Predictive Machine Learning Model for Bottom Hole Flowing and Average Formation Pressure in Underground Gas Storage

Abstract

Abstract Considering that injection–production of underground gas storage (UGS) is characterized by periodic and dramatic change, effective and fast model for predicting the pressure of UGS would not only be a valuable tool to figure out pressure variety but also of great benefit in optimizing injection and production. This study proposes a practical pressure prediction procedure for UGS to adapt the imbalances between injection and production on a timely basis. In this work, a first step in establishing a novel correlativity measure algorithm to screen out the objective injector–producer wells is proposed. A continuous feature selection strategy aims at selecting and filtrating feature to form the input variables of the pressure predictive model. Eventually, the long-short term memory model is used to fit the variation of pressure. Besides, an in-depth discussion illustrates the importance of well site division and model sensitivity analysis. The predictive capability of the proposed approach is verified by a real application scenario. Experimental results reveal that predictive relative error is less than 5%, which proves that the above procedure exhibits better prediction performance. The novelty of this work is that it is a purely data-driven approach that can directly interpret conventional surface measurements into intuitive subsurface pressure parameters, ideal for field applications of UGS.