Data-driven forecasting of naturally fractured reservoirs based on nonlinear autoregressive neural networks with exogenous input

Abstract

In this paper we discuss the results of the modeling of naturally fractured reservoir based on the application of the nonlinear autoregressive neural network with exogenous inputs (NARX). We show that the NARX network can be efficiently applied to multivariate multi-step ahead prediction of reservoir dynamics. Predictability of the time series is studied using the Hurst exponent. We show that preliminary clustering of the time series can increase the precision of the forecasting. We evaluate the proposed approach using a real-world data set describing the dynamic behavior of a naturally fractured oilfield asset located in the coastal swamps of the Gulf of Mexico. This paper is not only intended for proposing a new model but to study carefully and thoroughly several aspects of the application of ANN models to reservoir modeling and to discuss conclusions that could be of the interest for petroleum engineers. • The NARX network is a feasible model for reservoir modeling. • It shows competitive accuracy in 1-year oil production prediction. • The use of both static and dynamic data extends the predictive capabilities of the ANN model. • Preliminary clustering can be a good means to increment the precision of the forecasting.