SPE DISTINGUISHED LECTURER SERIES is funded principally through a grant of the SPE FOUNDATION

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1 SPE DISTINGUISHED LECTURER SERIES is funded principally through a grant of the SPE FOUNDATION The Society gratefully acknowledges those companies that support the program by allowing their professionals to participate as Lecturers. And special thanks to The American Institute of Mining, Metallurgical, and Petroleum Engineers (AIME) for their contribution to the program.

2 Integrated Reservoir Modeling Challenges and Solutions Mohan Kelkar The University of Tulsa

3 Background Approach Outline Hierarchical Descriptions Dynamic Data Integration Ranking Upscaling History matching of multiple descriptions Uncertainty representation Future Challenges Conclusions

4 Background What is integrated reservoir modeling? Integration of various qualities and quantities of data to generate inter well reservoir properties of interest so that uncertainties in future reservoir performance can be predicted.

5 Background What are the challenges? Scale and resolution of input and output Size of geomodel vs. size of simulation model Quantification of Uncertainties Solutions of inverse problem, especially during history matching of production data

6 Background Drawbacks related to Conventional History Matching Geological and geophysical uncertainties Uncertainties in future performance. The relationship between scale and uncertainty. Drawbacks related to Automatic History Matching Computationally intensive Customization to appropriate input parameters Objective Function Initial Guess Dependent

7 Approach Work Flow Structural Modeling ell Logs Generation (Rock Type, Perm) Spatial Modeling Hierarchical Realizations Property Modeling Seismic Porosity Integration Limited Dynamic Data Integration Fluid in Place Calculations Ranking of Realizations 3 Selected Realizations Upscaling Of Prop. Se H Ma

8 Topics of Concentration Hierarchical Descriptions Well Test Integration Upscaling using dynamic characteristics Objective history matching Future uncertainties representation

9 Hierarchical Multiple Realizations Rank the uncertain parameters from the largest to the smallest scale Discretize the range of uncertainties if possible Use fewer number of realizations for small scale uncertainties Limit the potential number of realizations to less than hundred Use methods such as experimental design to efficiently sample the range of uncertainties in input parameters

10 Limited Dynamic Data Integration Well Test Data Adjustment of fine scale permeabilities through adjustment factors accounting for fractures, multi-phase and scale PLT (Production Log Testing) Data Vertical adjustment to account for flow Determination of fracture conductivity

11 Matching Permeability Procedure Well Test Simulated Fine Scale Permeability Distribution Re Stop Yes Radia Upscal KH - Well Test Match? No KH Sim Fracture Enhanced Permeability

12 Alteration without Fractures Calculate the upscaled value of kh from fine scale description Calculate the ratio of (kh)( well test to (kh)( upscale. Interpolate the ratio across the field using kriging or similar technique Adjust the fine scale permeability value accordingly

13 Matching Permeability Background Enhancement Definition : Enhancement required to match well test when there is no fracture. Physical Interpretation : Enhancement required due to micro fracture/fissures which are not captured by seismic curvature analysis

14 Matching Permeability Log (EF) vs Fracture Density Background Enhancement

15 Permeability before and after enhancement Layer 35 Not-enhanced Layer 35 Enhanced

16 Permeability before and after enhancement Layer 35 Enhanced Layer 35 Enhanced

17 Permeability Anisotropy

18 Permeability Anisotropy Assume that permeability in the direction of fractures is maximum permeability and the one perpendicular to that is the minimum permeability. Minimum value is the base value. The enhanced permeability is calculated as: Based on tensor relationship

19 Dynamic Ranking Use the information from all the realizations Use different methods to rank realizations Permeability connectivity Streamline simulation Finite difference simplified simulation Use observed parameter of interest Select three to five realizations for history matching

20 Dynamic Ranking 1.30 Realization 4 Normalized Sweep Realization 15 Realization Normalized STOIIP

21 Upscaling Vertical Upscaling Optimization - Procedure Fine Scaled Model Fine Scaled Flow Behavior Upscaling Technique Streamline Simulator Select Prev. Level No Similar? Upscaled Model Upscaled Flow Behavior Further Upscaling

22 Upscaling Scenarios Coarsen the geo-cellular grids while preserving the necessary level of heterogeneity Use streamline simulator to calculate the sweep efficiency of each vertical layer Combine vertical layers having similar displacements Test the vertical upscaling scenarios with Streamline simulator Sweep efficiency of each vertical layer of the upscaled model should be close to the sweep efficiency of the fine scale model. Fine tune the scenarios if needed

23 Optimum Upscaling Level Fine Scale (93 layers) Coarse Scale (66 layers)

24 Optimum Upscaling Level Fine Scale (93 layers) Coarse Scale (50 layers)

25 Optimum Upscaling Level Fine Scale (93 layers) Coarse Scale (30 layers)

26 Optimum Upscaling Level Fine Scale (93 layers) Coarse Scale (20 layers)

27 Upscaling Optimization 246 Layers 100 Layers 75 Layers 55 Layers 46 Layers 31 Layers Sweep Efficiency, % Layer Number

28 Rock Type Upscaling 246 Layers 57 Layers

29 Porosity Upscaling 246 Layers 57 Layers

30 Permeability Upscaling 246 Layers 57 Layers (Kx)

31 Compare Well Logs Before and After Upscaling Optimum Level Fine Scale

32 History Matching Define objective standards for history matching Vary dynamic parameters within the range of uncertainty Explore the impact of input parameters on observed performance Simultaneously history match multiple realizations

