Determination of oil well placement using convolutional neural network coupled with robust optimization under geological uncertainty

Abstract

This study integrates a convolutional neural network (CNN) within the framework of robust optimization for determining the placement of an oil production well at a petroleum reservoir under geological uncertainty. The proposed algorithm identifies the well location that maximizes the expectation of cumulative oil production of equiprobable realizations. CNN, which inputs near-wellbore permeability and outputs cumulative oil production at a feasible well location, is trained to correlate between petrophysical spatial data and hydrocarbon productivity with several realizations. The trained CNN estimates the productivity at every feasible well location for all given realizations. The approach is tested with application to a set of realizations describing a channelized oil reservoir with an aquifer. The validity of the approach is evaluated by comparing the similarity between the full-physics reservoir simulation (RS) and CNN results for qualified well locations. The most productive well location based on the simulation results is selected as the optimal well placement. The performance of the CNN is also compared with that of an artificial neural network (ANN) for cases with varied numbers of realizations that provide training data with the neural networks. CNN outperforms ANN because of its feature extraction of multi-dimensional data from convolution. A sensitivity analysis is conducted to analyze the effects of the volume of training data on the predictability of the neural networks with k-fold cross-validation. The findings highlight that the integrated approach can be used as an efficient decision-making tool for solving well placement optimization problems with accuracy at an affordable computational cost. © 2020 Elsevier B.V.