Geophysical Interpretation and Static Reservoir Modeling of Structurally Complex Reservoir, RG Oil Field Area, Sirte Basin, Libya

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Fall 2017

Geophysical interpretation and static reservoir modeling of structurally complex reservoir, RG oil field area, Sirte Basin, Libya

Abdalla A. Abdelnabi

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Recommended Citation Abdelnabi, Abdalla A., "Geophysical interpretation and static reservoir modeling of structurally complex reservoir, RG oil field area, Sirte Basin, Libya" (2017). Doctoral Dissertations. 2753. https://scholarsmine.mst.edu/doctoral_dissertations/2753

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GEOPHYSICAL INTERPRETATION AND STATIC RESERVOIR MODELING OF

STRUCTURALLY COMPLEX RESERVOIR, RG OIL FIELD AREA,

SIRTE BASIN, LIBYA

ABDALLA ALI ABDELNABI

A DISSERTATION

Presented to the Faculty of the Graduate School of the

MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY

In Partial Fulfillment of the Requirements for the Degree

DOCTOR OF PHILOSOPHY

GEOLOGY AND GEOPHYSICS

2017

Approved by:

Kelly Liu, Advisor

Stephen Gao

Mingzhen Wei

Ralph E. Flori

Jawad Rustum

iii

PUBLICATION DISSERTATION OPTION

This dissertation has been prepared in the form of four papers for publication as follows:

Pages 3-46 have been published at Society of Exploration Geophysics (SEG, 2016) and being finalized for submission to interpretation Journal- Society of Exploration

Geophysics (SEG).

Pages 47-65 have been published at Society of Exploration Geophysics (SEG,

2017) and be finalized for submission to interpretation Journal- Society of Exploration

Geophysics (SEG).

ABSTRACT

Cambrian-Ordovician and Upper Cretaceous reservoir formations are found in the central western Sirte Basin, the main oil producing region in the Sirte Basin, Libya. As a result of changes in sedimentary environments and structural activities, a number of irregularities in reservoir continuity have developed, which negatively affected the overall performance of the reservoir. Effective simulation of such complex reservoirs can be achieved by integrating geophysical, geological, and petrophysical data to construct a reliable full-field static model, which has the potential to simulate the vertical and lateral variations in the reservoir formations. In this study, 2D and 3D seismic data acquired in the central western Sirte Basin are used to construct a fault and structural model which is an important component of the static model. The median filter and spectral whitening are applied to enhance the data quality and remove noise resulted from acquisition and processing effects. Spectel whitening is used to enhance the resolution of the data while mediam filter is used as noise filter. Coherence and curvature attributes are used to delineate faults and fracture zones. Coherence adequately detect major faults and minor faults. Most positive and most negative curvatures are used to define upthow and downthrow faults blocks. Curvatures attributes are also used to define fracture zones in reservoir formations. The most common fracture zone are identified in the northwest and southeast in the Cambrian-Ordovician reservoir formation in the study area.Well data are incorporated with core data to construct a property model that integrates a range of reservoir properties including facies, porosity, permeability, and net-to-gross ratio. A fine- scale geo-cellular model is created by integrating the fault, structural, and property models for the entire field.

ACKNOWLEDGMENTS

First, I must express my special sincere gratitude to my advisors, Dr. Kelly Liu and

Dr. Stephen Gao, for guiding and teaching me over the past four years. They provided me with an excellent learning environment and it has been a great pleasure to work with them.

I want to extend my appreciation to my advisory committee members, Dr. Jawad

Rustum, Dr. Mingzhen Wei and Dr. Ralph E. Flori for the time and advice they provided when reviewing my dissertation.

I would like to express my gratitude to the staff of Geosciences and Geological and

Petroleum Engineering Department for all the assistance over the past several years. I want to thank my colleagues of the geophysics group for their help.

I am grateful for my parent, my brothers, and my sisters. They offered continuous support and encouragement. Last but not least, I would like to thank my wife. She always believed in me and helped me during my study. I cannot accomplish my doctoral dissertation without her understanding, support, encouragement, and constant love.

TABLE OF CONTENTS

Page

PUBLICATION DISSERTATION OPTION ...... iii

ABSTRACT ...... iv

ACKNOWLEDGMENTS ...... v

LIST OF ILLUSTRATIONS ...... ix

LIST OF TABLES ...... xiii

NOMENCLATURE ...... xiv

SECTION

1. INTRODUCTION ...... 1

PAPER

I. A FULL FIELD STATIC MODEL OF THE RG-OIL FIELD, CENTRAL SIRTE BASIN, LIBYA ...... 3

ABSTRACT ...... 3

1. INTRODUCTION...... 4

2. GEOLOGICAL SETTING ...... 7

3. DATA ...... 9

4. METHODS ...... 11

4.1 SEISMIC HORIZON AND FAULT INTERPRETATION ... 11

4.2. PETROPHYSICS ...... 12

5. RESULTS AND DISCUSSIONS ...... 17

5.1. FAULT MODELING ...... 17

5.2. STRUCTURAL INTERPRETATION ...... 17

vii

5.3. STRUCTURAL MODELING ...... 19

5.4. UPSCALING WELL LOGS...... 19

5.5. PROPERTY MODELING ...... 31

5.6. FACIES MODELING ...... 31

5.7. POROSITY AND PERMEABILITY MODELING ...... 32

5.8. NET-TO-GROSS (NTG) RATIO ...... 38

5.9. VOLUMETRIC ESTIMATION ...... 38

6. CONCLUSIONS ...... 41

ACKNOWLEDGMENTS ...... 42

REFERENCES ...... 42

II. SEISMIC ATTRIBUTES AIDED FAULT DETECTION AND ENHANCEMENT IN THE SIRTE BASIN, LIBYA ...... 47

SUMMARY ...... 47

1. INTRODUCTION...... 48

2. DATA, METHODOLOGY, AND RESULTS ...... 50

2.1. DATA CONDITIONING ...... 50

2.2. SIMILARITY OR COHERENCE ATTRIBUTES ...... 57

2.3. CURVATURE ATTRIBUTES ...... 58

2.4. FAULT INTERPRETATION AND FAULTS VISUALIZATION………………………………………….62

3. CONCLUSIONS ...... 64

ACKNOWLEDGMENTS ...... 64

REFERENCES ...... 64

viii

SECTION

2. CONCLUSIONS ...... 66

VITA ...... 68

LIST OF ILLUSTRATIONS

Figure Page

PAPER I

1. Location map of the study area showing the sedimentary basins in Libya including the Ghadames, Murzuq, Kufra, and Sirte basins...... 5

2. A stratigraphic-lithologic column of the central Sirte Basin ...... 6

3. Model development workflow showing the process of integrated geology, geophysics, and petrophysics data to construct a full field static reservoir model of the RG-oil field using all the available data such seismic, well log, and core description data...... 7

4. Structural map showing the main structural features affected the sedimentation pattern in the study area...... 8

5. (Left) Base map of wells analyzed. (Right) data acquisition in Libya in 2004, RG Field, Sirte Basin, Libya...... 10

6. (Left) Topographic map of Libya. The red box presents the area covered by 3D seismic data, and black lines represent the area covered by 2D seismic lines. (Right) A map showing the distribution of the well location. Blue stars represent wells that have check shots...... 10

7. The north-south seismic section is showing the tie of 2D and 3D seismic data, Seismic to well tie for well 57, well 08 and well 04 represented by yellow trace on the seismic section and the ties are relatively good for three wells. ……14

8. Synthetic seismogram generated for well 70 showing the good tie...... 15

9. Average velocity of the upper Cretaceous Waha Formation using seismic time horizons and the well top depth at each well...... 15

10. Original seismic section at inline1215 showing the interpreted faults in the RG oil field...... 16

11. Left tracks show lithology estimation within the reservoir formation of upper Cretaceous Waha formation and Cambrian-Ordovician Gargaf Formation of typical well 100...... 16

