Petrophysical inversion with machine learning-based geologic priors
US11668853B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 6, 2020 |
| Grant date | Jun 6, 2023 |
| Priority date | — |
| Expiry date | Nov 8, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01V2210/667
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and system for modeling a subsurface region include applying a trained machine learning network to an initial petrophysical parameter estimate to predict a geologic prior model; and performing a petrophysical inversion with the geologic prior model, geophysical data, and geophysical parameters to generate a rock type probability model and an updated petrophysical parameter estimate. Embodiments include managing hydrocarbons with the rock type probability model. Embodiments include checking for convergence of the updated petrophysical parameter estimate; and iteratively: applying the trained machine learning network to the updated petrophysical parameter estimate of a preceding iteration to predict an updated rock type probability model and another geologic prior model; performing a petrophysical inversion with the updated geologic prior model, geophysical seismic data, and geophysical elastic parameters to generate another rock type probability model and another updated petrophysical parameter estimate; and checking for convergence of the updated petrophysical parameter estimate.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.