地热储层开发的机器学习模拟优化:现状与展望

IF 9 1区 地球科学 Q1 ENERGY & FUELS
M. Rajabi, Mingjie Chen
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引用次数: 2

摘要

:在地热储层管理中,联合模拟优化是实现最佳井位和操作的一种实用方法,可最大限度地提高能量回收率和储层寿命。机器学习模型的使用通常是使模拟优化计算可行的关键。机器学习的工具可以用来构建数据驱动的、通常不涉及物理的数值模型响应近似值,计算时间通常比油藏数值模型所需的时间小几个数量级。在这个简短的视角中,我们解释了地热储层管理中基于机器学习的组合模拟优化的背景和现状,并讨论了可能形成未来方向的几个关键问题
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulation-optimization with machine learning for geothermal reservoir recovery: Current status and future prospects
: In geothermal reservoir management, combined simulation-optimization is a practical approach to achieve the optimal well placement and operation that maximizes energy recovery and reservoir longevity. The use of machine learning models is often essential to make simulation-optimization computational feasible. Tools from machine learning can be used to construct data-driven and often physics-free approximations of the numerical model response, with computational times often several orders of magnitude smaller than those required by reservoir numerical models. In this short perspective, we explain the background and current status of machine learning based combined simulation-optimization in geothermal reservoir management, and discuss several key issues that will likely form future directions
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来源期刊
Advances in Geo-Energy Research
Advances in Geo-Energy Research natural geo-energy (oil, gas, coal geothermal, and gas hydrate)-Geotechnical Engineering and Engineering Geology
CiteScore
12.30
自引率
8.50%
发文量
63
审稿时长
2~3 weeks
期刊介绍: Advances in Geo-Energy Research is an interdisciplinary and international periodical committed to fostering interaction and multidisciplinary collaboration among scientific communities worldwide, spanning both industry and academia. Our journal serves as a platform for researchers actively engaged in the diverse fields of geo-energy systems, providing an academic medium for the exchange of knowledge and ideas. Join us in advancing the frontiers of geo-energy research through collaboration and shared expertise.
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