12. 40 faults identified in the study area using 3D and 2D seismic data ...... 20

13. A top view showing the fault model employed in the static model and the distribution of the wells...... 21

14. Map showing 20 selected faults used in the full static model including two major faults (F1and F2)...... 21

15. Isochore map based upon seismic, wireline-log, and core data showing thickness distribution of the Upper Cretaceous Waha Formation...... 22

16. Structural depth map of the Upper Cretaceous Waha Formation showing the main structural features in the study area...... 23

17. Isochore maps based upon seismic, wireline-log, and core data showing thickness distribution of the Upper Cretaceous Bahi Formation...... 24

18. Structural depth map of the Upper Cretaceous Bahi Formation showing the main structural features in the study area...... 25

19. Structural depth map of the Cambro-Ordovician reservoir formation showing the main structural features in the study area...... 26

20. Isochore map based upon seismic, wireline-log, and core data showing thickness distribution of the Cambro-Ordovician Gargaf reservoir formation...... 27

21. 3D structural model of the study area showing selected Cambrian-Ordovician and Upper Cretaceous horizons...... 28

22. 3D structural and grid models showing the subdivision of the Cambrian- Ordovician and Upper Cretaceous reservoir formations...... 28

23. A total of 12 subzones of the Cambrian-Ordovician Gargaf Formation...... 29

24. Histograms comparing distributions of computed well log and upscaled properties facies showing the good agreement between well log values (red), upscaled cells (green)...... 29

25. Histograms comparing distributions of computed well log and upscaled properties of porosity...... 30

26. Histogram comparing distributions of computed well log and upscaled properties of permeability...... 30

27. Example of the cross-plot of a modern well used to distinguishing Calcareous Sandstone from limestone in the Upper Cretaceous reservoir Formation...... 33

28. 3D facies model distribution of the Upper Cretaceous Waha Formation using the sequential indicator simulation method...... 33

29. 3D facies model distribution of the Cambrian-Ordovician Gargaf Formation using the sequential indicator simulation method...... 34

30. 3D facies model distribution of the Upper Cretaceous Bahi Formation using the sequential indicator simulation method...... 34

31. Full-field 3D facies model distribution of the Cambrian-Ordovician and Upper Cretaceous reservoir formations using the sequential indicator simulation method...... 35

32. 3D porosity model distribution of the Upper Cretaceous Waha Formation using sequential Gaussian simulation (SGS)...... 35

33. 3D Permeability model distribution of the Upper Cretaceous Waha Formation using sequential Gaussian simulation (SGS)...... 36

34. 3D porosity model distribution of the Upper Cretaceous Bahi Formation using sequential Gaussian simulation (SGS)...... 36

35. 3D porosity model distribution of the Cambro-Ordovician Gargaf Formation using sequential Gaussian simulation (SGS)...... 37

36. 3D Permeability model distribution of the Cambro-Ordovician Gargaf Formation using sequential Gaussian simulation (SGS)...... 37

37. 3D net-to-gross ratio distribution of the Cambrian-Ordovician and Upper Cretaceous reservoir formations...... 39

38. 3D structural model showing the identified fluid contacts, the regional gas-oil contact is at 5,050 ft (green surface), and regional water-oil contact is at 5,500 ft (blue surface)...... 40

PAPER II

1. Topography map showing the major sedimentary basins in Libya, including the Sirte, Kufra, Murzuq, and Ghadames basins...... 49

2. A cross-section showing the main structural features related to the sedimentation pattern in the study area...... 50

3. Workflow used in the processing of data conditioning and seismic attributes...... 52

xii

4. (a) The input amplitude spectrum. (b) The comparison of concept and practical use spectral whitening ...... 53

5. East-west cross section of the original inline before any filters ...... 54

6. East-west cross section after applying the spectral whitening...... 54

7. East-west cross section after applying a median filter...... 55

8. Original seismic inline 1215 before any filters...... 55

9. Seismic inline 1215 after application of whitening...... 56

10. Seismic inline 1215 after applying the median filter is enhanced...... 56

11. Time slice at 950 ms extracted over the enhanced 3D seismic data...... 57

12. Coherence slice extracted at 950 ms over conditioned data showing horse, graben, and major, and minor faults...... 58

13. Most negative curvature overlain seismic section at inline 1215 showing the high negative value in blue color indicate downthrow of the faults...... 59

14. Most negative curvature extracted at inline 1687 showing the high negative value which indicates the downthrow of the fault...... 60

15. Most positive curvature overlain seismic section at inline 1215 showing the high positive value in red color indicate upthrow of the faults...... 60

16. Most positive curvature extracted at inline 1687...... 61

17. Most negative curvature slide at 950 ms...... 61

18. Most positive curvature slide at 950 ms...... 62

19. Seismic inline 1215 showing major and minor faults identified within the upper Cretaceous and Cambro-Ordovician reservoir formation in the RG-Oil field...... 63

20. Show fault polygon of the interpreted faults which labeled F1, F2… F17...... 63

xiii

LIST OF TABLES

Table Page

PAPER I

1. Volumetric estimation of the Cambrian-Ordovician and Upper Cretaceous reservoir formations...... 40

xiv

NOMENCLATURE

Symbol Description

Ф Porosity

ФN Neutron porosity

ФD Density porosity

Фs Sonic porosity

SW Water saturation

R.F Recovery factor

SP Spontaneous potential

R Resistivity

Rt True resistivity

Rw Formation water resistivity

GWC Gas water contact

OWC Oil water contact

HCPV Hydrocarbon pore volume

OOIP Original oil in place

1. INTRODUCTION

The understanding of uncertainties involved in reservoir modeling is an essential tool to support decisions in the petroleum industry. The Sirte Basin is one of the most prolific oil producing fields in the world. Because of its complex structure, it is crucial to understand detailed relationships between fault networks and stratigraphy of the area for future field development. The RG oil field is one of the most producing fields in the Sirte

Basin. Integration geophysical, geological, and petrophysical provide the best tools for a future developing plan of RG-oil field. In general, however, combining these sources of data poses a challenge in the petroleum industry. Combined analyses will reduce the uncertainty of the model parameters that affect the production scenarios, enhance reservoir recovery, and provide the reservoir engineer with a trustworthy basis for reservoir simulation and management to aid in predicting the future performance of the RG oil field.

The Dissertation is mainly composed of two sections. The main focus of the first section is the connectivity of the various modules which extract as much data from as many sources as possible. This beginning with the construction of a most accurate static model that include structural maps, isopach maps, OOIP, Net Pay, 3D seismic and horizon visualizations, volumetric calculations, fluid volume calculation, porosity model, permeability model, and facies model. The static model will operate as a basis for constructing a dynamic simulation model of the RG oil field for future field performance.

The second section demonstrates the use of data conditioning and seismic attribute analysis to enhance fault interpretation and defines fracture zone in the Cambrian-

Ordovician and Upper Cretaceous reservoir formations in Sirte Basin. Spectral Whitening and Median filter are used to improve the data quality and remove noise of the conventional

3D Seismic data. Curvature attribute such as most positive attribute and most negative attribute are used to determine fault blocks and identify the fracture zone in Cambrian-

Ordovician Gargaf formation. Coherence attribute is used to determine the major and minor faults in the upper Cretaceous reservoir formations and determine horse and graben in the

Sirte basin.

PAPER

I. A FULL FIELD STATIC MODEL OF THE RG-OIL FIELD, CENTRAL SIRTE BASIN, LIBYA

Abdalla A. Abdelnabi, Kelly H. Liu, and Stephen S. Gao

ABSTRACT

Upper Cretaceous and Cambrian-Ordovician reservoir formations in the central

Sirte Basin, the main oil producing Formations in Sirte Basin is structurally complex reservoirs. Lateral and vertical heterogeneity of the reservoir formations are not yet understood, which negatively affected the general performance of the reservoir. Efficient full field static model of such complex reservoirs which can explain the lateral and vertical variations in the reservoir formations can be achieved by integrating geophysical, geological, and Petrophysical data. In this study, seismic data are used to construct a fault and structural model. By generating structural, isochore and fault maps of main producing formations, we explained the degree of connection between the three main reservoir formations of Waha, Bahi, and Gargaf and their relationship to the faults. Our analysis also defined thickness and distribution of Upper Cretaceous Waha and Bahi formations.

Cambrian-Ordovician Gargaf formation base is not yet penetrated by drilling and seismic data quality decrease with an increase of depth which makes it very hard to define the base and thickness of Gargaf formation. To solve this issue, we applying techniques of adding the isochores map of the overlying of Waha and Bahi formations and determined the

Gargaf base and thickness for the first time. Well data are incorporated with core data to build a property model of the reservoir formations such as facies, porosity, permeability,

4 and net-to-gross ratio. A fine-scale geo-cellular of model Upper Cretaceous and Cambrian-

Ordovician reservoir formations cover an area of 350 km2 are created for the entire RG- field. The established full field static model created is unique to the study area and for the whole area of Sirte Basin. It can be applied to any field in the Sirte Basin and can be updated at any time with new data following the production and drilling activities.

1. INTRODUCTION

The Sirte Basin is among the world’s top 20 petroleum provinces and is one of the four known sedimentary basins in Libya (Figure 1). It covers a total area of 600,000 km² and is the richest and youngest sedimentary basin in the country, with known reserves of over 43.1 billion barrels (Barr, 1972; Futyan and Jawzi, 1996; Wennekers et al., 1996;

Coward and Ries, 2003; Craig et al., 2004; Abadi et al., 2008). The present major structure of the Sirte Basin was developed between the late Paleozoic and much of the Early

Cretaceous, during which a continuous uplift created the Sirte Arch. During the Late

Cretaceous, extensional tectonics resulted in a partial collapse of the Sirte Arch and caused a system of horsts and grabens; Klitzsch, 1971; Bellini and Massa, 1980; Van Houten,

1983; Ahlbrandt, 2001; Hallett, 2002). The study area has three primary reservoir units, the Waha, Bahi, and Gargaf, with irregular vertical and horizontal continuities (Figure 2;

Cain, 2001). In spite of numerous studies, no consensus has been reached on the degree of connection between the different layers of the reservoirs and on the factors that distinguish these formations. As a result of its limited spatial distribution and thickness, the Bahi

Formation represents the least significant unit of the reservoir regarding hydrocarbon reserve. However, it is of significance because it acts as a good vertical and lateral reservoir

5 communicator between the Waha and Gargaf reservoir units wherever it is present.

Previous studies suggested that it is problematic to differentiate Bahi detritus and quartzitic materials from similar deposits found in either the Waha or Gargaf units (De Golyer and

Macnaughton, 2010).

Figure 1. Location map of the study area showing the sedimentary basins in Libya including the Ghadames, Murzuq, Kufra, and Sirte basins.

Integrated Geology, Geophysics, and Petrophysics data into one complete model for the whole field is an important process to help better understanding the complexity of reservoir formations and provide significant gains in productivity in terms of identifying and managing reservoirs (Zou et al., 2006; Denney, 2013; Adeoti et al., 2014). It provides reservoir engineers with a reliable and comprehensive basis upon which they can simulate and manage the behavior of a reservoir (Al-Harbi et al., 2013; Karamitopoulos et al., 2013).

In general, however, combining these various forms of data represents a significant challenge in the petroleum industry, mostly because of the fact that reliable integration requires a comprehensive understanding of the inherent uncertainties of the numerous components in a given geological modeling system (Hosseini et al., 2012; Kamali et al.,

2013; Musa et al., 2013; Rodríguez Torrado et al., 2015). Here we present study of such an integrated approach using a combined data set from the Sirte Basin in Libya. This case study uses a method by which a comprehensive blend of geological, geophysical, petrophysical, reservoir engineering information and the accompanying interpretations are linked in one static model. The modeling approach presented here is unique to the study area. One of the aims of this study is to identify the main heterogeneity of the main producing formations is the study area. The workflow of the construction of the full field static reservoir model of the RG-oil field is illustrated in details in Figure 3.

Figure 2. A stratigraphic-lithologic column of the central Sirte Basin (the left figure was modified after Barr and Weegar, 1972). The figure depicts the Upper Cretaceous and Cambro-Ordovician reservoir formations of the study area, based on well tops and formation tops showing the Upper Cretaceous Waha, Upper Cretaceous Bahi, and Cambro-Ordovician Gargaf formations (right).

Figure 3. Model development workflow showing the process of integrated geology, geophysics, and petrophysics data to construct a full field static reservoir model of the RG-oil field using all the available data such seismic, well log, and core description data.

2. GEOLOGICAL SETTING

The area of focus for the present study is located in the central-western part of the

Sirte Basin. The major structural features that have affected the sedimentation pattern in this area include four northwest-southeast-trending structures (Van Houten, 1980;

Anketell, 1996; Schroter, 1996) which are the Zallah and Tumayan Troughs to the west, the Beyda Platform in the central part of the area, and the Maradah Graben to the east (

Figure 4) (Mansour and Magairhy, 1996; Parsons et al., 1980; Capitanio et al., 2009). All platforms in the region are covered by limestone and remained relatively stable throughout the Early Paleocene. However, the shale-filled troughs continued to subside and deepen to the north until recently (Gumati and Kanes, 1985; Baric et al., 1996; Belhaj, 1996; Johnson and Nicoud, 1996; Heselden et al., 1996).

Figure 4. Structural map showing the main structural features affected the sedimentation pattern in the study area. The red rectangle represents the study area location. Blue areas represent major highs, red areas represent gas field distribution, and green areas represent oil field (modified after Rusk, 2001).

The dominant petroleum system in the study area is the Sirte-Zelten. The thick

Upper Cretaceous marine shaly syn-rift sequence contains the principal source rocks, whereas the reservoirs are shallow-marine clastics and carbonate deposits situated on horst blocks (Gumati and Schamel, 1988; Hamyouni et al., 1984; El-Alami, 1996a; Hallett and

Ghoul, 1996; Boote et al., 1998). Subsidence in the basin occurred at different rates, and

9 four major phases of extensions had impacted the deposition throughout the basin (Gumati and Nairn, 1991; Roohi, 1996a; Roohi, 1996b; Sinha and Mriheel, 1996). Sediments in the study area comprise of Lower Paleozoic clastics (Gargaf Group or Hofra Formation) and Upper Cretaceous (Bahi, Waha) and Tertiary marine deposits (Bezan et al., 1996;

Brennan, 1992). The reservoir was formed by the onlap of Cretaceous rocks (Waha and

Bahi) onto the Paleozoic Gargaf. The Hagfa Shale forms the cap (Figure 3; Bonnefous,

1972; Baird et al., 1996). The trap is a large irregular dome-shaped structure that covers an area of about 25,000 acres. It is bounded on the west by a major NE-SW-trending normal fault with minor faults cutting into the interior of the structure (Boweman, 1962; Hassan,

1971).

3. DATA

The seismic data employed in this study consists of 23 two-dimensional (2D) migrated lines covering an area of approximately 4000 Km2 and one three-dimensional

(3D) seismic cube acquired in 2004, covering an area of about 351 km² (Figure 5). Both the 2–D and 3–D seismic data quality ranges from fair to good. Check shots used to tie the well tops to the seismic data are available for several wells. Well data from 110 wells are also used for the study (Figure 6). The seismic-to-well ties are evaluated by constructing synthetic seismograms at well locations where sufficient sonic log data are available. We examined a total of 110 wells for the petrophysical study in support for the construction of properties modeling. Core description data available for 30 wells are also used in determining the distribution of reservoir properties in the models.

Figure 5. (Left) Base map of wells analyzed. (Right) data acquisition in Libya in 2004, RG Field, Sirte Basin, Libya.

Figure 6. (Left) Topographic map of Libya. The red box presents the area covered by 3D seismic data, and black lines represent the area covered by 2D seismic lines. (Right) A map showing the distribution of the well location. Blue stars represent wells that have check shots.

4. METHODS

4.1. SEISMIC HORIZON AND FAULT INTERPRETATION

An integrated seismic interpretation of the primary producing formations of Waha,

Bahi, and Gargaf was carried out using both 2D and the 3D seismic volume. We tie the 2-

D and 3-D seismic data, and the tie was reasonably good (Figure 7). Then we start the seismic interpretation by tying seismic to well data. The ties are evaluated by constructing synthetic seismograms at well locations with sufficient sonic log data (Figure 8). About sixty well have sonic data. The tie is made with a sonic log. The tie is good and represents the best ties from the 3–D survey. Shallow horizons are easy to track over the study area.

Data quality deteriorates with increasing depth and increases the uncertainty of the interpretation of the deep horizons. Nevertheless, there is enough well control to constrain the interpretation and produce accurate structural maps of the deep horizons. We conducted the depth conversion over the full 3d seismic data using the well data. Establishing a time- depth relationship is a key achievement, where it will impact further depth conversion and improve seismic to well tie. We calculated the average velocity using seismic time horizons and the well top depth at each well (Figure 9). Then we convert the time horizon to depth by multiplying the time horizon values by the average velocity. We also use both 2D and

3D seismic data to identify the faults. We picked the faults every inline and crossline. It is crucial to determine the faults and understand their relationship to the reservoir. We identified 40 faults in the study area, and only 17 faults are detected to be connected to the reservoirs network (Figure 10).

4.2. PETROPHYSICS

We examined one hundred forty wells for petrophysical analysis inside and outside the study area. We use different techniques appropriate for each specific reservoir properties depending on the company that ran the tool and the year the log was recorded.

We estimated reservoir Properties for 100 wells and made Lithostratigraphic correlations for 140 wells in addition to Quantitative analyses of the petrophysical properties of core samples for 30 wells. We use three different techniques to estimate the porosity for each specific well, depending on, the presence of core data, well log data, and whether the reservoir zone had significant shale content. For the well drilled recently, we determined the porosity mostly based Density-Neutron cross plot technique the general most accepted methods to determine the porosity for mixtures of calcite and silica such as rocks of the study area. The Shale volume in the study area Formation is low and distributed as a distinct bed rather than intimately mixed. As a result, we used DT and NEUT logs to estimate the porosity for the old well because it is eliminating the need of shale correction to porosity. We primarily use DT logs to determine the porosity for 80 wells and used it as final results for 60 wells. Neutron (NEUT) log was used where no sonic logs data available.

Microlaterology MLL logs were run as porosity device for ten wells with DT and NEUT logs. We do not use the MLL log because the washout is extreme in these wells. We used core porosity available for 15 well for the calibrations of the logs. We used Resistivity and

Spontaneous Potential logs as indirect permeability indicators. We use the Spontaneous

Potential (SP) as a Boolean permeability indicator because the reservoir lithology is relatively shale-free and SP is known to measures the electrical potential generated at the interface between permeable and impermeable beds. We also used two significant shallow-

13 reading electrode pad resistivity devices: the microblog and the micro-spherically focused log (MSFL) as a permeability indicator during the estimation of lithology. Microlog has two simultaneous measurements, the micronormal (MNOR) and the microinverse (MINV) logs. MNOR log reads approximately 2 inches into the formation, and the MINV log reads approximately 1 inch into the formation and the Permeability is indicated when the log readings different. Micro-spherically focused log (MSFL) was available for five wells drilled in mid-90 and was used as qualitative permeability indicator during the estimation of lithology.

We used deepest reading resistivity measurements (Rw) in the estimation of water saturation (Sw). The Rw expressed in ohm-m. We use Archie equation to estimated water saturation

∗ / = , ∗

Where

PHIE= Porosity, fraction

a = Archie tortuosity constant, = 1

m = Archie cementation factor, = 2

n = Archie saturation exponent, = 2

RT = deepest reading resistivity at the well

After the estimation of water saturation, we determine the bulk volume of water

(BVW) using the following equation BVW = Sw × PHIE, and then we determined the oil- water contact OWC and gas-oil contact (GOC) at the time of drilling. We estimation lithology and lithostratigraphic correlations in new wells directly from wireline. GR appear to be affected by uranium which is unrelated to clay mineral or shales in the reservoir

14 formations. In well 1 GR shows present of shale in the reservoir formation, and there is no indication of the shale present in other logs (Figure 11). It is very hard to know how much

Uranium is natural and how much is related to fluid movements. Instead, we used spectral gamma ray that was available for thirty wells. CGR is designed to respond to only Thorium and Potassium, and ignoring the present of Uranium. We also use SP, porosity and resistivity logs as the primary tools for both lithostratigraphic correlation and lithology estimation. We use cutting description and lithology logs in a final well report in the lithostratigraphic correlation and the estimation of lithology in the old wells.

Figure 7. The north-south seismic section is showing the tie of 2D and 3D seismic data, Seismic to well tie for well 57, well 08 and well 04 represented by yellow trace on the seismic section and the ties are relatively good for three wells. Major faults F1 and F2. Horizon interpretation of the main formation in the study area.

Figure 8. Synthetic seismogram generated for well 70 showing the good tie.

Figure 9. Average velocity of the upper Cretaceous Waha Formation using seismic time horizons and the well top depth at each well.

Figure 10. Original seismic section at inline1215 showing the interpreted faults in the RG oil field.

Figure 11. Left tracks show lithology estimation within the reservoir formation of upper Cretaceous Waha formation and Cambrian-Ordovician Gargaf Formation of typical well 100. It also shows the anomalies in GR logs and how there is no support to interpret high spike in GR log as shale in other lithology logs such Caliper and SP logs. The right tracks show the logs used to calculate and determine oil water contact, porosity, bulk volume of water (BVW) and water saturation (SW).

5. RESULTS AND DISCUSSIONS

5.1. FAULT MODELING

Fault modeling represents the first step in the construction of the structural model.

A total of 40 faults are identified from the seismic data (Figure 12). To simplify the model, we exclude from the structural model the faults that are not connected to the reservoir fault network. Consequently, 17 of the 40 faults are used to construct fault polygons that are imported into the geological model (Figure 13). The major fault (F1) is located on the west side of the field, trending northeast-southwest with a net throw of about 250 m. This fault effectively seals the structure on this flank and appears to diminish in the Hagfa Shale at the top of the reservoir. The major fault (F2) with an exhibited throw of 130 m also seems to seal the structure on the east side of the producing area (Figure 14). Smaller faults of small magnitude that cut the interior of the structure and are sub-parallel to the major faults are also identified. The throws of these faults range from 20 to 100 m. Previous studies suggested that these faults were created in response to extensional tectonic forces that are common in the platform area (El-Ghoul, 1996; Mansour and Magairhy, 1996).

5.2. STRUCTURAL INTERPRETATION

We constructed series of structural and isochores maps using the seismic data, well log data, and core data to define the thickness and distribution of the main reservoir formations of the study area and provide a framework for the 3D static model.

