Integrated Reservoir Interpretation- Disk 2

Integrated Reservoir Interpretation

Reservoir management, integrated data interpretation, multidisciplinary asset teams, synergy—these are the buzzwords of modern reservoir engineering. They point to the efficient use of all types of data to better understand reservoirs and to ultimately produce more hydrocarbon less expensively. But is vision outpacing the tools available? We describe how better reservoir understanding is being achieved in practice.

Ed Caamano Every field is unique, and not just in its geol- disciplines work together, hopefully in close Ken Dickerman ogy. Size, geographical location, production enough harmony that each individual’s Mick Thornton history, available data, the field’s role in expertise can benefit from insight provided Conoco Indonesia Inc. overall company strategy, the nature of its by others in the team. There is plenty of Jakarta, Indonesia hydrocarbon—all these factors determine motivation for seeking this route, at any how reservoir engineers attempt to maxi- stage of a field’s development. Reservoirs Chip Corbett mize production potential. Perhaps the only are so complex and the art and science of David Douglas commonality is that decisions are ultimately characterizing them still so convoluted, that Phil Schultz based on the best interpretation of data. For the uncertainties in exploitation, from Houston, Texas, USA that task, there is a variability to match the exploration to maturity, are generally higher fields being interpreted. than most would care to admit. Roopa Gir In an oil company with separate geologi- In theory, uncertainty during the life of a Barry Nicholson cal, geophysical and reservoir engineering field goes as follows: During exploration, Jakarta, Indonesia departments, the interpretation of data tends uncertainty is highest. It diminishes as to be sequential. Each discipline contributes appraisal wells are drilled and key financial Dwi Martono and then hands over to the next discipline. decisions have to be made regarding expen- Joko Padmono At the end of the line, the reservoir engineer sive production facilities—for offshore Kiagus Novias attempts to reconcile the cumulative under- fields, these typically account for around Sigit Suroso standing of the reservoir with its actual 40% of the capital outlay during the life of Pertamina Sumbagut behavior. Isolated from the geologist and the field. As the field is developed, uncer- Brandan, North Sumatra, Indonesia geophysicist who have already made their tainty on how to most efficiently exploit it contributions, the reservoir engineer can diminishes further. By the time the field is Gilles Mathieu play with parameters such as porosity, satu- dying, reservoir engineers understand their Clamart, France ration and permeability, but is usually field perfectly. barred, because of practical difficulties, from A realistic scenario may be more like this: Zhao Yan adjusting the underlying reservoir geometry.1 During exploration, uncertainty is high. But China National Petroleum Company This scenario is giving way to the inte- during appraisal, the need for crucial deci- Beijing, China grated asset team, in which all the relevant sions may encourage tighter bounds on the reservoir’s potential than are justifiable. For help in preparation of this article, thanks to Tom In this article, ELAN (Elemental Log Analysis), Finder, Later, as the field is developed, production Bundy, Conoco Indonesia Inc., Jakarta, Indonesia; Gilles Fortress (Formation Reservoir Test System), GeoFrame, fails to match expectations, and more data, Bitoun and Rune Hope, Total Indonesie, Jakarta, Indone- Geopulse, Geoshare, GeoViz, IES (Integrated Exploration sia; Dharmawan Samsu, Arco, Jakarta, Indonesia; Bill System), LogDB, MeshBuilder, ModelBuilder, RFT for example 3D seismic data, have to be Harmony, Gerry Dyer and John Rice of Maxus, Jakarta, (Repeat Formation Tester), RM (Reservoir Modeling) TDT acquired to plug holes in the reservoir Indonesia; Ron Boulter, Beijing, China; John Bradfield, (Thermal Decay Time) and WellTie are marks of Schlum- understanding. Uncertainties begin to Abraham Baktiar and Ron Mobed, GeoQuest, Jakarta, berger; Eclipse is a mark of Intera ECL Petroleum Tech- Indonesia; Hashem Bagherpour, Mustafa Biterge, Gra- nologies; Excel is a mark of Microsoft Corporation; Sig- increase rather than diminish. They may ham Bunn, Andrew Carnegie, Metin Karakas, Bahman maView is a mark of Western Atlas; Zycor is a mark of even remain high as parts of the field Samimi, GeoQuest, Dubai, United Arab Emirates; Ben Landmark Graphics Corp. become unproducible due to water break- Lovell, Simon Robson, Steve Simson and Robert 1. For an overview of reservoir management: Sorensen, GeoQuest, Gatwick, England; Christine through and as reservoir engineers still Briggs P, Corrigan T, Fetkovich M, Gouilloud M, Lo T- Economides, Schlumberger, Houston, Texas, USA; Dou- w, Paulsson B, Saleri N, Warrender J and Weber K: struggle to fathom the field’s intricacies. glas Gray-Stephens, Schlumberger Cambridge Research, “Trends in Reservoir Management,” Oilfield Review 4, Cambridge, England. no. 1 (January 1992): 8-24. Oilfield Review 50 Lake Baikal

