Example 3 part_7_case_studies 9-10(oil field)

Case Studies: Marathon Oil
Case Study: Statfjord Formation in the Statfjord Field
Case Study: Major Arabian Carbonate
Stochastic Modeling of Surfaces

1. Case Studies Case Studies: Marathon Oil Case Study: Statfjord Formation in the Statfjord Field Case Study: Major Arabian Carbonate Stochastic Modeling of Surfaces

2. Case Study: 3-D Reservoir Characterization for Improved Reservoir Management SPE 37699 M J. Uland, S. W. Tinker, D. H. Caldwell, Marathon Oil

3. Permeability Cross-Section North Brae Field Permeability Cross-Section using the 2D maps from the original 13-layer Simulation Model.

4. Net-to-Gross Maps North Brae Field Three of the original 13-layer model net-to-gross 2D maps used as aerial templates for both the deterministic and stochastic 3D models.

5. Permeability Cross-Section North Brae Field Permeability cross-section for the 140 layer deterministic 3D model. Note the increased reservoir heterogeneity as compared to the homogeneous 13 layer simulation model.

6. Permeability Cross-Section North Brae Field Permeability cross-section for the 120 layer stochastic 3D model. Note the difference in the permeability distribution between this model and the deterministic 140 layer model.

7. Permeability Cross-Section North Brae Field Permeability cross-section for the 27 layer simulation 3D model that was upscaled from the 120 layer geostatistical model.

8. Net Pay Map and Model Lawrence Field Original 2D waterflood netpay map showing one continuous grainstone reservoir Stratigraphic 3D model showing individual grainstone bars that had different waterflood responses

9. Connected Geobodies Lawrence Field Connected geobodies from the 3D model. The small red colored geobodies represent infill drilling targets

10. Production Results Lawrence Field The 20-acre infill drilling results are shown in green. In yellow is the base production from the 40-acre waterflood.

11. Simulation Models Anonymous Field The original 5-layer simulation model using 2D maps Upscaled porosity for the 21 flow-units used in the secondary recovery 3D model

12. Geobody Analysis Anonymous Field Geobody analysis from the 3D model indicates that a minimum of 20 flow-units would be needed to capture the higher permeability intervals for use in a secondary recovery simulation model

13. Flow Unit Cross-Sections Anonymous Field Fine-layer porosity within the simulation flow units Upscaled flow-unit permeability using a porosity-to-permeability transform. Superimposed on the permeability flow-units are the vertical transmissibility grids (shown in red) at the interface of each flow unit

14. Cross-Sections Yates Field Cross sections showing stratigraphic framework used to construct the 3D geologic model (top) and the porosity distribution within the 3D model. Stratigraphic grids and lithofacies regions are superimposed on bottom right section

15. Fence Diagram of Permeability Yates Field Fence diagram of permeability. Permeability was calculated in every cell as a function of porosity, lithology, pore type, texture and calcite cement. White boxes indicate actual permeability from core analysis

16. Structural Cross-Section Yates Field Structural cross section showing porosity distribution in upper figure with well control (vertical white lines). Porosity from the stratigraphic model was extracted and used to populate a 3D elevation slice model composed of 140 five-foot thick layers. The figure on the right is porosity from the elevations slice model. Note how the porosity structure is preserved.

17. Turbidite Lobe GeoBodies Ewing Bank 873 Field Pre-development wells. Five turbidite lobes based on seismic and well control that were used to constrain the reservoir porosity distribution in the initial 3D model (left) Post-development wells. Eight turbidite lobes based on seismic and well control that were used to constrain the reservoir porosity distribution in the current 3D model (right)

18. Porosity Distribution Ewing Bank 873 Field Porosity distribution for the current 3D model using the 8 turbidite lobes as constraints

19. Case Study: Stochastic Modeling of Incised Valley Geometries Statfjord Field AAPG Bulletin V 82. No 6 (June 1998) A. C. MacDonald, L. M. Falt, A Hekton

20. Conceptual Framework for Bounding Surfaces Conceptual Framework for bounding surface development driven by cyclic base-level fluctuations. 1, base-level fall leads to the development of a regional erosion surface with incised valleys, sequence boundary(SB1). 2, low rates of base-level rise/aggradation and confinement of rivers within the valley produce a sand-rich valley fill that can be capped by a significant base-level rise or flooding surface (FS1). 3, higher rates of base-level rise/aggradation and a wide, nonconfined alluvial plain leads to the preservation of isolated channels within mudstone-rich overbank deposits. 4, renewed base-level fall causes the development of the next regional erosion surface (SB2)

