Analytics and machine learning method for estimating petrophysical property values
US11555936B2 · kind B2 · utility
1Cited by
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18Claims
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Key dates
| Filing date | Jan 9, 2020 |
| Grant date | Jan 17, 2023 |
| Priority date | — |
| Expiry date | Jul 16, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01V2210/614
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Property values inside an explored underground subsurface are determined using hybrid analytic and machine learning. A training dataset representing survey data acquired over the explored underground structure is used to obtain labels via an analytic inversion. A deep neural network model generated using the training dataset and the labels is used to predict property values corresponding to the survey data using the DNN model.
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