Integrated optimization of reservoir production and layer configurations using relational and regression machine learning models

This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization. For the first time, relational machine learning models are applied in reservoir development optimization. Traditional regression-based models often struggle in complex scenarios, but the proposed relational and regression-based composite differential evolution (RRCODE) method combines a Gaussian naive Bayes relational model with a radial basis function network regression model. This integration effectively captures complex relationships in the optimization process, improving both accuracy and convergence speed. Experimental tests on a multi-layer multi-channel reservoir model, the Egg reservoir model, and a real-field reservoir model (the S reservoir) demonstrate that RRCODE significantly reduces water injection and production volumes while increasing economic returns and cumulative oil recovery. Moreover, the surrogate models employed in RRCODE exhibit lightweight characteristics with low computational overhead. These results highlight RRCODE's superior performance in the integrated optimization of reservoir production and layer configurations, offering more efficient and economically viable solutions for oilfield development.