Upper Cretaceous Waha formation constitutes the upper and major reservoir in the study area. We constructed combined structural map and isochore map to delineate the thickness and distribution of Waha formation. We found that Waha formation thickness

18 ranges from 200 ft on the north side of the study area to over 800 ft southwestern part of the study area. We also found that Waha formation is absent in northeast, apparently because this side of the study area was above sea level during the deposition of Waha formation, which makes Waha formation bald Gargaf formation (Figure 15). (Figure 16) indicated that the crest of the Study area is at -4800 ft., maximum structural closer observed is 800 ft, and maximum relief is about 1500 ft. The Bahi reservoir unit is the least important of the three unit due to its limited thickness and distribution. However, it is a good reservoir communicator between the underlying Gargaf formation and overlying Waha Formation.

Most of the previous studies trace Bahi formation in south part of the study area. In this study, we succeeded to locate the Bahi near the crest and in east side with a thickness of

150 ft. In the southwest part of the study area, Bahi formation recorded maximum thickness of 250ft (Figure 17). We believed that the active Cretaceous faulting created a high and low area which limited the distribution and caused thickness variation of Bahi Formation in the study area (Figure 18). The Cambrian-Ordovician Gargaf Formation is the second reservoir unit in the study area. It is overlain unconformably by clastices and carbonates of the Upper Cretaceous Waha formation and/or clastics of the Bahi formation. Previous studies revealed that the Gargaf formation has unknown thickness because of the base of

Gargaf are not penetrated. We seismically determine the top structural map of the Gargaf formation by depth-tying the well and seismic data (Figure 19). Then we created the isochore map of the Gargaf by adding the total isochores maps of the overlying Upper

Cretaceous Waha Formation and Upper Cretaceous Bahi Formation (Figure 20).

5.3. STRUCTURAL MODELING

We use seismic data to construct the structural maps and tie the maps to control well. The identified faults and structural surfaces are imported into the structural model and are refined to produce the best representation of the seismic and well data (Figure 21).

We use all the available wireline logs, but with an emphasis on the GR, resistivity, DT, and

NEUT logs to subdivide three main zones into subzones.The main zones Waha, Bahi, and

Gargaf formations are subdivided into 23 lithologically and petrophysically distinct subzones.The subzones of the Waha, Bahi, and Gargaf formations are further divided by adding layers to prepare the model for property distribution (Figure 22). We choose the layer thickness that best captures the vertical heterogeneity that the petrophysical properties exhibit. Each zone is layered with cells with a minimum thickness of 1 m. The application of a minimum cell thickness is found to be helpful because it prevents the accumulation of thin cells at the unconformities. The 3D grid has a horizontal dimension of 16.9 km along the X-axis and 20.8 km along the Y-axis, covering a surface area of 351.52 km2. We use a corner-point 3D grid to create grids based on the fault and structural models. The areal cell size for the model is chosen as 100 m by 100 m, and the thickness of the cells is proportional to the overall zone thickness. The resulting static model has a fine-scale dimension of 248 by 265 by 466, or 30.6 million cells (Figure 23).

5.4. UPSCALING WELL LOGS

In order to the upscaling wireline log data into the 3–D grid, values are assigned to the 3–D grid cells that are penetrated by wells. Different property data types require different upscaling methods to derive the best possible cell value. Discrete data, such as

20 facies, are averaged by assigning the most frequently occurring facies values to each cell.

Arithmetic averages of continuous-property data (e.g., porosity and permeability) are biased by facies. These upscaled data constitute the foundation for facies and petrophysical modeling. Upscaled log-data values are checked for accuracy against the raw wireline log data by comparing them in well sections. Additionally, data are checked by comparing the raw-log property proportion and distribution to the upscaled data in histograms with their summary statistics. Figures 24, 25, and 26 show a comparison between raw and upscaled values for facies, porosity, and permeability. For all properties, the results are in good match with the raw-log data.

Figure 12. 40 faults identified in the study area using 3D and 2D seismic data.

Figure 13. A top view showing the fault model employed in the static model and the distribution of the wells.

Figure 14. Map showing 20 selected faults used in the full static model including two major faults (F1and F2).

Figure 15. Isochore map based upon seismic, wireline-log, and core data showing thickness distribution of the Upper Cretaceous Waha Formation.

Figure 16. Structural depth map of the Upper Cretaceous Waha Formation showing the main structural features in the study area. The black lines represent faults location in study area. Black dots represent wells locations.

Figure 17. Isochore maps based upon seismic, wireline-log, and core data showing thickness distribution of the Upper Cretaceous Bahi Formation.

Figure 18. Structural depth map of the Upper Cretaceous Bahi Formation showing the main structural features in the study area. The black lines represent faults location study area. Black dots represent wells location.

Figure 19. Structural depth map of the Cambro-Ordovician reservoir formation showing the main structural features in the study area. The black lines represent fault locations in study area. Black dots represent well location.

Figure 20. Isochore map based upon seismic, wireline-log, and core data showing thickness distribution of the Cambro-Ordovician Gargaf reservoir formation.

Figure 21. 3D structural model of the study area showing selected Cambrian-Ordovician and Upper Cretaceous horizons. The faults are represented as fault polygons in the 3D structural model.

Figure 22. 3D structural and grid models showing the subdivision of the Cambrian- Ordovician and Upper Cretaceous reservoir formations. The Upper Cretaceous Waha is divided into ten subzones, and the Upper Cretaceous Bahi is considered as one zone.

Figure 23. A total of 12 subzones of the Cambrian-Ordovician Gargaf Formation. Layers were added in the Cambrian-Ordovician and Upper Cretaceous reservoir formation subzones.

Figure 24. Histograms comparing distributions of computed well log and upscaled properties facies showing the good agreement between well log values (red), upscaled cells (green).

Figure 25. Histograms comparing distributions of computed well log and upscaled properties of porosity. The well log values (orange) agree well with upscaled cells (green).

Figure 26. Histogram comparing distributions of computed well log and upscaled properties of permeability.The well log values (orange) agree well with upscaled cells (green).

5.5. PROPERTY MODELING

With the purpose of build precise property model, we apply upscale well log data into the 3D model and distribute the reservoir properties by using sequential Gaussian simulation (SGS) for continuous property data (e.g., porosity and permeability) and sequential indicator simulation (SIS) for discrete data (e.g., facies).

5.6. FACIES MODELING

We have identified a total of 10 facies in the reservoir formations using well log and core description data. We defined the Shale as very low-porosity with indications of impermeability on the SP log. We used Crossplots of RHOB/NPHI logs as primary method used to distinguish limestone from calcareous sand in Upper Cretaceous Waha Formations for estimating lithology in the new wells (Figure 27). We found that the Upper Cretaceous

Waha Formation comprises calcareous limestone, sandstone, and shale. Argillaceous and

Sideritic limestone and are only present with significant thicknesses in a few wells (Figure

28). The Cambrian-Ordovician Gargaf Formation containing quartzite, scattered shale, and mafic intrusive igneous rocks is found in a few wells (Figure 29). We also found that the

Cenomanian Bahi Formation contains siliceous sand, conglomerate, and rare shale (Figure

30). We used resistivity cutoffs on the NPHI and DT logs to distinguish shale from quartzite and siliceous sand in the Bahi and Gargaf Formations. Siliceous sand was distinguished from quartzite by an increase in porosity and boulder beds by a decrease in porosity. We populated identified facies of the Waha, Bahi and Gargaf formations across the 3D model using the SIS statistical method. The facies model constructed for the three formations are used as a reference to assign porosity and permeability values (Figure 31).

