Enhancing Hydraulic Fracturing Productivity via Model-based Feedback Control

本计画将建立一个系统化的水力压裂过程的计算模型与控制架构，以进行水力压裂过程的即时回馈控制。 该框架的目标是提高低渗透油藏的生产率，同时最大限度地减少能源消耗和水的使用。尽管实时测量技术，如井下压力分析和微地震监测的进步，仍然缺乏一个系统的框架，以提高低渗透油藏的生产力。 这种差距是由于与水力压裂操作产生的移动边界相关的建模和控制器设计挑战。 拟议的研究有可能导致水力压裂的变革性进展，并可能扩展到具有类似挑战的其他问题。提出了一项基础研究，使闭环操作的水力压裂提高生产率在低渗透油藏的水力压裂过程中的移动边界方面的计算效率的模型简化，空间变化的参数估计，和基于模型的反馈控制策略相结合。具体而言，所提出的研究将设计方法来构建本地的时间降阶模型的水力压裂，这是适合于控制器和状态估计器的设计，和实时实现。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。基于这些模型，非线性模型预测控制设计将被开发，以产生实际可实施的反馈控制系统，用于低渗透油藏的提高产量。还将开发一种参数估计技术，用于解决岩层中空间变化地质特性的反问题。除了培训研究生外，主要研究者还计划在西班牙裔学生人数较多的高中开展针对高中学生和教师的外联活动，并举办关于水力压裂基本原理的研讨会。研究成果将被整合到一个新的课程的基础上的应用程序的过程系统工程原理在水力压裂作业。将与德克萨斯农工大学的玛丽凯奥康纳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，该研讨会的指导部分旨在建立西班牙裔学生与来自石油和天然气行业的德克萨斯农工校友之间的互动。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.