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Tuesday, October 15 • 11:00am - 11:15am
Near Real-Time Hydraulic Fracture Event Recognition using Deep Learning Methodology

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Historically, the real-time hydraulic fracturing analytics system (Real-Time Completion system, RTC) has relied heavily on manual labeled data. The manual tasks, including fracture stage start/end labeling and ball pumpdown/seat event labeling, suffer from human bias and inconsistent errors, and can easily take days to review and correct. This paper provides the development and technical details of the automated stage-wise KPI report generator that fills the manual task gaps and provides industry-leading performance. The generator is constructed with two machine learning models that detect the stage start and end and identify the ball pumpdown and seat operations. These tasks are performed based on the reliably available measurements of slurry rate and wellhead pressure, which enable the real-time automated stage-wise KPI analysis, and they also lay the foundation for further advanced analysis regarding real time hydraulic fracture operational decision making.


Yuchang Shen

Anadarko Petroleum Corporation

Dingzhou Cao

Presenter, Anadarko Petroleum Corporation

Kate Ruddy

Anadarko Petroleum Corporation

Tuesday October 15, 2019 11:00am - 11:15am CDT
BRC 103

Attendees (3)