Methods and systems for machine-learning based simulation of flow
US10198535B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 19, 2011 |
| Grant date | Feb 5, 2019 |
| Priority date | — |
| Expiry date | Jan 30, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2113/08
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
There is provided a method for modeling a hydrocarbon reservoir that includes generating a reservoir model comprising a plurality of sub regions. At least one of the sub regions is simulated using a training simulation to obtain a set of training parameters comprising state variables and boundary conditions of the at least one sub region. A machine learning algorithm is used to approximate, based on the set of training parameters, an inverse operator of a matrix equation that provides a solution to fluid flow through a porous media. The hydrocarbon reservoir can be simulated using the inverse operator approximated for the at least one sub region. The method also includes generating a data representation of a physical hydrocarbon reservoir can be generated in a non-transitory, computer-readable, medium based, at least in part, on the results of the simulation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.