Invention Application
- Patent Title: METHOD FOR PREDICTING SUBSURFACE FEATURES FROM SEISMIC USING DEEP LEARNING DIMENSIONALITY REDUCTION FOR REGRESSION
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Application No.: US17275309Application Date: 2019-09-10
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Publication No.: US20220113441A1Publication Date: 2022-04-14
- Inventor: Donald Paul GRIFFITH , Sam Ahmad ZAMANIAN , Russell David POTTER
- Applicant: SHELL OIL COMPANY
- Applicant Address: US TX HOUSTON
- Assignee: SHELL OIL COMPANY
- Current Assignee: SHELL OIL COMPANY
- Current Assignee Address: US TX HOUSTON
- International Application: PCT/EP2019/074086 WO 20190910
- Main IPC: G01V1/30
- IPC: G01V1/30 ; G06N3/08
Abstract:
A method for training a backpropagation-enabled regression process is used for predicting values of an attribute of subsurface data. A multi-dimensional seismic data set with an input dimension of at least two is inputted into a backpropagation-enabled process. A predicted value of the attribute has a prediction dimension of at least 1 and is at least 1 dimension less than the input dimension.
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