Invention Grant
- Patent Title: Integrated machine learning framework for optimizing unconventional resource development
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Application No.: US17082793Application Date: 2020-10-28
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Publication No.: US11746651B2Publication Date: 2023-09-05
- Inventor: Hui Zhou , Benjamin Lascaud
- Applicant: ConocoPhillips Company
- Applicant Address: US TX Houston
- Assignee: ConocoPhillips Company
- Current Assignee: ConocoPhillips Company
- Current Assignee Address: US TX Houston
- Agency: Polsinelli PC
- Main IPC: E21B49/08
- IPC: E21B49/08 ; E21B47/003 ; G01V3/34 ; G06N20/00 ; E21B7/04 ; E21B43/00 ; G06Q10/04 ; G06Q10/0639 ; G06Q50/02 ; G06F17/18

Abstract:
Implementations described and claimed herein provide systems and methods for developing resources from an unconventional reservoir. In one implementation, raw reservoir data for the unconventional reservoir is obtained. The raw reservoir data includes geology data, completion data, development data, and production data. The raw reservoir data is transformed to transformed data. The raw reservoir data is transformed to the transformed data based on a transformation from a set of one or more raw variable to a set of one or more transformed variables. The set of one or more transformed variables is statistically uncorrelated. Resource development data is extracted from the transformed data. Performance analytics are generated for the unconventional reservoir using the resource development data. The performance analytics are generated through ensemble machine learning. The unconventional reservoir is developed based on the performance analytics.
Public/Granted literature
- US20210123343A1 INTEGRATED MACHINE LEARNING FRAMEWORK FOR OPTIMIZING UNCONVENTIONAL RESOURCE DEVELOPMENT Public/Granted day:2021-04-29
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