene mineralization is located adjacent to primary Geology and pyrite/chalcopyrite mineralization. Copper oxide and carbonate mineralization is best Resource Domaining developed over carbonate lithologies and the Cu in this mineralization has been re-mobilized during the weathering process. Domaining for resource modeling and estimation honours the oxidation/weathering state and metallurgical properties (a combination of Cu, S, Fe and Mn grades and logged mineralogy) of the mineralization. 3,729 wax-immersion bulk density determinations from across the deposit form the basis for assigning bulk densities to the resource estimate. The amount of available density data has Tonnage Factors (in increased by 275%. Lithology weighted average densities are applied to each of the situ bulk densities) mineralized domains. Average dry bulk densities of 1.97g/cc, 1.57g/cc and 2.79g/cc were determined for Chalcocite, Copper Oxide/Carbonate and Primary mineralization within the deposit. Block models were constructed in Datamine by AMC consultants using LXML generated wireframes as well as flagged drill hole and composite data. Geostatistical analysis was also performed by AMC and these parameters were used in the grade estimation process. The Grade Estimation following discussion details the Datamine block model process.

Grade was interpolated into blocks with a parent cell size of 25m x 25m x 5m (X, Y, Z). The block size was selected based on drill spacing in the more sparsely drilled areas and because

Page 68 Oxiana Limited 2007 Mineral Resource Explanatory Notes

these dimensions are multiples of 5 which were requested for the proposed production models.

All data is in the Sepon site grid SPG06.

Origin:

Origin Easting (m) Northing (m) Rl (m) Origin (minimum) 16,350 73,200 0 Origin (maximum) 20,000 76,000 600 Total Number of Cells 146 112 120 Cells Size 25 25 5 Minimum Sub Cell Size 5 2.5 1

To build the block models: • three dimensional solids were filled with parent cells and sub cells • the models were then added over each other in a specific order to honour detailed geological and grade solids.

The model is coded to include data in three different geological/metallurgical and statistical domain types that are used to control estimation of grade into the model. These domain types include; interpreted lithology, oxidation state and metallurgical mineralisation orientation.

Resource drilling (both RC and diamond) is dominantly sampled at nominal 1m intervals. A 2m composite was chosen for the Thengkham North statistical analysis and grade estimation after looking at a range of composite lengths from 1m to 5m. The selection of 2m composites took into consideration grade statistics and the bulk mining techniques being used at Sepon. The compositing process was checked and validated.

The geostatistical analysis was undertaken by AMC consultants (Brisbane office) using the Isatis geostatistical software package. From this work Variography and estimation parameters were developed for copper, gold, silver, molybdenum sulphur, calculated pyrite, manganese, iron, calcium, magnesium, aluminium.

Grade capping did not make a significant difference to the global statistics for each of the modelled domains except for domains with low numbers of composites. Where groups of high grades were grouped together then generally they were not considered as outliers.

Relatively high nugget variances up to 25 percent combined with significant short range structures close to the average drillhole spacing made interpretation of the semivariograms difficult. As a result experimental semivariograms were generated using a gaussian transformation.

Copper grades were estimated into blocks by Ordinary Kriging (OK) using Datamine software. One search pass was required to fill the copper model with grades. The first search pass filled all of all cells with copper values. The second search pass, which was twice the size of the first pass, was required for some minor elements. Additional quality estimators were informed by two passes; if uninformed they were left absent.

The minimum number of composites used to inform a cell with grade was 4 and the maximum was 10 for the first search pass. The second search pass used a minimum number of 3 Page 69 Oxiana Limited 2007 Mineral Resource Explanatory Notes

samples and maximum number of 10 samples.

Both Absolute and Relative Kriging variances were calculated. All of the above information was stored in the models for use in the classification process.

Resource classification is based solely on confidence in copper grade and volume (tonnage) estimation.

Three dimensional contour surfaces (shells) were created at appropriate values for the following criteria:

Sample moisture Sample recovery Sample type Relative Kriging Variance Kriging slope of regression Number of samples used to inform the cell with grade Number of search passes required to fill a cell with a gold grade Distance to closest drillhole composite Resource

Classification These shells were then assessed on a domain by domain basis in conjunction with a subjective assessment of the confidence in the interpretation of geological controls on mineralisation and the spatial configuration of drilling within the domain.

No resources are reported from the four waste domains as the geological and grade continuity of potentially economic intersections within these domains has not been demonstrated.

Resource models have been validated both statistically and visually.

Validation and Audit AMC consultants have conducted a review of the Thengkham North resource models. No significant issues were identified with regard to the methodology.

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Resource Estimation Methodology – PVN

The 2007 Phavat North Resource Estimate deals with the near surface Phavat North Gold Deposit located 10km west of the Sepon Mine area in South Eastern Laos.

The 2007 Phavat North Resource Estimate is an update of the February 2006 Resource Estimate. The update has confirmed and improved the confidence in the previous estimate through the inclusion of 60 additional drill holes (4,931m) into the database for geological modelling and grade interpolation. The geology of the resource is similar to other Sepon gold deposits where Oxide mineralisation is hosted predominantly along a shallow dipping dolomite/calcareous-shale contact zone and along the margins of rhyodacite porphyry sills and structurally controlled dykes. Partial Oxide and Primary mineralization is hosted in the down-dip extensions of the same zones. The deposit is drilled on 50x50m spacing, with the main identified mineralized zones in-filled to 25x25m spacing.

Contributing Experts

The Resource Estimate was coordinated by Duncan Hackman of Hackman & Associates Pty Ltd and supported by contributions from the experts listed in Table 12. The information supplied by the experts was used without alteration and in the context supplied.

Table 12: Contributing Persons

Expert Person / Company Area of Expertise and Contribution Information Source

Jason McNamara Supervision of data compilation, data extraction, geological interpretation, Superintendent Geology wire-framing and classification by LXML geologists. LXML

Saut Parulian Resource Database compilation. QAQC for drill hole, sampling, and SG LXML Senior Resource Geologist data. Database validation. Geological interpretation, Block Modelling and Grade Interpolation

Michael Stewart Variography and Estimation Parameters (2006) QG Consultants.

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Table 7: Summary of Resource Estimation Methods and Key Inputs for the Phavat North Model

A total of 321 resource-delineation drill holes comprising 70 Diamond, 114 Face Sampling Reverse Circulation and 137 RC pre-collar with DD tail have been drilled in a predominantly 25mX25m grid pattern at Phavat North. Most holes are drilled at -60 O towards 180 O with eight holes drilled towards the north to test and confirm geological interpretation. Eight holes were drilled to twin and replace poor quality RC holes drilled in the western portion of the resource.

The resource-delineation drilling grid now encompasses the main body of mineralization at Phavat North Drilling at 25m spacing. Peripheral areas are defined at 50m spacing. The oxide mineralization is considered to Configuration be well constrained however primary mineralisation is still open in the north, south and at depth along the rhyodacite porphyry dyke contact as well as down-dip along the dolomite calcareous shale contact.

(The 2007 resources include an additional 60 resource-delineation drill holes comprising 32 diamond, 18 face sampling RC and 10 RC pre-collar with DD tail total meter age Diamond and RC is 2,896.2m, 2,035m respectively.[these holes are included in the figures above]. The additional holes were drilled in the west and north of the resource to test and confirm the continuity of mineralisation in these areas)

Correct drill hole locations were visually verified using the topographic DTM and plans showing the tracks and road access. Drill hole orientation and depth were checked against site generation cross- section.

All drill hole collars were converted from UTM/Indian60 projection to SPG06 coordinate systems. Approximately 10% (34 holes) were resurveyed using SPG06 for validating the conversion. No significant difference was identified (average difference < 2m).

16 Diamond to Reserve Circulation twin-holes have been drilled to assess the suitability of drilling techniques. Obvious contamination and smearing of grade is observed in the wet and moist intervals within RC holes. Assays were assessed for inclusion-in/exclusion-from the resource dataset (see notes on recovery data, below). Where a DD twin hole existed, the RC holes were excluded from the resource dataset.

Diamond Drilling recoveries average 93%. There is no obvious relationship between Diamond Sample recoveries and Au grade Drillhole QA/QC The average calculated recovery for RC drilling is 65% for all samples data used. Samples from mineralized domains average 61% calculated recovery while sample non-mineralized domains average 72% calculated recovery. As identified in previous studies, RC recovery for moist samples is notably reduced and it is interpreted that material retention is an issue for these samples. Post November 2005, the introduction of new drilling procedures eliminated the introduction of wet RC samples with holes being converted to Diamond Drilling at the first encountered moist sample. There is no obvious relationship between RC recovery and Au grade.

Each assay within the database was assessed with respect to its moisture content, sample weight overall grade profile (for the mineral intercept) and its relationship spatially to the local tenor of mineralization. 511 RC assays and 2DD assays were rejected from the resource estimate dataset as they were deemed to be significantly compromised by poor or high recoveries; 683 RC assays were flagged as suspect but remain in the resource data set. The flagged data was employed to identify areas where estimates are potentially compromised and appropriate classification has been applied (JORC, 2004). 435.9kt of potentially Indicated mineralization has been classified as Inferred Resources due to this uncertainty in assay values caused by poor recoveries.

(Eight additional holes were drilled to twin and replace poor quality RC holes drilled in the western portion of the resource. [These holes are included in the figures above].)

The resource database used comprises of assays from 14,898 HQ triple tube diamond ½ core samples and 15,314 130-140mm diameter RC samples (split to a nominal 3-4kg sample wt). Sampling The final dataset within mineralized domains (Au gt 0.2ppm) consist of 3,147 RC samples and 4,858 DD samples, and these samples from the basis of the grade interpolation for the 2007 resource estimate

Page 72 Oxiana Limited 2007 Mineral Resource Explanatory Notes

(This represents a total increase of 2,951 DD samples and 2,112 RC samples from the 2006 resource dataset. Within the mineralized domains 940 RC and 2587 DD samples have been added)

All samples were analyzed for Au by 30g Fire Assay – AAS determination with high grade samples Analysis repeated with gravimetric determination. Ag and Cu grades were analyzed by Mixed Acid Digest – ICP- AES determination.

QA/QC coarse-blank, pulp blank, grade and matrix-matched certified reference-standards and duplicate samples were routinely inserted into the analytical stream at an interval of 1 to 25 samples.

Results returned for blanks, shows that cross contamination between consecutive samples is negligible.

Reference Au field standards and Laboratory standards show good precision, no significant bias and all average within 4% of the certified values which indicates that the assaying is of good standard and reliable. 2 batches were identified as being outside the acceptable limits and have been flagged for re- assay. The 3 holes contained within these batches have been included in the estimation process as the expected impact on the economic viability of the deposit is considered negligible. One Ag field standard and a number of Laboratory Ag standards from the 2006 resource update showed periods of positive Assay QA/QC assay bias, however as the deposit shows relatively low Ag grades, the expected impact of this on the economic viability of the deposit is considered negligible.

Field duplicate on diamond core confirms that the sample protocol is appropriate and correctly implemented; whilst field duplicate on RC samples confirms deriving bias in dry and wet samples, therefore reducing of RC samples in sample protocol is appropriate and correctly implemented.

A total of 11 batches were re-assayed after having been identified as having poor QA/QC in the 2006 resource update. The drill hole data contained din these batches were flagged and employed to identify areas where estimates are potentially compromised and appropriate classification has been applied (JORC, 2004).

Details of rock-type, alteration, mineralization and weathering were logged onto field sheets and appended to an SQL database, once it passing stringent and rigorous validation tests. Careful planning Logging of the logging process and codes has produced a fertile database with respect to domaining and modelling the economic geology features of the deposit.

The bulk of the gold and associated silver mineralization at Phavat North exists in both sub vertical domains along the contact aureole of an east-west striking rhyodacite porphyry (RDP) dyke and in sub horizontal to gently northerly dipping domains along the dolomite to calcareous shale contact and the overlaying calcareous shale to chert contact. The remainder of the mineralization is interpreted to be hosted with in structures (and associated RDP dykes) in the west and north east regions of the deposit, and along the margins of some RDP sills that intrude to the south of the main RDP dyke.

0.2ppm Au grade shells were modeled following the geological controls and used to constrain and guide the grade interpolation. Mineralization

Geology and 0.3% Cu grade shells were modeled following the geological controls and used to constrain and guide Resource the grade interpolation. The 0.3% threshold was chosen as it is at this level that copper grade will have a Domaining detrimental effect to the economics of gold recovery based on current mining data.

Oxidation state is interpreted from geological logging. The degree of subjectivity required in interpretation of oxidation state is high, and the confidence in the assignment of oxidation state to estimates is thus moderately low. Oxidation state is important in assigning bulk density and metallurgical recovery to the estimate. There is no evidence that gold distribution is affected by oxidation state, and consequently estimates for gold were not domained by oxidation.

(Interpretations were updated based on the additional 55 resource delineation holes. Mineralisation domains were “tightened” in an attempt to remove some areas of low grade material from within the

Page 73 Oxiana Limited 2007 Mineral Resource Explanatory Notes

0.2g/t domain. Due to sporadic Leach Well data a surface representing the boundary of transitional material (i.e. reduced oxide recovery) could not be generated)

2,581 wax-immersion bulk density determinations were collected from 173 diamond drill holes across the deposit and form the basis the basis for assigning bulk densities to the resource estimate. Average densities are applied to each of the mineralized domains according to geological setting and oxidation state. Average dry bulk densities of 1.66g/cc, 1.96g/cc and 2.77g/cc were determined for Oxide, Partial Tonnage Oxide and Fresh material with in the deposit. Factors (in situ bulk densities) (This represents a total increase of 615 DD samples from an additional 31 holes when compared to the 2006 resource dataset. The recent data has been reviewed and the 2007 bulk density data set compares well to the 2006 data therefore the average bulk densities for Oxide and Transitional and Primary material have remained unchanged.)

All other gold resource estimates at Sepon have been aligned to fit neatly over a site-wide grade control grid of 5mX x 3mY x 2.5mZ. Most gold estimates have used blocks of 25mX x 6mY, a dimension initially chosen to deal with the moderate dips present in Discovery East, and match the resource drilling grid. The estimation domains at Phavat North are either sub-horizontal or sub-vertical in orientation. Testing showed that blocks of 25m in X were too large, and resulted in too many blocks with poor estimation geometry. Two smaller block dimensions were considered: firstly blocks of 12.5mX (and 12.5mY), which are increments of the resource drill spacing, but not of the grade control grid; and secondly blocks of 15mX (and 12mY) which match the grade control grid, but are out of step with the drill spacing. There is little material difference between the two estimates. Optimization and reporting of the Phavat North resource is based on a grade estimation carried out using Minesight™ software into a 3D block model with cells of 15mX x 12mY and 2.5mZ.

