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Integrated Characterization and Simulation of the Fractured Tensleep Reservoir at Teapot Dome for CO2 Injection Design A. Ouenes, Prism Seismic, T. Anderson, RMOTC, D. Klepacki, Prism Seismic, A. Bachir, Prism Seismic, D. Boukhelf, Prism Seismic, U. Araktingi, Prism Seismic, M. Holmes, Digital Formation, B. Black, RMOTC, V. Stamp, RMOTC

Copyright 2010, Society of Petroleum Engineers This paper was prepared for presentation at the Western North America Regional Meeting held in Anaheim, California, USA, 26–30 May 2010. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract This paper describes a workflow that fully utilizes the post-stack seismic attributes to derive reliable geologic and fracture models that are validated with multiple blind wells and reservoir simulation. The first step in the workflow is to run post-stack seismic processes, which includes volumetric curvature, post-stack inversion and spectral imaging. The second step consists of using the various post-stack seismic attributes to derive 3D geologic and fracture models. The third step is to use the derived models in a reservoir simulator to verify the validity of the models. This workflow was applied to the Tensleep reservoir at Teapot Dome in Wyoming. A large number of post-stack seismic attributes were generated in time and then depth converted within a 3D geocellular grid. These seismic attributes were used as input in REFRACTTM, Prism Seismic fracture modeling software, to create geologic and fracture models. An effective permeability was estimated by using a linear combination of the scaled fracture density and the matrix permeability. Two reservoirs unknowns were estimated by history matching in a black oil simulator: the strength of the aquifer and the scaling factor used to convert fracture density to fracture permeability. Water cut was matched at all the wells, confirming the reliability and accuracy of the derived geologic and fracture models and the usefulness of the workflow. With the derived dynamic model, a compositional simulator was used to test various CO2 injection rates and their effects on the breakthrough time.

Introduction

Injecting CO2 in hydrocarbon reservoirs is an established EOR (enhanced oil recovery) process in the oil and gas industry. CO2 injection can usually extend an oilfield’s production and increase the ultimate oil recovery. Proper planning of a CO2 injection project requires an accurate characterization and simulation of the reservoir, with the goal of optimizing the oil recovery. The presence of faults and fractures in the reservoir complicates this process. Fractures can enhance reservoir permeability, and thus allow larger volumes of CO2 to be injected. However, these fractures could also create early CO2 breakthroughs in existing wells, or leakage through fractures and non-sealing faults to other formations or even to the surface. The reservoir characterization and simulation must accommodate these complications in order to ensure a successful result. This paper describes a robust characterization and simulation workflow for a fractured reservoir considered for CO2 injection. This workflow has been successfully applied to various oil and gas fields1-8, and is illustrated in this paper using the Tensleep reservoir at Teapot Dome, Wyoming.

Data Requirements Producing an accurate fractured reservoir characterization and simulation requires the input of geophysical, geological, and engineering data. The input geophysical data is a 3D seismic survey that appropriately images the reservoir. As is shown in this study, information on azimuthal anisotropy (derived from a wide-azimuth or multi-component survey), is not necessary,

