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

ABSTRACT: 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

SUBMITTER: Aghayev Z 

PROVIDER: S-EPMC12007002 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature