Hybrid CNN–GA framework for optimal oil well placement under geological uncertainty

Abstract A sequential well-placement strategy is important for field-development planning under geological uncertainty because reservoir conditions evolve between drilling stages. This study proposes a hybrid framework that combines convolutional neural networks (CNNs) and a genetic algorithm (GA) to optimize well locations while reducing the number of full-physics reservoir simulations required during optimization. Open Porous Media (OPM) Flow was used to generate training datasets for two consecutive six-month periods, allowing the CNN proxies to learn the relationship between permeability realizations, well coordinates, and cumulative oil production (COP). The trained proxies were then coupled with the GA to perform sequential well-placement optimization. The methodology was evaluated using the Egg reservoir model with three selected permeability realizations. The CNN proxies achieved coefficients of determination of 0.84 and 0.81 for the training subsets and 0.71 and 0.74 for the testing subsets in Periods I and II, respectively. The corresponding mean absolute percentage errors were 2.3% and 1.3% for the testing subsets. After validation with OPM Flow, the proxy-derived optimal solutions showed relative differences below 1% in ensemble-mean COP compared with the reservoir simulator. The proxy-based workflow required about 26% of the computational time of direct optimization in Period I and about 15% in Period II. The main contribution of this work is a sequential CNN–GA workflow in which separate proxy models are trained for successive drilling periods, allowing well-placement decisions to account for the evolved reservoir state between development stages.