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although it could be incorporated into the workflow if available. The engineering input requires a good characterization of the reservoir fluids, including a swelling test (laboratory measurements to determine the phase behavior of oil and CO2), production history of the field, and a few well tests that provide reservoir permeability values. The geological data input consists of well logs (sonic, neutron, density, resistivity, gamma ray) from several wells, formation image logs with coverage over both fractured and non-fractured reservoir intervals in at least one well, and core measurements of porosity and permeability in fractured and non-fractured reservoir zones. Implicit in the building of the reservoir model is an understanding of the stratigraphy and depositional environment of the reservoir units. Geology of the Tensleep Formation, Teapot Dome Teapot Dome is an asymmetric, doubly plunging Laramide-age anticline located near the southwestern edge of the Powder River Basin in Natrona County, Wyoming (Figures 1 and 2). A total of nine productive horizons are present in the field, with the Pennsylvanian Tensleep Formation being the deepest and one of the most prolific producers in the field. The Tensleep Formation has an average gross thickness of 320 feet and consistsof eolian sandstones interbedded with sabkha and marine dolomites (Figure 3). The Tensleep reservoir is divided into the A Sand, B Dolomite, B Sand, C1 Dolomite and C1 Sand. The A and B sands are oil producers; the C1 sand is wet. Core descriptions and image logs indicate that the Tensleep Formation is fractured9. Most of the fractures and faults at Teapot Dome are interpreted to have formed during deformation associated with the Laramide basement-cored thrusting10. Well tests indicate high fracture connectivity in the field. The presence of these connected open fractures produces permeability anisotropy, which must be accounted for in the planning of a CO2 injection project. Integrated Reservoir Characterization and Simulation Workflow The workflow used to derive an accurate description of the Tensleep reservoir must quantitatively integrate the geophysical, geologic and engineering data. Once a reliable reservoir description is achieved and verified, various CO2 injection scenarios can be investigated using a compositional reservoir simulator. Such a workflow11 (Figure 4) consists of the following major steps: interpreting key seismic horizons and faults and generating seismic attributes; using the horizons, faults, and seismic attributes to build seismically constrained geocellular models of porosity, water saturation, etc.; combining the derived geocelluar models with seismic attributes and geomechanical models to derive high-resolution three-dimensional (3D) fracture models; and validating the 3D fracture models in a dynamic reservoir simulator by testing the ability of the models to match well performance history. Step 1 – Geophysical Analysis of the Tensleep Reservoir The initial geophysical work is familiar to every seismic interpreter – generate synthetic seismograms and correlate them with the seismic data to identify key seismic events, and interpret horizons and faults. In addition to the seismic amplitude data (Figure 5), additional seismic attributes such as similarity, volumetric curvature, and colored inversion were generated to aid in the structural interpretation. Building reservoir models requires detailed seismic interpretation in the reservoir interval; five key horizons were interpreted in the Tensleep formation and tied to the formation tops in time (Figure 6). Spectral decomposition, instantaneous phase, and multiple stochastic inversions generated additional seismic attributes for use in building reservoir models and identifying structural features (Figure 7). The Tensleep reservoirs are thin enough that they generally fall below the tuning thickness. As the reservoir sands exhibit lateral variations in both thickness and porosity, we cannot uniquely determine reservoir thickness or porosity from the seismic amplitude or impedance data. However, we can accurately determine porosity thickness values from the impedance (Figure 8), and the resulting porosity thickness map correlated with known porosity thickness variations observed in the well data. Once the key horizons and faults were interpreted, these objects were used to construct the structural framework in the time domain. In this structural framework, all object intersections (fault-fault intersections and horizon-fault intersections) truncate cleanly, providing the sealed 3D framework necessary for the following work. With the 3D structural framework built, a 3D geocellular grid was generated within this container, dividing the Tensleep reservoir into three zones: A Sand / B dolomite with 9 layers, B sand, with 13 layers and C sand with 3 layers. This layering ensured that the cell thickness is approximately 1 ms in the time domain or 2m (7 ft) in the depth domain. In the areal direction, the cells are 67m x 67m, resulting in a 3D geocellular grid with 376,025 cells (Figure 9). As the seismic bins are rectangular and current reservoir simulators still require orthogonal grids, the derived 3D grid with its rectangular cells and vertical columns is the appropriate bridge to the geology and reservoir engineering domains. The seismic attributes are copied into the 3D geocellular grid, using an appropriate averaging technique, providing the seismic information needed for reservoir

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modeling. The 3D geocellular time grid built is then depth converted, along with the seismic attributes contained within the grid. These seismic attributes are now available for use in the seismically constrained geologic and fracture modeling. Step 2 – Seismically Constrained Sequential Geologic Modeling of the Tensleep Reservoir The sequential modeling approach uses multiple seismic attributes as a building block to estimate, in a sequential manner, related petrophysical properties such as facies, shale volume, porosity, water saturation, and permeability. This process is sequential because the first model created is the reservoir property that potentially controls the distribution of most of the other reservoir properties. In this study, the shale volume was chosen as the primary reservoir property. The shale volume model was built using a neural network approach, constrained by many seismic attributes. Shale volume logs calculated from 11 different wells were used as the input data to the shale volume model. The neural network approach provides multiple realizations that are screened based on various criteria and blind wells. The best realizations were used to compute an average shale volume model. This shale volume was subsequently used to estimate another reservoir property – density.

