Numerical Simulation and Multiple Realizations for Sensitivity Study of Shale Gas Reservoir

Transcription

1 SPE Numerical Simulation and Multiple Realizations for Sensitivity Study of Shale Gas Reservoir A.Kalantari-Dahaghi, S.D.Mohaghegh,SPE, Petroleum Engineering and Analytic Research Laboratory(PEARL) West Virginia University Schlumberger Private SPE 2011 Production and Operation Symposium Oklahoma,March 27-29

2 Outline Objective Introduction Approach Result and Discussion Schlumberger Private

3 Objective Integrated workflow to capturing the essential characteristics of shale gas reservoirs Development of Single well Shale Surrogate reservoir model (S 3 )

4 Introduction Shale gas in the United States went almost instantly from a practically invisible resource to massive reserves that challenge the largest conventional gas accumulations in the world. Unconventional gas reservoirs are characterized by: complex geological petrophysical systems Horizontal drilling and multi-stage stimulation technologies are driving the successful development of shale plays.

5 Shale Gas Field Development Typical Characteristics Nano-Darcy Matrix Permeability Complex Fracture Network Distribution (natural and induced fractures) Adsorbed gas in organic materials Development Challenges Well spacing and total well count Length of lateral segment and landing point Completion and stimulation strategy Quantifying producible volumes Understanding local and regional stresses (Krishna et al.2010)

6 Shale Gas Field Development 3D Seismic Heterogeneity Lateral length Well count Stresses Dynamic simulation Completions & Stimulation (Krishna et al.2010)

7 Simulating a Shale Gas Reservoir Why? Frac Stage Spacing & Well Length Well Spacing Estimated Ultimate Recovery (EUR) Reserves Estimation Modeling the adsorbed gas content (Krishna et al.2010) Reservoir simulators simulates the physics of shale gas reservoirs: Multi-porosity system (Micro porosity, fractures and organic content) Accounts for transient flow in matrix Langmuir adsorption isotherms Inter-block non-darcy flow and flow dependent skin

8 (Song 2010)

9 Methodology

10 The Well/Field data Gas Composition: 85.1, 3.12, 0.25, 0.1, 0.2, respectively (mol %) C1, C2, C3, C4-6, CO 2,and N2 Reservoir temperature:85(deg F) Initial reservoir pressure:780 psi Number of grid cells:139*42*5 Grid size: 50*50 ft Net pay thickness:100ft

11 Results and Discussions Natural fracture modeling and up scaling DFN based on 80 acre spacing Two fracture sets Complex DFNs were up-scaled using both Oda and flow-based methods Fracture sets Distribution Geometry Orientation Fracture Set Fracture area/vol Sides Elongation Ratio Length Shape Scale Mean Dip Mean Dip Azimuth Concentratio n Power Power Fracture aperture was less than 10 micrometer or even less than 5 micrometer based on core analysis. Fracture Aperture

12 Fracture permeability Fracture porosity Sigma

13 Hydraulic fracture design and modeling Hydraulic fractures and logarithmic local grid refinement around them with global grid.

14 Sensitivity Analysis Matrix Discretization In traditional dual porosity models: Matrix to fracture flow is in steady state, Matrix cell can be regarded as a single cell. In shale gas reservoirs: The flow is not instantaneous requires matrix subdivision to capture transient nature of the matrix to fracture flow.

15 Hydraulic fracture length Matrix permeability Matrix porosity Fracture permeability Fracture porosity Sigma Fracture half length Hydraulic frac. height Hydraulic frac. spacing Hydraulic frac. conductivity Number of Matrix sub grids Non-Darcy coefficient Rock compaction Diffusion coefficient Poisson ratio Young s modulus Max. Gas content Sorption type Hydraulic fracture spacing

16 Single wells Shale Surrogate Reservoir Model (S 3 ) development 39 different simulation runs -13 shale reservoir properties(design parameters)- 21 scenarios Intelligent data partitioning has been performed 70%, training 15% calibration 15 % initial verification 0 and 1 for instant and time dependent sorption Included Cum (t-1) and Cum (t-2) in the training Kf/Km contrast between two permeabilities. Used hydraulic fracture height to net pay thickness ratio Log( input, output)

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18 Run # Cumulative(mscf) Real SRM Time(month) SRM prediction-initial verification SRM predication (blind data set) (Changing only 1 input parameter) Final verification SRM predication (blind data set) (Changing 6 input parameters simultaneously) Final verification

19 Conclusion An integrated workflow, which demonstrates a quantitative platform for shale gas production optimization through capturing the essential characteristics of shale gas reservoirs, using Petrel-Eclipse has been proposed. Comprehensive sensitivity study has been performed on all parameters, which play role on production from shale gas reservoirs The ultimate objective of this study is developing an AI-assisted history matching tool for shale formation with multiple stages of hydraulic fracture for the first time. Shale surrogate reservoir model has been developed successfully and it has been validated by two steps verification even though it has some limitations which will be covered in near future. The SRM could predict production profile for 15 years with high accuracy so quick

20 Acknowledgment Special thanks to: ISI Schlumberger