The extent of the block model is:

Project Min Max Dimension Number X 15300 16800 15 100 PVN Y 73700 74684 12 82 Z 0 650 2.5 260

A composite length of 2m downhole was chosen (small intervals at the end of hole are merged to the last composite if <0.99m). Compositing for Au and Cu were performed separately. The compositing Grade process has been checked and validated. Estimation Minimal domain modifications resulted from the 2007 update. As a consequence Variography and search neighborhood optimization for gold was adopted from the 2006 parameters which were generated using Isatis geostatistical software. In general, the spatial continuity of gold shows a moderate nugget effect ratio between 10% and 50% and short to moderate ranges.

Copper domains were grouped based on two orientation criteria, flat and sub-vertical. Copper Variography was performed using Compass-Minesight software and generally, it shows a low nugget effect ratio less than 10% with moderate ranges of spatial continuity.

Gold and copper grades were estimated by Ordinary Kriging (OK) using Minesight™ software. In addition to grade, the number of informing samples, the distance to the closest sample and the kriging variance were stored to each block.

The sub-horizontal, stratigraphy-parallel gold and copper domains show a zonation of grade away from the RDP, in keeping with the hypothesis that the dykes and associated faulting provided the conduits for gold bearing fluids during mineralization. High grade copper is coincident with high grade gold. Due to different orientation and spatial distribution, grade domain boundaries were treated as “hard boundaries” during the estimation process.

Gold grade estimation was carried out in a single pass in each domain. Resultant estimates were checked visually, to ensure appropriate blocks were estimated. Estimate validation for each domain was carried out by examining the global estimation statistics (tonnage, minimum, maximum, mean, and

Page 74 Oxiana Limited 2007 Mineral Resource Explanatory Notes

method was utilized to restrict the area of influence of high grades (99th Percentile) to 20m

Silver grades were composited along with gold, using Au as the controlling variable, and silver estimates were made into Au domains only, using the same parameters as the gold estimates. Although the resultant estimates will be sub-optimal, the current value of contained silver (where Au >1.0g/t) is <10% that of gold and probably contributes a considerably lower recovered value and not have a material impact on the economics of the deposit. In addition, silver estimates for other deposits at Sepon have been conducted in similar fashion and shown to be robust at the global scale (through production reconciliation) and it is anticipated that the same will be seen at Phavat North.

(Block size has been changed from the 2006 12.5mX x 12.5mY and 5mZ to allow more efficient reconciliation to grade control. Copper has been domained and estimated separately to gold in the 2007 update. Instead of a top-cut of 30g/t Au being applied as in 2006, a grade distance restriction method was used for the 2007 update)

In addition to grade, an estimate was also made of the influence of wet or moist samples (suspect) that contribute to Au grade estimates. These were created by assigning an indicator based on logged Resource moisture content (dry=0, moist or suspect=1), and making estimates of the indicator using the same Classification kriging plan as for Au estimation. Areas of the model estimated with >30% suspect source composites have been classified as Inferred as have peripheral areas of the model where drill spacing is greater than 25X25m. The remainder of the resources are classified as Indicated (JORC 2004). Classification is based solely on the Au estimate.

The block model and grade estimates were validated using statistical and visual methods with good reconciliation established between estimated grades and the composited drill hole data.

Quantitative Group (QG) conducted a cursory review of the Phavat North model prior to finalisation. QG Validation and listed made several recommendations with regard to modelling methodologies. As a result further up- Audit dates to the model were undertaken prior to reporting.

Comparison between 2006 resource against 2007 resource shows upgrading of inferred material into indicated within oxide, transition and primary zones due to replacement of poor wet RC holes by twin diamond holes.

Page 75 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Resource Estimation Methodology – LOL

The 2007 Muang Luang Resource Estimate deals with the near surface Muang Luang Gold Deposit located adjacent to and North West of the Discovery (DIS) resource of the Sepon Gold and Copper Operations Central South Eastern Laos.

The 2007 Muang Luang Resource Estimate is an update of the 2004 Multiple Indicator Kriged model. This update has confirmed and improved the confidence in the previous estimate through the inclusion of 62 additional drill holes (10941.8m) and by using updated geological modelling as well as more detailed statistical analysis of all data and the use of the superior Ordinary Kriging interpolation technique. The geology of the resource is generally similar to other Sepon gold deposits and in particular the nearby Discovery (DIS) area where Oxide mineralization is hosted predominantly along the shallow to gentle dipping dolomite/calcareous- shale contact zones and also along the margins of rhyodacite porphyry contacts. Some additional high grade mineralization is often associated with structurally controlled shear zones particularly in the vicinity of some localized Jasperoid occurrences. The Partial Oxide (Transition) and Primary mineralization is seen to hosted some down-dip extensions of the main zones. The deposit is drilled on a reasonably regular 25x25m spacing which generally encompasses and mostly “closes off” most of the known mineralization domains. The deposit may still extend for a significant distance to depth as primary mineralisation.

Contributing Experts

The Resource Estimate was conducted by Stephen Hyland and was additionally supported by contributions from Oxiana-LXML Personnel working on-site at the Sepon Gold and Copper operations. The contributing persons involved in generating the new Muang Luang Resource Block model and Resource Estimation are listed in Table 2 below. The information supplied by the experts was used without alteration and in the context supplied.

Compliance with the JORC Code

Paul Quigley. BAppSc, AUSIMM Lane Xang Minerals Ltd.

Table 2: Contributing Persons

Expert Person / Company Area of Expertise and Contribution Information Source Stephen Hyland Resource Modelling - Including Geology and Mineralization Definition, Principle Consultant – Resource Geostatisitcal Review and Variography, Block Model Construction, Geologist and Qualified Person. Interpolation runs for Resource Estimation, Block Model Validation, and Ravensgate Resource Consultants Resource Classification.

Page 76 Oxiana Limited 2007 Mineral Resource Explanatory Notes

FIG 1 – General View Of Muang Luang “Oxide + Primary Gold” Project Area Model Showing Major Mineralization Structures - ZONE=1 (Pink) and ZONE=2 (Blue) Domains.

Page 77 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Table 7: Summary of Resource Estimation Methods and Key Inputs for the Muang Luang Model

Muang Luang (LOL) Drilling.

The following tabulation summarises the drilling used in the December 2007 Muang Luang Au Resource Estimates:

Table 3-1.

# Au Assayed # Au Deposit Drillhole Type # Holes Metres LW* Metres Assays Assays DDH 20 2566.00 2530.20 1585 23 LOL RC 81 7744.00 7465.00 7465 1678 RC+DD “Tail” 34 8210.80 7755.00 4583 1093

Drilling All available information relating to the drilling campaigns conducted at Muang Luang, including mapping, Configuration drill-hole logging, as well as all validated assay data was carefully reviewed as part of the standard resource modelling and resource estimation process.

The majority of drilling carried out at Muang Luang was during the period 2002 - 2005. One of the outcomes of a preliminary review of this data was the increased understanding of the distribution of Au Ore particularly as it was distributed with respect to the known geological and structural regime previously modelled at Muang Luang. The density of drilling used at Muang Luang is approximately 25x25m on a relatively regular N-S oriented grid, with most of the holes drilled towards Grid Azimuth 180 degrees and at angles of approximately -60 degrees from horizontal. All of the drill-holes have been collared at the natural topographic surface. The overall geological and mineralization modelling frame-work has been developed from the 135 drill-holes available for this area. The most recent versions of the new interpreted mineralization zone geometries have been constructed using this data-set as passed to the LXML Sepon Resource Geology Division as at December 01st 2007. The new resource block model ultimately re-constructed is essentially a straight-forward up-date of the earlier “first pass” Inverse Distance Resource Block Model, but with much more detailed 3-D geometrical geological, structural and mineralization modelling incorporated

The December 2007 Mineral Resources have been defined with drillhole data from both RC and diamond resource definition drilling programs. Holes greater than 50m were routinely downhole surveyed through the drill rods for (chrome rods for RC holes). It is judged that sample location errors on shorter holes (ie. Less than 50-70m) are immaterial as holes are generally at high angles to mineralisation geometries. Missing survey data are unlikely to significantly affect resource estimates. Drillhole

QA/QC During preparation of the LOL estimate, at least one drillhole collar (DIS613) was identified as presenting

location problems, possibly arising from the use of two coordinate systems. The issue relating to questionable hole location for this drill-hole was not resolved as at the time of modelling. To mitigate this problem, it was decided that as a conservative approach that this drill-hole despite is carrying some quite high grade Au assays, be completely removed from the resource model coding, block model interpolation and resource reporting process

Resource estimates for the Muang Luang Gold are based predominantly on RC and minor diamond core samples. Diamond core samples comprise approximately 35% of the total drilling. RC resource drilling is generally sampled at regular 1m intervals while diamond core is sampled on variable intervals sometimes dictated by geology, although generally a default sample length 1m is used. The established practice used at Sepon is to riffle split RC samples to a maximum of 5kg, with wet samples being air dried prior to splitting. Sampling of both drilling methods follows established procedures used at Sepon. Sampling The presence of down-hole sample contamination below expected locations of high grade mineralisation and particularly where it was observes to “extend” into rock types that would not generally be expected to host significant mineralisation were noted, and the interpretations adjusted to exclude these intervals. The problematic drill-hole DIS613 was identified in this way.

There is only a limited selection of close spaced RC drill-hole “twins” available for direct assay comparison

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and analysis, and for the most part the assays from each of these sets of holes show good agreement. To date, at Muang Luang, as with other Sepon gold project areas it has been difficult to arrive at statistically robust conclusions about the integrity of wet RC sampling. The data available suggests a potential positive grade bias from wet RC sampling although this analysis is not definitive.

All sample collection and splitting of RC samples was performed under the supervision of trained LXML staff and the procedures used are well understood.

Pre July 2003 All samples were analyzed by ALS Chemex in Australia for Au by 30g Fire Assay – AAS determination with high grade samples repeated with gravimetric determination. Ag and Cu grades were analyzed by Mixed Acid Digest – ICP-AES determination. Analysis After July 2003, samples were analysed at the Sepon Site laboratory. Analytical protocols require that all samples are assayed for Au by 30g FA (LXML lab standard). Samples with a FA grade greater than 0.4g/t are subsequently analysed via rapid cyanide leach (PM200, LW) for cyanide recoverable Au.

Extensive QA/QC was undertaken during the drilling, sampling, and laboratory processes using a Assay combination of standards, blanks, field duplicates, lab duplicates, checks, twin holes, and monitoring of QA/QC sample recoveries.

Details of rock-type, alteration, mineralization and weathering were logged onto field sheets and appended to an SQL database, once it passing stringent and rigorous validation tests. Careful planning of the logging Logging process and codes has produced a fertile database with respect to domaining and modelling the economic geology features of the deposit.

Most of the Au mineralization at Muang Luang is Sulphide / Fresh (Primary) mineralization.

The relatively small amount of oxide gold mineralization as it occurs at Muang Luang is predominantly located in horizontally-oriented surface accumulation zones. This mineralization is remnant of the mostly steeply north dipping sub-vertical zones along a possible structural contact between of an east-west striking Rhyodacite Porphyry (RDP) and dolomite (DOL) or calcareous shale (CSH) Lithologies. Some localized development of Jasperoid (JAS) is evident, and as elsewhere at Sepon is often responsible for the deposition of higher concentrations of gold. The remainder of the mineralization is interpreted to be hosted within with other fault or splay fault structures (associated RDP and DOL or CSH contacts).

A set of mineralization shells were developed from the existing Drill-hole assays using a nominal 0.3-0.5ppm Au lower cut-off. Where possible the mineralization shells were constrained according to the local geology and associated geology rock mass model.

The 0.3-0.5ppm Au lower cut-off threshold was chosen as the gold distribution at this level tended to display Mineralization relatively continuous observable zones of mineralization. Geology

Oxidation state surfaces were also modelled and these were interpreted from drill-hole geological logging data. Oxidation state logging by nature particularly when observing RC Drill chips inherently suffers from some subjectivity and it should be noted that the confidence in the assignment of oxidation state to estimates may not always be reliable. An attempt to accurately define oxidation state in all parts of the deposit is important the assignment of bulk density values as well as helping predict some metallurgical recovery estimates. Due to limited data it was unclear if gold distribution is affected by oxidation state. Experiences from reviewing other Au deposits at Sepon including the Discovery (DIS) area adjacent to the Muang Luang deposit suggest that variations in gold distribution according to oxidation state is not significant. Consequently, for the Muang Luang assay and composite statistics reviews or the block model interpolation runs for gold were not dominated by oxidation state.

The 3-D mineralization domains when finalized were not sub-divided into separate domains according to underlying rock type. There is almost certainly some Au distribution variation within some of the lithologies particularly the Jasperoid zones. It was however determined that due to the relatively limited amount of drilling data in these zones, coupled with the relative uncertainly of the size or extent of the volumetrically relatively small Jasperoid zones that no significant improvement to the resource model could be expected. Page 79 Oxiana Limited 2007 Mineral Resource Explanatory Notes

The lithology domains however, were directly used however in conjunction with the oxidation state designation surfaces in the resource block model to assign variable bulk densities.

The standard wax-immersion bulk density determination methods were continued throughout the most recent drilling programs carried out at Muang Luang however area specific data is limited. As a result bulk density determinations available from the nearby Discovery (DIS) were deemed suitable for use at Muang Tonnage Luang since similar lithologies are observed to be present. Factors (in situ bulk At this time the oxidation state definition surfaces were extensively up-dated and consequently the average densities) bulk densities were again re-calculated using the lithology domains, and according to the up-dated oxidation state. The bulk density determinations once categorized in this way were applied to whole blocks in the block model based on a majority 50% block-in / 50% block-out coding basis.

Visual assessment of the relationship between grade distribution and underlying geology and known structural regime supports the use of tightly constrained grade-based mineralization domains for defining and estimating the Au and Ag distribution at Muang Luang. The decisions related to defining an appropriate mineralization domaining regime are also supported by using general statistical analysis of Au and Ag spatial distribution within any give area. This is particularly important in areas where a clear step in grade is observed across mineralisation boundaries.

There were 2 main mineralization domain defined and used for the LOL Au, one was for the surface “remnant” horizontal domains and the steeply dipping “main” zones. These mineralization domains were constructed as 3-D wire-frame mineralization envelopes using all available drill data as at December 01st, 2007.

Resource The underlying approach used for defining these 3-D wire-frame mineralization shell envelopes was to try to Domain capture the majority of Au drill intervals above a “nominal” 0.3-0.5g Au/t lower cut-off. The general rule Modelling applied to mineralization definition was that captured intervals had to be at least 2 sequential “above grade” Au mineralized intervals or longer. An “exclusion rule” was also applied whereby 2 or more sequential “waste” or below grade intervals may be present within any given “above grade” interval.

The mineralization definition regime described here and used for the most recent Muang Luang Au modelling up-date and is a very similar approach to that used in the recent Primary Gold Modelling Development Project modelling. The main mineralization domains were sub-divided into 4 domains based on orientation.

