Methods and systems for machine-learning based simulation of flow
US9187984B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 19, 2011 |
| Grant date | Nov 17, 2015 |
| Priority date | — |
| Expiry date | Feb 11, 2032 |
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 that has a plurality of coarse grid cells. A plurality of fine grid models is generated, wherein each fine grid model corresponds to one of the plurality of coarse grid cells that surround a flux interface. The method also includes simulating the plurality of fine grid models using a training simulation to obtain a set of training parameters, including a potential at each coarse grid cell surrounding the flux interface and a flux across the flux interface. A machine learning algorithm is used to generate a constitutive relationship that provides a solution to fluid flow through the flux interface. The method also includes simulating the hydrocarbon reservoir using the constitutive relationship and generating a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable medium based on the results of the simulation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.