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

水力压裂增产是页岩气开发的关键技术之一。尽管压裂裂缝网络在油气工业中得到了广泛的应用，但由于页岩气地层复杂，观测资料缺乏，如何定量表征页岩气开发中的压裂裂缝网络仍然是一个巨大的挑战。因此，很难评估和预测其效率和环境影响。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来研究水力刺激引起的裂缝网络。借助微地震数据和产量曲线，他们开发了一个裂缝网络的统计动力学系统，模拟了在外加液压下的网络形成过程。贝叶斯框架用于参数推断，利用先验知识的地质结构领域的研究。地下水流数学模型和先进的数值方法，分区法，被用来连接一个给定的裂缝网络的生产产量曲线，提供地下不可观测的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，该项目解决了一个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压裂过程，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了有益的指导。这些知识有助于决策者作出更明智的决定和更准确的成本分析。该项目涵盖研究和教育两个方面，包括跨学科研究的本科生，博士生和博士后学生的培训，跨越统计分析和数值分析的边界，在一个重要的应用。由此产生的软件，可在公共领域，将被用作教育，研究和工程工具远远超出了项目的持续时间。

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.