Enhancing Hydraulic Fracturing Productivity via Model-based Feedback Control

该项目将为水力压裂过程的实时基于模型的反馈控制建立一个系统的计算建模和控制框架。这一框架的目标是提高低渗透油藏的产能，同时最大限度地减少能源消耗和水的使用。尽管井下压力分析和微地震监测等实时测量技术取得了进展，但仍缺乏提高低渗透油藏产能的系统框架。这一差距是由于与水力压裂作业引起的移动边界相关的建模和控制器设计挑战造成的。拟议的研究有可能导致水力压裂方面的变革性进展，并可能扩展到具有类似挑战的其他问题。提出了一项基础性研究，旨在通过将水力压裂过程的动边界方面与计算高效的模型简化、空间变化参数估计和基于模型的反馈控制策略相结合，来实现水力压裂的闭环系统操作，以提高低渗透油藏的产能。具体地说，该研究将设计出适合于控制器和状态估计器设计和实时实现的水力压裂局部时间降阶模型的构建方法。这些模型将预测可控变量对影响油井动态的关键参数的影响。在这些模型的基础上，将发展非线性模型预测控制设计，以产生实用的反馈控制系统，以提高低渗透油藏的产能。还将开发一种参数估计技术，用于解决岩层中空间变化的地质属性的反问题。除了培训研究生外，首席调查员还计划向拉美裔学生人数较多的高中学生和教师开展外联活动，并开发关于水力压裂基础知识的研讨会。研究成果将被整合到一门基于过程系统工程原理在水力压裂作业中的应用的新课程中。将与德克萨斯农工大学玫琳凯·奥康纳工艺安全中心合作，组织一次关于水力压裂安全问题的研讨会，其中有一个指导部分，旨在建立拉美裔学生与德克萨斯农工大学石油和天然气行业校友之间的互动。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project will establish a systematic computational modeling and control framework for real-time model-based feedback control of the hydraulic fracturing process. The goal of this framework is to enhance productivity in low permeability oil reservoirs, while minimizing energy consumption and water usage. Despite advances in real-time measurement techniques, such as downhole pressure analysis and micro-seismic monitoring, there remains a lack of a systematic framework to enhance productivity in low permeability oil reservoirs. This gap is due to modeling and controller design challenges associated with moving boundaries arising from the hydraulic fracturing operations. The proposed research has the potential to lead to transformative advances in hydraulic fracturing and could be extended to other problems with similar challenges. A fundamental study is proposed to enable closed-loop operation of hydraulic fracturing for enhanced productivity in low permeability oil reservoirs by integrating the moving boundary aspects of hydraulic fracturing processes with computationally efficient model reduction, spatially varying parameter estimation, and model-based feedback control strategies. Specifically, the proposed research will devise methods to construct local in time reduced-order models of hydraulic fracturing, which are suitable for controller and state estimator designs, and real-time implementations. The models will predict the effect of controllable variables on key parameters that affect well performance. Based on these models, nonlinear model predictive control designs will be developed to produce practically implementable feedback control systems for enhanced productivity in low permeability oil reservoirs. A parameter estimation technique for solving the inverse problem of the spatially varying geological properties in the rock formation will also be developed. In addition to training graduate students, the principal investigator plans to pursue outreach activities to high-school students and teachers in high schools with large Hispanic student populations and develop workshops on the fundamentals of hydraulic fracturing. The research findings will be integrated into a new course based on applications of process systems engineering principles in hydraulic fracturing operations. A workshop on safety issues in hydraulic fracturing will be organized in collaboration with the Mary Kay O'Connor Process Safety Center at Texas A&M University with a mentoring component aimed at establishing interactions between Hispanic students and Texas A&M alumni from the oil and gas industry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.