Invention Grant
- Patent Title: Machine learning for production prediction
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Application No.: US16067157Application Date: 2016-12-29
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Publication No.: US11053789B2Publication Date: 2021-07-06
- Inventor: Gabriel Maher , Daniel D'Souza
- Applicant: Schlumberger Technology Corporation
- Applicant Address: US TX Sugar Land
- Assignee: Schlumberger Technology Corporation
- Current Assignee: Schlumberger Technology Corporation
- Current Assignee Address: US TX Sugar Land
- Agent Colin L. Wier
- International Application: PCT/US2016/069301 WO 20161229
- International Announcement: WO2017/117445 WO 20170706
- Main IPC: G01V1/40
- IPC: G01V1/40 ; E21B44/00 ; E21B43/00 ; G06N20/00 ; G06N3/02 ; G06N5/00 ; G06N3/08 ; G06N20/20

Abstract:
Estimating a production prediction of a target well includes computing, based on production time series from training wells, a smoothed production history curves. Each smoothed production history curve corresponds to a training well. Based on the smoothed production history curves, a fitting function defined by a set of fitting coefficients is selected. A machine learning process determines, based on a set of well parameters for each training well, a set of predicted fitting coefficients as a function of a set of well parameters of the target well. Estimating the production prediction further includes applying the predicted fitting coefficients to the fitting function to compute a production prediction curve for the target well, and presenting the production prediction curve.
Public/Granted literature
- US20190024494A1 Machine Learning for Production Prediction Public/Granted day:2019-01-24
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