Collaborative Research: Modeling and Analysis of Fracture Network for Shale Gas Development and Its Environmental Impact

合作研究：页岩气开发裂缝网络建模与分析及其环境影响

• 批准号: 1209124
• 负责人: Guan Qin
• 金额: $ 10万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1209124&HistoricalAwards=false
• 关键词: Collaborative; Research; Modeling; Analysis; Fracture

Hydraulic fracturing stimulation is one of the key technologies in shale gas development. Though it has been widely used in oil and gas industry for many years, it remains a great challenge to quantitatively characterize the hydraulic fracturing induced fracture network in shale gas development due to the complex shale gas formation and the lack of observational data. Consequently, it is extremely difficult to evaluate and predict its efficiency and environmental impacts. In this collaborative research project the investigators study the hydraulic stimulation induced fracture network through advanced statistical analysis and mathematical modelling. Armed with micro-seismic data and production yield curve, they develop a statistical dynamical system for the fracture network, mimicking the network formation process under imposed hydraulic pressure. A Bayesian framework is used for parameter inferences, utilizing prior knowledge of the geological structure of the field under study. A mathematical subsurface flow model and an advanced numerical method, the sub-region method, is used to link a given fracture network to the production yield curve, providing vital information of the unobservable fracture network underground. By combining creative statistical analysis with advanced mathematical modelling and numerical method, the project addresses a very challenging application with important national interests. The project yields a better understanding of the hydraulic stimulation process, provides useful guidance for control and optimization of hydraulic fracturing process as well as assessing its environmental risks and consequences. Such knowledge helps policy makers to make more informed decision and more accurate cost analysis. The project spans both research and education aspects, including training of undergraduate, doctoral and post-doctoral students in interdisciplinary research, crossing the boundary of statistical analysis and numerical analysis, on an important application. The resulting software, available in public domain, will be used as an educational, research, and engineering tool well beyond the project duration.

水力压裂增产是页岩气开发的关键技术之一。尽管它已在油气行业广泛应用多年，但由于页岩气地层复杂且缺乏观测数据，定量表征页岩气开发中水力压裂诱导裂缝网络仍然是一个巨大的挑战。因此，评估和预测其效率和环境影响极其困难。在这个合作研究项目中，研究人员通过先进的统计分析和数学建模研究水力增产诱发的裂缝网络。借助微地震数据和产量曲线，他们开发了裂缝网络的统计动力系统，模拟了施加水压下的网络形成过程。贝叶斯框架用于参数推断，利用所研究领域的地质结构的先验知识。数学地下流动模型和先进的数值方法（子区域方法）用于将给定裂缝网络与产量曲线联系起来，提供地下不可观测裂缝网络的重要信息。通过将创造性的统计分析与先进的数学建模和数值方法相结合，该项目解决了具有重要国家利益的非常具有挑战性的应用。该项目使人们更好地了解水力增产过程，为水力压裂过程的控制和优化以及评估其环境风险和后果提供有用的指导。这些知识有助于政策制定者做出更明智的决策和更准确的成本分析。该项目涵盖研究和教育两个方面，包括对本科生、博士生和博士后学生进行跨学科研究、跨越统计分析和数值分析边界的重要应用的培训。由此产生的软件可在公共领域使用，在项目持续时间结束后将用作教育、研究和工程工具。