During hydraulic fracturing jobs, engineers must monitor the wellhead pressure and adjust the pumping schedule in real time to avoid screenout, optimize the proppant and fluid amounts, and minimize cost. In this paper, we use machine learning to predict wellhead pressure in real time during hydraulic fracturing. The new algorithm can assist engineers in monitoring and optimizing the pumping schedule.

We explored several neural network models. For each hydraulic fracturing stage, we train a machine learning (ML) model with the data from the first several minutes and predict the wellhead pressure for the next several minutes; we then add the data for the next several minutes, train a second ML model and predict the pressure for the next couple of minutes; and so on. We used several performance metrics to compare different models and select the best model for deployment to the Cloud, where a real-time completions platform is developed and hosted.

We selected more than 100 hydraulic fracturing stages from several wells completed in the Delaware Basin and tested several ML methods on the historical data. The wellhead pressure can be predicted with an acceptable accuracy by a slightly nonlinear machine learning model. We tested the ML model on the Cloud, where real-time streaming data such as slurry rate and proppant concentration are gathered. The computation is fast enough that a real-time wellhead pressure can be predicted.

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