In general the mineralisation zones and orientation domain boundaries were adjusted according to the presence of both the shallow-dipping lithological contacts and any of the distinct observed mineralization “offsets” that may be associated with the major steeply dipping fault zones. Consideration was also given to the presence of a possible “supergene” mineralisation zones at or near the Oxide and Transition interfaces.

The block size selected for use in the new Muang Luang Au resource model was 15m(X) x 6m(Y) x 2.5m (Z). The block size was chosen to help adequately define material the relatively small ore zones observed at Muang Luang and more accurately define the variations seen in the oxide-transition-fresh rock surfaces. The block size ultimately determined also was not deemed to unduly compromise sample or block support consideration with respect to the currently defined drilling grid and drill-hole spacing. The new smaller block is also a somewhat closer match to the typical Selective Mining Unit (SMU) commonly in use with respect to Sepon Oxide Au mining. The extents of all models are tabulated below. Grade Estimation The extent of the block model is:

Project Min Max Dimension Number X 25500 26550 15 70 LOL Y 76600 77200 6 100 Z -400 350 2.5 300

The modelling process used for the new Muang Luang commenced with the generation of new 3-D wire frames. The Resource drilling (both RC and diamond) predominantly sampled at 1m intervals and although

Page 80 Oxiana Limited 2007 Mineral Resource Explanatory Notes

variances with respects to composite length. It was decided however to continue to use a 2m down-hole composite length which was the same as was used for the nearby Discovery main (DSM) area. The 2m composite length is considered appropriate as it closely matches the typical mining bench height. The change of sample support incorporated by compositing to this length will to some extent also reduce the overall sample variance, and this effect in turn has to some extent assisted in the generation of some of the semi-variograms for the main mineralization domains. Standard Log probability plots were also generated to review localized composite distribution and also help with selecting upper cut-off grade ranges to be applied to block model interpolation. Overall the coefficient of variation for most of the deposit areas was relatively low. (ie ~CV=1.0 1.5)

The Variography used for the new Muang Luang Au resource model were newly generated from the existing 2m down-hole composite dataset. The important parameters derived were the “nugget”, “sill” values and indicative search neighbourhood (distance) parameters for each domain. The semi-variograms generated were calculated and modelled using Minesight™ program M303V1. In general, the spatial distribution of the gold and silver composites display moderate to high nugget to sill ratios (+33%) and usually short to moderate ranges with respect to spatial continuity. These observations were expected when considering previous statistical analysis work carried out for the nearby Discovery Main (DSM) deposit area. The underlying statistics observed for the Muang Luang are fairly typical for the gold deposits observed at Sepon and for gold deposits generally.

Gold grades from the 2m down-hole composite data-set were interpolated into the resource model blocks by use of the Ordinary Kriging (OK) technique - (Using MineSight™ program M624V1). Estimation was performed in a single pass for each of the 4 AREA domains. Most domains were sampled on a reasonable regular grid pattern and as such it was decided that that quadrant searching or block discretization was not necessary. Sample selection however, used during the interpolation process was constrained and specifically, a maximum of 3 composites from any given drill-hole was allowed to be used to interpolate any given block. The minimum and maximum numbers of composites allowable to interpolate a block were set as 1 and 24 respectively. In addition to the interpolated grade items, some additional “ancillary” items including the “Number of Composites” the “Distance to the nearest composite” and the “Kriging variance” were also stored into each interpolated block. These ancillary items were later used to help define some additional relative levels of interpolation confidence items that were in turn used for resource classification.

The silver sample data-set is not as extensive as the gold sampling data as the selection decisions related to Ag assaying has changed over time. Statistical analysis shows that Ag and Au are somewhat correlated and probably more closely than in the nearby Discovery Main (DSM) area. Au vs Ag correlation appears to be best in the sulphide / fresh domains, but mopre analysis is probably required in this regard at Muang Luang. As silver is being considered as a relatively “low value” element for the Sepon Mining Operations, and for the purposes of estimation at Muang Luang, it was only interpolated into “equivalent” blocks that were initially interpolated where there was Au composite sample coverage. Silver grades were again interpolated using MineSight™ program M624V1 and new down-hole variogram parameters were derived and used for each respective AREA domain as necessary.

The final reporting item developed for the new Muang Luang Au block model is the P1AU item. The final resource estimate is then generally reported at 0.5g 1.0g and 1.5g Au/t lower cut-off’s and further tabulated and sub-divided by material oxidation state and and resource classification confidence.

Resource classification was based on some significant modelling input and some ancillary block model items :

There was also relative data density considerations used at Muang Luang. There is an expectation that mineralization continuity should be good in areas where there is a lot of close spaced drilling particularly when using a regular drilling grid. If this situation is realized, then there should be a reasonably high degree Resource of resource modelling confidence associated with the overall reported resources. Classification During the normal kriging interpolation process used at Muang Luang, a series of ancillary estimation items were used to help further classify the modelled areas using the current resource drilling data-set. These ancillary items are “Kriging variance”, “Number of composites” used in a search ellipse used to interpolate any given block and “distance from nearest composite” to block centroid. These ancillary items were interrogated from the block model and plotted as histograms to review their overall distribution. From these, some general ancillary item thresholds are chosen which are then used to assign relative levels of interpolation confidence to each resource model block. For the New Muang Luang Page 81 Oxiana Limited 2007 Mineral Resource Explanatory Notes

resource block model this item was designated as the CONF (relative confidence) item. This CONF item then again “condensed” to an additional “final” reporting item designated as the QLTY item. The QLTY item has an allowable range of 1, 2, 3 or 4. (Representing “Measured”, “Indicated”, “Inferred” and “Un-Classified” respectively). QLTY=4 material has been “hard-wired” as blocks within the main mineralized zones greater than 40 metres from the nearest composite.

Classification was applied on a block-by-block basis. Whilst the resulting “pattern” of classification seen is somewhat complex it does give a clear indication of where there are areas of classification uncertainty within the block model.

Classifications were applied regardless of oxidation state (which affects recovery and treatment option. This is however the typical regime carried forward from previous resource modelling in areas such as NLU (oxide) and the nearby Discovery Main (DSM) Primary Gold modelling project area. Likewise the presence of and inorganic / “preg-robbing” carbon within Au mineralized zones was not used to modify the final resource classification, since the confidence of carbon material definition within the oxide and transitional zones is relatively uncertain

The Muang Luang Au Resource models have been validated both statistically and visually and also by internal Oxiana-LXML review.

This is a first pass Resource model for the Muang Luang Area and as such it should be subject to further

additional review and remodelling later if necessary. It is expected that considering the overall deposit Validation geometry that the identified estimated resource base for this area will not change significantly following any and Audit future deposit or resource modelling review. There are currently no known or significant issues identified that

present any serious risk relating to the new Muang Luang Au resource model. Most of the physical parameters that have been measured through drilling and sampling have been done comprehensively and rigorously. There is limited scope for the deposit to extend laterally in any direction. The deposit still possibly remains open at depth.

Page 82 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Resource Statement and Estimation Methodology – TKS-Au

Resource Statement

The 2007 Thengkham South Resource Statement documents the Resource Estimate for the near surface Thengkham South Copper Deposit located 7 km west of the Sepon Mine area in South Eastern Laos.

Silver grades have not been estimated because they are so low that there is no reasonable prospect of eventual economic extraction of silver from Thengkham South.

Gold Resources

The Resource Estimates are reported by 2004 JORC Code categories in Table 1.

Contributing Experts

Compliance with the JORC Code

Paul Quigley. BAppSc, AUSIMM Lane Xang Minerals Ltd.

Page 83 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Table 1: 2007 Thengkham South Gold Resource Estimate

0.5 g/t Au Cutoff – in Material containing <0.5% Cu Resource Category (2004 JORC Code) Au Grade Au Ag Grade Ag Cu Grade Cu Tonnes (Mt) (g/t) (koz) (g/t) (koz) (%) (Kt) Measured - Indicated ------Inferred 0.68 0.74 16 - - 0.38 3 Measured & Indicated ------Measured, Inferred & Indicated 0.68 0.74 16 - - 0.38 3

1.0 g/t Au Cutoff – in Material containing <0.5% Cu Resource Category (2004 JORC Code) Au Grade Au Ag Grade Ag Cu Grade Cu Tonnes (Mt) (g/t) (koz) (g/t) (koz) (%) (Kt) Measured ------Indicated ------Inferred 0.10 1.10 3 - - 0.32 0 Measured & Indicated ------Measured, Inferred & Indicated 0.10 1.10 3 - - 0.32 0

1.5 g/t Au Cutoff – in Material containing <0.5% Cu Resource Category (2004 JORC Code) Au Grade Au Ag Grade Ag Cu Grade Cu Tonnes (Mt) (g/t) (koz) (g/t) (koz) (%) (Kt) Measured ------Indicated ------Inferred ------Measured & Indicated ------Measured, Inferred & Indicated ------

Decimal places do not imply precision. Cutoff grades approximate those employed at the nearby Khanong Copper Mine.

Table 2: Contributing Persons

Expert Person / Company Area of Expertise and Contribution Information Source

Jason McNamara Supervision of data compilation, data extraction, geological interpretation, Superintendent Geology wire-framing and classification by geologists. LXML

QAQC for drill hole, assay, sampling, and SG data. Elizabeth Zbinden

Senior Geologist – Resources LXML Craig Michael Senior Resource Geologist Geological Interpretation LXML

Kerrin Allwood Resource database compilation, database validation, geological Geomodelling Ltd. interpretation, block modelling, model validation and resource classification.

Variography and geostatistical analysis. Steve Hyland, Ravensgate .

Page 84 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Resource Estimation Methodology

Table 3 summarizes the methodology in determining the Resource Estimate stated in Table 1.

Table 3: Resource Estimation Methodology

JORC CODE - Estimation and Reporting of Resources.

(This represents an increase compared to the 2004 resources of 21 Diamond, 49 Face Sampling Reverse Circulation and 147 RC-Precollar with DD tails resource-delineation drill holes)

7 Diamond to Reverse Circulation twin-holes were drilled to assess the suitability of drilling techniques. Obvious contamination and smearing of grade is observed in the wet and moist intervals within some RC holes.

44% of the assay data within mineralized domains is from diamond core and the remaining 56% from RC drilling

Drilling QA/QC Diamond Drilling recoveries average 92% in both mineralised and waste domains.

The large proportion of RC data and the poor quality of some of the RC data was a contributing factor to the decision to report no measured or indicated resources from Thengkham South.

The resource database comprises of assays from 10,603 diamond ½ core samples (95% HQ) and 16,945 130-140mm diameter RC samples (split to a nominal 3-5kg sample wt). Of this database, 4,519 (44%) DD samples and 5,818 (65%) RC samples are within mineralised Sampling domains and so used for grade interpolation.

(This represents an increase compared to the 2004 resource of 8,856 for assays from diamond ½ core samples and 7,804 RC samples. This is a threefold increase in higher quality diamond core.)

Copper grades have been determined by mixed acid digest ICP-AES determination for those samples with grades less than 0.5%Cu and by the ore-grade aqua-regia digest – IPC-AES Analysis detection method for those samples with grades above 0.5%Cu. Ag grades were determined by aqua regia digest ICP-AES determination. Au grades were routinely determined by 30g Fire Assay AAS analysis with high grade Au samples re-assayed by 30g Fire Assay, gravimetric analysis.

A thorough and rigorously implemented sampling protocol was adopted for the resource drilling Assay QA/QC at Thengkham South. Quality control coarse-blanks, pulp-blanks, grade and matrix-matched certified reference-standards and duplicate samples were routinely inserted into the analytical stream at rates of 1 in 25 samples. Page 85 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Data quality appears uniform and acceptable over all drilling campaigns.

Blanks show that cross contamination between consecutive samples is negligible.

Reference Cu field standards and Laboratory standards show acceptable precision. Standards generally show a slight positive bias.

The bulk of the copper and associated Au and Mo mineralization at Thengkham South exists in moderately south dipping tabular to semi-tabular zones within the weathering profile. The mineralized zones occur over a strike length of 3 kilometres and typically show an along-strike to cross-strike ratio of 10:1. Chalcocite supergene mineralization is located adjacent to primary pyrite/chalcopyrite mineralization. Copper oxide and carbonate mineralization is best developed further downslope and the Cu in this mineralization has been re-mobilized during the Mineralization weathering process. Geology and Resource Domaining Domaining for resource modeling and estimation honours the oxidation/weathering state and metallurgical properties (a combination of Cu, S, Fe and Mn grades and logged mineralogy) of the mineralization.

The domain interpretations excluded zones of suspected downhole contamination. Further drilling to twin these holes for potential exclusion from future resource models is planned.

767 wax-immersion bulk density determinations from across the deposit form the basis for assigning bulk densities to the resource estimate. Average densities are applied to each of the Tonnage Factors (in mineralized domains. Average dry bulk densities of 2.0g/cc, 1.6g/cc and 2.8g/cc were situ bulk densities) determined for Chalcocite, Copper Oxide/Carbonate and Primary mineralization within the deposit.

A block models was constructed using Minesight software by Geomodelling Ltd. using wireframes to flag drill hole and composite data. Geostatistical analysis was also performed by Ravensgate and these parameters were used in the grade estimation process. This discussion details the Minesight block model process.

All data is in the local Sepon grid, SPG06.

Four grade / metallurgical and two oxidation domain wireframes were constructed from sectional interpretation snapped to drill holes. Assay and two metre composite data was coded using these domains. The coding process was checked and validated

Grade Estimation Grade was interpolated into a regular block model with cell dimensions of 20m x 12m x 2.5m (X, Y, Z). The block size was selected based on drill spacing in the well drilled areas and because these dimensions are multiples of the cell dimensions in the proposed production models.

Block model extents:

Minimum Maximum Size number Easting (m) 16,400 21,000 20 230 Northing (m) 73,000 75,004 12 167 RL (m) 150 450 2.5 120 Page 86 Oxiana Limited 2007 Mineral Resource Explanatory Notes

The block model was coded by the geological / metallurgical and oxidation domains using majority logic.

The geostatistical analysis was undertaken by Ravensgate. From this work Variography models and estimation parameters were developed for copper, gold, molybdenum and sulphur.

Minimal grade capping did not make a significant difference to the global statistics for each of the modelled domains.

The absolute kriging variances and the slope of kriging regression for copper were stored in each block as an aide to the classification process.

Resource classification is based solely on confidence in copper grade and volume (tonnage) estimation.

The block model has been validated both statistically and visually.

Validation and Audit The estimate was also checked by an alternative interpolation method (inverse distance squared within a nominal 0.2% Cu grade domain).

