Invention Grant
- Patent Title: Recurrent neural network model for bottomhole pressure and temperature in stepdown analysis
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Application No.: US17043129Application Date: 2018-04-12
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Publication No.: US11441404B2Publication Date: 2022-09-13
- Inventor: Srinath Madasu , Yogendra Narayan Pandey , Keshava Rangarajan
- Applicant: Landmark Graphics Corporation
- Applicant Address: US TX Houston
- Assignee: Landmark Graphics Corporation
- Current Assignee: Landmark Graphics Corporation
- Current Assignee Address: US TX Houston
- Agency: Kilpatrick Townsend & Stockton LLP
- International Application: PCT/US2018/027341 WO 20180412
- International Announcement: WO2019/199313 WO 20191017
- Main IPC: E21B43/26
- IPC: E21B43/26 ; E21B47/06 ; G06N3/04 ; G06N3/063

Abstract:
A method for fracturing a formation is provided. Real-time fracturing data is acquired from a well bore during fracturing operation. The real-time fracturing data is processed using a recurrent neural network trained using historical data from analogous wells. A real-time response variable prediction is determined using the processed real-time fracturing data. Fracturing parameters for the fracturing operation are adjusted in real-time based on the real-time response variable prediction. The fracturing operation is performed using the fracturing parameters that were adjusted based on the real-time response variable prediction.
Public/Granted literature
- US20210017845A1 RECURRENT NEURAL NETWORK MODEL FOR BOTTOMHOLE PRESSURE AND TEMPERATURE IN STEPDOWN ANALYSIS Public/Granted day:2021-01-21
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