Density logs were available at 9 wells. In addition, the shale volume model and the available seismic attributes were used to generate the density model. As a result, the density models are constrained by both geologic and geophysical attributes. The density model was used, along with the shale volume model, in subsequent modeling. This sequential process similarly estimated other reservoir properties, including log derived porosity, core porosity, core permeability and oil saturation estimated from core data. The two particular reservoir properties that require special attention are the oil saturation and core permeability.

Unlike previous property models, the core permeability model must take into account the effects of fractures, hence the need for adding geomechanical attributes such as slopes, curvatures, deformation and distance to faults. Using the available core permeability data at 11 wells, the previously derived shale volume, density, log and core porosity models, the multiple seismic attributes available in 3D, and the computed geomechanical drivers, various core permeability models were estimated and validated. An average core permeability model was derived by combining many realizations. This average core permeability model does not take into account large-scale effects of fractures and primarily represents the matrix permeability. This matrix permeability was used as an input to compute the effective permeability, which required the modeling of the fracture density.

The final reservoir property required for the simulation is the oil saturation. Given the tilted oil-water contact in the reservoir, this property requires special attention. Using the available core oil saturation data at 11 wells, the previously derived shale volume, density, log porosity, core porosity, and permeability models, along with the multiple 3D seismic attributes, various core oil saturation models were estimated and validated, and an average core oil saturation model was derived by combining many realizations. This average core oil saturation model was used as an input for the reservoir simulation. The only property left to generate is the fracture density, which is used to estimate the effective permeability required for the simulation.

Step 3 – Fracture Modeling of the Tensleep Reservoir In order to capture fracture effects at multiple scales and simultaneously integrate the available core, log, seismic, and well test data information, the continuous fracture modeling (CFM) technique was used to model the Tensleep fractures. The CFM approach does not focus on identifying the fractures themselves but rather on identifying the factors that control where fracturing occurs. It is common knowledge that lithology, structure, proximity to faults, and other geological factors control the location and intensity of fracturing. These factors, known as fracture drivers, can be identified using seismic data (available over the entire reservoir) as well as borehole data. These fracture drivers are then related to fracture indicators, demonstrating the existence of a fracture at a specific location in a well. The fracture indicators are derived from interpretation of core descriptions, image logs, production logs, et cetera. Once the relationship between fracture drivers and fracture indicators in the wells is established, the fracture drivers can be used to predict the location of fractures elsewhere in the reservoir. A neural network is used to find possible relationships between the fracture drivers and fracture indicators observed at the wellbore. The neural network approach first ranks all the fracture drivers according to how reliably they correlate with the fracture indicators. The modeler then reviews the results in light of what is known about the significance and robustness of each fracture driver, and how the ranking compares to what is understood about the physical distribution of fractures in the reservoir. The best-correlated fracture drivers that make sense geologically are then subjected to a training and validation process before being used to generate a suite of equi-probable fracture density realizations. These realizations can then be screened and calibrated to permeability-thickness (kh) data obtained from well tests if available. The resulting effective

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permeability model can then be exported for use in numerical simulation and development planning.

In the Tensleep reservoir, geophysical, geological, and geomechanical drivers, described above, were available for generating fracture models. The fracture indicator logs were generated from interpreted image logs at five wells, providing fracture density logs (using only open fractures) at these five wells (Figure 10). Fracture directions recorded in the image logs are not used as input in the CFM approach, and are kept for validation purposes. The computed fracture models provided a 3D distribution of the fracture density (Figure 11). Given the derived fracture models, the CFM workflow provides the fracture directions at each layer. Figure 12 shows the fracture density and direction in layers 10 and 15. In both layers, the fracture density model shows the NW-SE fracture direction observed in the image logs, and also reveals other fracture orientations observed in outcrop data10 but not captured in the image logs. The ability to compute fracture orientations not present in image logs is a key advantage of the CFM approach over other fracture modeling methods that depend solely on the image logs for the fracture directions. The fracture models were validated using two blind wells. In each test, the blind well is removed from the modeling, and the estimated fracture density at the blind well is extracted from the model and compared to the actual fracture indicator log. For both blind wells, the model was able to reasonably predict the low, medium, and high fracture density zones as shown in Figure 13. Step 4 – Reservoir Simulation The ultimate objective of this workflow is to derive dynamic models that are able to reproduce past individual well performances without the need for any time-consuming history matching. This provides an additional validation of the fracture models derived using the CFM approach. Using the reservoir parameter models generated in steps 2 and 3, both black oil and compositional reservoir simulations were run to verify that these models would produce a history match to the production data.