Page 87 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Resource Estimation Methodology – DKY

Classification Oxidation 0.5g/ t Au Cut off Deposit (JORC, 2004) State Mt Au (g/t) Ag (g/t) Au (kOz) Ag (kOz) Measured OX - - - - - Indicated OX - - - - - Inferred OX 2.05 1.12 3.9 74 254 Meas,Ind&Inf OX 2.05 1.12 3.9 74 254 Measured POX - - - - - Indicated POX - - - - - DKY Inferred POX 1.06 1.07 3.7 36 124 Meas,Ind&Inf POX 1.06 1.07 3.7 36 124 Measured PRIMARY - - - - - Indicated PRIMARY - - - - - Inferred PRIMARY 4.38 1.10 5.7 155 798 Meas,Ind&Inf PRIMARY 4.38 1.10 5.7 155 798 Meas,Ind&Inf OX/POX/PRIM 7.49 1.10 4.9 265 1,177

A 2007 resource estimate was prepared for the Dankoy (DKY) Au deposit. The deposit is located within the Sepon MEPA approximately 8km to the west of the active Sepon mining area, and situated along the north-west flanks of the Phu Thengkham porphyry center.

The estimate is an update of the November 2006 Scoping Inventory and now has Inferred (JORC, 2004) as the highest resource classification. The update has confirmed and improved the confidence in the geological model and grade interpolation with the inclusion of 87 additional drill holes (9,848m).

The deposit is drilled on a broad 50x50m spacing, with the main core central mineralized zones in-filled to 25x25m spacing.

Compliance with the JORC Code

The DKY resource estimate follows the guidelines of the 2004 Australasian Code for the Reporting of Mineral Resources and Ore Reserves (the “JORC Code”). The details within this section describes the factors considered in assigning resource classification under the code. It follows the format of Table 1 of the JORC Code.

The competent person signing off on the resource statement is Christopher Gerteisen, who has over 10 years of relevant mining and exploration geology experience as outlined in the JORC Code.

Contributing Experts

All drilling programs, data collection, quality control, and geological interpretation was carried out and supervised by site-based personnel. Database compilation and validation, QA/QC, geological interpretation and 3-D modelling, geostatistical modelling, resource estimate and statement was coordinated and/or carried out by Christopher Gerteisen with support from the LXML Sepon exploration department.

Page 88 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Table 11: Contributing Persons

Expert Person / Company Area of Expertise and Contribution Information Source

Christopher Gerteisen All critical resource modelling and estimation tasks LXML

Vanhdone Photisane Database validation and geological Interpretation LXML

Page 89 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Resource Estimation Methodology

The table below summarizes the methodology in determining the Resource Estimate.

Table : Resource Estimation Methodology

JORC CODE - Estimation and Reporting of Resources.

A total of 200 reverse circulation and diamond drillholes were drilled in several campaigns, and for data analysis recognized as pre-November 2006 (Scoping Inventory drill programs) and post- November 2006 (Inferred Resource drill program). The most recent Inferred Resource drilling was as RC precollars with DD tails were drilled using the wet sampling drill protocol, implemented post November 2005. Drilling Configuration The drill pattern within the deposit area is on a broad 50 x 50m spacing, with significant 25 x 25m coverage in the central mineralized zones. Most holes are drilled at -60O towards 180O, which is appropriate for the orientation of the orebody. There are also a few holes drilled at oblique angles which adds confidence to the interpretation.

The mineralization remains open to the west and additional drilling is planned.

Correct drill hole location, orientation and depths were checked on plans and cross-sections, and compared to planned locations as holes were drilled. The topography was corrected to include additional collar location points.

Seven Diamond to Reverse Circulation twin-holes were drilled to assess the suitability of drilling techniques, and determine the effect of wet sampling and downhole recovery. Obvious contamination and smearing of grade is observed in some of the wet and moist intervals within RC holes. A total of 378 assays, or about 11% of samples within the mineralized domains were identified as wet or moist and excluded from the estimation dataset.

A total of 39% of samples drilled in the modelled mineralized domains were from DD holes.

The average calculated recovery for RC drilling is 65% for samples included in the mineralized domains. There is a marked increase in calculated RC recovery for moist and wet samples which is attributed to the extra weight from water saturation. In terms of grade, moist samples within the Drilling QA/QC mineralized zones have the highest grade. This is attributed to sample clogging in the system during drilling, resulting in a serious problem of downhole contamination. This is a further justification for all moist and wet samples being removed from the dataset for estimation.

Diamond drilling recoveries average 94% within the mineralized zones. Diamond sample grades on average have a slight overall positive correlation to recovery. No assays were excluded as a result of these observations, however these trends highlight potential risk and require further analysis.

Post November 2005, wet RC sampling was eliminated from the project with holes being converted to Diamond Drilling at the first encountered moist or wet sample.

Each assay within the database was assessed with respect to its moisture content, sample weight, overall grade profile (for the mineralized intercept) and its relationship to the spatially local tenor of mineralization. No assays were rejected from the resource estimate dataset due to recovery trends, and an adequate study to quantify the effects of these has not been completed.

The raw dataset for the designated DKY resource area has a total of 11,947 assays from 8,780 HQ diamond ½ core samples and 13,793 130-140mm diameter RC samples (split to a nominal 3-4kg

sample wt). Sampling

The final dataset represents 2 meter composites and within the mineralized domains (Au gt 0.2ppm) consists of 2,104 RC samples and 1,342 DD samples. These samples form the basis of the grade interpolation in the resource model. Page 90 Oxiana Limited 2007 Mineral Resource Explanatory Notes

All samples were analyzed for Au by 30g Fire Assay – AAS determination with high grade samples Analysis repeated with gravimetric determination. Ag grades are analyzed by Mixed Acid Digest – ICP-AES determination. A LeachWell Au analysis was done on samples >0.4g/t Au. The preferred lab was ALS, with sample preparation carried out in Vientiane and grade determinations at several labs in Australia.

A thorough and rigorously implemented sampling protocol was adopted for the resource drilling at DKY. QA/QC coarse-blanks, pulp-blanks, grade and matrix-matched certified reference-standards and duplicate samples were routinely inserted into the analytical stream at an interval of 1 in 25 samples.

Blanks show that cross contamination between consecutive samples is negligible.

Assay QA/QC Reference Au field standards and Laboratory standards show good precision, no significant bias and overall average within 1.5% of the certified values which indicates that the assaying is of good standard and reliable. No batches were rejected for re-assaying during the Inferred Resource drilling program.

Field duplicates (DD and RC) confirm that precision is good and the sample reduction protocols were appropriate and correctly implemented at DKY.

Details of rock-type, alteration, mineralization, oxidation and weathering were logged onto field sheets and appended to an SQL database, once passing stringent and rigorous validation tests. Careful Logging planning of the logging process and codes has produced a fertile database with respect to domaining and modeling the economic geology features of the deposit.

The mineralization at DKY is mainly a continuous low-grade zone extending the full E-W length of the deposit. Mineralization style is typical of other Sepon deposits and consists of primary, partially and fully oxidised zones that are hosted in chert/siltstone, calc-shale, and dolomite rock types that dip shallow to moderately to the north. The mineralization is centered around and radiates from a major steep (vertical) E-W trending fault with a dip- slip component that offsets stratigraphy by up to ~80m. Along this structure, there are several locations where mineralization is thicker, mainly in the vicinity of N-S faults and RDP dykes that bisect the deposit. The RDP dykes can be barren or mineralized with respect to gold. Away from the main ore-controlling structure they are generally barren. Within the mineralized envelope some sections of RDP are strongly brecciated and mineralised throughout, while some RDP dykes have barren cores with a thin mineralised skin. Mineralization Geology and Resource A 3-D geological model was constructed using Micromine software and contains the geological Domaining features as described above, including wireframes of lithology, structure, and gold domains. The 0.2ppm Au grade shells were modelled following the geological controls and used to constrain and guide the grade interpolation. Two meter composites were used as the data points for the gold domains.

The Dankoy deposit was divided into 4 zones for gold resource estimation, this includes 3 mineralized domains and waste. The mineralized domains are recognized as:

• Domain 1 – steep structurally controlled, mostly contained on the north side of the fault domain contact with flat domains radiating off this main zone are in most instances arbitrary.

• Domain 2 – flat near surface, generally high volume low grade (possibly formed by supergene processes) Page 91 Oxiana Limited 2007 Mineral Resource Explanatory Notes

• Domain 3 – flat stratigraphic contact and internally subparallel, found along both CHE-CSH/CSH and CSH-DOL contacts, as well as scatty bits and pieces in all units.

The domains were modelled on gold grades with the geology model used as a guide to ensure sensible geometries and continuity to the interpretation. As the mineralization is mostly controlled by contacts and structures the domains overprint lithologies. The deposit model interprets that some of the mineralization to be strataform, but not strata bound. The three modelled domains accommodate the change in the orientation of mineralisation from steep to flat dipping areas. In the flat dipping domains there is also a separation into potential near surface supergene zone and pods of hypogene mineralization.

Oxidation state is interpreted from geological logging. The partial and complete oxidation surfaces were also modelled using the drill hole oxidation codes: primary = OX code 0 and 1, transition/partial oxide = Ox Codes 2 and 3 and oxide = OX codes 4 and 5. The degree of subjectivity required in interpretation of oxidation state is high, and the confidence in the assignment of oxidation state to estimates is thus moderately low. Oxidation state is important in assigning bulk density and metallurgical recovery to the estimate. As such, metallurgical testwork available at the time supported the use of three oxidation designations, Oxide, Partial Ox and Fresh. There is no strict evidence that gold distribution is affected by oxidation state, and consequently estimates for gold were not domained by oxidation. The bulk of the mineralization lies below the complete oxidation surface at DKY and is thus classified mostly as partial oxide and primary.

1479 wax-immersion dry bulk density determinations were collected from 60 diamond drill holes across the deposit and form the basis for assigning tonnage factors to the resource estimate. Average dry bulk densities are applied to each of the mineralized domains according to geological setting and oxidation state. Average dry bulk densities of 1.71g/cc, 2.51g/cc and 2.77g/cc were determined for Oxide, Partial Ox and Fresh material, respectively, within the deposit. Detailed dry bulk densities are tabulated below:

DKY SG Mineralised Count Min Mean Max OX CHE-CSH 50 1.18 1.63 2.81 CSH 30 1.08 1.57 2.67 DOL 30 1.08 1.57 2.67 Tonnage Factors RDP 19 1.44 1.84 2.24 POX CHE-CSH 10 1.09 1.77 2.83 CSH 15 1.35 2.42 2.84 DOL 3 1.70 2.08 2.82 RDP 4 2.15 2.23 2.31 PRIM CHE-CSH 44 1.16 2.72 2.87 CSH 188 1.83 2.78 2.97 DOL 29 2.66 2.82 2.88 RDP 21 2.64 2.78 2.94 Non-Mineralised Count Min Mean Max OX 47 1.07 1.71 2.81 POX 71 1.10 2.51 2.98 PRIM 916 2.01 2.77 2.99

The gold resource estimates at DKY has been aligned to fit neatly over a site-wide grade control grid of 5mX x 3mY x 2.5mZ. A resource estimation block dimension of 25mX x 12mY x 2.5mZ was chosen. The main estimation domains at DKY are large low-grade zones that are controlled by the steep feeder structure and/or flat stratigraphy. There is also a flat tabular near surface mineralization Grade Estimation (supergene?). The block size chosen should adequately deal with the orientation of the ore body, match the resource drilling grid, and also match the bench height for future mining. Optimization and reporting of the DKY resource is based on grade estimation into a 3D block model carried out using Micromine™ software.

Page 92 Oxiana Limited 2007 Mineral Resource Explanatory Notes

The extents of the model:

Item X Y Z

Origin 596200 1874000 75 Maximum 597400 1875200 540 Block Size 25 12 2.5 Number of Blocks 49 101 187 Model Length 1200 1200 465

A composite length of 2m downhole was chosen, and the compositing process checked and validated.

Variography and search neighborhood optimization for each domain was performed using Micromine software. In general, the spatial continuity of gold grades shows a moderate nugget effect ratio between 25 and 50%, and moderate-long ranges of spatial continuity.

Gold grades were estimated by Ordinary Kriging (OK) using Micromine™ software. In addition to grade, the number of informing samples, the number informing holes, the kriging standard error, and the kriging variance were stored to each block.

The low-grade domains show a gradational contact with the barren wallrock, whereby varying the cutoff significantly changes the boundary. The mineralized domains were treated as hard boundaries during estimation, including only those flagged samples belonging to the domain.

The search in X and Y directions are based on 50m radii, which is 2 times the data spacing (25 x 25m) in the better drilled areas of the deposit. The initial first pass search is set at 66.7% of 50m to delineate the areas of closest data density and confidence level. The second pass search is set at the base radius of 50m, 2 times the closest sample spacing. This is then expanded by a factor of 2 in the third pass, and by a factor of 4 for the fourth and final pass. The search in Z was selected to ensure the search ellipsoid totally enclosed the blocks. The minimum number of samples, ellipse sector constraints, and drillhole counts were also relaxed for subsequent search passes. The fourth and final search was used simply to fill the domain wireframe with blocks and these represent a very low confidence estimate.

Silver grades were composited along with gold, using Au as the controlling item, and silver estimates were made into Au domains only, using the same parameters as the gold estimates. Although the resultant estimates will be sub-optimal, the current value of contained silver (where Au >1.0g/t) is <10% that of gold and probably contributes a considerably lower recovered value and not have a material impact on the economics of the deposit. In addition, silver estimates for other deposits at Sepon have been conducted in similar fashion and shown to be robust at the global scale (through production reconciliation) and it is anticipated that the same will be seen at DKY.

Gold domaining was done on a broad 0.2 g/t cutoff grade and guided by the geology. Estimates for each domain were produced using data from the mineralized domain being modelled only, ie treated as a hard boundary. Analysis of domain statistics support this approach. Each domain was assigned its variogram model for the estimation. Waste domain was also treated as a hard boundary.

No top cut was used.

The oxide/partial oxide and fresh zones in the deposit were all estimated together because grade statistics do not vary greatly and the bulk of the mineralization is hosted in the oxide/partial oxide zone. Blocks were flagged by oxidation zone after grade estimation, using a wireframe surface of the base of oxidation and the top of fresh rock.

The model was also flagged by Domain (Domcode) and Rocktype (Rockcode) and assigned an SG after grade estimation.

Page 93 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Validation and Audit The block model and grade estimates were validated globally using statistical comparison and more locally using visual methods. Good reconciliation was established between the estimated grades and the composited drill hole data.

The highest confidence level in the deposit is classified as Inferred (JORC, 2004). There are some lower confidence areas which were given an in-house Scoping Inventory classification. The classification sheme is based on the search pass and proportion of mineralization in each block:

Category Pass Min. Prop. Comment 1 >30% Potential resource upgrade – initial grade control area Inferred 1 <30% 2 >30% <30% 2 Scoping All Inventory 3 All Used to fill in domain wireframe 4

While the highest confidence resource classification for the estimate is Inferred (JORC 2004), there is potential to improve the resource classification to higher confidence. Further peer review and external audit of the model is recommended.

