Near Real-Time Hydraulic Fracturing Event Recognition Using Deep Learning Methods
- Yuchang Shen (Anadarko Petroleum Corporation) | Dingzhou Cao (Anadarko Petroleum Corporation) | Kate Ruddy (Anadarko Petroleum Corporation) | Luis Felipe Teixeira de Moraes (Anadarko Petroleum Corporation)
- Document ID
- Society of Petroleum Engineers
- SPE Drilling & Completion
- Publication Date
- September 2020
- Document Type
- Journal Paper
- 478 - 489
- 2020.Society of Petroleum Engineers
- hydraulic fracture event recognition, hydraulic fracture KPI analytics, real-time hydraulic fracture analytics, real-time completion, deep learning/machine learning
- 33 in the last 30 days
- 174 since 2007
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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.
|File Size||3 MB||Number of Pages||12|
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