History Matching of Stimulated Reservoir Volume of Shale-Gas Reservoirs Using an Iterative Ensemble Smoother

被引:16
|
作者
Chang, Haibin [1 ]
Zhang, Dongxiao [2 ]
机构
[1] Peking Univ, Coll Engn, Dept Energy & Resources Engn, Beijing, Peoples R China
[2] Peking Univ, Coll Engn, Beijing, Peoples R China
来源
SPE JOURNAL | 2018年 / 23卷 / 02期
基金
中国国家自然科学基金;
关键词
FACIES DISTRIBUTION; KALMAN FILTER; MODEL; PREDICTION; FRACTURE; ENKF;
D O I
10.2118/189436-PA
中图分类号
TE [石油、天然气工业];
学科分类号
0820 ;
摘要
Economic production from shale-gas reservoirs typically relies on the drilling of horizontal wells and hydraulic fracturing in multiple stages. In addition to the creation of hydraulic fractures, hydraulic-fracturing treatment can also reopen existing natural fractures, which can create a complex-fracture network. The area that is covered by the fracture network is usually termed the stimulated reservoir volume (SRV), and the spatial extent and properties of the SRV are crucial for shale-gas-production behavior. In this work, we propose a method for history matching of the SRV of shale-gas reservoirs using production data. For each hydraulic-fracturing stage, the fracture network is parameterized with one major fracture of the hydraulic fractures and the SRV that represents minor hydraulic fractures and reopened natural fractures. The major fracture is modeled explicitly, whereas the SRV is modeled by the dual-permeability/dual-porosity (DP/DP) model. Moreover, the spatial extent of the SRV is parameterized by the level-set-function values on a predefined representing-node system. After parameterization, an iterative ensemble smoother is used to perform history matching. Both single-stage-fracturing cases and multistage-fracturing cases are set up to test the performance of the proposed method. Numerical results demonstrate that by use of the proposed method, the SRV can be well-recognized by assimilating production data.
引用
收藏
页码:346 / 366
页数:21
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