5.7. POROSITY AND PERMEABILITY MODELING

We use four distinct porosity log technologies and three distinct deep-reading resistivity log technologies in addition to quantitative analyses of the petrophysical properties of core samples to determine porosity and permeability values. Different techniques appropriate for each specific porosity and permeability values are applied, and the resulting estimation is compared favorably to the core data and test results. The identified porosity and permeability values are subsequently distributed across the model using SGS. The resulting porosity of the Upper Cretaceous Waha Formation ranges from

25% in the north part of the field to 15% in the south part, and the permeability ranges from

10 MD to 350 MD (Figures 32 and 33).

The resulting model suggests that the porosity of the Bahi conglomeratic sandstone decreases with depth and thickness in all directions. The porosity of the Bahi ranges from

30% in the south and southwest of the field to 5% in the southeastern part, an area in which the Bahi thickness exceeds 150 feet (Figure 34). The porosity model indicates that the average porosity of the basal fractured Gargaf quartzites formation, excluding the fracture, is 2%, and the average permeability is less than 0.1 MD. However, in the center of the field, the Gargaf Formation exhibits relativity high porosity and permeability, where the porosity reaches 9%, and permeability reaches 0.54 MD. (Figures 35 and 36).

Figure 27. Example of the cross-plot of a modern well used to distinguishing Calcareous Sandstone from limestone in the Upper Cretaceous reservoir Formation.

Figure 28. 3D facies model distribution of the Upper Cretaceous Waha Formation using the sequential indicator simulation method.

Figure 29. 3D facies model distribution of the Cambrian-Ordovician Gargaf Formation using the sequential indicator simulation method.

Figure 30. 3D facies model distribution of the Upper Cretaceous Bahi Formation using the sequential indicator simulation method.

Figure 31. Full-field 3D facies model distribution of the Cambrian-Ordovician and Upper Cretaceous reservoir formations using the sequential indicator simulation method.

Figure 32. 3D porosity model distribution of the Upper Cretaceous Waha Formation using sequential Gaussian simulation (SGS).

Figure 33. 3D Permeability model distribution of the Upper Cretaceous Waha Formation using sequential Gaussian simulation (SGS).

Figure 34. 3D porosity model distribution of the Upper Cretaceous Bahi Formation using sequential Gaussian simulation (SGS).

Figure 35. 3D porosity model distribution of the Cambro-Ordovician Gargaf Formation using sequential Gaussian simulation (SGS).

. Figure 36. 3D Permeability model distribution of the Cambro-Ordovician Gargaf Formation using sequential Gaussian simulation (SGS).

5.8. NET-TO-GROSS (NTG) RATIO

The final grid required for the 3D geological model is the net-to-gross (NTG) model. NTG values ideally range from zero to one in a reservoir rock. In the study, NTG ratio of the Cambrian-Ordovician and Upper Cretaceous reservoir formations is 1.0 over large volumes in most of the reservoir rock. All the wells in the study area are vertical wells, and the greatest part of the drilled rock is a net reservoir. The lateral and vertical variations observed regarding NTG ratio are very limited which makes generating separate variograms for the description of these minimal variations in NTG ratio impossible (Dashti et al., 2013). We chose the NTG model in accordance with a net and pay zone that comprises of ones and zeros. Ones imply for the layers that passed the cut-offs and area treated as reservoir layers while Zeros imply for the layers failed cut-offs and are treated as non-reservoir. After testing the method, we found that the same variograms employed to distribute the porosity grid can be successfully used to distribute the NTG grid (Figure

37).

5.9. VOLUMETRIC ESTIMATION

Reservoir volumetric evaluation is the process of which the quantity of hydrocarbon in a reservoir is estimated. We calculate water saturation using well log data with emphasis on the deepest reading resistivity measurement logs and then match on a foot-by-foot basis and correct for structural elevation based on the computed saturation-height gradient observed in the wells. After water saturation is estimated, we identified two fluid contacts to the field based on well logs and core data. The resulting oil water contact (OWC) and the gas-oil contact (GOC) are at 5,500 feet TVDSS and 5,050 feet TVDSS, respectively

(Figure 38). After all the fluid contacts and necessary models, including structural, facies, porosity, permeability, and net to gross ratio models, are established, we estimated hydrocarbon pore volume and original oil in place (OOIP) for each formation of Cambrian-

Ordovician and Upper Cretaceous reservoir in the full field Geological model (Table

1). We found that the largest hydrocarbon volumes were reservoirs in the Upper

Cretaceous Waha Formation. The total OOIP for the full field is estimated to be 2.548×109

STB, which is comparable to the last published estimation of approximately 2.05×109 STB

(Brennan, 1992).

Figure 37. 3D net-to-gross ratio distribution of the Cambrian-Ordovician and Upper Cretaceous reservoir formations. Yellow or one represents the reservoir layer, and gray or zero represents a non-reservoir layer.

Figure 38. 3D structural model showing the identified fluid contacts, the regional gas-oil contact is at 5,050 ft (green surface), and regional water-oil contact is at 5,500 ft (blue surface). Columns in the middle indicate the wells.

Table 1. Volumetric estimation of the Cambrian-Ordovician and Upper Cretaceous reservoir formations.The table shows the volumetric estimates after modeling for each zone.

Zones Bulk Volume Net Volume Pore Volume HCPV Oil OOIP

(106 RB) (106 RB) (106 RB) (106 RB) (106 RB)

Waha 25,176.6 25,176.6 3727.8 2466.3 1644.3

Bahi 2850.1 2.850.1 209.4 156.2 105.1

Gargaf 42,724.1 42,724.1 1,781.0 1,198.4 799.1

6. CONCLUSIONS

In this case study, integrated geophysical, geological, and pertophysical interpretations are employed to construct a full-field static model of the Upper Cretaceous reservoir and Cambrian-Ordovician formations using relevant data and connectivity of various modules. Pertophysical analyses incorporated core data are used to identify and construct the reservoir properties model, and geophysical interpretation of the data is mainly used to create a fault and structural model. Structural and isochores maps created from seismic and well data were used to determine the distribution and thicknesses of the main producing formations of Waha, Bahi, and Gargaf.

The model comprises of three main zones. The Upper Cretaceous Waha zone is divided into ten subzones according to the stratigraphy, and the Cambrian-Ordovician

Gargaf zone has divided into 12 lithologically and petrophysically distinct zones. Because the Upper Cretaceous Bahi Formation is a heterogeneous succession with variable thicknesses and lateral extents, we represent the Bahi Formation as one reservoir zone. We distribute the reserve properties using sequential index simulation method and sequential

Gaussian simulation method.The full-field static model created includes interpreted faults, structural maps, isopach maps, net pay, fluids contacts, structural and properties models, and volumetric calculations. The original oil in place is calculated for the field by examining the reservoir layers, using reservoir properties cut-offs. The OOIP was estimated to be 2.548 billion barrels. Our results suggest that over 50% of the estimated oil is located in the Waha Formation. The resulting geological model clearly defines the lateral and vertical heterogeneities of the primary producing formation of the study area and can be used with confidence for the future development of plans for the field.

ACKNOWLEDGMENTS

The authors wish to thank the National Oil Corporation (NOC) Libya and the Sirte

Oil Company for providing the information and data used in this study. The first author was sponsored by the Ministry of Education in Libya.

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II. SEISMIC ATTRIBUTES AIDED FAULT DETECTION AND ENHANCEMENT IN THE SIRTE BASIN, LIBYA

Abdalla A. Abdelnabi, Kelly H. Liu, and Stephen S. Gao

SUMMARY

Cambrian-Ordovician and Upper Cretaceous reservoir formations, the primary producing formations in the Sirte Basin, Libya have complex structures which affect the performance of the reservoirs. It is critical to understand the complicated relationships between fault networks, fractures, and stratigraphy of the area for future field development.

However, detecting faults especially subtle faults and fractures is a challenging task using conventional seismic data due to the low signal-to-noise ratio. Seismic attributes provide effective tools in identifying and enhancing fault and fracture interpretation beyond the seismic resolution of the conventional seismic data. In this study, we focus on coherence and curvature attributes extracted from the post-stack 3D seismic data acquired in the central Sirte Basin to delineate subtle fault and fracture zones. We applied a median filter and spectral whitening to enhance the data quality and remove noise resulted from acquisition and processing effects. We utilized these methods to produce high-resolution data and preserve structural features. A total of 17 faults have been identified in the study area. The most common fractures in the Cambrian-Ordovician reservoir formations are in the northwest and southeast of the field. Seismic data conditioning and seismic attribute analyses applied on the 3-D seismic data effectively increased our understanding of the reservoir complex and help detect and identify subtle faults and fracture zones in the study area.

