Transfer Learning with Recurrent Neural Networks for Long-Term Production Forecasting in Unconventional Reservoirs

Abstract

Summary Robust production forecasting allows for optimal resource recovery through efficient field management strategies. In hydraulically fractured unconventional reservoirs, the physics of fluid flow and transport processes is not well understood and the presence of and transitions between multiple flow regimes further complicate forecasting. An important goal for field operators is to obtain a fast and reliable forecast with minimal historical production data. The abundance of wells drilled in fractured tight formations and continuous data acquisition effort motivate the use of data-driven forecast methods. However, traditional data-driven forecast methods require sufficient training data from an extended period of production for any target well, which may have limited practical use when the effective production life of wells is relatively short. In this paper, a deep recurrent neural network (RNN) model is developed for long-term production forecasting in unconventional reservoirs. As input data, the model takes completion parameters, formation and fluid properties, operating controls, and early (i.e., 3–6 months) production response data. The model is trained on a collection of historical production data across multiple flow regimes, control settings, and the corresponding well properties from multiple shale plays. The proposed RNN model can predict oil, water, and gas production as multivariate time series under varying operating controls. Once the forecast model is trained, it can be used to obtain a one-step forecast by feeding the model with input well properties, operating controls, and a short initial production. The long-term forecast is obtained by either recursively feeding the model with forecast results from the preceding timesteps or by training the model for multistep ahead predictions. Unlike other applications of RNN that require a long history of production data for training, our model employs transfer learning by combining early production data from the target well with the long-term dynamics captured from historical production data in other wells. We illustrate our approach using synthetic data sets and a case study from Bakken Play in North Dakota.