Near Real-Time Hydraulic Fracturing Event Recognition Using Deep Learning Methods

Abstract

This paper provides the technical details of developing models to enable automated stage-wise analyses to be implemented within the real-time completion (RTC) analytics system. The models—two of which use machine learning (ML), including the convolutional neural network (CNN) technique (LeCun et al. 1990) and the U-Net architecture (Ronneberger et al. 2015)—detect the hydraulic fracture stage start and end, identify the ball seat operation, and categorize periods of pump rate. These tasks are performed on the basis of the two reliably available measurements of slurry rate and wellhead pressure, which enable the models to run automatically in real time, and also lay the foundation for further hydraulic fracturing advanced analyses. The presented solution provides real-time automated interpretations of hydraulic fracture events, enabling auto-generation of key performance indicator (KPI) reports, dispelling the need for manual labeling, and eliminating human bias and errors. It replaces the manual tasks in the RTC workflow/data pipeline and paves the way for a fully automated RTC system.