MONGOLIA KAZAKHSTAN Karamay Zger basin

Urumqi

XINJIANG

Kashgar

Tarim basin GANSU ery, to simply estimating the reserves in a new discovery about to be exploited. QUINGHAI Whatever the scenario, the tactic of stringing together diverse packages creates several 500 km problems. First is data compatibility. Since 0 500 Miles the industry has yet to firm up a definitive geoscience data model, each package is nXinjiang province in western China, where the RM Reservoir Modeling package has likely to accept and output data in slightly been selected for several oil fields. Since the early 1990s, China National Petroleum different ways (see “Managing Oilfield Data Company (CNPC) has placed increased emphasis on reservoir characterization to bet- Management,” page 32). This forces a cer- ter estimate field reserves and to optimize development drilling. Xinjiang province is best known for the Tarim basin, the largest basin in the world tain amount of data translation as the inter- still awaiting significant exploration. The RM package is being deployed farther north pretation moves forward—indeed, a small in the Zger basin that contains the large Karamay oil field. Three new fields have been industry has emerged to perform such trans- recently discovered here and the RM package coupled with GeoQuest seismic interpre- lation. Second, the data management system tation software played a key part in their discovery. Two of these fields are currently undergoing development drilling, and the drilling success rate has exceeded 90%. supporting this fragmented activity must somehow keep track of the interpretation as Asset teams go a long way toward maxi- For example, this could be the worksta- it evolves. Ideally, the reservoir manager mizing understanding of the reservoir and tion/package lineup for XYZ Oil Company: needs to know the history of the project, placing a realistic uncertainty on reservoir • GeoQuest’s IES Integrated Exploration who made what changes, and if necessary behavior. They are the best bet for making System for seismic interpretation how to backtrack. Third, and most impor- most sense of the available data. What they • Landmark Graphics Corp.’s Zycor map- tant, the tactic of stringing together frag- may lack, however, are the right tools. ping package for mapping mented packages discourages integrated Today, interpretation is mainly performed on • Western Atlas’ SigmaView package for interpretation. Crudely put, as the interpreta- workstations with the raw and interpreted seismic inversion tion progresses, putting things into reverse is data paraded in its full multidimensionality • Stratamodel Inc.’s package for geological always more of a hassle than continuing to on the monitor. Occasionally, hard-copy model building and 3D visualization move forward. It takes an iron will to accept output is still the preferred medium—for • GeoQuest’s GeoFrame platform for petro- that anomalous production data may mean example, logs taped to walls for correlating physical log interpretation going back to the beginning and rethinking large numbers of wells. • Intera’s PVT analysis, gridding and Eclipse the basic reservoir description. There are workstation packages for 3D simulation packages for reservoir engi- The answer to all these problems is a fully seismic interpretation, for mapping, for neering integrated package that performs most, if viewing different parts of the reservoir in • GeoQuest’s Finder package for database not all, the functionality listed above for three-dimensions, for petrophysical interpre- management XYZ Corporation, a package that is easy to tation in wells (see “Beating the Exploration • Microsoft Corporation’s Excel spreadsheet load with data and that keeps the data Schedule with Integrated Data Interpreta- program for collating data and making together in a unified format, a package that tion: Cam Oil’s Experience,” page 10), for reserve estimates. tracks the progress of the interpretation, a performing geostatistical modeling in Any number of combinations is possible. package that is easy to use, and, finally, a unsampled areas of the reservoir, for creat- The choice depends on oil company prefer- package that in its construction draws geol- ing a grid for simulation, for simulating ences, the history of the field and the prob- ogist, geophysicist, petrophysicist and reser- reservoir behavior, and more. But for the lem being addressed. Modeling a mature voir engineer into close and constant inter- reservoir manager, these fragmented offer- elephant field in the Middle East with hun- action. The quest for this ideal has not been ings lack cohesion. In a perceived absence dreds of wells and poor seismic data may easy. One solution, GeoQuest’s RM Reser- of an integrated reservoir management require a different selection of tools than a voir Modeling package, has proved success- package, many oil companies pick different newly discovered field having high-quality ful worldwide, especially in South America packages for each specific application and 3D seismic coverage and a handful of and Asia (above). then connect them serially. appraisal wells. Reservoir management problems vary from replanning an injection July 1994 strategy for mature fields, to selecting hori- 51 zontal well trajectories for optimum recov- Data loading Seismic Logs Petrophysics Geology Production

Data loading and QC

Seismic data Well data

Seismics Correlation Borehole/ Pick surface geologic seismic match tops

Tie seismic horizons and Impedance geologic tops inversion

Attribute mapping

Velocity mapping

Correlate and Geologic assign layer modeling shapes Depth horizons

Reservoir parameters Seismic-guided Petrophysics Seismic attributes log property mapping averaging

Reservoir model 3D reservoir Geology data model building

Reservoir engineering Material balance Reservoir data

Simulation

nModule functionality and data flow in GeoQuest’s RM package.

52 Oilfield Review The RM package presents geophysics, geology, petrophysics and reservoir engineering on one workstation, deals with a single data base that is loaded with all relevant data, and allows multiple interpretations and backtracking as the reservoir model is firmed up. Its functionality is leading edge in some areas, and standard in others. Its main asset, however, is that it permits an integrated approach. Let us peruse its functionality, drawing on case studies from a new offshore producing field of Conoco Indonesia, Inc., a middle-aged field in the North Sea, and Pertamina Sumbagut’s producing field, Parapen, in North Sumatra.2 Although the RM package is highly modu- lar—there are over 20 separate modules— the package basically provides tools to perform these key functions (previous page): • Loading of all available data—seismic, log, geologic and production data—into a common data base. No mean feat this, as we describe below; • User-defined display of data from up to nQuality control for well data using well summary module. Stratigraphy information four wells at a time, enabling quality con- (left) is manually input by the user. This is combined with log data and drilling and trol checks and picking of geologic tops; geologic input (right), including core results, fossil indications, hydrocarbon shows and • Postprocessing of seismic data to improve geologic top information. All data can be output to hard copy at any scale. (From the North Sea case study.) the match to well data, and also to provide acoustic impedance sections and Data Loading and Quality Control may then be required to perform quality attribute maps; Data loading is frequently underestimated in control on the entire data set. This may be • Combining of seismic and well data to both scope and importance. Since the RM the most important stage of reservoir man- make the best correlation between wells package obviates any need for messy data agement. Data quality is crucial to all subse- and create the building blocks of a strati- translation between incompatible software quent interpretation, and the difficulty in fied reservoir model; modules, it is to be expected that the initial achieving it sometimes comes as a rude • Estimating reservoir engineering parame- loading requires time and care. The array of shock. The problem is that most data by ters in every layer anywhere in the model, data in the oilfield is large. First, there are themselves look fine. It is only when they using powerful interpolation algorithms; geographic data pertinent to the field—posi- are brought into juxtaposition with other • Constructing the optimum reservoir tion coordinates, lease lines, coastlines and data types that inconsistencies, glaring or model for reserve estimation, using a other landmarks. Then, there are huge pro- subtle, become obvious. A few examples: model builder that integrates previously cessed 3D seismic data sets that can be Seismic time is standardized to two-way obtained model building blocks and imported in either SEG-Y format or from travel time, but some data may be refer- reservoir engineering parameters; seismic interpretation workstations using a enced to one-way travel time and must be • Performing sophisticated material balance Geoshare link (see “Geophysical Interpreta- corrected. Well depths can be referenced to analysis and preparing for simulation tion: From Bits and Bytes to the Big Picture,” different surface datums. True vertical depth using Intera’s Eclipse simulator package. page 23). Interpreted seismic horizons and may be confused with measured depth. The Any combination of these functions can be seismic attributes can be imported through polarity convention on surface seismic and combined to answer specific reservoir prob- American Standard Code for Information borehole seismic data may be opposite to lems. In every functionality, the RM package Interchange (ASCII) loading or, again, using each other, requiring a flipping of one or the is equipped to handle and include data from a Geoshare link. Well data can be imported other. In fact, mistakes may emerge almost deviated and horizontal wells. The latest from GeoQuest’s Finder, LogDB and multi- anywhere, and errors are spotted only by version of the RM system, currently in test- well (MWDB) data bases. Alternatively, log review of all data together. ing, helps predict average properties of pro- data can be imported from Log Information One quality-control tool in the RM pack- ducing layers in a reservoir given any hypo- Standard (LIS) files or just ASCII files. Log age is the well summary module, in which thetical well trajectory. data should include, where available, pro- all available well data for a single well are duction logs and wireline testing results. displayed versus depth and can therefore be Petrophysical interpretations can be input readily cross-checked (above). Data may from ASCII, LIS files or proprietary Schlum- berger formats. Facies information, geologic 2. See also an RM case study described in: tops and surfaces, core data, all of this gen- Ramli R, Nugroho SB, Bradfield J and Hansen S: “Reservoir Modeling in the Bunyu Tapa Gas Field—an erally in ASCII format, can also be input to Integrated Study,” Proceedings Indonesian Petroleum the data base. Finally, well test results are Association 22nd Annual Convention, October 1993. input manually. For a field of 20 wells, data loading may July 1994 take about two weeks. Two more weeks 53 n include a stratigraphic column, a lithologic Base map display showing deviated column, any amount of manually input geo- well trajectories logic information such as core and fossil from a single plat- descriptions, all wireline logging data and form (wells have images including testing data and produc- been renamed tion logs, and petrophysical interpretations. after minerals) and position of a reser- Once the data are loaded, data access is voir unit (the Oslo) facilitated by reference on the workstation on the trajectories screen to a base map of the field. This with unit thickness shows geographical features and a plan indicated. (From the North Sea case study.) view of the trajectory of the wells. As the interpretation ensues, this base map illustrates progressively more of the interpreted features, for example the exact location of geologic tops along a deviated well trajectory or a map (right).

Postprocessing Seismic Data The interpretation path obviously depends on the data available. For two of the three fields considered here, there were excellent 3D seismic data. And in all three fields, there was at least one well with a borehole seismic survey. The first goal in working with accurately assessing the signature of the In Conoco Indonesia, Inc.’s field, the sur- seismic data is to ensure that the borehole acoustic source. This is straightforward in face seismic and borehole seismic data ini- seismic and the surface seismic at the bore- borehole seismics because the measured tially matched poorly (next page). With the hole trajectory look as similar as possible. If signal can be split into its downgoing and residual processing module, the mismatch is that is achieved, then the surface seismic can upgoing components, and the former yields resolved by comparing the frequency spec- be tightly linked to events at the borehole the source signature. In surface seismics, the tra of the two data sets and designing a filter and subsequently used to correlate structure downgoing field is unmeasured and statisti- to pull the surface seismic data into line and evaluate properties between wells. If no cal techniques must be used to assess the with the borehole seismic data.3 In this case, borehole seismic data are available, an alter- signature, leading to less reliable results. the postmatch alignment is excellent. How- native is to use synthetic seismograms, computed from acoustic and density logs. Differences in seismic data arise because of difficulties in achieving a zero-phase response, a preferred format for displaying seismic results in which each peak on the trace corresponds exactly to an acoustic impedance contrast, and, by inference, geologic interface. Processing seismic data to obtain a zero-phase response depends on

3. Gir R, Pajot D and Des Ligneris S: “VSP Guided Reprocessing and Inversion of Surface Seismic Data,” paper OSEA 88105, presented at the 7th Offshore South East Asia Conference, Singapore, February 2-5, 1988. Schultz P, Kordula J, Lawyer L, Metrailer F, Nestvold W, Raikes S and Nguyen T: “Integrating Borehole and Seismic Data,” Oilfield Review 3, no. 3 (July 1991): 36-45.

nAcoustic impedance section, obtained using the inversion module, with superimposed well log acoustic impedance. Tools needed for the job include the low-frequency trend of acoustic impedance derived from well logs (left track, top right), and frequency spectra of both seismic and log data. (From the Conoco Indonesia, Inc. case study.)

54 Oilfield Review After match ever, if the alignment resulting from this Before match treatment remains poor, it may prove necessary to vary the design of the filter versus Cross- Surface Borehole Surface Cross- two-way time. This is achieved by con- correlation seismic seismic seismic correlation structing filters for several specific time intervals along the well trajectory and then interpolating between them to obtain a rep- resentative filter at any two-way time. The next step is to perform a seismic inversion on the matched seismic data, using the inversion module. This process converts the matched seismic data to acous- Time, msec tic impedance, defined as the product of rock density and acoustic velocity. Acoustic impedance can be used to classify lithology and fluid type. Mapped across a section in two dimensions or throughout space in three dimensions, acoustic impedance provides a valuable stratigraphic correlation tool. For Conoco Indonesia, Inc., inversion provided valuable insight into the lateral extent of the reservoir (previous page, bottom). The inversion computation requires the full spectrum of acoustic frequencies. Very low frequencies are missing from the seismic record, so these are estimated interactively from acoustic well logs. Between wells, this low-frequency information is interpolated, and for a 3D inversion, the information must be mapped everywhere in reservoir space. Inversion results can be output from the RM package to a seismic interpretation workstation for detailed horizon picking. The residual processing and inversion modules prepare the seismic data for the next step, correlation.

nTop: Crosscorrelation between borehole seismic and surface seismic data before and after matching, using the residual processing module. Matching the surface seismic required a time shift of 9 milliseconds and a phase rotation of 90 degrees. Note that after matching, the crosscorrelation function and its envelope are symmetric about zero time. Bottom: Before and after match comparison between surface and borehole seismic data. Note the excellent alignment between the two data sets after matching. (From the Conoco Indonesia, Inc. case study.)

July 1994 55 Correlating Seismic and Well Data played for up to four wells simultaneously, aid in this process, with core information, Correlation is performed in several stages. the interpreter can correlate horizons from petrophysical log interpretations, wireline The first is establishing geologic tops on one well to the next, registering consistent testing results and production logs equally each well using the detailed correlation geologic tops in every well across the field able to contribute to identifying significant module. With individual well data dis- (below). All well data have the potential to geologic horizons. The next step signals the beginning of the merging of seismic and well data. In the WellTie module, the 3D seismic trace at a given well is displayed versus two-way time alongside all pertinent well data, which are already converted to time using borehole seismic or check-shot data (bottom left). The main purpose of this combined display is to tie events recognized on the seismic trace—seismic horizons—to the recently established geologic tops found from the well data. These ties, or links, between the two data types are crucial at several subse- quent stages during the construction of the reservoir model. In addition, seismic mark- ers found at this stage can be transferred to a seismic interpretation workstation for horizon tracking. The first use of the tie, or link, between seismic and well data is in the velocity mapping module that enables the 3D seismic record versus time to be converted to a record versus depth. This crucial step subsequently allows the seismic data to guide the mapping of geologic horizons between wells. In the case of Conoco Indonesia, nDisplays of up to four wells using the detailed correlation module for picking and correlating geologic tops across the field. Here, each example displays different types of Inc.’s case study, seismic depth maps data and speaks to a different specialist: to the geophysicist (top left), to the geologist including fault positions were already avail- (top right), to the reservoir engineer (bottom). This diversity encourages integration of all able from previous interpretation work and available data and permits addressing different stages of a field’s life. (From the North were imported directly into the RM pack- Sea case study.) age. When depth maps are not available, however, they can be obtained using the RM package, as follows: A velocity map for each layer is first assessed from the stacking velocities used in the 3D seismic processing. These are average velocities to the depth in question and must be converted to interval velocities using Dix’s formula.4 The interpreter then maps these velocities for a given horizon, using one of four available algorithms including the sophisticated kriging tech-

4. For a review of Dix’s formula: Sheriff RE and Geldhart LP: Exploration Seismology Volume 2: Data-Processing and Interpretation. Cam- bridge, England: Cambridge University Press, 1987. 5. For an explanation of kriging: Isaaks EH and Srivastava RM: An Introduction to Applied Geostatistics. Oxford, England: Oxford Uni- versity Press, 1989.

nDisplay from the WellTie module, in which seismic horizons are tied to geologic tops. (From the Conoco Indonesia, Inc. case study.)

56 Oilfield Review n nique, and reviews their appearance in plan Structural dip azimuth plots (white) 5 view. Gradual changes in velocity are nor- superimposed on a mal, but anomalies such as bull’s-eye map derived from effects—isolated highs or lows—that are velocity mapping geologically unacceptable can be edited out. and time-to-depth Next, values of velocity at the intersec- conversion. In this example, the trends tions of horizons with wells are compared of the plots do not with velocity values obtained from acoustic follow lines of great- log or borehole seismic data. The differ- est slope indicated ences, determined in all wells, are also on the map. Some interactive editing mapped and then used to correct the origi- of the map may nal velocity map. Finally, the corrected therefore be velocity map is used to convert the 3D seis- required. (From the mic record to depth. To check the result, North Sea case study.) structural dip azimuth as estimated from dipmeter logs can be superimposed on the resulting map—structural dip azimuth should follow the line of greatest slope as indicated by the map (above, right). Structural View With seismic data converted to depth, the interpreter can begin building a stratified model of the reservoir using the correlation module. First, seismic data acting as a guide allow geologic tops in one well to be firmly correlated with tops in adjacent wells (right). This display may be further enhanced by superimposing dipmeter stick plots and other forms of dipmeter interpretation along the well trajectories. Another display that shifts data to an arbitrary datum, generally an already correlated horizon, provides a stratigraphic perspective (below). Second, each geologic correlation is allocated descriptors that determine how it relates geometrically to its neighbors above and below. These descriptors are later used to build up the actual reservoir model. Third, all available information about reservoir compart- mentalization—for example, saturation interpretations from well logs and wireline testing results—are used to identify flow barriers, Stratigraphic View such as a sealing faults, so the reservoir can be divided into a set of isolated volumes called tanks, essential for correctly estimating reserves. Sometimes, the interpreter may want to manually dictate the geometry of a horizon

nTwo displays available in the correlation module. Top: A structural view shown in true depth with each well’s ELAN Elemental Log Analysis petrophysical interpretation superimposed on the depth-converted seismic section. This provides a key visual comparison between seismic and well data. A small map (upper right) shows the zig-zag course of the displayed section. Bottom: A stratigraphic view referenced in depth to a specific marker, used to check geologic tops from well to well. (From the Conoco Indonesia, Inc. case study.)

July 1994 57 or other feature—such as a fault, bar, channel, etc.—rather than let it be guided by established horizons on the 3D seismic data. This can be accomplished using the section modeling module, which offers an array of graphic tools to create and edit elements of the reservoir model in vertical section (left). The same elements must be created in every other vertical section containing wells and then sampled on a reg- ular grid of points in preparation for model building. This labor-intensive manual cre- ation of a reservoir model becomes manda- tory when there are no seismic data or only sparse 2D data. One source of data that may contribute to the definition of tanks and faults is the well test. Well tests give an approximation of tank size and, in particular, provide distance estimates from the well to sealing faults. Azimuth to the fault, however, is undeter- mined. In the Geopulse module, the RM system permits viewing well test results in a plan view and includes the ability to rotate a sealing fault to see if it can be aligned with faults already established from seismic interpretation or by using the section modeling nBuilding a geologic model manually using the section modeling module. A wide vari- module (below). ety of colors and patterns are available to visualize the construction. (From the North Sea case study.)

nViewing well test results on a reservoir base map. The well test indicated a sealing fault at a certain distance from a well, but cannot indicate its azimuth (left). Using the Geopulse module, interpreters could rotate the sealing fault until it coincided with a fault already established in the model (right). (From the North Sea case study.)

