A data-driven approach for optimizing horizontal well placement in thin oil rim reservoirs using deep learning

Abstract

It is challenging to develop thin oil rim reservoirs economically using conventional wells. Horizontal wells are now widely used to overcome the shortcomings of vertical wells. The deciding factor in ensuring successful horizontal wells application is optimum well placement. However, the conventional optimization approach is time and resource-intensive. A data-driven approach was proposed to optimize heel and toe locations by deploying a deep learning model. A synthetic database comprised of nine fundamental parameters that influence recovery mechanisms in thin oil reservoirs was generated to train the model. The accuracy and computation time of a deep-learning model trained on a synthetic database were compared to a novel optimization method that combines a genetic algorithm and a particle swarm optimization（hybrid GA-PSO）algorithm. The deep-learning model predicted optimum well placement（heel and toe points）with an accuracy comparable to the hybrid GA-PSO algorithm. Furthermore, the prediction obtained by the deep-learning model takes significantly less computation time than the hybrid GA-PSO algorithm. The developed optimization method offers a rapid and reliable initial guess of well placement for detailed optimization by simulation. The developed model is universally applicable for various thin oil rim characteristics, especially in the scarcity of data to build a reliable reservoir model.