100% Matrix 100% Fracture 100% Matrix 100% Fracture Matrix Dominated Fracture Dominated % of Total Porosity % of Total Permeability IV III II I After Nelson.

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100% Matrix 100% Fracture 100% Matrix 100% Fracture Matrix Dominated Fracture Dominated % of Total Porosity % of Total Permeability IV III II I After Nelson (2001) Fractured Reservoir Simulation Milind Deo and Craig Forster University of Utah

Account for Irregular Geometries Hypothetical ‘Real System’ Regularized Equivalent ECLIPSE Equivalent Common Model Properties Impermeable matrix with  = 0 (Type I, basement reservoir system) Domain = 1,000 ft by 1,000 ft by 200 feet deep Total feature length = 30,000 feet Reference Case: Feature k = 1,000 md,  = 14 %, width = 0.5 feet OOIP = 53,580 STB Injection Pressure = 4,300 psi Injection Well Production Well

Time (days) Oil Production (STB/day) Primary Production Hypothetical ‘Real System’ CVFE Regularized Equivalent CVFE ECLIPSE Equivalent Model Comparison at 900 days ‘Real System’ CVFE ECLIPSE Regularized Equiv. CVFE SoSo

Time (days) Oil Production (STB/day) Primary Production Parameter Sensitivity (900 days) 1000 md Base Case SoSo GM 100 md 50:50 GM 100 md 33:33: md Base Case Geom. Mean 100 md 50:50 Random k Geom. Mean 100 md 33:33:33 Random k

Time (days) Oil Production (STB/day) Primary Production Parameter Sensitivity (900 days) 1000 md Base Case SoSo 1000 md Base Case 100 md Case 10 md Case Case 10md Case 100md

Different Angle Domain (900 days) Base CaseDifferent Angle Case Oil Production (STB/day) SoSo Base Case Different Angle Case Time (days) Primary Production

Different Height Domain (900 days) Base Case Oil Production (STB/day) SoSo Base Case Half Top Case Time (days) Half-bottom Case Half-top Case Half Bottom Case Primary Production

Sandstone Faulted-Fractured Reservoir System Fault Increasing Fractures Increasing Fractures Upper Reservoir Lower Reservoir Shale k (md)  (%) ft 1000 ft kh (md-ft) Fault thickness = 0.3 ft Production Wells Secondary Injection Wells (production wells during primary prod’n)

20 days Primary Production SoSo 600 days Primary Production 3000 days Water Flood Start after 600 days 6000 days Water Flood Start after 600 days Viewed From Bottom of Model Domain Faulted-Fractured Reservoir Production Well Injection Well

SoSo Production Well Injection Well Fault Lower Res. Primary Production Water Cut (vol/vol) Time ( days ) Primary Production Oil Production Rate (STB/day) Time ( days ) Production From Primary Production 20 days Impact of Fault k Primary Production 600 days Secondary Production after 600 days 3000 days Secondary Production after 600 days 6000 days Fault k = 100 mdFault k = 10,000 md

1800 feet 200 feet 1m Joint Zone High k Features Outcrop-to-Simulation Production Primary 600 days Production BHP 2200 psi Injection BHP 3200 psi Line Drive 2 Line Drive 1 k (md)  (%) kh (md-ft)

Line Drive 1 (N to S) Line Drive 2 (W to E) 600 days Primary Production Oil Production Rate (STB/day) Time (days) Gas Oil Ratio Time (days) 2100 View From Bottom of Model Domain Well Placement Strategies Line Drive 1 Line Drive 2 Line Drive 1 Line Drive days 2000 days 4000 days 6000 days SoSo

Different Height Fracture Set Case Study Time (days) Oil Production Rate (STB/day) Base Case1000 md ("real-case") Half-bottom fracture set Half-top fracture set Half-top fracture set CST OOIP Half-bottom fracture set CST OOIP

Different Height Fracture Set Case Study Time (days) Oil Production Rate (STB/day) Base Case1000 md ("real-case") Half-bottom fracture set Half-top fracture set Half-top fracture set CST OOIP Half-bottom fracture set CST OOIP

Water Cut Time (days) Water Cut Test01 Test02 Test03 Test04 Center Producer w/o injection

Status and Challenges Preserve geologic integrity while constructing simulation models Relationship between discrete-fracture models and dual-porosity models –DFN as upscaling/calibration tool for DP models? –Field-wide DFN models in the near future? Fundamental work –Additional physics? –Upscaling –Integrating geomechanics –Dynamic data updaing –Different discrete-fracture implementations –Discretization schemes –Gridding –Efficiency of solvers –High-performance (parallel) computing

Acknowledgements  U.S. DOE Contract DE-FC26-04NT15531 through the National Energy Technology Laboratory.  Schlumberger Inc. – Eclipse academic license  Sandia National Laboratories – CUBIT license  Argonne National Laboratory – PETSc  Our eam –Jim Evans, Professor, Utah State University, Logan, Utah –Tom Doe, Golder and Associates –Yi-kun Yang, Post-doc –Sriram Balasubramaniam, Graduate student –Ganesh Balasubramaniam, Graduate student –Yao Fu, Graduete student –Kan Huang, Graduate student –Zhiqiang Gu, Graduate student –Huabing Wang, Graduete student