Invention Application
- Patent Title: SUPERVISED MACHINE LEARNING-BASED WELLBORE CORRELATION
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Application No.: PCT/US2021/070891Application Date: 2021-07-16
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Publication No.: WO2023287454A1Publication Date: 2023-01-19
- Inventor: SERVAIS, Marc Paul , BAINES, Graham
- Applicant: LANDMARK GRAPHICS CORPORATION
- Applicant Address: 3000 N. Sam Houston Pkwy E.
- Assignee: LANDMARK GRAPHICS CORPORATION
- Current Assignee: LANDMARK GRAPHICS CORPORATION
- Current Assignee Address: 3000 N. Sam Houston Pkwy E.
- Agency: PEACOCK, Gregg A. et al.
- Priority: US17/305,861 2021-07-15
- Main IPC: E21B41/00
- IPC: E21B41/00 ; E21B47/04 ; E21B47/12 ; G06N20/00
Abstract:
A method for performing wellbore correlation across multiple wellbores includes predicting a depth alignment across the wellbores based on a geological feature of the wellbores. Predicting a depth alignment includes selecting a reference wellbore, defining a control point in a reference signal of a reference well log for the reference wellbore, and generating an input tile from the reference signal, the control points, and a number of non-reference well logs corresponding to non-reference wellbores. The well logs include changes in a geological feature over a depth of a wellbore. The input tile is input into a machine-learning model to output a corresponding control point for each non-reference well log. The corresponding control point corresponds to the control point of the reference log. Based on the corresponding control points output from the machine-learning model, the non-reference well logs are aligned with the reference well log to correlate the multiple wellbores.
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