Enhancing Hydraulic Fracturing Productivity via Model-based Feedback Control

本项目将为水力压裂过程的实时基于模型的反馈控制建立系统的计算建模与控制框架。该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步，但仍然缺乏提高低渗透油藏产能的系统框架。这种差距是由于水力压裂作业中产生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的革命性进步，并可以扩展到其他具有类似挑战的问题。提出了一项基础研究，通过将水力压裂过程的移动边界方面与计算效率高的模型简化、空间变化参数估计和基于模型的反馈控制策略相结合，实现水力压裂闭环操作，以提高低渗透油藏的产能。具体而言，本研究将设计构建水力压裂局部时间降阶模型的方法，该模型适用于控制器和状态估计器的设计，并可实时实现。该模型将预测可控变量对影响油井动态的关键参数的影响。基于这些模型，非线性模型预测控制设计将被开发出来，以产生实际可实施的反馈控制系统，以提高低渗透油藏的产能。还将开发一种用于求解地层空间变化地质性质逆问题的参数估计技术。除了培训研究生外，首席研究员还计划向西班牙裔学生较多的高中学生和教师开展推广活动，并开展有关水力压裂基础知识的研讨会。研究成果将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。将与德克萨斯州a&m大学的Mary Kay O'Connor过程安全中心合作组织一个关于水力压裂安全问题的研讨会，其中包括一个指导部分，旨在建立西班牙裔学生与德克萨斯州a&m石油和天然气行业校友之间的互动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.