Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow

This paper will present a robust workflow to address multi-objective optimization (MOO) of CO2-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) enhanced oil recovery (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 employed to demonstrate the proposed optimization workflow. Since 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 utilized to construct proxies. An iterative self-adjusting process prepares the training knowledgebase 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 Multi-objective 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 employing a multi-layer neural network 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 proven 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 knowledgebase 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 multi-objective optimization problems in various projects with similar desired outcomes.