Abstract
SummaryDeclineācurve analysis (DCA) is an easy and fast empirical regression method for predicting future well production. However, applying DCA to shaleāgas wells is limited by long transient flow, a unique completion design, and highādensity drilling. Recently, a long short-term-memory (LSTM) algorithm has been widely applied to the prediction of timeāseries data. Because shaleāgasāproduction data are timeāseries data, the LSTM algorithm can be applied to predict future shaleāgas production. After information for 332 shaleāgas wells in Alberta, Canada, is obtained from a commercial database, the data are preprocessed in seven steps, including cutoffs for well list, data cleaning, feature extraction, train and test sets split, normalization, and sorting for input into the LSTM model. The LSTM model is trained in 405 seconds by two features of production data and a shutāin (SI) period from 300 wells. The twoāfeature case shows a better prediction accuracy than both the oneāfeature case (i.e., production data only) and the hyperbolic DCA, where the three methods are tested on unseen data from 15 wells. The twoāfeature case can predict future production rates according to the SI period and provide a stable result for available timeāseries data.
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