Invention Grant
- Patent Title: Data-driven domain conversion using machine learning techniques
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Application No.: US16841890Application Date: 2020-04-07
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Publication No.: US11614557B2Publication Date: 2023-03-28
- Inventor: Samiran Roy , Soumi Chaki , Sridharan Vallabhaneni
- 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: G01V1/48
- IPC: G01V1/48 ; G01V1/36 ; G06N3/08 ; G06N20/00 ; G06V10/70 ; G06V10/82 ; G06F18/21

Abstract:
Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the training seismic data corresponds to the training area, and wherein the study seismic data corresponds to the study area, calculating target depth data corresponding to the training area, training a machine learning model using training inputs and training targets, wherein the training inputs comprise the training seismic data, and wherein the training targets comprise the target depth data, computing, by the machine learning model, output depth data corresponding to the study area based at least in part on the study seismic data; and modifying one or more subsurface operations corresponding to the study area based at least in part on the output depth data.
Public/Granted literature
- US20210311221A1 DATA-DRIVEN DOMAIN CONVERSION USING MACHINE LEARNING TECHNIQUES Public/Granted day:2021-10-07
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