Reservoir characterization using machine-learning techniques
US11703608B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 29, 2020 |
| Grant date | Jul 18, 2023 |
| Priority date | — |
| Expiry date | Jun 9, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01V2210/63
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
A system can determine a location for future wells using machine-learning techniques. The system can receive seismic data about a subterranean formation and may determine a set of seismic attributes from the seismic data. The system can block the set of seismic attributes into a set of blocked seismic attributes by distributing the set of seismic attributes onto a geo-cellular grid representative of the subterranean formation. The system can apply a trained machine-learning model to the set of blocked seismic attributes to generate a composite seismic parameter. The system can distribute the composite seismic parameter in the subterranean formation to characterize formation locations based on a predicted presence of hydrocarbons.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.