21. Sequence Boundaries Composed of Incised Valleys, Terraces and Interfluve Sequence boundaries are composed of incised regions (valleys) and flatter regions (terraces and interfluves). Significant flooding surfaces can occur within the valley (FS1), at the top of the valley (FS2), or within the nonconfined alluvial plain (FS3)

22. Stochastic realizations of Sequence Boundaries Realizations of 2D gaussian functions in map view and in cross section. The two surfaces are simulated with identical parameters (and random seed numbers), except that realization (1) uses and exponential variogram and realization (2) uses a gaussian variogram. Note the anisotropy that is oriented 45 degrees with respect to the x-axis. Scale is in meters

23. Parameterization of Valley Geometry These figures illustrate the various steps involved in describing a single valley associated with a single sequence boundary

24. Well Control and Sequence Stratigraphic Correlations

25. Flooding Surface (FS4) Map View and Cross Section Realizations

26. Sequence Boundary Realizations Realizations of sequence boundary 5 in map and cross-section. The average depth map (lower right) is based on 100 simulations.

27. Cross-Sections through two 3D Realizations of Reservoir Stratigraphy Gamma ray logs are at well locations. Sandstone-rich valley-fill units (VF1-5) are in reds, yellows and greens; mudstone-rich units (HS0-4) are in blues and purples

28. Stochastic Realizations of 3D Model 3D reservoir architecture of realizations 58 and 86. The valley fills are illustrated consecutively from the base and upward. The thickness of each new valley fill is illustrated with rainbow colors where the reds and yellow illustrate areas with relatively thick valley fills, and blues and illustrate relatively thin valley file and interfluve/terrace areas. Well data:yellow reservoir sandstone; purple - mudstone rich barriers

29. Statfjord Field Study Results The simulated geometry's provided an improved description of reservoir distribution, connectivity and barrier distribution The improved reservoir description provided a better basis for predicting reservoir performance and for designing well locations in complex fluvial reservoirs Uncertainty in the reservoir architecture was accounted for by generating multiple realizations

30. Statfjord Field Study One Final Comment “The main drawback to developing flexible, realistic models is that the number of parameters that need to be estimated increases dramatically. The danger is that overestimating these parameters will become overly tedious. Although there is clearly a trade-off, this problem cannot be avoided totally, thus, geologists must equip themselves with the analog data and develop appropriate procedures to simplify the complex parameter estimation”

31. Integrated Reservoir Modelling of a Major Arabian Carbonate Reservoir SPE 29869 J.P. Benkendorfer, C.V Deutsch, P.D LaCroix, L.H. Landis, Y.A Al-Askar, A. A. Al-AbdulKarim, J. Cole

32. Major Arabian Carbonate Reservoir Oil production from wells on a one-kilometer spacing with flank water injection. There has been significant production and injection during the last 20 years This has had rapid and erratic water movement uncharacteristic of the rest of the field and reason for building a new geological and flow simulation models

33. Modeling Process Novel aspect was modeling permeability as the sum of a matrix permeability and a large-scale permeability fractures vuggy and leached zones bias due to core recovery Typical modeling procedure that could be applied to other carbonates and to clastic reservoirs

34. Indicator Simulation of Lithology Presence / absence of limestone / dolomite was modeled with indicator simulation on a by-layer basis

35. Gaussian Simulation of Porosity Variogram model for porosity in limestone: Variogram model for porosity in dolomite:

36. Gaussian Simulation of Porosity Porosity models for limestone and dolomite were built on a by-layer basis then put together according to the layer and lithology template

37. Indicator Simulation of Matrix Permeability

38. Gaussian Simulation of Large-Scale Permeability Matrix permeability at each well location yields a K•h matrix Well test-derived permeability at each well location yields a K•total Subtraction yields a K•h large Vertical distribution of K•h large scale on a foot-by-foot basis is done by considering multiple CFM data

39. Gaussian Simulation of Large-Scale Permeability Large-scale permeability models were built on a by-layer basis with SGSIM Matrix permeability and large-scale permeability models were added together to yield a geological model of permeability A calibrated power average was considered to scale the geological model to the resolution for flow simulation

40. Flow Simulation: First History Match

41. Flow Simulation: Fourth History Match

42. Stochastic Modeling of Surfaces

43. Stochastic Modeling of Surfaces To assess uncertainty in pore volume or reservoir performance predictions requires adding uncertainty to the gridded surface elevations. Characteristics of the uncertainty essentially zero at the well locations varies smoothly away from the wells variance depends on the quality of the seismic and the distance from the wells Uncertainty at wells is 0 Uncertainty increases away from wells