- Patent Title: Subsurface fluid-type likelihood using explainable machine learning
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Application No.: US17119181Application Date: 2020-12-11
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Publication No.: US11630224B2Publication Date: 2023-04-18
- Inventor: Samiran Roy , Shashwat Verma
- 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
- Main IPC: G01V1/28
- IPC: G01V1/28 ; G01V1/30

Abstract:
A system is described for determining a likelihood of a type of fluid in a subterranean reservoir. The system may include a processor and a non-transitory computer-readable medium that includes instructions executable by the processor to cause the processor to perform various operations. The processor may receive pre-stack seismic data having seismically-acquired data elements for geometric locations in a subterranean reservoir. The processor may determine, using the pre-stack seismic data, input features for each geometric location and may execute a trained model on the input features for determining a likelihood of a type of fluid in the subterranean reservoir and for determining a list of features affecting the likelihood. The processor may subsequently output the likelihood and the list of features.
Public/Granted literature
- US20220187484A1 SUBSURFACE FLUID-TYPE LIKELIHOOD USING EXPLAINABLE MACHINE LEARNING Public/Granted day:2022-06-16
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