A comparison of machine learning surrogate models for net present value prediction from well placement binary data

Abstract

Net Present Value (NPV) is an important indicator to guide investment decisions. In oil production planning, NPV is employed to evaluate and select among different production strategies. However, NPV estimation requires computational costly numerical simulations. So, evaluating as many production strategies as is desirable may be prohibitive. Therefore, one can only evaluate a small part of the search space, decreasing the chance of finding a near-optimal production strategy. To speed up the searching process, a much faster, but error-prone, surrogate model is used to approximate the simulator output. Data-driven surrogate modeling depends on both: 1) building a simple model to reproduce the quality of a high-fidelity model, while 2) considering a large volume of data to build it. In this work, we address the well placement optimization task by considering a binary data representation, indicating the presence or absence of a given well in a production strategy. We show the possibility of predicting the NPV from binary data, thus reducing data dimension and model complexity. Specifically, we compare six machine learning regression algorithms to predict the NPV. The simulations conducted in a benchmark case, based on a real field, showed that some regression algorithms can be used as a surrogate model to the simulator to efficiently perform well placement optimization considering binary data. The best results were obtained with Multi-Layer Perceptron, whose estimations covered a wide range of NPV with a small and constant error.