Black oil reservoir simulation of the Tensleep production

In a black oil simulator, where the two main fluid phases are oil and water, the complex effects related to the injection of CO2 are not considered. In the case of the Tensleep reservoir, CO2 injection did not yet occur and therefore the reservoir model could be validated with a black oil model. The aspects related to CO2 injection will be considered in the next section using a compositional simulator. The three major reservoir properties needed for reservoir simulation are permeability, porosity, and oil saturation. Given the presence of the fractures with high connectivity, the matrix permeability is expected to be enhanced. The degree of this permeability enhancement depends on the fracture density. Given the extensive fracturing present in the Tensleep formation, there is no evidence of a dual porosity or dual permeability medium, and the reservoir appears to behave as a single porosity medium with an enhanced effective permeability. The matrix porosity seems to provide the storage for the oil, which has a tilted oil-water contact. Water production rates indicate that the Tensleep formation has a strong aquifer, resulting in a pressure drop of less than 100 psi throughout the Tensleep Formation. Consequently, water drive is considered the primary producing mechanism in the reservoir. Initial water saturations (Swi) are between 12.5% to 22.1%, and residual oil saturations (Sro) are between 28.7% and 56.3%. Measurements and tests of the Tensleep Formation show that the initial reservoir pressure at a depth of 5400 ft is approximately 2350 psia at a temperature of 190 0F. Analysis of producing wells in the Tensleep Formation shows that all of the production occurs in section 10, near the crest of the structure. Oil producing wells exhibit a fairly good production history for the first 2 years, followed by a rapid breakthrough of water. The oil production is replaced by a high water production, which confirms a very high mobility of water in the reservoir. The dynamic model uses the derived matrix porosity and oil saturation as input in the reservoir simulator. The key reservoir property is the effective permeability, as the dynamic model uses a single porosity system. In this case, the reservoir permeability is simply the effective permeability computed as follows: K eff = K m + C · f Where

effK : Effective permeability of the combined matrix and fracture flow in millidarcies

mK : Matrix permeability in millidarcies

f : Fracture density [number of fractures / meter] C: Scaling factor to be estimated by history matching The matrix permeability and fracture density were estimated in the previous steps, and the scaling factor is estimated during

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the history matching process. The history matching process consists of finding three reservoir parameters that are unknown: 1) the scaling factor C, required to compute the effective permeability, 2) the relative permeability curves, and 3) the strength of the aquifer. After a few tests, these three unknowns were easily estimated and the individual well performances of all the wells were matched as shown in Figure 14. The dynamic model allowed a better understanding of the current fluids distribution, which in the case of Teapot shows that the strong aquifer is pushing the oil from the lower layer of the B sand to the top of the A sand as shown in Figure 15. This dynamic model validated the geologic and fracture models, and we can use it for various reservoir management strategies, including CO2 injection.

Compositional simulation of the Tensleep production and CO2 injection When planning a CO2 injection, a compositional simulator must be used to account for various fluid effects that cannot be accounted for in a black oil simulator. In compositional simulation many additional fluid data inputs are required. At Teapot Dome, the detailed fluid analysis of the oil is available at 2 wells. These valuable fluid analyses provided the necessary input for Equation of State (EOS) tuning to simulate the PVT experiments and the phase behavior. The fluid components were grouped into four pseudo-components (CO2, N1-C1, C2-C3, C4+) and after many trials a good fit of the laboratory experiments was derived. The geologic models remained the same as those used in the black oil model. The simulation of the CO2 injection is limited to section 10 where a Local Grid Refinement (LGR) is added around the injector (Figure 16). Prior to using the compositional simulator to test various CO2 injection scenarios, the first step is to ensure that the individual historical well performances are matched. The compositional simulation results in each well are compared with those of black oil model. A good match is seen in both models which validates both the geological model and the EOS for fluid behavior. Given the availability of a reliable geologic model that was validated with the black oil simulator, this verification took only one simulation run and produced a good match for all the wells. Using this validated geologic model and the appropriate fluid information, the testing of different CO2 injection scenarios will be examined. In this paper, we are limiting the CO2 injection scenarios to the study of the effect of the gas injection rate on the breakthrough time. We have selected to inject CO2 in one well (44-1-TpX-10) at the rates listed in Table 1 and observe the time it took for the CO2 to reach the other wells. We assumed that the faults bounding the area, where most of the wells are located, are sealing. The CO2 was injected at the top of the A sand since any injection in the lower A sand or B sand brings the injected gas very quickly to the top of the A sand through the pervasive fracture system. In the areal direction, the injected CO2 reached the wells 75-TpX-10 and 55-TpX-10 through a preferential flow path in the times indicated in Table 1.

Injection rate (Mscf/day) Breakthrough time at 75-TpX-10 Breakthrough time at 55-TpX-10

500 6 months 10 months 1,000 6 months 6 months 10,000 4 months 4 months 1,000,000 18 days 18 days

Table 1: Breakthrough time at wells 75-TpX-10 and 55-TpX-10 as a function of the injection rate at well 44-1-TpX-10 Given a reliable dynamic model, the evolution of CO2 saturation in the reservoir can be predicted prior to injection both in the areal (Figure 17) and vertical directions between any wells such as the injector 44-1-TpX-10 and the well 55-TpX-10 (Figure 18) for an injection rate of 10,000 Mscf/day. In the case shown in Figures 17-18, the gas breakthrough occurs after 4 months as shown in Table 1. To delay the CO2 breakthrough in these wells, a well control strategy could be used by shutting and re‐opening the producing wells after a certain time when the gas saturation decreases around the well. The time needed to keep the wells shut-in could be tested with the available model. When considering the injection rate of 10,000 MscfD, the model shows an oil recovery factor of 54%. With a robust model the effect of injection rates on the breakthrough time, or any other reservoir performance criterion, could be estimated prior to injection. Depending on the goal of the CO2 injection, the derived model could be used to optimize the injection rates, the choice of the injection well, or any other injection parameter. Better knowledge of the reservoir also helps to avoid surprises during the injection project12.

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Conclusions The technology and relevant workflows currently exist that can quantitatively integrate geophysical, geological, and engineering data to develop reservoir models for fractured reservoirs. The CFM methodology, as shown in this example from the fractured Tensleep reservoir at Teapot Dome, can successfully generate static and dynamic reservoir models that have predictive capability in this fractured reservoir. Given the necessary input data, application of this technique to fractured reservoirs should lead to more efficient field development and better planning of CO2 injection projects in these reservoirs. References 1. Boerner, S, Gray, D., Todorovic-Marinic, D. Zellou, A., Schnerck, G., “Employing Neural Networks to Integrate Seismic

and Other Data for the Prediction of Fracture Intensity:” paper SPE 84453 2. Christensen, S.A., Ebbe Dalgaard, T., Rosendal, A., Christensen, J.W., Robinson, G., Zellou, A., Royer, T, “Seismically

Driven Reservoir Characterization Using an Innovative Integrated Approach: Syd Arne Field:” paper SPE 103282. 3. Gauthier, B., Zellou, A., Toublanc, A., Garcia, F., Petit, J.M., “Integrated Fractured Reservoir Characterization: a Case

Study in a North Africa Field:” paper SPE 65118 4. Laribi, M., Boubaker, H, Beck, B., Chen, H.K, Amiri-Garroussi, K, Rassas, S, Rourou, A, Boufares, T, Douik, H., Saidi,

N., and Ouenes, A., “Integrated Fractured Reservoir Characterization and Simulation: Application to Sidi El Kilani Field, Tunisia.” Journal of Petroleum Technology, August 2004.

5. Ouenes, A., Robinson, G., Balogh, D., Zellou, A., Umbsaar, D., Jarraya, H., Boufares, T., Ayadi, L., Kacem, R., “Seismically Driven Characterization, Simulation and Underbalanced Drilling of Multiple Horizontal Boreholes in a Tight Fractured Quartzite Reservoir: Application to Sabria Field, Tunisia:” paper SPE 112853

6. Pinous, O., Sokolov E.P., Bahir, S.Y., Zellou, A., Robinson, G., Royer, T., Svikhnushin, N., Borisenok, D., Blank, A. “Application of an integrated approach for the characterization of a naturally fractured reservoir in the West Siberian basement (example of Maloichskoe Field):” paper SPE 102562

7. Ouenes, A., Robinson, G., Balogh, D., Zellou, A., Umbsaar D., Jarraya, H., Boufares, T., Ayadi, L, Kacem R.: “Seismically Driven Characterization, Simulation, and Underbalanced Drilling of Multiple Horizontal Boreholes in a Tight Fractured Quartzite Reservoir: Application so Sabria Field, Tunsia.,” paper SPE 112853.

