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Integration of Shale-Gas-Production Data and Microseismic for Fracture and Reservoir Properties With the Fast Marching Method
- Jiang Xie (Texas A&M University) | Changdong Yang (Texas A&M University) | Neha Gupta (Texas A&M University) | Michael J. King (Texas A&M University) | Akhil Datta-Gupta (Texas A&M University)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- July 2014
- Document Type
- Journal Paper
- 2014.Society of Petroleum Engineers
- 6.5.8 History Matching, 6 Reservoir Description and Dynamics, 6.5.6 Dynamic Model Update Algorithms, 6.9 Unconventional Hydrocarbon Recovery, 6.5 Reservoir Simulation, 6.9.2 Shale Gas
- Microseismic data , Unconventional reservoirs , Fast Marching Method, History Matching
- 37 in the last 30 days
- 313 since 2007
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We present a novel approach to calculate drainage volume and well performance in shale gas reservoirs by use of the fast marching method (FMM) combined with a geometric pressure approximation. Our approach can fully account for complex fracture-network geometries associated with multistage hydraulic fractures and their impact on the well pressure and rates. The major advantages of our proposed approach are its simplicity, intuitive appeal, and computational efficiency. For example, we can compute and visualize the time evolution of the well-drainage volume for multimillion-cell geologic models in seconds without resorting to reservoir simulation. A geometric approximation of the drainage volume is then used to compute the well rates and the reservoir pressure. The speed and versatility of our proposed approach make it ideally suited for parameter estimation by means of the inverse modeling of shale-gas performance data. We use experimental design to perform the sensitivity analysis to identify the "heavy hitters" and a genetic algorithm (GA) to calibrate the relevant fracture and matrix parameters in shale-gas reservoirs by history matching of production data. In addition to the production data, microseismic information is used to help us constrain the fracture extent and orientation and to estimate the stimulated reservoir volume (SRV). The proposed approach is applied to a fractured shale-gas well. The results clearly show reduced ranges in the estimated fracture parameters and SRV, leading to improved forecasting and reserve estimation.
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