Time series forecasting of oil production in Enhanced Oil Recovery system based on a novel CNN-GRU neural network

Abstract

An accurate prediction of oil production is critical for the oilfield development, and many deep learning models have been widely employed for this purpose. However, those methods show insufficiencies in extracting complex features from multivariable time series datasets, which leaves the prediction of oil production still challenging. In this study, a novel CNN-GRU model combining Convolutional Neural Networks (CNN) and Gate Recurrent Unit (GRU) neural network was proposed to accurately predict oil production for Enhanced Oil Recovery (EOR) performance. The CNN layer can extract the features from variables affecting oil production, and the GRU layer models temporal information using the transmitted features for prediction. The Bayesian Optimization algorithm (BO) was employed to design the optimal hyper-parameters of CNN-GRU. For evaluation purpose, two case studies were carried out with the production data from a CO2-EOR project and a waterflooding project. The prediction performance of the proposed approach was compared with typical deep learning methods and a hybrid (statistical and machine learning) method. The results of experiments and comparisons indicate that the proposed CNN-GRU model outperforms other prediction approaches. The CNN-GRU model provides future oil production of wells, enabling engineers to make informed decisions in development plan of reservoirs.