8. Bejaoui, R. Ben Salem, R., Ayat H., Kooli, I., Balogh, D., Robinson, G., Royer, T., Boufares, T., Ouenes, A.:”Characterization and Simulation of a Complex Fractured Carbonate Field Offshore Tunisia,” paper SPE 128417.

9. Lorenz, J. “Summary of Published Information on Tensleep Fractures,” http://eori.uwyo.edu/downloads/Tensleep_FractureStudy1.doc

10. Cooper, S., Goodwin, L., Lorenz, J. “Fracture and fault patterns associated with basement-cored anticlines: The example of Teapot Dome, Wyoming,” AAPG Bulletin, V. 90, No 12 (December 2006)

11. Jenkins, C., Ouenes, A., Zellou, A., Wingard, J.: “Quantifying and predicting naturally fractured reservoir behavior with continuous fracture models, AAPG Bulletin, V. 93, No 11 (November 2009)

12. Ringrose P. et al.: “Plume Development around well KB-502 at the In Salah CO2 storage site,” First Break Volume 27, January 2009

Figure 1. Teapot Dome oil field and the Naval Petroleum Reserve #3 are located in Natrona County, Wyoming, USA.

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Figure 2. Stucture map on the Tensleep Formation, showing wells with sonic and density logs used in the study.

Figure 3. Stratigraphic column for the deeper units at Teapot Dome, including the Pennsylvanian Tensleep Formation - the focus of this study.

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Figure 4. Schematic workflow to produce the reservoir models for simulation, integrating data from various domains.

Figure 5. Representative line from the 3D survey, showing the location of the Tensleep Formation on the seismic data.

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Figure 6. Welltie cross-section showing the structural interpretation overlain on the colored inversion. Significant lateral impedance changes are visible in the A and B sands. Note the ability of the colored inversion volume to visually differentiate the various layers contained in the Tensleep reservoir.

Figure 7. Instantaneous phase (left) highlights lineaments possibly related to small-offset faulting in the NE-SW direction. Acoustic impedance (colored impedance in center and stochastic impedance on right) is used as both a reservoir quality indicator and as a fracture indicator; high fracture density can generally be correlated with thinner, higher impedance strata.

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Figure 8. Correlation of porosity thickness with impedance in the Tensleep (top) and a map displaying areas of porosity thickness greater than 2 m derived from impedance data (bottom).

Figure 9. 3D geocellular grid of the Tensleep reservoir.

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Figure 10. Fracture density derived from the image log data using only the open fractures. The mean fracture density in the B sand that results from the fracture modeling effort is shown to highlight the lateral variations.

Figure 11: Fence diagram showing the 3D distribution of the fracture density in the Tensleep reservoir.

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Figure 12. Anisotropy maps and discrete fracture displays at the top and middle of the B sand. The fracture density appears to increase downward in the B sand.

Figure 13. Extracted fracture density compared to actual fracture density in 2 blind wells. For both blind wells, the model was able to reasonably predict the low, medium, and high fracture density zones.

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Figure 14. History match at 6 wells of the water cut (blue) given the oil rate (green) used as input in the black oil reservoir simulator. The continuous lines are the simulated results and the stars are the field measurements. All the other 8 wells have a good match similar or better to those shown in this figure

Figure 15. Distribution of the oil saturation in layer 3 (top of A sand) in 1978 (top) and 2009 (bottom) illustrating the effect of the strong aquifer pushing the oil to the upper layers while depleting the lower layers.

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Figure 16: Portion of the Teapot Dome simulation grid representing Section 10 where CO2 injection is simulated. A Local Grid Refinement (LGR) is added around the proposed CO2 injector well.

Figure 17. Distribution of the CO2 Saturation over time in section 10. Notice the anisotropy created by the presence of the fractures.

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Figure 18. Distribution of the CO2 Saturation over time between wells 44-1-TpX-10 and 55-TpX-10. Notice the preferential path created by the fractures that leads to the gas breakthrough at 55-TpX-10.