Integrated Characterization of the Fracture Network in Fractured Shale Gas Reservoirs—Stochastic Fracture Modeling, Simulation and Assisted History Matching
- Yonghui Wu (China University of Petroleum-Beijing, and Texas A&M University) | Linsong Cheng (China University of Petroleum-Beijing) | John E. Killough (Texas A&M University) | Shijun Huang (China University of Petroleum-Beijing) | Sidong Fang (Sinopec Exploration and Production Research Institute) | Pin Jia (China University of Petroleum-Beijing) | Renyi Cao (China University of Petroleum-Beijing) | Yongchao Xue (China University of Petroleum-Beijing)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 30 September - 2 October, Calgary, Alberta, Canada
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Ensemble Kalman Filter, Edge-based Green Element Method, Discrete Fracture Model, Integrated Workflow, Fracture Network Modeling
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- 387 since 2007
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The large uncertainty in fracture characterization for shale gas reservoirs seriously affects the confidence in making forecasts, fracturing design, and taking recovery enhancement measures. This paper presents a workflow to characterize the complex fracture networks (CFNs) and reduce the uncertainty by integrating stochastic CFNs modeling constrained by core and microseismic data, reservoir simulation using a novel edge-based Green element method (eGEM), and assisted history matching based on Ensemble Kalman Filter (EnKF).
In this paper, the geometry of CFNs is generated stochastically constrained by the measurements of hydraulic fracturing treatment, core, and microseismic data. A stochastic parameterization model is used to generate an ensemble of initial realizations of the stress-dependent fracture conductivities of CFNs. To make the eGEM practicable for reservoir simulation, a steady-state fundamental solution is applied to the integral equation, and the technique of local grid refinement (LGR) is applied to refine the domain grids near the fractures. Finally, assisted-history-matching based on EnKF is implemented to calibrate the DFN models and further quantify the uncertainties in the fracture characterization.
The proposed technique is tested using a multi-stage fractured horizontal well from a shale gas field. After analyzing the history matching results, the proposed integrated workflow is shown to be efficient in characterizing fracture networks and reducing the uncertainties. The advantages are exhibited in several aspects. First, the eGEM-based Discrete-Fracture Model (DFM) is shown to be quite efficient in assisted history matching of large field applications because of eGEM’s high precision with coarse grids. This enables simulations of CFNs without upscaling the fractures using continuum approaches. In addition, CFNs geometry can be generated with the constraints of core and microseismic data, and a primary conductivity of CFNs can be generated using the hydraulic fracturing treatment data. Moreover, the uncertainties for CFNs characterization and EUR predictions can be further reduced with the application of EnKF in assimilating the production data.
This paper provides an efficient integrated workflow to characterize the fracture networks in fractured unconventional reservoirs. This workflow, which incorporated several efficient techniques including fracture network modeling, simulation and calibration, can be readily used in field applications. In addition, various data sources could be assimilated in this workflow to reduce the uncertainty in fracture characterization, including hydraulic fracturing treatment, core, microseismic and production data.
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