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Prediction of the Continuous Probability of Sand Screenout Based on a Deep Learning Workflow


 Sand screenout is one of the most serious and frequent challenges that threaten the efficiency and safety of hydraulic fracturing. Current low prices of oil/gas drive operators to control costs by using lower viscosity and lesser volumes of fluid for proppant injection—thus reducing the sand-carrying capacity in the treatment and increasing the risk of screenout. Current analyses predict screenout as isolated incidents based on the interpretation of pressure or proppant accumulation. We propose a method for continuous evaluation and prediction of screenout by combining data-driven methods with field measurements recovered during shale gas fracturing. The screenout probability is updated, redefined, and used to label the original data. Three determining elements of screenout are proposed, based on which four indicators are generated for training a deep learning model [gated recurrent units (GRU), tuned by the grid search and walk-forward validation]. Training field records following screenout are manually trimmed to force the machine learning algorithm to focus on the prescreenout data, which then improves the prediction of the continuous probability of screenout. The Pearson coefficients are analyzed in the STATA software to remove obfuscating parameters from the model inputs. The extracted indicators are optimized, via a forward selection strategy, by their contributions to the prediction according to the confusion matrix and root mean squared error (RMSE). By optimizing the inputs, the probability of screenout is accurately predicted in the testing cases, as well as the precursory predictors, recovered from the probability evolution prior to screenout. The effect of pump rate on screenout probability is analyzed, defining a U-shaped correlation and suggesting a safest-fracturing pump rate (SFPR) under both low- and high-stress conditions. The probability of screenout and the SFPR, together, allow continuous monitoring in real time during fracturing operations and the provision of appropriate screenout mitigation strategies.