58 Oilfield Review Obtaining Reservoir Engineering offs is made with the help of sensitivity plots Parameters in Each Layer showing how the averaged parameter varies Once the reservoir geometry has been with cutoff value, and preferably in a well defined, if not actually computed, one step with well-test data to validate the cutoff remains before synthesizing the complete choices. The effect of adjusting cutoffs can reservoir model. This is the estimation of key be observed in real time on the workstation reservoir engineering parameters in each and for all wells simultaneously. defined interval across the areal extent of the Second, the averaged parameters for each reservoir. Key parameters are net thickness, interval must be gridded or mapped across porosity, oil, gas and water saturations, and the reservoir. In the log property mapping horizontal and vertical permeabilities. The module, the RM package brings into play computation proceeds in two stages. powerful algorithms that use seismic data to First, in each well, the parameters must be guide the mapping. The key to the method averaged for each interval from the petro- nPrinciple of seismic-guided log property is establishing a relationship at the wells physical interpretations. This is performed in mapping. A correlation between seismic between some attribute of the seismic data the component property module and relies attributes at the well intersections and the and a combination of the averaged well logging parameter must first be estab- on careful selection of cutoffs to exclude lished. The correlation is then used to parameters, and then using the relationship sections of formation that do not contribute map the logging parameter throughout to interpolate the averaged parameter every- to fluid movement (below). Choice of cut- the seismic cube. where in the reservoir (left). The seismic attribute could be amplitude, or acoustic impedance calculated earlier using the inversion module, or one of several attributes that are routinely calculated on seismic interpretation workstations and then imported to the RM system, or simply depth—for example, saturation is often related to depth. The relationship may be linear—that is, the combination of averaged parameters is defined as a simple weighted sum of seismic attributes—or nonlinear, in which an elabo- rate neural network approach juggles several linear relationships at the same time, picking the best one for given input.6 Linear relationships easily handle smooth dependencies such as between acoustic impedance and porosity. The nonlinear approach is required for averaged parameters, such as saturations, that may vary abruptly across a field. In practice, the log property mapping module guides the interpreter through the

6. Schultz PS, Ronen S, Hattori M and Corbett C: “Seis- mic-Guided Estimation of Log Properties. Part 1: A Data-driven Interpretation Methodology,” The Lead- ing Edge 13, no. 5 (May 1994): 305-310, 315. nChoosing cutoffs for the averaging of interval parameters, as displayed in the compo- Ronen S, Schultz PS, Hattori M and Corbett C: “Seis- nent property module. Top: tables of averaged parameters for various intervals (called mic-Guided Estimation of Log Properties. Part 2: Using Artificial Neural Networks for Nonlinear after major cities) and wells (called after gemstones) for a particular cutoff selection: Attribute Calibration,” The Leading Edge 13, no. 6 red means average value is fixed, blue means it can be edited. Bottom left: optimiza- (June 1994): 674-678. tion plot to help select cutoffs. Bottom right: cutoff sensitivity plots to show percentage Schultz PS, Ronen S, Hattori M, Mantran P and Cor- net thickness versus chosen cutoff parameters. (From the North Sea case study.) bett C: “Seismic-Guided Estimation of Log Properties. Part 3: A Controlled Study,” The Leading Edge 13, no. 7 (July 1994): 770-776.

July 1994 59 Conoco Indonesia Inc. essential stages: choosing the interval to Building the Reservoir Model and map, comparing seismic data at the well Estimating Reserves intersections with the averaged well data, The stage is set for the RM package’s most establishing relationships that show a good powerful functionality—the ModelBuilder degree of correlation and then proceeding module. This module fully characterizes the with the mapping. The advantage of log reservoir by integrating the geometric inter- property mapping over conventional map- pretation established with the correlation ping was demonstrated in both the Conoco and section modeling modules, including Indonesia, Inc. and Pertamina Sumbagut definitions of reservoir tanks and fluid lev- case studies (below). Research continues els, with the reservoir engineering parame-

clay into finding ways of using all available data ters established using the component prop- V to assist the mapping of log data across the erty and log property mapping modules. reservoir (see “Inversion for Reservoir Char- The main task is constructing the exact acterization,” page 62). shape of the reservoir layers. This is

Pertamina Sumbagut

nSeismic-guided log property mapping results. For Conoco Indone- nFor Pertamina Sumbagut, averaged seismic amplitude for sia, Inc., seismic-derived acoustic impedance averaged over 60 each interval was used to map net-to-gross ratio. The map milliseconds was used to map averaged clay percentage (top). derived from logs alone (right) is clearly inferior to the seismic- A map of clay distribution for one of the reservoir intervals derived guided map. The latter shows a channel (blue) and tidal inlets from well data alone (bottom) is enhanced when seismic data are that are consistent with the fault. used as a guide (middle). The latter clearly shows clean sands to the right (yellow and orange) and dirty sands to the left (blue) for that interval. The reservoir is bounded on the south by a major thrust fault.

