Multistage hydraulic fracturing has gained global popularity as more tight geologic formations are developed economically for hydrocarbon resources. However, screenout is a major issue caused by the blockage of proppant inside the fractures. The complete paper presents a screenout-classification system based on Gaussian hidden Markov models (GHMMs) trained on simulated data that predicts screenouts and provides early warning by learning prescreenout patterns in surface-pressure signals. The methodology is a useful tool for early screenout detection and shows the promise of other fracturing time-series data analysis.

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