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Resource Estimation Methodology – YNG

Classification Oxidation 0.5g/ t Au Cut off Deposit (JORC, 2004) State Mt Au (g/t) Ag (g/t) Au (kOz) Ag (kOz)

Measured OX - - - - - Indicated OX - - - - - Inferred OX 0.91 3.10 8.0 91 234 Meas,Ind&Inf OX 0.91 3.10 8.0 91 234 Measured POX - - - - - Indicated POX - - - - - YNG Inferred POX - - - - - Meas,Ind&Inf POX - - - - - Measured PRIMARY - - - - - Indicated PRIMARY - - - - - Inferred PRIMARY 0.12 3.84 7.9 15 30 Meas,Ind&Inf PRIMARY 0.12 3.84 7.9 15 30 Meas,Ind&Inf OX/POX/PRIM 1.03 3.19 8.0 106 265

The 2007 Houay Yeng (YNG) Resource Estimate deals with a near surface gold deposit located 10km west of the Sepon Mine area in South Eastern Laos. The deposit is situated along the north side of the northern ridge containing the western tailings storage facility.

The estimate is an update of the January 2007 Internal Scoping Inventory and now has Inferred as the highest resource classification (JORC Code, 2004). The update has confirmed and improved the confidence in the geological model and grade interpolation with the inclusion of 50 additional drill holes (4,330m).

Mineralization occurs predominantly as karst infill collapse breccias, hosted along the chert/siltstone (hangingwall) and limestone (footwall) lithology contact. There is a steep dipping east-west bounding structure running along the strike of the deposit which separates the geology into a chert/siltstone/limestone mineralized package on the hangingwall side (north) and an interbedded siltstone/sandstone, generally barren package, on the footwall side (south) of the structure. The mineralization has a distinct rod- like, WNW trending elongate geometry. The mineralization is centered in the hinge area of an interpreted fold in the stratigraphy. A further zone of colluvial mineralization occurs as a surficial blanket downslope to the east of the main zone. The bulk of the mineralization lies above the oxidation boundary and is classified accordingly as Oxide. The deposit is drilled on 50x50m spacing, with the main identified mineralized zones infill drilled to 25x25m spacing.

Compliance with the JORC Code

The YNG resource estimate follows the guidelines of the 2004 Australasian Code for the Reporting of Mineral Resources and Ore Reserves (the JORC Code, 2004). The details within this section describe the factors considered in assigning resource classification under the code. It follows the format of Table 1 of the JORC Code.

The competent person signing off on the resource statement is Christopher Gerteisen, who has over 10 years of relevant mining and exploration geology experience as outlined in the JORC Code.

Contributing Experts

All drilling programs, data collection, quality control, and geological interpretation was carried out and supervised by site-based personnel. Database compilation and validation, QA/QC, geological interpretation and 3-D modelling, geostatistical modelling, the resource estimate and statement was coordinated and/or carried out by Christopher Gerteisen with support from the LXML Sepon exploration department.

Table 11: Contributing Persons

Expert Person / Company Area of Expertise and Contribution Information Source

Christopher Gerteisen All critical resource modelling tasks

Page 95 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Expert Person / Company Area of Expertise and Contribution Information Source LXML

Vanhdone Photisane Database validation and geological interpretation LXML

Page 96 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Resource Estimation Methodology

Table 12 summarizes the methodology in determining the Resource Estimate stated in Tables 1 to 3.

Table 12: Resource Estimation Methodology

JORC CODE - Estimation and Reporting of Resources.

A total of 125 reverse circulation and diamond drillholes were drilled in three campaigns, 2005, 2006, and 2007, respectively. The most recent drilling was as RC precollars with DD tails as per the wet sampling drill protocol, implemented post November 2005. The drill pattern within the deposit area is on a broad 50 x 50m spacing, with significant 25 x 25m coverage of the central mineralized zones. Drilling Most holes are drilled at -60O towards 180O, which is appropriate for the orientation of the orebody. Configuration There are also a number of holes drilled at oblique angles adding confidence to the interpretation.

The main zone of mineralization remains wide open to the WNW and further drilling is planned. Additional smaller zones of mineralization also remain open in a number of directions and many isolated intercepts in peripheral drillholes require follow-up.

Actual drill hole location, orientation and depths were checked on plans and cross-sections, and compared to planned locations as holes were drilled.

Five Diamond to Reverse Circulation twin-holes were drilled to assess the suitability of drilling techniques by determining the effect of wet sampling and downhole recovery. Obvious contamination and smearing of grade is observed in some of the wet and moist intervals within RC holes. A total of 39 assays, or about 6% of all samples within the mineralized domains are identified as wet or moist and were excluded from the estimation dataset. In addition, all RC holes that are twinned with Diamond holes are excluded from the resource dataset. These total 95 assays, or about 14% of all samples within the mineralized domains that were excluded from the estimation dataset.

A total of 56% of the samples drilled in the mineralized zones were from DD holes. This includes 64% DD sampling within the main mineralized zone. This highlights a significant portion of assays are from the higher confidence drilling method.

The average calculated recovery for RC drilling is 67% for all samples. Samples from mineralized domains average 59% calculated recovery while samples from non-mineralized domains average 73% calculated recovery. There is a marked decrease in calculated RC recovery for moist samples. This is a common phenomenon at other deposits and is likely a material retention issue. Within the mineralized domains RC grades have a positive correlation with recovery and a negative correlation Drilling QA/QC with moisture content.

Diamond Drilling recoveries average 91%, including 88% within the mineralized zones. Diamond sample grades on average have a negative correlation to recovery. No assays were excluded as a result of these observations, however these trends highlight potential risk and require further analysis.

Post November 2005, wet RC sampling was eliminated from the project with holes being converted to Diamond Drilling at the first encountered moist sample.

The highest confidence level in the deposit is classified as Inferred (JORC, 2004). There are some lower confidence areas which were given the in-house Scoping Inventory classification so that areas with potential could be recognised. No higher classification were given mainly due to the fact that no thorough peer review or external audit of the resource model and estimate has been completed. Following this it may be justified to assign a higher confidence resource classification (JORC Code, 2004) to some areas of the deposit.

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The raw databset for the designated YNG resource area has a total of 11,947 assays from 3,266 HQ triple tube diamond ½ core samples, 107 PQ diamond ½ core samples, and 7,693 130-140mm diameter RC samples (riffle split to a nominal 3-4kg sample wt). Sampling The final dataset represents 2 meter composites and within the mineralized domains (Au gt 0.2ppm cutoff model) consists of 302 RC samples and 376 DD samples. These samples form the basis for grade interpolation in the resource model.

A thorough and rigorously implemented sampling protocol was adopted for the resource drilling at YNG. QA/QC coarse-blanks, pulp-blanks, grade and matrix-matched certified reference-standards and duplicate samples were routinely inserted into the analytical stream at an interval of 1 in 25 samples.

Blanks show that cross contamination between consecutive samples is negligible.

Assay QA/QC Reference Au field standards and Laboratory standards show good precision, no significant bias and all average within 4% of the certified values which indicates that the assaying is of good standard and reliable. The 2006 drilling campaign where most of the assays are from shows a difference in average grades of 0.40% (measured to expected). No batches were rejected for re-assaying.

Field duplicates (DD and RC) confirm that precision is good and the sample reduction protocols are appropriate and correctly implemented at YNG.

The bulk of the gold mineralization occurs predominantly as karst infill collapse breccias, hosted along the chert/siltone (hangingwall) and limestone (footwall) lithology contact. There is a steep dipping east-west bounding structure running along the strike of the deposit which separates the geology into a chert/siltstone/limestone mineralized package on the hangingwall side (north) and an interbedded siltstone/sandstone, generally barren package, on the footwall side (south) of the structure. The mineralization has a distinct rod-like, WNW trending elongate geometry. The mineralization centered in the hinge area of an interpreted fold in the stratigraphy. This folding is interpreted to have resulted in brittle deformation of the limestone unit, providing pathway for fluid flow focussed along the fold axis vector. Fluid ingress resulted in the typical alteration of decalcification and silicification, which was Mineralization responsible for limestone karst development, subsequent collapse and clay infill. The karst infill Geology and collapse breccias are polymictic with clasts of siltstone, limestone, and jasperoid within a MnO clay Resource matrix. High grades can be correlated visually with higher MnO and jasperiod content. While the Domaining mineralization is mostly constrained to the hangingwall side (north) of the main east-west bounding structure, there is no evidence within the deposit area to suggest this structure is a feeder. As the rocks on either side are distinctly different, this structure may in fact represent a later offset. An additional zone of colluvial mineralization also occurs as a surficial blanket downslope to the east of the main zone, and generally constitutes low grade.

A 3-D geological model was contructed using Micromine software and contains the geological features as described above, including wireframes of lithology, structure, and gold domains. The 0.2ppm Au grade shells were modelled following the geological controls and used to constrain and guide the grade interpolation. Two meter composites were used as the data points for the gold domains.

Oxidation state is interpreted from geological logging. The degree of subjectivity required in Page 98 Oxiana Limited 2007 Mineral Resource Explanatory Notes

interpretation of oxidation state is high, and the confidence in the assignment of oxidation state to estimates is thus moderately low. Oxidation state is important in assigning bulk density and metallurgical recovery to the estimate. As such, metallurgical testwork available at the time supported the use of a single boundary designating only two oxidation zones, Oxide/Partial-Ox and Fresh. There is no evidence that gold distribution is affected by oxidation state, and consequently estimates for gold were not domained by oxidation. The bulk of the mineralization lies above the oxidation boundary and is classified accordingly as Oxide. Preliminary metallurgical testwork has indicated excellent +90% Au recovery in the Oxide/Partial Ox zone.

652 wax-immersion dry bulk density determinations were collected from 50 diamond drill holes across the deposit and form the basis for assigning tonnage factors to the resource estimate. Average dry bulk densities are applied to each of the mineralized domains according to geological setting and oxidation state. Average dry bulk densities of 2.09g/cc and 2.78gcc were determined for Oxide/Partial-Ox and Fresh material within the deposit. Detailed dry bulk densities are tabulated below: YNG Tonnage Factors Tonnage Mineralised Factors OX DOM1 2.10 DOM2 2.12 DOM3 2.12 DOM4 1.84 FRESH DOM1 2.78 Non-Mineralised CHE 2.35 LST 2.74 SLT 2.43

The gold resource estimates at YNG has been aligned to fit neatly over a site-wide grade control grid of 5mX x 3mY x 2.5mZ. Accordingly, an estimation block dimension of 25mX x 12mY x 2.5mZ was chosen. The gold estimation domains at YNG are primarily rod-like geometries. There is also a flat tabular domain for near surface mineralization. The block size chosen should adequately deal with moderate dips present in the orebody. The block size should also be appropriate for the existing resource drilling grid and match the bench height for future mining. Optimization and reporting of the YNG resource is based on a grade estimation carried out using Micromine™ software into a 3D block model.

The extents of the model:

Item X Y Z

Origin 598850 1871800 120 Maximum 599600 1872268 350 Grade Block Size 25 12 2.5 Estimation Number of Blocks 30 39 92 Model Length 750 468 230

A composite assay length of 2m downhole was chosen, and the compositing process was checked and validated.

Gold grades were estimated by Ordinary Kriging (OK) using Micromine™ software. In addition to grade, the number of informing samples, the number of informing holes, the kriging standard error, and the kriging variance were stored to each block and used in resource classification.

The rod-shaped karst collapse breccia domains show a sharp contact with the barren wallrock, and

Page 99 Oxiana Limited 2007 Mineral Resource Explanatory Notes

varying the cutoff does not drastically change the boundary, ie relatively sharp contact. As such, the mineralized domains were treated as hard boundaries during estimation, including only those flagged samples belonging to the domain.

The search in X and Y directions are based on 50m radii, which is at least 2 times the data spacing (25 x 25m) in the better drilled areas of the deposit. The search radius in the Z direction is set to a minimum of 1.25m. The search radii selected ensure the search ellipsoid totally encloses the blocks in every pass. The initial first pass search is set at 66.7% of 50m to delineate the areas of closest data density and confidence level. The second pass search is set at the base radius of 50m, 2 times the closest sample spacing. This is then expanded by a factor of 2 in the third pass, and by a factor of 4 for the fourth and final pass. The minimum number of samples, ellipse sector constraints, and drillhole counts were also relaxed for subsequent search passes. The fourth and final search was used simply to fill the domain wireframe with blocks and these represent a very low confidence estimate.

Silver grades were composited along with gold, using Au as the controlling item, and silver estimates were made into Au domains only, using the same parameters as the gold estimates. Although the resultant estimates will be sub-optimal, the current value of contained silver (where Au >1.0g/t) is <5% that of gold and probably contributes a considerably lower recovered value and not have a material impact on the economics of the deposit. In addition, silver estimates for other deposits at Sepon have been conducted in similar fashion and shown to be robust at the global scale (through production reconciliation) and it is anticipated that the same will be seen at YNG.

A global top cut of 12 g/t Au was used. This affected 39 samples or about 5% of the mineralized domain samples.

The model was also flagged by Domain (Domcode) and Rocktype (Rockcode) and assigned a tonnage factor (DBD) after grade estimation.

The block model and grade estimates were validated globally using statistical comparison and more locally using visual methods. Good reconciliation was established between the estimated grades and the composited drill hole data.

The resource was classified as either Inferred or Internal Scoping Inventory. The classification sheme is based on the search pass and proportion of mineralization in each block:

Category Pass Min. Prop. Comment Validation and 1 >30% Potential resource upgrade – initial grade control area Audit Inferred 1 <30% 2 >30% <30% 2 Scoping All Inventory 3 All Used to fill in domain wireframe 4

While the highest confidence resource classification for the estimate is Inferred (JORC Code, 2004), there is potential to improve the resource classification to higher confidence. Further peer review and external audit of the model is recommended.

Page 100 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Golden Grove Mineral Resources Statement: June 30, 2007

Introduction

The Golden Grove June 2007 Resource Statement incorporates the primary zinc, primary copper, oxide copper and oxide gold Mineral Resources at the Scuddles and Gossan Hill deposits located 450 km northeast of Perth, Western Australia.

The Resources have been estimated using the Golden Grove geological database as at 30 June 2007. The majority of the Resources comprise primary zinc and copper mineralisation in and around the Scuddles and Gossan Hill underground operations. The near surface oxide gold and copper Resources located at Gossan Hill were estimated in 2003 (oxide gold by Newmont Australia Limited) and 1997 (oxide copper by Normandy Mining Limited), and were not updated for this Estimate.