33 Field Example 1 Carbonate, naturally fractured reservoir Influence of water influx as well as injected water Approximately 55 well strings Parameters adjusted: Relative permeability parameters Aquifer strength Local permeabilities at three wells

34 Field Study History Match 95% 59% 38% Well Testing History Matching Static Model Stage 2 Stage 1 Stage 3 Original Model Calibrated Model Final Model

35 Stage-1 (38%) Field-wide Match Cum.Oil Oil Rate Pressure Water Cut Stage-2 (59%) Stage-3 (95%) Cum.Oil Pressure Cum.Oil Pressure Oil Rate Water Cut Oil Rate Water Cut

36 Stage-1 (38%) Cum.Oil Reservoir Pressure Jan 1983 Jan 2004 Oil Rate Water Cut

37 Stage-2 (59%) Cum.Oil Reservoir Pressure Jan 1983 Jan 2004 Oil Rate Water Cut

38 Stage-3 (95%) Cum.Oil Reservoir Pressure Jan 1983 Jan 2004 Oil Rate Water Cut

39 Field Study History Match (cont d) Middle Zone Matrix Well BHFP Simulation RFT BHCIP/PBU Oil Rate U1 U2 U3 Model 96 % Water Cut PLT 100 %

40 Field Study History Match (cont d) Upper Zone Fracture Well BHFP BHCIP/PBU Oil Rate Water Cut Cut

41 Blind Tests : Field Study History Match (cont d) 7 Newly Drilled Wells 3 Rehorizontalized Wells 4 New Horizontal/High Deviated Wells 17 Pressure Observer Well Strings 10-months Extended Production Data Results : 6 out of 7 well production was successfully simulated 15 out 17 pressure observation wells were matched Excellent Field-wide performance during the extended period

42 Field Study Blind Test at Rehorizontalized Well BHP BHCIP/PBU Oil Rate Water Cut

43 Field Study Blind Test at New Fractured Well Fracture Realization: 1000 m BHFP BHFP BHCIP/PBU BHCIP/PBU New Well Oil Rate Oil Rate Water Cut Water Cut Well-43

44 Field Study Saturation Comparison at New Wells 2002/2 003 OH L og SW Match Map H 9 OWC 4055 J E F R 2+R3 8 B

45 Field Study Pressure Match at Observer Well Simulation RFT BHCIP

46 Field Study Blind Test at the Extended Production Time Period Cum. Oil Cum. Oil Reservoir Pressure Pressure Oil Rate Water Cut Cut

47 Field Example 2 Highly faulted sandstone reservoir (over 100 faults) Large uncertainty with respect to permeability values More than 110 wells producers and injectors Weak aquifer drive high water cut in many wells Parameters adjusted: Aquifer strength Relative permeability parameters Capillary pressure curves Fault transmissibilities

48 History Matching Field Level FLPR (stb/day) pessimistic likely optimistic historical Time, days FGOR (mscf/stb) Time, days

49 History Matching Field Level FOPR (stb/day) Time, days pessimistic likely optimistic historical 0.8 FWCT Time, days

50 History Matching Group Level GGPR mmscf/day Bloque#4 pessimistic likely optimistic historical Bloque#5 Time, days GGPR mmscf/day Time, days

51 History Matching Group Level 4 Bloque#4 pessimistic likely optimistic historical GOPR mstb/day 2 GOPR mstb/day Bloque#5 Time, days Time, days

52 History Matching Group Level 12 8 Bloque#4 pessimistic likely optimistic historical GWPR mstb/day Bloque#5 Time, days GWPR mstb/day Time, days

53 History Matching Well Level 400 W31 pessimistic likely optimistic historical WOPR stb/day W31 Time, days WWPR stb/day Time, days

54 History Matching Well Level WOPR stb/day W90 pessimistic likely optimistic historical W90 Time, days WWPR stb/day Time, days

55 History Matching Well Level 1200 W WOPR stb/day 400 WWPR stb/day W25 Time, days pessimistic likely optimistic historical Time, days

56 Future Challenges Right Scaling of Reservoir Model Generate reservoir description consistent with resolution of production data Generate reservoir description consistent with the flow process in the future Prioritize Observations Some observations are more important than others Large perturbations have more information content than small perturbations Eliminating large amount of observations prior to history matching will make the process cleaner and easier

57 Future Challenges Uncertainty Quantification in Future Performance Fit for purpose uncertainty quantification Quantification during the exploration phase Use of uncertainties prediction in future reservoir management

58 Conclusions A practical work flow allows an efficient history matching of multiple reservoir descriptions Partial integration of dynamic data makes the history matching more efficient Uncertainties in future performance can be quantified through multiple reservoir descriptions

59 Maintain Local Consistency among Attributes