Invention Grant
- Patent Title: Direct hydrocarbon indicators analysis informed by machine learning processes
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Application No.: US16776319Application Date: 2020-01-29
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Publication No.: US11434757B2Publication Date: 2022-09-06
- Inventor: Kurt J. Steffen , Cody J. MacDonald , Jie Zhang
- Applicant: ExxonMobil Upstream Research Company
- Applicant Address: US TX Spring
- Assignee: ExxonMobil Upstream Research Company
- Current Assignee: ExxonMobil Upstream Research Company
- Current Assignee Address: US TX Spring
- Agency: ExxonMobil Upstream Research Company—Law Department
- Main IPC: E21B49/08
- IPC: E21B49/08 ; G01V1/30 ; G06N20/00 ; E21B49/00 ; G01V99/00 ; G06F16/9038 ; G06F16/9035 ; E21B43/16 ; G06K9/62

Abstract:
Various embodiments described herein provide methods of hydrocarbon management and associated systems and/or computer readable media including executable instructions. Such methods (and by extension their associated systems and/or computer readable media for implementing such methods) may include obtaining geophysical data (e.g., seismic or other geophysical data) from a prospective subsurface formation (that is, a potential formation or other subsurface region of interest for any of various reasons, but in particular due to potential for production of hydrocarbons) and using a trained machine learning (ML) system for direct hydrocarbon indicators (DHI) analysis of the obtained geophysical data. Hydrocarbon management decisions may be guided by the DHI analysis.
Public/Granted literature
- US20200308961A1 Direct Hydrocarbon Indicators Analysis Informed By Machine Learning Processes Public/Granted day:2020-10-01
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