Since the previous Resource estimate at 30 June 2006, a further 28,212m of underground diamond drilling was completed at Gossan Hill. Approximately 27,600m was designed to upgrade Indicated Resources to Measured Resources, while the remainder was designed to upgrade Inferred Resources to Indicated Resources. A further 678m were drilled at Scuddles to upgrade Inferred Resources in the central copper area to Measured Resources.

Resource block modelling for primary zinc and copper mineralisation follows similar procedures to those adopted in previous years. Geological domains, generated using drill information and mine development mapping where available, serve as constraints for block modelling and grade estimation. Estimation of grades and densities into block models are carried out using ordinary kriging and inverse distance squared techniques.

The resource estimate has been classified based on data density, data quality, confidence in the geological interpretation and confidence in the estimation.

Results

Table 1 summarises the Mineral Resources as at 30 June 2007, which include Resources within Reserves.

The primary zinc and copper Resources were prepared by estimating the pre-mining Resource and then removing mined–out blocks, unmineable areas adjacent to stoping, and areas considered to be too far from existing development and/or too small in tonnage to be potentially economic.

Table 2 compares the June 2007 Mineral Resources with an estimate of the tonnage and grade at June 2006. The primary zinc and copper Resources have increased by 0.45 Mt, from 24.12 Mt to 24.57 Mt. This incorporates 1.86 Mt of additional material at Gossan Hill due to an increase in predicted long term metal prices expressed in AUD and 1.48 Mt decrease due to mining. Scuddles Resources were not recalculated as very minor additional information was added to the database and no mining activity took place. The last year’s study demonstrated that increasing metal price had very little effect on total Resources at Scuddles. The increase from June 2006 to now is considered immaterial.

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Table 1. Golden Grove Mineral Resources 30 June 2007

Mineral Resources Category Commodity Mine Tonnes Cu Pb Zn Ag Au Mt % % % g/t g/t Primary Zinc Gossan Hill 3.98 0.3 1.5 14.1 100 1.9 Primary Copper 6.47 3.4 0.0 0.2 16 0.4 Oxide Copper ------Measured Oxide Gold ------Primary Zinc Scuddles 1.04 0.6 1.0 12.2 92 1.1 Primary Copper 2.61 3.1 0.0 0.5 14 0.4 Total Measured 14.11 2.3 0.5 5.1 45 0.9 Primary Zinc Gossan Hill 0.92 0.4 1.7 15.6 83 1.9 Primary Copper 2.50 2.6 0.0 0.1 12 0.3 Oxide Copper 4.08 1.9 - - - - Indicated Oxide Gold 1.04 - - - 94 3.1 Primary Zinc Scuddles 0.06 0.8 0.7 8.5 72 0.5 Primary Copper 1.00 2.6 0.0 0.2 9 0.2 Total Indicated 9.59 1.8 0.2 1.6 23 0.6 Primary Zinc Gossan Hill 2.91 0.3 1.4 11.0 90 1.2 Primary Copper 2.03 2.3 0.1 0.4 20 0.6 Oxide Copper ------Inferred Oxide Gold 0.07 - - - 197 4.3 Primary Zinc Scuddles 0.45 0.5 0.8 9.4 69 1.0 Primary Copper 0.60 2.8 0.0 0.3 9 0.2 Total Inferred 6.06 1.2 0.7 6.1 58 0.9 Primary Zinc Gossan Hill 7.80 0.3 1.5 13.1 95 1.6 Primary Copper 11.00 3.0 0.0 0.2 16 0.4 Oxide Copper 4.08 1.9 - - - - Total Oxide Gold 1.11 - - - 100 3.2 Primary Zinc Scuddles 1.56 0.6 0.9 11.3 85 1.0 Primary Copper 4.21 2.9 0.0 0.4 12 0.3 Total Resources 29.75 1.9 0.5 4.2 41 0.8

Table 2. Comparison of 30 June 2007 Mineral Resources with June 2006 Mineral Resources Estimate (Measured + Indicated + Inferred)

30 June 2007 30 June 2006 Commodity Tonnes (Mt) Grade (%) Tonnes (Mt) Grade (%) Primary Zinc 9.36 12.8 9.00 14.0 Primary Copper 15.21 3.0 15.12 3.1 Oxide Copper 4.08 1.9 4.08 1.9 Oxide Gold 1.11 3.2 1.11 3.2

Page 102 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Compliance with JORC code assessment criteria

This Mineral Resource Statement has been compiled in accordance with the guidelines defined in the Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves (The JORC Code, 2004 Edition).

All information in this Statement which relates to Mineral Resources is based on, and accurately reflects reports prepared by the persons named below. All of the persons listed are Members of the Australasian Institute of Mining and Metallurgy or the Australian Institute of Geoscientists and have the necessary experience relevant to the style of mineralisation, the type of deposit and the activity undertaken to qualify as a ‘Competent Person’ under the JORC Code, 2004.

Deposit Competent Person

Primary Zinc and Copper Elizabeth Florkiewicz, Oxiana Golden Grove Pty Ltd

Oxide Gold Robert Singer, Chief Geologist at Golden Grove (2003) (Gossan Hill oxide gold Resource Estimate as of October 2003, not re-estimated for 30 Jun 2007)

Oxide Copper Paul Blackney, Snowden (Gossan Hill oxide copper resource estimate as of December 1997, not re-estimated for 30 Jun 2007)

Each of the Competent Persons has given their consent for the inclusion of the material in the form and context in which it appears.

Key Points relating to the Golden Grove June 2007 Resource Estimate

1. Drilling

The Golden Grove drilling database comprises 938,712m of surface and underground diamond coring of several diameters and 42,000m of reverse circulation (RC) drilling. 40% of the diamond core samples used to estimate Gossan Hill sulphide deposits are of LTK48, LTK60 or BQ diameter, 24% of NQ or HQ diameter while 36% of samples are of unknown size pre-1998 and not recorded. Less than 2% of all samples used were RC type samples. The total number of diamond drill holes used in the resource modelling is 1,970 for Scuddles and 4,227 for Gossan Hill. There are also 348 Gossan Hill RC holes in the database, of which 267 holes were used in the modelling of the gold oxide and copper oxide deposits. Only 81 RC holes were used for modelling of Gossan Hill primary deposits and none to model the Scuddles deposit.

In the second half of 2006 and the first half of 2007 underground diamond drilling concentrated on converting Indicated Resources to Measured Resources. A total of 13,249m was drilled at Hougoumont, 6,507m at Ethel and Catalpa, 3,595m at A & Q Copper, 2,711m at Amity, and 678m at Scuddles. In addition, a further 604m was drilled at Hougoumont to convert Inferred Resources to Indicated Resources.

2. Drilling Quality Assurance

All of the diamond drill hole collar locations and orientations are surveyed using an electronic theodolite. Downhole surveying is performed using a Maxibor or an Electronic Multi Shot tool. Many of the surface RC holes had “in rod” dip only Eastman Surveys and a large number of these holes were not originally downhole surveyed. Where possible, RC holes were subsequently resurveyed using an Electronic Multi Shot tool. Holes containing magnetite were gyro-surveyed. .

All drill hole locations and orientations were validated by plotting collar pick ups against existing surface or underground development and by comparing actual traces against designed hole paths. Core recovery is generally excellent for all core sizes, averaging 98% recovery. Recoveries from RC drilling were reported to be generally good except with lower recoveries reported for drilling in parts of the gossans.

3. Geological Logging

All drill core is geologically logged using codes set up for direct computer input. Rock type, stratigraphy, grainsize, weathering, colour, bedding, alteration style, type and intensity, structure, mineralisation style and percentage are recorded. Zones of sulphide mineralisation determined during geological logging are selected for assays.

Page 103 Oxiana Limited 2007 Mineral Resource Explanatory Notes

All RC chips were geologically logged. Prior to the 1998/99 drill programme and the subsequent resource estimation, a programme of drill hole re-logging was carried out on all of the holes previously drilled into the oxide gold deposit.

4. Sampling

Drill core is halved and sampled at nominal 1m intervals. Sample intervals are adjusted to conform to the lithological boundaries. The sampling protocol and the sampling volumes are considered to provide a representative sample for the style of massive sulphide mineralisation encountered at Golden Grove.

RC drilling samples were used extensively in the oxide deposits. Various sampling techniques were used for the RC drilling, which were most commonly sampled on 1m intervals in mineralised zones and as 4m composites outside of mineralised zones.

5. Analytical Methods

Prior to 2001 a number of commercial analytical laboratories were used for drill hole sample analysis. Since 2001, ALS Chemex at Malaga, WA has analysed the majority of samples. The 2005 and some of 2006 Hougoumont drill samples were analysed by to Ultra Trace Analytical Laboratories in Canning Vale, WA.

Samples undergo total pulverisation before an assay charge is extracted and analysed for a basic suite of seven elements (Zn, Cu, Pb, Fe, S, Ag and Au). At ALS Chemex, Zn, Cu, Pb, Fe, S and Ag are analysed using aqua-perchloric digest, ICPOES determination, Au is analysed by 50g fire assay, AAS determination. At Ultra Trace, Zn, Cu, Pb, Fe, S and Ag are analysed by sodium peroxide fusion, hydrochloric digest, ICPOES determination and Au by 40g Fire Assay, ICPMS determination.

6. Assay Quality Control and Quality Assurance

Since 1997, standard samples have been used to confirm assay results and represent approximately 4% of submitted samples. Before 1997, umpire assays were used to assess the accuracy of assays. In the oxide deposits assay quality control was performed using standards, blanks, duplicates and umpire assays.

Overall, all elements show an acceptable reconciliation with the expected value. The following issues have been noted, and are believed to represent limited risk to the June 2007 Resource Estimate:

• The base metal assay standards for primary mineralisation exhibit a negative bias relative to the expected value between -2 and -6%. Precision for base metal elements range up to +/- 7%, based on statistical analysis of standard populations. • Gold assay standards in primary mineralisation show negative bias with accuracy between +2 and -9%. Precision is generally poor for gold with average values at +/-28%. • In the oxide gold deposit the umpire assays results for the samples from the 1995 and 1998/99 drilling programmes show good correlation with original assays. Coarse crush and pulp repeat Au analyses of selected 1998/99 samples showed good correlation with the original assay, suggesting a low nugget effect for this mineralisation. For silver the mean value of the total for all batches agrees very well between the original assay and the repeat. Details of assay validation on earlier programmes are not available. • In the oxide copper deposit the results of various quality control programs are variable and it is difficult to draw meaningful conclusions based on the available data. Oxiana’s broad conclusions are that oxide copper assays are more likely to understate the true value than overstate them.

7. Bulk Density

All bulk density data collected from non dried drill core samples since 1997 (1m lengths or as specified for sampling) have been measured using a gravimetric method. As such the primary zinc and copper resources are based on high quality data. As drill core samples are of solid, non porous fresh rock, the measured density is assumed to be the dry bulk density. Non-sampled intervals (waste rock) are assigned an average density of 2.82 based on the mean density of analysed waste samples. In the primary zinc and copper deposits, bulk density is interpolated within the block model using the same methods as for the grade estimation.

In the oxide gold deposit bulk density was determined on selected holes using a gravimetric method. In the oxide copper deposit about 50% of samples have bulk in situ density measurements determined using a downhole density probe. Average bulk densities were assigned in the block models of oxide deposits according to the rock type and depth below surface.

Page 104 Oxiana Limited 2007 Mineral Resource Explanatory Notes

8. Interpretation

Resource modelling is carried out using VulcanTM software. Solid triangulations are produced around mineralised envelopes within which grade is interpolated. Numerous un-mineralised dacite, dolerite and rhyolite intrusives that dissect mineralisation are also modelled. In the Gossan Hill area, 104 mineralised domains and 109 waste intrusives are created. In the block modelling process, each domain is coded with a unique number. The same unique number is also used to code drill hole samples from that domain.

In the primary zinc mineralisation, lithological boundaries representing massive sphalerite-pyrite mineralisation are interpreted, and these boundaries roughly correspond to a 4% zinc cut-off. The primary copper mineralisation is interpreted using a 0.2% copper cut-off due to disseminated nature of this mineralisation. The oxide gold deposit is located above the primary zinc mineralisation within the oxidised massive sulphide lithology in the area where zinc was leached out. The boundary of the oxidised massive sulphide corresponds with a 0.2g/t Au cut-off, and this cut-off was used to interpret gold concentration.

9. Grade Estimation

The predominant method of estimating grade and bulk density into the block model areas, defined by the mineralisation triangulations, is ordinary kriging. Where the data does not permit geostatistical methods to be used, inverse distance squared method of grade estimation has been used. The Gossan Hill copper oxide resources was estimated using indicator kriging.

Samples were composited to 1m lengths in the primary zinc and copper deposits and to 2m in the oxide deposits. No top-cuts were applied except to gold in one of the Amity massive pyrite areas.

10. Cut-off Grade

The cut-off grade used for the primary zinc and copper Resource estimate approximates the site mining and processing break- even costs taking into account metallurgical recovery, concentrate transport costs, concentrate treatment and refinery charges, and royalties. Expressed as Nett Smelter Return (NSR) or mine gate value, the cut-off grade used for the Resource Estimate is AUD70.00/t.

The metal prices and exchange rate used for the estimation of the Mineral Resources are presented in the table below.

Table 3. Metal Prices and Exchange Rate

Metal Unit Price Zinc USD/lb 1.00 Copper USD/lb 2.00 Lead USD/lb 0.35 Gold USD/oz 500 Silver USD/oz 10.0 Exchange rate AUD/USD 0.70

The cut-off used for the oxide gold Resource estimate is 1g/tAu. The cut-off used for the oxide copper Resource estimate is 0.5%Cu.

11. Resource Classification

Resources were classified based on data density and quality. The drill density required for each area was assessed based on the geology and continuity of mineralisation in that area. The drill hole spacing used for each classification is presented in the table below.

Table 4. Resource Classification Criteria

Classification Hole Spacing Measured 10mx15m to 15mx15m Indicated 20mx30m to 30mx30m Inferred Wider spacing

Page 105 Oxiana Limited 2007 Mineral Resource Explanatory Notes

12. Block Model Validation

Resource block models were validated using various techniques. The sections and plans through the block models were visually compared to the drill hole data. Block and sample statistics were compared for selected domains. Sensitivities to estimation method, block size, minimum and maximum samples used, and interpolation parameters were assessed during the estimation process.

13. Recent Audits

Whist the June 2007 has not been audited, AMC Consultants have audited the estimation processes and methods used to determine the December 2005 Resources during February 2006. AMC concluded that it could not identify any issues that would materially impact the December 2005 Resources, and recommended some procedures should be reviewed to improve and simplify them. AMC also noted that the December 2005 Resources had been subjected to a more stringent application of the “reasonable prospects for eventual economic extraction” test defined in the JORC code and also that Oxiana ran the risk of not reporting Resources that might be extractable in the longer term. This issue was since rectified.

