A Deep-learning Based LSTM Approach for Multiphase Rate Transient Analysis in Tight and Ultratight Reservoir

Abstract During the production and operations of hydraulically fractured wells, large amounts of data are collected through numerous sensors or flowmeters, which can provide valuable understanding on the formation and hydraulic fractures. Although much studies try to use physical-justification based approaches to analyze these well history data, the analysis accuracy is significantly limited due to many assumptions made in physical models. This paper developed a LSTM-based deep learning for rate transient analysis in tight and ultratight (shale) reservoir and proposed a workflow to quantitatively evaluate fracture parameters. The proxy model is based on deep-learning algorithm of LSTM and is combined with a semi-analytical (base) model for multiphase water and hydrocarbon (oil or gas) flow in the hydraulically fractured reservoirs. To rigorously consider the multiphase flow mechanism in the semi-analytical model, LSTM and attention mechanism are introduced to forecast the key relationship of average saturation and pressure for semi-analytical model by training and predicting the time-dependent pressure and saturation series. We generated thousands of numerical simulation cases of wells in hydraulically fractured reservoirs, which provide production data and static reservoir data to train the deep-learning based proxy model. Model verification and comparison show that the proxy model can effectively predict pressure-dependent average saturation relationship with high accuracy. The numerical validation confirms the superiority of the proposed deep-learning based model than the semi-analytical model in accuracy with an error of less than 10% in estimating reservoir and fracture parameters and in calculation efficiency with the speed two orders of magnitude faster. The LSTM approach for rate transient analysis provides a more reliable method for evaluating reservoir performance, which can lead to improved production planning and resource allocation.