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

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 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 proposed approach is verified by a real application scenario. Experimental results reveal that predictive relative error less than 5%, which proves 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.