Page 106 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Prominent Hill Resource Statement: July 31, 2007

Introduction

The Prominent Hill July 2007 Resource Statement deals with the copper-gold and gold-only Mineral Resources for the Prominent Hill deposit located 150km south-east of Coober Pedy, South Australia.

The Resource model is based on the geological database as at 24th September, 2007, the geological interpretation by Patrick Say and Hamish Freeman of Oxiana Limited and solid modelling of the geology by Duncan Hackman of Hackman and Associates Pty. Ltd. Block model construction and grade estimation were undertaken by Jared Broome of Oxiana Limited in consultation with Duncan Hackman, using VulcanTM software.

The geological interpretation was based on all drill holes up to hole PH07D334, which was completed in July 2007. This includes data from 223 Diamond Drill holes (114,958m) and 113 Reverse Circulation holes (39,097m) containing 85,377 logged and assayed nominal 1m intervals. The geological model extends 1000m along strike East to West and covers the 300-400m width of the mineralized horizons.

Sample data was composited to five (5) metre intervals and flagged by the domains defined in the geological interpretation. Ordinary Kriging was used to estimate grades within the geological domains. Resources were estimated for both the coincident copper-gold mineralisation and the contiguous gold-only mineralisation in the deposit.

The resource estimate has been classified based on data density, data quality, confidence in the geological interpretation and confidence in the estimation.

Results

The July 2007 Copper Resource of 152.7Mt at 1.21% Cu, and 0.48g/t Au above a 0.3% Cu block cut-off and Gold Only Resource of 38.2Mt at 1.1g/t Au is reported from within the limits 555425 to 556225m East, above -450m RL (550m vertical extent) and 555300 to 555550m East from -1100 to -450m RL.

Tables 1 to 5 detail the Prominent Hill Resource as estimated in the model.

Table 2: Copper Resource at 0.3% Cu cut-off Prominent Hill Resource Cu > 0.3% Mineral Resource Category Tonnes Cu Au Ag JORC 2004 (,000) (%) (ppm) (ppm) Measured 42,898 1.69 0.52 4.1 Indicated 46,355 1.14 0.41 2.6 Inferred 63,490 0.94 0.50 2.4

Total Measured + Indicated 89,252 1.40 0.46 3.30

Total Measured + Indicated+ Inferred 152,743 1.21 0.48 2.93

Table 3: Copper Resource at 0.5% Cu cut-off Prominent Hill Resource Cu > 0.5% Mineral Resource Category Tonnes Cu Au Ag JORC 2004 (,000) (%) (ppm) (ppm) Measured 41,418 1.73 0.52 4.1 Indicated 40,088 1.26 0.45 2.8 Inferred 49,170 1.10 0.55 2.7

Total Measured + Indicated 81,506 1.50 0.49 3.47

Total Measured + Indicated+ Inferred 130,676 1.35 0.51 3.16

Page 107 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Table 4: Copper Resource at 1.0% Cu cut-off Prominent Hill Resource Cu > 1.0% Mineral Resource Category Tonnes Cu Au Ag JORC 2004 (,000) (%) (ppm) (ppm) Measured 31,767 2.02 0.55 4.7 Indicated 22,945 1.62 0.49 3.4 Inferred 22,095 1.55 0.65 3.6

Total Measured + Indicated 54,711 1.85 0.53 4.12

Total Measured + Indicated+ Inferred 76,806 1.77 0.56 3.98

Table 5: Gold Resource at 0.5ppm Au cut-off in material with less than 0.3% Cu Prominent Hill Resource Au > 0.5g/t and Cu <0.3% Mineral Resource Category Tonnes Cu Au Ag JORC 2004 (,000) (%) (ppm) (ppm) Measured 25 0.19 0.62 2.4 Indicated 16,798 0.04 1.29 1.3 Inferred 21,405 0.07 0.97 1.1

Total Measured + Indicated 16,824 0.04 1.28 1.28

Total Measured + Indicated+ Inferred 38,229 0.06 1.10 1.17

Table 6: Gold Resource at 1.0ppm Au cut-off in material with less than 0.3% Cu Prominent Hill Resource Au > 1.0g/t and Cu <0.3% Mineral Resource Category Tonnes Cu Au Ag JORC 2004 (,000) (%) (ppm) (ppm)

Measured - - - - Indicated 10,770 0.04 1.60 1.3 Inferred 1,344 0.07 1.51 1.2

Total Measured + Indicated 10,770 0.04 1.60 1.34

Total Measured + Indicated+ Inferred 12,114 0.06 1.56 1.30

Tables 1 to 5 show rounded estimates. This rounding may cause apparent computational discrepancies. Significant figures do not imply precision. Nominal lower Cu and Au grade cuts are applied.

Jared Broome and Duncan Hackman are members of the Australian Institute of Geoscientists. Both have sufficient experience relevant to the style of mineralisation and type of deposit under consideration and to the activity undertaken to qualify as Competent Persons as defined in the 2004 Edition of the Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves (The JORC Code, 2004 Edition).

Jared Broome Duncan Hackman B.App.Sc.(Hons), MSc, Grad. Dip. Business, MAIG B.App.Sc., MSc, MAIG Senior Geologist – Resources and Mining Consultant Geologist Oxiana Limited Hackman & Associates Pty. Ltd. Page 108 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Key points relating to the Prominent Hill July 2007 Resource Estimate:

1. The resource estimate applies to coincident copper and gold mineralization and peripheral gold-only mineralisation within haematite breccias from 555300E to 556225E, 6711800N to 6712850N and from the Permian Unconformity (~100RL) to -1100RL (at its deepest). This volume is an expansion of that reported in the June 2006 Resource Estimate.

2. The deposit is delineated by 210 diamond core (108,766m) and 74 reverse circulation drill holes (26,598m) drilled on nominal north-south 50m sections with 25m infill holes and sections located immediately below the unconformity and in the Eastern Gold zone (also tested with an additional oblique drilling grid). Within the copper-gold mineralisation, holes are drilled 50m apart on section; the eastern gold mineralisation has been drilled at 25m horizontal separation on section. The majority of holes are angled at approximately 60 degrees to the south, 25 holes drilled approximately 60 degrees to the north, there are 7 vertical holes and 29 holes drilled oblique to the drill sections (>30 degrees from section and include those targeting the Eastern Gold zone).

A further 13 diamond and 39 reverse circulation holes are drilled in the project area and have either not intersected significant mineralisation and/or the intercepts cannot be confidently modelled. Mineralization is open at depth below the deposit, at depth to the west and along its entire vertical extent to the south-east.

3. Samples are at a nominal 1m length. 64,417 half-NQ2 diamond core samples and 20,114 1/8th split 14cm diameter reverse circulation samples populate the Prominent Hill Database. Confidence in the data is split into two levels. There is high confidence in the quality of data collected during the 2004-07 drilling campaigns (70,888 samples), and lower confidence in the quality of drilling data collected pre 2004 (14,489 samples). Analysis and QA-QC relating to the physical properties of samples collected during 2004-07 (locations, recoveries, sampling procedures) shows no significant issues and it is most likely that earlier data (pre 2004) is of similar quality and thereby acceptable for use in estimating Measured, Indicated and Inferred Resources.

The 2004-07 Cu grades were determined by modified aqua-regia/perchloric acid digest ICPOES determination (AMDEL Adelaide ore-grade Cu method) and Au grades by 40g Fire Assay AAS (at AMDEL Adelaide). For the pre 2004 data, the AMDEL ore- grade Cu method was adopted for assays greater than 1%Cu in the initial HF/mixed-acid digest ICPOES assay results. Assay data quality was determined through submission of field and laboratory standards, blanks and repeats which were inserted at a nominal rate of 1 each per 25 drill samples. A description of the assay data quality follows:

For the May 2005 to 2007 data – the Cu and Au standards for all but one batch were within acceptable levels of precision and accuracy. The failed batch (282 samples) was resubmitted for re-assay to AMDEL and returned with improved results. The negative bias for U standards noted in earlier batches persists in this data.

For the 2004 to May 2005 data – 2,660 samples from 24 batches encompassing mineralised intercepts were submitted to AMDEL for re-assay and returned with improved precision and accuracy. Initially a negative relative bias of 2-3% was noted in a number of Cu standards, which is still evident in some of the re-assay data, however the precision has improved significantly. A positive relative bias of 2-4% is still noted in a number of Au standards and precision issues were noted for Au assays in the high-Cu standards. A consistent relative negative bias of 4-13% is noted in the U standards.

Check-laboratory assays on the Pre 2004 data show good correlation for Cu assays in samples with grades less than 5%Cu and a relative 6% bias in favour of the original assays for Cu grades greater than 5%Cu. A positive relative bias of 8% was found in favour of the original Au assays. Pre 2004 Fe assays were determined by an inappropriate method and were excluded from the Fe estimate.

4. For the copper-gold mineralisation; copper, gold, silver, uranium, iron, sulphur, calcium, silica, barium and fluorine grade estimation is conducted by Ordinary Kriging and guided by wire-framed geological and mineralogical domains. To assist in preserving observed local grade variability the Kriged Cu and Au grade-tonnage curves were matched against those from a theoretical model, generated from 14, 5m close-spaced drillhole data within the Prominent Hill Shear Zone. 15,134 five metre composites inform the grade interpolation. Parent cell estimates (25mE x 25mN x 12mRL) were written to a sub-blocked model using three composite search parameter sets to account for variable data distribution. High-grade copper assays were cut and high-grade gold samples have a restricted area of influence (diameter of 25mE x 25mN x 12mRL). Top-cuts have been applied to the other elements’ populations where applicable.

Grade estimation for the high-grade eastern gold-only mineralisation, within the silica-haematite breccia domain, was interpolated using Ordinary Kriging and guided by wire-framed mineralogical domains.

Domains used in the estimate are: Constrained tabular Cu mineralisation that persists over the entire extent of the deposit: • The Hangingwall Fault Zone – a chloritic and carbonaceous fault zone located to the north of the main zones of mineralisation. Contributes 1.0% of total (Measured, Indicated and Inferred) contained Cu metal (at a 0.3%Cu cut-off grade).

Page 109 Oxiana Limited 2007 Mineral Resource Explanatory Notes

• The Prominent Hill Shear Zone – a layered or foliated haematite matrix breccia located along the northern margin of the main mineralised zone. Contributes 55.2% of contained Cu metal (0.3%Cu cut-off grade) within the copper resource and 7.0% of Au metal of the gold resource. • BD1 breccia – a crystalline-haematite matrix breccia with dominant sedimentary clasts (trending to volcanic in the east) located along the southern margin of the main mineralised zone. Contributes 24.0% of contained Cu metal (0.3%Cu cut-off grade). Plunging breccia pipe mineralisation located in the eastern half of the deposit: • BD2 breccia – a crystalline-haematite matrix breccia with dominant sedimentary clasts (trending to volcanic at higher RLs). Contributes 9.8% of contained Cu metal (0.3%Cu cut-off grade). • BD3 breccia – a crystalline-haematite matrix breccia with dominant sedimentary clasts located in the southeast of the deposit and open to the east. Contributes 4.0% of contained Cu metal (0.3%Cu cut-off grade). This breccia also contains significant Au mineralisation within an internal copper depleted zone. 11.7% of the Au metal in the Au-only mineralisation is in this domain (0.5ppmAu cut-off grade). • BG2 breccia – a haematite diorite breccia located close to the centre of the deposit. Contributes 2.1% of contained Cu metal (0.3%Cu cut-off grade). • BG3 breccia – a haematite-silica breccia hosting Au mineralisation located to the east of the Cu mineralisation. Contributes 31.7% of Au metal in the Au-only mineralisation (0.5ppmAu cut-off grade). Plunging breccia pipe mineralisation located in the western half of the deposit: • BD4 breccia – a crystalline-haematite matrix breccia with dominant sedimentary clasts located in the southwest of the deposit at depth. Contributes 1.2% of contained Cu metal (0.3%Cu cut-off grade). This breccia also contains Au mineralisation within a Cu depleted zone. 12.0% of the Au metal within the Cu-Au mineralisation is in this domain (0.5ppm Au cut-off grade). • BG1 breccia – a crystalline-haematite matrix breccia with dominant sedimentary clasts located in the south west of the deposit at depth. Contributes 0.6% of contained Cu metal (0.3%Cu cut-off grade). This breccia also contains Au mineralisation within a Cu depleted zone. 12.2% of the Au metal within the Cu-Au mineralisation is in this domain (0.5ppm Au cut-off grade). Peripheral and detached areas of Cu or Au mineralisation: • These are located throughout the prospect within the hangingwall dolomites and skarn, greywacke and the footwall andesite. These zones contribute 2.1% of contained Cu metal (0.3%Cu cut-off grade) and 20.0% of the Au metal in the Au- only mineralisation.

The deposit is zoned at the macro scale. Chalcocite-bornite mineralisation is dominant at higher RLs, throughout the majority of the Prominent Hill Shear Zone and in the west of the deposit. Bornite-chalcopyrite mineralisation is prevalent below the chalcocite- bornite and in-general forms a shell around a core of chalcopyrite mineralisation at depth in the central-east area of the deposit.

5. Block Bulk Density factors were assigned for each domain using a polynomial regression applied to the estimated block Fe grade. The regression was based on Fe assay data and 32,120 bulk density measurements determined by Archimedes Principle. An average bulk density of 3.38 is determined for the haematite breccias in the deposit.

6. Measured, Indicated and Inferred Resource categories are applied to the Resource Estimate based on data density and quality, and confidence levels in the geological interpretation and grade estimation.

7. The 2007 Resource Estimate compares well with the 2006 BFS Resource Estimate, both globally and locally within the region reported for the 2006 resource estimate. Stephen Godfrey and Richard Gaze of Golder and Associates completed a parallel 2007 check estimate that compared closely to the July 2007 resource model.

8. The total Cu mineral resource for Prominent Hill of 152.7Mt at 1.21% Cu and 0.48g/t Au above a 0.3% Cu cut-off represents a 50.7Mt (50%) increase in tonnes from the June 2006 estimate with increases of 25% in contained copper metal and 40% in contained gold ounces.

The total Au mineral resource for Prominent Hill of 38.2Mt at 1.1g/t Au above a 0.5g/t Au cut-off for blocks less than 0.3% Cu represents an increase of 15.6Mt (69%) from the June 2006 estimate with an increase of 52% (0.47Moz) of contained gold.

Resources above -450mRL within the Prominent Hill open pit region, remain largely similar to the June 2006 mineral Resource estimate at comparable cut offs with changes outlined as follows: − Application of VulcanTM software for the July 2007 estimate instead of the Golder Associates in-house software as applied for the June 2006 estimate for grade interpolation resulted in an increase of 1% total tonnage due to changes within unconstrained inferred mineral resources. − Definition drilling resulted in alterations to geological domaining and a 7% increase in tonnes over the 2006 mineral resource estimate.

