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.
Issue Section:
Energy Storage/Systems
Keywords:
underground gas storage,
bottom hole flowing pressure,
average formation pressure,
correlativity measure algorithm,
continuous feature selection strategy,
long-short term memory model,
natural gas technology,
oil/gas reservoirs,
energy storage systems,
underground injection and storage,
wells-injection/oil/gas/geothermal
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