60 Oilfield Review n achieved by starting at a bottom reference Unconformity, truncate below Principle behind the ModelBuilder horizon and building up younger layers module. Shapes according to their assigned descriptors, are assigned to mimicking the actual processes of deposi- Sequential, conformable layers with refer- tion and erosion (right). For example, if a ence to mapped layer top has been defined as sequential and horizons above or Sequential, conformable below. Then, the Sequence # 2 Special, bar conformable, it will be constructed roughly Sequential, model is built up parallel to the layer’s bottom horizon. If a conformable from the bottom reference horizon has been described as an following simple unconformity, then underlying layers can Unconformity, truncate rules of deposition below, conformable and erosion. approach it at any angle, while layers above above can be constrained to track roughly parallel. The areal bounds on layers are determined within the ModelBuilder module by several factors. First, specific geometries can Sequential, Special, channel be imported. Second, areal bounds may be conformable

implied through the geometries created with Sequence # 1 the section modeling module. Third, the contours of petrophysical parameters estab- Sequential, reference lished during log property mapping can establish areal limits. Fourth, thickness maps of layers can be interactively created and edited prior to model building. The key dividend of model building is the establishment of reserve estimates for each tank. Oil in place, total pore volume, net- pay pore volume, water volumes, reservoir bulk volume, net-pay area and net-pay bulk thickness are some of the parameters that can be calculated and tabulated on the workstation. Conoco Indonesia Inc.’s estimates using the RM package were in close agreement with standard calculation proce- dures. During appraisal, when the oil company decides whether to proceed to development, establishing reserve estimates is crucial. As a result, the many steps leading to this moment will be reexamined and almost certainly rerun to assess different assumptions about the reservoir (right). A benefit of the RM package is that rerun- ning interpretations and testing new assumptions are not only straightforward but automatically monitored by the system’s version manager. The manager keeps track of con- current or parallel interpretation paths. The initial version is created when data are loaded, and becomes the top member of a nOne result of model building—porosity mapped according to a seismic surface versus depth. This 3D perspective was obtained by transferring the ModelBuilder results to a seismic workstation and viewing them with GeoViz software.

July 1994 61 Inversion for Reservoir Characterization

Alberto Malinverno Ridgefield, Connecticut, USA

Log data

Fundamental to reservoir characterization is assigning physical property values everywhere within the reservoir volume. The challenge of Model using all available data to choose the best assignment is being addressed by a group of scientists at Schlumberger-Doll Research in Ridgefield, Connecticut, USA. Available data could include seismic data, log data, well test results, knowledge of the statistical distributions of the sizes and orientations of sedimentary bodies, and even specific information about reservoir geometry. To incorporate all these diverse sources of information, the scientists use an inversion Seismic data method that begins by considering all possible assignments.1 Each assignment is represented by a single point in a multidimensional space that has as many dimensions as there are cells in the reservoir model. In assigning acoustic impedance in a reservoir model comprising 10 × 10 × 10 dis- crete cells, for example, each assignment would be represented by a unique point in a 1000-dimen- sional space. The available data are then used to determine sional space. A procedure to choose a single, which of these points are acceptable. This is best assignment is therefore required. The cur- achieved by representing each available data rent method starts with an initial guess and then set—3D surface seismic data, well data, or what- modifies it as little as possible until the intersec- ever—by a cloud of points corresponding to tion set is reached. assignments that fit that particular data set. Find- A synthetic example illustrates the method. ing an acceptable assignment then reduces to First, a reservoir model is constructed using 21 × finding a point that lies at the intersection of all 21 horizontal cells and 201 vertical cells, with an such clouds of points. acoustic impedance value assigned to each cell As the solution is always nonunique (more than (right). This synthetic model is equivalent to a nLeft: Reservoir model comprising 21 × 21 horizontal one assignment satisfies all the available infor- volume of about 1 km × 1 km [0.6 miles × 0.6 × 201 vertical cells, each assigned a value for acoustic impedance, scaled from blue to red. mation), this intersection set will not be a single miles] horizontally and 100 milliseconds (about Middle: Simulated acoustic impedance log (top) point but have some volume in the multidimen- 200 m) vertically. From this are generated two and 3D surface seismic data obtained in the reser- data sets that would be measured if the reservoir voir model. Right: Simple extrapolation of log data only (top), and were real: first, a log of acoustic impedance in a inversion using both log and surface seismic data. well through the center of the model; second, the surface seismic response, which displays a lower spatial resolution than the original model. The challenge is to reconstruct the original acoustic impedance model using the log and

62 Oilfield Review Simple extrapolation tree whose branches represent later interpre- Material Balance Analysis and tations. The tree is displayed on the worksta- Preparation for Simulation tion screen and work can be started at any For reservoir managers striving to improve branch with a double-click on the mouse the performance of developed fields—for (below). Several people can work on the example, investigating placement of new data set simultaneously; the original data are wells or reconfiguring existing producers never corrupted or lost; and the history of and injectors to improve drainage—the RM the interpretation is automatically recorded. package has two more modules to offer. Say, for example, a geologist is working One provides a sophisticated material bal- on correlating logs and creating geologic ance analysis that assesses whether the tops, while the geophysicist is preparing an established reservoir model is compatible inversion to obtain acoustic impedance. If with historical production data. The second both want to work concurrently, the version converts the reservoir model into a format manager simply grows two branches. At the suitable for simulating reservoir behavior end of the day, both branches are saved and and predicting future production.7 both specialists can pick up where they left Material balance analysis is performed off in the morning by double-clicking with using the Fortress Formation Reservoir Test the mouse on their respective branches. System module. In traditional material bal- Similarly, a reservoir engineer may wish ance analysis, reservoir volume is estimated to try several scenarios for mapping the dis- by noting how reservoir pressure decreases Inversion tribution of porosity within a layer—say by as fluids are produced. The more fluids pro- mapping well log values only and alterna- duced, the greater the expected pressure tively by using seismics to guide the map- decrease. Exactly how much depends on ping with the log property mapping module. the compressibility of the fluids, which can Two versions can be made in parallel with a be determined experimentally from down- branch for each scenario. Several further hole samples through pressure-volume-tem- steps along each interpretation path may be perature (PVT) analysis, the compressibility necessary before it becomes clear which mapping technique is better. The final inter- 7. For a simulation case study, see: pretation proceeds from the end of the suc- Bunn G, Minh CC, Roestenburg J and Wittmann M: “Indonesia’s Jene Field: A Reservoir Simulation Case cessful branch. Study,” Oilfield Review 1, no. 2 (July 1989): 4-14.

seismic data only. A reasonable starting model can be obtained from a simple extrapolation of the well log data. This clearly fails to reproduce structural variations away from the well that appear in the original model. However, modifica- tions to this first guess using in addition the surface seismic data produces a reconstruction that is much closer. Scientists at Schlumberger-Doll Research anticipate that this method will adapt readily to a wide variety of input data and provide a much sought after generalized approach to constrain the assignment process.

1. Han S-P: “A Successive Projection Method,” Mathemati- cal Programming 40 (1988): 1-14. Combettes PL: “Signal Recovery by Best Feasible Approx- imation,” IEEE Transactions on Image Processing 2 nA view of the RM system’s version manager, showing the tree structure of a project’s (1993): 269-271. progress. After step 61, “Seismic Guided LPM of SW,” four activities were simultaneously launched. Each is preserved and can be developed independently. (From the North Sea case study.)

July 1994 63 of the rock, which can be determined from core samples in the lab, and, of course, reservoir volume. Faster declines in pressure than expected from such an analysis might indicate a smaller reservoir than first thought. Slower declines might indicate a high-volume aquifer driving production or, less rarely, connected and as yet undiscov- ered extensions to the reservoir. This traditional analysis of reservoir size and drive mechanism requires no a priori knowledge of reservoir geometry, only production, pressure and PVT data. The Fortress module uses these basic prin- ciples of material balance, but applies them within the geometrically defined reservoir tanks of the established reservoir model.8 This allows not only verification of tank volumes, but also estimation of fluid communi- cation between tanks (right). Communica- tion between tanks could be due to an intervening low-permeability bed or a fault being only partially sealing. Another result is the prediction of how fluid contacts are moving. In a sense, the Fortress module n allows the interpreter to ensure that the Displays from the RM package’s Fortress module that uses material balance compu- tations in each of the model’s defined tanks to monitor fluid movements throughout the reservoir model established by the RM reservoir. Top right: an oil-water contact is shown moving from solid purple contour to package is compatible with known produc- dashed purple contour. (From the North Sea case study.) tion, pressure and PVT data obtained in the field. This is a minimum requirement before embarking on the more laborious and expensive business of a reservoir simulation. The last RM module is the MeshBuilder module that prepares the reservoir model for input to a simulator (right). This pro- nTypical cell ceeds in two steps. First, an areal mesh is geometries for simulation. The cells constructed covering the part of the reser- are constructed by voir to be simulated. The mesh’s perimeter taking a grid and may be irregular to honor the typically irreg- slicing it through ular bounds of a reservoir. Second, this the reservoir model mesh is pushed down through the layers of like a cookie cutter. the reservoir, like a cookie cutter, to create thousands of small cells required for the simulator. Each cell is characterized by the reservoir engineering parameters established during model-building. The output of the MeshBuilder module can be input directly to Intera’s Eclipse simulator. In today’s tough business climate, characterizing reservoirs is a key activity for oil companies, and its complexities demand the power of high-speed workstations. The RM package represents an option that favors input, and not, by tradition, just when it’s 8. Ehlig-Economides CA: “Application of Multiphase the approach of integrated interpretation, in his or her turn. Like most other workstation Compartmentalized Material Balance,” paper SPE 27999, to be presented at the SPE/University of Tulsa which each specialist provides continuous packages, the RM system is being continu- Centennial Petroleum Engineering Conference, Tulsa, ously evolved to improve its functionality Oklahoma, USA, August 29-31, 1994. and provide new leading-edge tools. The likely winner in this workstation game will combine integrated functionality with ease of use. —HE

64 Oilfield Review