Summary This paper will present a robust workflow to address multiobjective optimization (MOO) of carbon dioxide (CO2)-enhanced oil recovery (EOR)-sequestration projects with a large number of operational control parameters. Farnsworth unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) EOR, will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU’s numerical model is used to demonstrate the proposed optimization workflow. Because using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian-SVR) is used to construct proxies. An iterative self-adjusting process prepares the training knowledge base to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian optimization to achieve better generalization performance. Trained proxies will be coupled with multiobjective particle swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology using a multilayer neural network (MLNN) to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proved that the workflow coupling Gaussian-SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledge base while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian-SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale MOO problems in various projects with similar desired outcomes.

中文翻译：

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使用机器学习辅助工作流程优化水交替二氧化碳注入现场操作

总结 本文将提出一个稳健的工作流程，以解决具有大量操作控制参数的二氧化碳 (CO2) 提高采收率 (EOR) 封存项目的多目标优化 (MOO)。Farnsworth 单元 (FWU) 油田是一个经历 CO2 交替注水 (CO2-WAG) EOR 的成熟油藏，将用作验证所提出的优化方案的现场案例。这项工作的预期成果将是多个目标函数的帕累托最优解的存储库，包括石油采收率、碳储存量和项目经济性。FWU 的数值模型用于演示建议的优化工作流程。因为使用 MOO 需要计算密集型程序，引入基于机器学习的代理来替代高保真模型，从而减少总计算开销。向量机回归结合高斯核（Gaussian-SVR）用于构造代理。一个迭代的自我调整过程准备训练知识库以开发强大的代理并最大限度地减少计算时间。代理的超参数将使用贝叶斯优化进行优化设计，以实现更好的泛化性能。训练有素的代理将与多目标粒子群优化 (MOPSO) 协议相结合，以构建帕累托前沿解决方案存储库。该工作流程的结果将是一个存储库，其中包含 CO2-WAG 项目中考虑的多个目标的帕累托最优解决方案。建议的优化工作流程将与使用多层神经网络 (MLNN) 的另一种已建立的方法进行比较，以验证其在处理具有大量要控制的参数的 MOO 方面的可行性。使用的优化参数包括可能用于控制 CO2-WAG 过程的操作变量，例如注水/注气期的持续时间、生产井底压力 (BHP) 控制以及数值模型中包含的每口井的注水率. 证明了工作流耦合 Gaussian-SVR 代理和迭代自调整协议的计算效率更高。通过压缩所需培训知识库的大小，同时保持优化结果的高精度，MOO 过程变得更加快速。优化研究的结果显示了在考虑多个目标函数的情况下成功建立解决方案存储库的可喜结果。还通过使用获得的优化控制参数验证 Pareto 前沿与模拟结果来验证结果。这项工作的成果可以为现场运营商提供一个机会，让他们有机会使用来自储层模型的尽可能多的输入来设计一个 CO2-WAG 项目。所提出的工作引入了一个新概念，将 Gaussian-SVR 代理与自调整协议相结合，以提高所提出工作流的计算效率并保证获得的优化结果的高精度。更重要的是，该工作流程可以优化复杂 CO2-WAG 过程中使用的大量控制参数，