期刊
JOURNAL OF UNCONVENTIONAL OIL AND GAS RESOURCES
卷 9, 期 -, 页码 1-17出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.juogr.2014.09.001
关键词
Shale gas reservoir; Hydraulic fracture optimization; Horizontal wellbore placement; Covariance matrix adaptation evolution; strategy; Simultaneous perturbation stochastic; approximation algorithm; Genetic Algorithm
资金
- Crisman Institute at Texas AM University
Rapid advances in horizontal well drilling and hydraulic fracturing have made these technologies standard development strategies in unconventional gas reservoirs. Further improvements in these practices by means of numerical optimization of wellbore locations and hydraulic fracture (HF) stages spacing can enhance shale gas reserves and increase revenue from the unconventional projects. In order to solve these two challenges simultaneously as an integrated optimization problem, an automated framework for placement of horizontal wellbores and HF stages is developed and tested in this paper. Coupled with expert knowledge and engineering judgment, this workflow allows to produce unconventional assets economically. This paper presents specifics of our novel optimization framework that improves the design and placement of HF stages in shale gas reservoirs and increases production and the net present value (NPV) of the projects by judicious application of numerical optimization algorithms. In particular, we test several gradient- based and gradient-free methods, namely, simultaneous perturbation stochastic approximation (SPSA), Genetic Algorithm (GA), and covariance matrix adaptation evolution strategy (CMA-ES). Application of these optimization strategies to a suite of test cases illustrates that it is not necessary to assume even spacing between HF stages because the algorithms have a capability to optimize HF stages spacing in homogeneous and heterogeneous geologic systems. (C) 2014 Elsevier Ltd. All rights reserved.
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