Invention Grant
- Patent Title: Building scalable geological property models using machine learning algorithms
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Application No.: US17585441Application Date: 2019-12-03
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Publication No.: US12189075B2Publication Date: 2025-01-07
- Inventor: Mehran Hassanpour , Gaetan Bardy , Genbao Shi
- 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
- International Application: PCT/US2019/064262 WO 20191203
- International Announcement: WO2021/040763 WO 20210304
- Main IPC: G01V20/00
- IPC: G01V20/00 ; G06N3/091

Abstract:
A method of predicting rock properties at a selectable scale is provided, including receiving coordinates of locations of respective sample points, receiving measurement data associated with measurements or measurement interpretations for each sample point, receiving for each sample point a scale that indicates the scale used to obtain the measurements and/or measurement interpretations, wherein different scales are received for different sample points. A deep neural network (DNN) is trained by applying the received coordinates, measurement data, and scale associated with each sample point and associating the sample point with a rock property as a function of the coordinates, measurement data, and scale applied for the sample point. The DNN is configured to generate rock property data for a received request point having coordinates and a selectable scale, wherein the rock property data is determined for the request point as a function of the coordinates and the selectable scale.
Public/Granted literature
- US20230367031A1 BUILDING SCALABLE GEOLOGICAL PROPERTY MODELS USING MACHINE LEARNING ALGORITHMS Public/Granted day:2023-11-16
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