期刊
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
卷 108, 期 -, 页码 304-315出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.petrol.2013.04.019
关键词
shale gas; unconventional resources; optimization; well placement; reduced-order modeling
资金
- industrial affiliates of the Stanford Reservoir Simulation Research (SUPRI-B)
- Smart Fields Consortia
- Stanford Graduate Fellowship program
The economics of oil and gas field development can be improved significantly by using computational optimization to guide operations. In this work, we present a general framework for applying optimization to the development of shale gas reservoirs. Starting with a detailed three-dimensional full-physics simulation model, which includes heterogeneous geology, highly resolved fracture networks, dual-porosity, dual-permeability regions, and gas desorption, the approach first entails the generation of a much simpler, and much more computationally efficient, reduced-physics surrogate model. This reduced-physics model is tuned using a procedure akin to history matching to provide results in close agreement with the full-physics model. The surrogate model is then used for field development optimization. During the course of the optimization, the surrogate model is periodically 'retrained' to maintain agreement with the full-physics representation. In the optimizations considered here, we seek to determine the optimal locations, lengths, and number of fracture stages for a set of horizontal wells. A direct search optimization procedure (generalized pattern search) is applied. In two examples, involving models with properties representative of the Barnett Shale, optimization is shown to provide field development scenarios with net present values that are considerably higher than those of base case designs. In addition, speed-ups of about a factor of 100 are achieved through the use of the surrogate modeling procedure. (C) 2013 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据