Page 110 Oxiana Limited 2007 Mineral Resource Explanatory Notes

The July 2007 mineral Resource estimate applied a 0.3% Cu cut off based on preliminary estimates of the mine operating cut off to be applied to low-grade copper stocks. The change in reporting from a 0.5% cut off to a 0.3% Cu cut off is estimated to have added 19.5Mt of mineralisation conaintaining 87Kt of contained copper and 107Koz contained gold metal.

Resources below -450mRL represent newly added mineral resources as part of the July 2007 mineral resource estimate, which are the result of exploration drilling beneath the June 2006 mineral resource region that has been undertaken between June 2006 and July 2007. The additional Cu resources above a 0.3% Cu cut-off total 25.4Mt at 1.02% Cu and 0.56g/t Au. Additional Au resources above a 0.5g/t Au cut-off for blocks less than 0.3% Cu total 14.7Mt at 1.05g/t Au.

Indicated and Inferred categories for both the Cu and Au mineral resources increased significantly in tonnes and metal, which is largely due to exploration drilling and to a lesser extent to cut off grade alterations and definition drilling undertaken between June 2006 and July 2007. The Indicated resource tonnage increase is 17.6Mt (61.4%) for the Cu resource and 8.2Mt (95.4%) for the Au resource. The Inferred resource tonnage increase is 29.6Mt (87.1%) for the Cu resource and 7.5Mt (54.1%) for the Au resource.

The Measured category of the Cu mineral resource tonnes and metal increased by a lesser margin 3.5Mt (which is a 9.0% increase in tonnes or 21.4Kt contained Cu metal and 53.4Koz contained Au metal). The measured category of the Au only mineral resource is unchanged.

Page 111 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Martabe Mineral Resources Statement: December 31, 2007

Introduction

The Martabe 2007 Resource Statement incorporates the gold and silver Mineral Resources at the Martabe Project in North Sumatra Indonesia.

Since the previous Resource Statement, no remodelling of the Purnama (Pit1) or Baskara (Ramba Joring) has been done. An infill program in the central part of Pit 1 between 167150N and 167300N was completed in late December 2007, with remodelling scheduled for the first quarter of 2008. Although not all assay results have been received, initial indications are that similar widths and grades of mineralisation have been returned on the holes received to date. Variability in results has been typical for gold deposits of this style. Additional drilling has been completed in the north Pelangi area and the Resource model was updated in December 2007. A total of 61 additional holes for 9027m were drilled between 167200N and 166600N to both extend known mineralisation and to infill drill selected areas using 25-meter spaced holes.

The Pelangi Resource has been estimated using the Martabe geological database as at November 2007. Resource block modelling for gold mineralisation at Pelangi follows similar procedures to those adopted in the previous model in 2006. A geological interpretation was generated using geological drill logging information. A 0.5g/t Au lower cut off was used to define a coherent mineralised zone interpretation. Ordinary Kriging was used to estimate gold (Au) and cyanide-soluble gold (AuCN) grades into the block model. The resource estimates have been classified considering data density, data quality, confidence in the geological interpretation and confidence in the estimation.

Results

Table 1 summarises the Martabe Mineral Resources as at 31 December 2007.

Deposit Category Tonnes Au Au oz Ag Ag oz ‘000 gpt ‘000 gpt ‘000 Purnama Indicated 48,750 1.8 2,859 24 37,400 Inferred 42,527 1.1 1,512 13 17,400 Total 91,277 1.5 4,371 19 54,800 Baskara Indicated 0 0 0 0 0 Inferred 36,557 1.0 1,191 4 5,207 Total 36,557 1.0 1,191 4 5,207 Pelangi Indicated 0 0 0 Inferred 10,390 1.1 368 - - Total 10,390 1.1 368 - -

Martabe Indicated 48,750 1.8 2,859 Summary Inferred 89,474 1.1 3,071 Au ONLY Total Mineral Resource 138,224 1.3 5,930

Purnama and Indicated 48,750 1.8 2,859 24 37,400 Baskara ONLY Inferred 79,084 1.1 2,703 9 22,607 Au and Ag Total Mineral Resource 127,834 1.4 5,562 15 60,007

Table 2 compares the June 2007 Mineral Resources with an estimate of the tonnage and grade at November 2006, for the Pelangi Mineral Resource. The Purnama and Baskara Mineral Resources remain unchanged.

Comparison of Pelangi Mineral Resource at 31st December 2007 Mineral Resources with November 2006 Mineral Resources Estimate.

Dec 31 2007 Dec 31 2006 Category Tonnes (Mt) Grade Au g/t Au kozs Tonnes (Mt) Grade Au g/t Au kozs Inferred 10.39 1.1 368 5.48 1.2 218

Page 112 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Compliance with JORC code assessment criteria

Deposit Competent Person Ingvar Kirchner, Principal Resource Geologist Coffey Mining Ltd. Pelangi (Barani) Iain MacFarlane, Senior Consultant Resources, Coffey Mining Ltd. Graham Petersen, Geology Manager Martabe, PT Agincourt Minerals Purnama and Baskara Ingvar Kirchner, Principal Resource Geologist Coffey Mining Ltd. Graham Petersen, Geology Manager Martabe, PT Agincourt Minerals

Each of the Competent Persons has given their consent for the inclusion of the material in the form and context in which it appears.

Key points relating to the Martabe December 2007 Resource Estimate:

12. Drilling

All mineral deposits in the Martabe December 2007 Resource statement have been drilled by diamond drilling technique of predominantly HQ and PQ size with lesser NQ diameter core. The Purnama deposit has 222 drill holes (35,706m), Baskara has 146 drill holes (22,819m) and Pelangi (Barani) has 119 drill holes (17,857m).

13. Drilling Quality Assurance

All of the diamond drill hole collar locations and orientations are surveyed using an electronic theodolite. Down hole surveying is performed using Eastman single shot cameras.

Drill hole locations and orientations were validated by plotting collar pick ups against the drill design. Core recovery is generally excellent for all core sizes, averaging 93% recovery.

14. Geological Logging

All drill core is photographed and geologically logged for rock type, weathering, colour, alteration style, type and intensity, structure and mineralisation style. Zones of silicic alteration and quartz veining determined during geological logging are selected for assaying.

15. Sampling

Drill core is halved and sampled at varying intervals between 1-3m and are commonly 1m sample intervals. Most drill holes are completely sampled. All samples are of core from triple tube diamond drilling that is stored in plastic lined core trays.

16. Analytical Methods

All samples have undergone sample preparation at the ITS sample preparation facility in Padang prior to 250gm samples being forwarded to ITS laboratory in Jakarta for assay determination. Multi-element assays were processed in a Melbourne Laboratory.

Sample preparation since the 10th July, 2004 involved the crushing of samples to 2mm before splitting to approximately a 1.5kg sample, which is then pulverised to -200 mesh and further split down to a 250 gram sample for analysis. Prior to 10th July, 2004, samples were crushed to 10mm before splitting to an approximate 1.5Kg sample, with all other processing remaining unchanged.

Au is analysed by 50g fire assay, AAS determination, Zn, Cu, Pb, Fe, Mn and Ag are analysed using HCl/HClO4 digest, Mo, As, Sb and Te by XRF, Hg by cold vapour AAS and Bi by Hydride generation with AAS finish. Most samples are analysed for Au, Ag, Cu, Pb, Zn, As and Hg only but sample suites assayed varied over time.

Page 113 Oxiana Limited 2007 Mineral Resource Explanatory Notes

17. Assay Quality Control and Quality Assurance

Approximately 3% of pulps have been duplicated by Genalysis Australia. Standards were submitted at the rate of 1 for every 20 normal samples including blank material sourced locally.

For 2004, 2005 and 2007 periods, standard reference sample assay results suggest reasonable replication of expected Au values i.e. +/- 2 standard deviations of the mean. The exception to this was for standard G903-3 which had a mean of 1.078g/t Au with an expected value of 10.04g/t Au, which is almost certainly a labelling error.

18. Bulk Density

Dry bulk density was measured on selected representative core samples of approximately 0.2m length. Mineralised drill core has significant porosity associated with vughs formed by acid-sulphate leaching. Bulk density measurements at Martabe account for this porosity by sealing samples prior to measurement. Measured samples were oven dried with the majority then plastic wrapped before applying Archimedes principle of measurement. A lesser number of samples were sealed by wax immersion before measurement.

Purnama applied 2,939, Baskara 2,437 and Pelangi (Barani) 468 bulk density measurements to the block model estimates used for the December 2007 Resource Statement.

19. Interpretation

Resource modelling is carried out using Vulcan software. The controls on geology and gold mineralisation are moderately well understood. However, interpretation of the high-grade gold mineralisation between sections was difficult, so a 0.5 g/t Au lower cutoff was used to define the mineralised zones. Gold mineralised intervals are generally contained within low-temperature quartz vein material and hydrothermal breccia.

20. Grade Estimation

Ordinary Kriging was used to interpolate Au, AuCN and Ag grades into the block model. However, Ag was not modelled for the Pelangi deposit but average silver grades are in the order or 1-3 g/t Ag.

Sample data were composited to 2m downhole intervals. Detailed statistical and geostatistical investigations utilised the composite data. Gold grade top-cuts were applied to Zones 20, 40 and 70 of 10g/t, 8g/t and 10g/t Au respectively.

21. Cut-off Grade

A gold cut-off grade of 0.50g/t Au was used for reporting. No metallurgical testing has been completed but AuCN grades give an approximation of expected recoveries.

22. Resource Classification

Resources were classified based on data density, confidence in interpretation, number of samples involved in the estimate and confidence in the input data. Material has only been classified as Inferred. Additional drilling and mining and metallurgical studies are required to gain further confidence in the continuity of the mineralisation, particularly at higher cut off grades.

14. Block Model Validation

Resource estimates were validated against the data used to create them both visually and statistically.

Page 114 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Wiluna Nickel Laterite Statement: June 30, 2005

Introduction

The Wiluna Nickel Laterite June 2005 Resource Statement deals with the nickel laterite Mineral Resources located west of the Wiluna town site, Western Australia.

The Resource model is based on the geological database as at June, 2005. Snowden Mining Industry Consultants completed geological interpretation (0.5% Ni domains and high-Mg discontinuity surfaces), triangulation modelling, statistical and spatial analysis, block model construction, grade estimation and reporting. A review of the work has been conducted by Andrew Ross of Snowden Mining Industry Consultants.

The geological interpretation was based on data from 70 diamond, 552 reverse circulation and 972 air core drill holes containing 21,266 nickel assays of predominantly 2m intervals. The nickel laterite mineralisation has been interpreted as five discrete mineralised lenses within the lower saprolite zone overlying Mg-rich units of the Perseverance Ultramafic sequence over a strike length of 19km.

Sample data was composited to two (2) metre intervals and flagged by the domains defined in the geological interpretation. Ordinary Kriging was used to estimate grades within the geological domains.

The resource estimate has been classified as Inferred on the basis of data density, data quality, confidence in the geological interpretation and confidence in the estimation.

Results

The June 2005 Inferred Mineral Resource of 80.5Mt at 0.77% Ni (above a 0.5% Ni cut-off) is reported from five >0.5%Ni envelope domains within AMG grid coordinates of 214800 to 224800m East, 7047200 to 7063800m North and 425 to 540m RL.

Table 1 outlines the June 2005 Wiluna Nickel Laterite Inferred Mineral Resource estimate. Limonite Nickel Laterite mineralisation has been defined as material above the MgO discontinuity, defined at a level of 10% MgO. Saprolite Nickel Laterite mineralisation is defined as material below this discontinuity.

Table 7: Inferred Nickel Resource at Ni cut-off grade of 0.5% Ni

MgO Domain Nickel Laterite Tonnes (Mt) % Ni % Co %MgO Mineralisation Type Above Limonite 54.5 0.77 0.059 4.6 Below Saprolite 26.0 0.77 0.055 17.8 Total 80.5 0.77 0.058 8.8

Tables 1 shows rounded estimates. This rounding may cause apparent computational discrepancies. Significant figures do not imply precision.

Andrew Ross is a Fellow of the Australasian Institute of Mining and Metallurgy, and has sufficient experience relevant to the style of mineralisation and type of deposit under consideration and to the activity undertaken to qualify as Competent Person as defined in the 2004 Edition of the Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves (The JORC Code, 2004 Edition).

Andrew Ross Principal Consultant – Snowden Mining Industry Consultants Page 115 Oxiana Limited 2007 Mineral Resource Explanatory Notes

Key points relating to the Wiluna Nickel Laterite June 2005 Resource Estimate:

1. The resource estimate applies to nickel laterite mineralisation hosted within the lower saprolite zone overlying the Mg-rich units of the Perseverance Ultramafic sequence from 214800 to 224800m East, 7047200 to 7063800m North and 425 to 540m RL (AMG Grid 84_51).

2. The deposit is delineated by 70 diamond core, 552 reverse circulation and 972 air core drill holes drilled on nominal north east- south west sections spaced 400m apart with 120m spacing on section. A small area has been drilled on sections spaced 200m apart, and there is a 2.4km break in the drilling over the Wiluna airstrip. The majority of holes are angled at approximately 60 degrees to the south-west (248o), with fewer holes drilled 60 degrees to the north-east and sub-vertically.

3. Samples intervals are mostly of 2m length with some of 1m length. Assay results are of uncertain quality as suitable QA/QC data is unavailable, which limits the mineral resource estimation to an Inferred classification.

4. Five mineralised envelopes of greater than 0.5% nickel were defined by cross-sectional interpretation which was followed by the construction of solid triangulations. Triangulations have a minimum down-hole thickness of 4m, a maximum internal waste thickness of 4m, are extrapolated up to 60m on section, 100m in strike or halfway between drill holes.

5. Nickel, cobalt and magnesia grade estimation is interpolated by Ordinary Kriging and guided by wire-framed grade and geological domains. Parent cell estimates (100mE x 300mN x 2mRL) were written to a sub-blocked model using two estimation passes for all blocks reported in the resource estimate. The first pass applied a search neighbourhood of 800m x 800m x 20m with a minimum of 8 and a maximum of 48 composite samples, and a restriction of four composites per drill hole. The second pass retained the same search radii but decreased the minimum number of samples to 4.

6. The bulk density value of 1.80g/cm3 has been applied uniformly to all blocks, which is the same value applied in previous estimates. No density data was available for the resource estimate.

7. The mineral resource is entirely classified as Inferred on the basis that nickel grade continuity has not been established on the broadly spaced drill hole data, there is a lack of QA/QC information for the drill hole data and there is no available density data.

8. The Wiluna Nickel Laterite mineral resource of 80.5Mt at 0.77% Ni (above a 0.5% Ni cut-off) previously owned by Agincourt Resources has been reported in the Agincourt Resources 2006 annual report. No further work has been conducted on the Wiluna Nickel Laterite mineral resource since the June 2005 estimate.

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