Neural-network-based mapping of potential leakage pathways of subsurface carbon dioxide storage
US12182226B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 10, 2021 |
| Grant date | Dec 31, 2024 |
| Priority date | — |
| Expiry date | Jun 2, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0985
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The disclosed technology is generally directed to carbon capture and storage. In one example of the technology, a first neural network is trained with synthetic data that is associated with seismic images of synthetic simulated subsurfaces. The first neural network extracts features from multiple resolutions of the seismic images of the synthetic simulated subsurfaces. The ground truth includes synthetic labels that indicate probabilities of potential carbon dioxide leakage pathways of the synthetic simulated subsurfaces. A seismic image of a first subsurface is received. At least the trained first neutral network is used to generate output labels that indicate probabilities of potential leakage pathways of carbon dioxide storage of the first subsurface.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.