Invention Application
- Patent Title: METHOD FOR PREDICTING GEOLOGICAL FEATURES FROM THIN SECTION IMAGES USING A DEEP LEARNING CLASSIFICATION PROCESS
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Application No.: PCT/EP2022/062162Application Date: 2022-05-05
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Publication No.: WO2022238232A1Publication Date: 2022-11-17
- Inventor: FALIVENE ALDEA, Oriol , KLEIPOOL, Lucas Maarten , AUCHTER, Neal Christian
- Applicant: SHELL INTERNATIONALE RESEARCH MAATSCHAPPIJ B.V. , SHELL USA, INC.
- Applicant Address: Carel van Bylandtlaan 30; P.O. BOX 576
- Assignee: SHELL INTERNATIONALE RESEARCH MAATSCHAPPIJ B.V.,SHELL USA, INC.
- Current Assignee: SHELL INTERNATIONALE RESEARCH MAATSCHAPPIJ B.V.,SHELL USA, INC.
- Current Assignee Address: Carel van Bylandtlaan 30; P.O. BOX 576
- Agency: SHELL LEGAL SERVICES IP
- Priority: US63/187,144 2021-05-11
- Main IPC: G06V10/25
- IPC: G06V10/25 ; G06V10/82 ; G06V20/69 ; G06V20/698
Abstract:
A method for predicting an occurrence of a geological feature in a geologic thin section image uses a backpropagation-enabled classification process trained by inputting extracted training image fractions having substantially the same absolute horizontal and vertical length and associated labels for classes from a predetermined set of geological features, and iteratively computing a prediction of the probability of occurrence of each of the classes for the extracted training image fractions. The trained backpropagation-enabled classification model is used to predict the occurrence of the classes in extracted fractions of non-training geologic thin section images having substantially the same absolute horizontal and vertical length as the training image fractions.
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