Multistep Ahead Multiphase Production Prediction of Fractured Wells Using Bidirectional Gated Recurrent Unit and Multitask Learning

Abstract

Summary Relying on its strong nonlinear mapping ability, machine learning is found to be efficient and accurate for production prediction of fractured wells compared with conventional analytical methods, numerical simulations, and traditional decline curve analysis. However, its application in forecasting future multistep time series production remains challenging, with complications of error accumulation, growing uncertainty, and degraded accuracy. To this end, we propose a novel multistep ahead production prediction framework based on a bidirectional gated recurrent unit (BiGRU) and multitask learning (MTL) combined neural network (BiGRU-MTL), which can improve prediction performance by sharing task-dependent representations among tasks of multiphase production prediction. The forecasting strategies and evaluation setups for multiple timesteps are elaborated to avoid unfair assessment caused by mixing different prediction confidences over several days. In this framework, BiGRU is in charge of capturing nonlinear patterns of production variation by utilizing both forward and backward sequence information. MTL methods including cross-stitch network (CSN) and weighting losses with homoscedastic uncertainty are incorporated to automatically determine the sharing degree of multiple tasks and the weight ratio of the total loss function. By this means, domain knowledge contained in tasks of multiphase production prediction is deeply leveraged, shared, and coupled to enhance multistep ahead prediction accuracy while meeting the need for multiphase production forecasting. The proposed framework is applied to a synthetic well case, a field well case, and a field multiwell case to progressively prove the feasibility, robustness, and generalization of the BiGRU-MTL model. Experiment results show that the proposed framework outperforms conventional single-task models and commonly used recurrent neural networks (RNNs), furnishing a reliable and stable tool for accurate multistep ahead production prediction. This work promises to provide insights into dynamic production optimization and management in oil- and gasfield sites.