1. INTRODUCTION

The Sirte Basin located in the north central part of Libya is one of the most prolific oil producing fields in the world (Figure 1). From the beginning of the Late Cretaceous, extensional tectonics caused a partial collapse of the Sirte Arch that resulted in the development of a system of horsts and grabens (Ahlbrandt, 2001; Hallett, 2002). The major structural features that have affected the sedimentation pattern in the study area include four northwest-southeast-trending structures, i.e., the Zallah trough and Dahra platform to the west, Beda Platform in the central part of the area, and Zelten platform and Ajdabiya

Trough to the east (Figure 2). Numerous faults and fractures located in the Cambrian-

Ordovician and Upper Cretaceous reservoir formations have a significant effect in reservoir producing and efficiency (Abadi et al., 2008; Brennan, 1992). The study area is situated within the Beda Platform in the southern part of the Sirte Basin (Figures 1 and 2). The trap in the RG-oil Field is a large irregular dome-shaped structure covering an area of about

25,000 acres. It is bounded on the west by a major NE-SW-trending normal fault with minor faults cut into the interior of the structure. Faults and fractures play a significant role in the field. Faults are believed to affect the distribution and thickness of the primary producing Cambrian-Ordovician and Upper Cretaceous reservoir formations. Natural fractures in the reservoir formation in the study area represent the most significant factor in the reservoir capacity and performance (Abdelnabi et al., 2016; Brennan, 1992). Because of its complex structure, it is crucial to understand the relationships between the fault network and stratigraphy of the area for future field development.

Figure 1. Topography map showing the major sedimentary basins in Libya, including the Sirte, Kufra, Murzuq, and Ghadames basins. The study area is located in the Sirte Basin marked by the red rectangle box.

It has long been recognized that seismic attributes can reveal features hidden in seismic data. Among hundreds of seismic attributes developed, coherency or similarity and curvature are proven to be the most effective attributes in the interpretation of structural discontinuities. The objective of this study is to utilize the seismic attributes to determine the faults and fractures systematically in the RG-oil Field in the north central Sirte Basin and to better understand their relationship to the reservoir formations.

Figure 2. A cross-section showing the main structural features related to the sedimentation pattern in the study area. From west to east, the structures include the Zallah trough, Dahra platform, Beda Platform in the central, Zelten platform, and Ajdabiya Trough to the east (Modified after Abdi et al., 2008.

2. DATA, METHODOLOGY, AND RESULTS

The data set utilized in this study includes a 3-D post-stack seismic volume acquired in 2004 covering a surface area of 350 Km2. The data consist of 746 inlines and 1465 crosslines with a line spacing of 20 m. The procedure to map and enhance fault interpretation in this study includes data conditioning and seismic attribute analysis.

2.1. DATA CONDITIONING

Seismic attributes used in this study to detect the discontinuity are typically sensitive to noise, and most attributes calculated from seismic data are only as good as the quality of the input. Post-stack attributes quantify stratigraphic and structural properties.

The post-stack attributes consist of complex trace attributes, time-frequency attributes, waveform, and discontinuity. Post-stack attributes can be computed using many methods that include complex trace analysis, correlation, semblance, principle component analysis, filtering and dip scanning (Barnes, 2016; Chopra and Marfurt, 2005, 2007). The data quality decreases with increasing depth due to several factors occurring during data acquisition and processing. Random noise, acquisition related noise, and processing related noise are present in the post-stack data despite the effort and careful process of the pre- stack data. It is essential to remove the unwanted signal (noise) resulted from acquisition and processing and to improve the data resolution before any attribute analysis. Filters applied to the original data tend to remove noise and some component of the signal and show another significant element of the signal hidden in seismic data needed in the interpretation. The workflow employed in this study of the application of data condition and seismic attributes analysis is shown in details in figure 3.

Data whitening is known concept in data processing and can be applied to post- stack data. Figure 4 shows the concept of spectral whitening. Figure 4a showing the typical migration section of an input amplitude. The goal of the spectral whitening is to increase the frequency to achieve a better resolution as shown in green line in Figure 4b. However, achieving absolute resolution is impossible because of the noise at the low and high frequency. The ideal result of applying data whitening is shown in blue line in Figure 4b (

Hardy, 2001). In general, spectral data whitening mainly uses the information contain in low frequency to correct and predict the high frequency and improve the resolution of the seismic data without adding any noise to the original data (Helmore, 2009). In this study, we use data whitening methods to enhance the resolution of the data. A median filter was

52 also applied to post-stack data and used as a noise filter. We used the median filter to enhanced lateral continuous and removed random noise by filtering noise across the structural dip. A median filter and whitening were utilized at each time we extract attributes to improve the resolution (Figures 5, 6, and 7). As indicated in Figures 8,9,10 faults are significantly enhanced.

Figure 3. Workflow used in the processing of data conditioning and seismic attributes.

Figure 4. (a) The input amplitude spectrum. (b) The comparison of concept and practical use spectral whitening (modified after Hardy, 2001).

Figure 5. East-west cross section of the original inline before any filters.

Figure 6. East-west cross section after applying the spectral whitening.

Figure 7. East-west cross section after applying a median filter.

Figure 8. Original seismic inline 1215 before any filters.

Figure 9. Seismic inline 1215 after application of whitening. The fault is enhanecd compared to the original seismic section in Figure 8.

Figure 10. Seismic inline 1215 after applying the median filter is enhanced. The fault is enhanced compared to the original seismic section in Figure 8.

2.2. SIMILARITY OR COHERENCE ATTRIBUTES

Coherence or Similarity attribute quantifies the similarities between two or more traces. A similarity of “1” means seismic reflections have consistent phase and amplitude.

The similarity of “0” means seismic reflections have inconsistent phase and amplitude and reveals the break in the trace which could result from faults, channel, fracture, and pinch- outs (Chopra and Marfurt, 2010; Dewett and Henza, 2016). The attribute is used in this study to improve delineation of the major faults and detect small faults and fracture zones.

We extract the coherence attribute at 950 ms at the top of reservoir formation from the 3-

D seismic data (Figures 11 and 12). It apparently detects major faults and minor fault and to some degree fracture zones.

Figure 11. Time slice at 950 ms extracted over the enhanced 3D seismic data.

Figure 12. Coherence slice extracted at 950 ms over conditioned data showing horse, graben, and major, and minor faults.

2.3. CURVATURE ATTRIBUTES

Different types of curvature attributes can be computed from seismic data which include most positive, most negative, along dip, and along strike. Most positive and most negative curvatures are the best attributes in defining fractures and faults, and they are widely used to determine the upthrown fault blocks and downthrown fault blocks (Bailey et al., 2016; Chopra and Marfurt, 2007; Schneider et al., 2016).

In this study, we use both most positive and most negative curvatures to determine upthow and downthrow fault blocks. We used most the Positive curvature to indicate the upthrown side of the fault and most negative curvature to indicate the downthrown side of the faults as shown is figures 13, 14, 15, and 16.

The most positive and most negative curvature slices are used to detect the fracture zones which typically occur at or near anticlines, synclines, or in folded layers of brittle

59 strata. Using the attribute, we have detected fractures in the study area in the northwest and southeast in the Cambrian-Ordovician reservoir formation (Figures 17 and 18). The better defined fracture zones are crucial for the development of the reservoir, as it depends on the natural fracture zones for its production.

Figure 13. Most negative curvature overlain seismic section at inline 1215 showing the high negative value in blue color indicate downthrow of the faults.

Figure 14. Most negative curvature extracted at inline 1687 showing the high negative value which indicates the downthrow of the fault.

Figure 15. Most positive curvature overlain seismic section at inline 1215 showing the high positive value in red color indicate upthrow of the faults.

Figure 16. Most positive curvature extracted at inline 1687.

Figure 17. Most negative curvature slide at 950 ms. The black circles indicates the location of the fracture zones at the Cambro-Ordovician reservoir formation of the study.

Figure 18. Most positive curvature slide at 950 ms. The black circles indicates the location of the fracture zones at the Cambro-Ordovician reservoir formation of the study.

2.4. FAULT INTERPRETATION AND FAULTS VISUALIZATION

Data conditioning and seismic attributes help better understand the location, trend of major and minor faults. The interpreted faults are labeled in Figure 20. The major F1 is a NE-SW-trending normal fault with a throw of 800 ft. It is believed to seal the structural on the west side of the field. The major fault F2 has a throw of 400 ft and is believed to seal the structural on the east side of the field. With the use of seismic attributes, we have identified numerous minor faults which cut the interior of the structure with throw ranging from 50 to 150 ft. After mapping the faults in the Cambrian-Ordovician and Upper

Cretaceous reservoir formations, we have constructed a fault model (Figures 19 and 20).

Figure 19. Seismic inline 1215 showing major and minor faults identified within the upper Cretaceous and Cambro-Ordovician reservoir formation in the RG-Oil field.

Figure 20. Show fault polygon of the interpreted faults which labeled F1, F2… F17.

3. CONCLUSIONS

Data conditioning is a critical tool to improve the data quality and remove random noise. Median filter and spectral whitening significantly improved the data quality and increased the resolution of the data set, and preserved edges and structural features. The integration of the attributes has improved confidence in the seismic mapping of the faults and the fractures which were difficult to identify in the input data. Coherence and curvature attributes efficiently detected and enhanced faults and fracture zones of the Cambrian-

Ordovician and Upper Cretaceous reservoir formations in the study area. Coherence or similarity attribute works the best in delineating fault with sharp edges, while the curvature attributes (most positive, most negative) are efficient in defining the fracture zone and determining the upthrow and downthrow of the faults.

ACKNOWLEDGMENTS

The authors wish to acknowledge the National Oil Company and the Sirte Oil

Company for providing data used in this study.

REFERENCES

Abadi, A. M., J. D. Van Wees, P. M. Van Dijk, and S. A. P. L. Cloetingh, 2008, Tectonics and subsidence evolution of the Sirte Basin, Libya, AAPG Bulletin 92, 993–1027.

Abdelnabi, A. K. Liu, and S Gao, 2016, A full field static model of the RG-oil field, central Sirte Basin, Libya, 86th Annual International Meeting SEG Expanded Abstracts, 2057-2061.

Ahlbrandt, T. S., 2001, The Sirte basin province of Libya Sirte-Zelten total petroleum system, U.S. Geological Survey Bulletin 2202-F, 31.

Bailey, A. H., R. C. King, S. P. Holford, and M. Hand, 2016, Extending interpretations of natural fractures from the wellbore using 3D attributes, The Carnarvon Basin, Australia, Interpretation, 4(1), SB107-SB129.

Barnes, A. E., 2016, Handbook of post-stack seismic attributes, Society of Exploration Geophysicists, Tulsa, OK, 254 p.

Brennan, P., 1992, Raguba Field, Libya, Sirte Basin, in E.A. Beaumont and N.H. Foster, eds., AAPG Treatise of Petroleum Geology, Atlas of Oil and Gas Fields, Structural Traps VII 7, 267-289.

Chopra, S., and K. J. Marfurt, 2005, Seismic attributes - a historical perspective: Geophysics, 70, 3SO-28SO.

Chopra, S., and K. J. Marfurt, 2007, Seismic attributes for prospect identification and reservoir characterization, Society of Exploration Geophysicists, Tulsa, OK, 456 p.

Chopra, S., and K. J. Marfurt, 2010, Integration of coherence and curvature images, The Leading Edge, 29, no. 9, 1092–1107, http:// dx.doi.org/10.1190/1.3485770.

Dewett, D. T., and A. A. Henza, 2016, Spectral similarity fault enhancement,Interpretation, 4(1), SB149-SB159.

Hallett, D., 2002, Petroleum geology of Libya, New York, Elsevier, 503 p.

Hardy, R., 2001. Basic Seismic Processing for Interpreters.

Helmore, S., 2009, Dealing with the noise - Improving seismic whitening and seismic inversion workflows using frequency split structurally oriented filters: 79th Annual International Meeting, SEG Expanded Abstracts, 3367–3371.

Schneider, S., C. G. Eichkitz, M. G. Schreilechner, and J. C. Davis, 2016, Interpretation of fractured zones using seismic attributes-Case study from Teapot Dome, Wyoming, USA, Interpretation, 4(2), T249-T260.

SECTION

2. CONCLUSIONS

The geophysical interpretation of the data is primarily used to construct a fault and structural model. Forty-one faults are identified in the study area, and 17 faults are used in final fault polygon. Isochores and structural maps are used to understand and define the variable thickness and distribution of Cambrian-Ordovician and Upper Cretaceous reservoir formations. Seismic attribute analysis and coherence attribute are used to identified faults and fracture in RG oil field. This study detected fractures in the study area in the northwest and southeast in the Cambrian-Ordovician reservoir formation.

The Upper Cretaceous Waha zone is divided into ten subzones according to the stratigraphy. Because the Upper Cretaceous Bahi Formation is a heterogeneous succession with variable thicknesses and lateral extents, we represent the Bahi Formation as one reservoir zone. Cambrian-Ordovician Gargaf zone has divided into 12 lithologically and petrophysically distinct zones. Petrophysical analyses incorporated core data are employed to identify and construct the reservoir properties model such as facies, Porosity, permeability, and net to gross ratio model.

The model consists of three main zones, which are subdivided into 23 lithologically and petrophysically distinct subzones and are further divided by adding layers to prepare for property distribution. This results in the development of a static model that contains

30.6 million cells. A total of ten facies are identified using well-log and core data and are distributed through the model using sequential index simulation method. Porosity, permeability, and net-to-gross ratio are estimated and distributed through the model using

67 sequential Gaussian simulation method. Hydrocarbon pore volume and original oil in place

(OOIP) are estimated for each formation of Cambrian-Ordovician and Upper Cretaceous reservoir in the full field Geological model. The total original oil in place for the full field is estimated to be 2.548 × 109 STB and largest hydrocarbon volumes were reservoirs in the Upper Cretaceous Waha Formation. The full-field static model created includes interpreted faults, structural maps, isopach maps, net pay, fluids contacts, structural and properties models, and volumetric calculations. The established model is unique to the study area and for the entire Sirte Basin. It is applicable to any field in the Sirte Basin and can be updated at any time with new data acquired following production and drilling activities.

VITA

Abdalla Abdelnabi was born in Tobruk, Libya. He received his diploma in

Petroleum Industries from the Petroleum Training and Qualifying Institute (PTQI) in June

2000. Upon graduation, he was hired by the Arabian Gulf Oil Company (AGOCO) as an

Instrument Engineer. He received his bachelor’s degree in Physics from the Omar Al-

Mukhtar University in Libya in September of 2005.

During his work at AGOCO, he was awarded a Ministry of Education Scholarship in conjunction with AGOCO Company to study abroad. He enrolled at Missouri University of Science and Technology and received his master’s degree in Geology and Geophysics in the summer of 2011.

In January 2013, Abdalla joined the Geology and Geophysics program at Missouri

University of Science and Technology (Missouri S&T) to study for his Ph.D.

He received the Doctor of Philosophy in Geology and Geophysics in December

2017.