Surrogate-Assisted Optimization of Highly Constrained Oil Recovery Processes Using Classification-Based Constraint Modeling

Real-world problems often involve constraints that must be carefully managed for feasible and efficient operations. In optimization, this becomes especially challenging with complex, high-dimensional problems that are computationally expensive and subject to hundreds or even thousands of constraints. We address these challenges by optimizing the highly constrained waterflooding process using a surrogate model of the reservoir and a classification-based constraint handling technique. Our study uses benchmark reservoir simulations, beginning with the low-dimensional Egg model and extending to the high-dimensional UNISIM model. We employ a Feedforward Neural Network (FFNN) surrogate for objective quantification and use classification-based modeling to transform the numerous constraints into a binary problem, distinguishing between feasible and infeasible reservoir settings. Our methodology involves an offline phase to develop and train models using reservoir simulation data, achieving high predictive accuracy (<i>R</i> ² > 0.98) with 20,000 bottom-hole pressure (BHP) settings and net present value (NPV) outputs. The classifier algorithms are then trained to model the constraints, ensuring that the solutions identified during optimization are feasible. In the online phase, we employ different model-based and search-based optimizers to find the optimal BHP settings that maximize the NPV throughout the production horizon. By integrating a highly accurate surrogate model and classification-based constraint handling, our approach significantly reduces the computational burden while ensuring that the solutions remain feasible, optimized for maximum economic gain, and yield better results compared to the deterministic approach.