Training of machine learning algorithms for generating a reservoir digital twin
US11715034B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 16, 2020 |
| Grant date | Aug 1, 2023 |
| Priority date | — |
| Expiry date | Jan 14, 2042 |
Classification
- Technology area (CPC E)Fixed Constructions
- CPC primaryE21B2200/22
- WIPO fieldCivil engineering
- WIPO sectorOther fields
Abstract
Methods for training machine learning algorithms for generation of a reservoir digital twin include receiving information obtained from hydrocarbon wells. The information includes porosity logs, petrophysical data, rock typing data, pressure transient test results, vertical production logs, reservoir pressure logs, reservoir saturation logs, production performance, and injection performance. The reservoir saturation logs are normalized in accordance with time. A machine learning algorithm is trained using the reservoir pressure logs, the production performance, and the injection performance to provide variations in reservoir pressure of the hydrocarbon reservoir in accordance with time. The machine learning algorithm is trained to provide variations in reservoir saturation of the hydrocarbon reservoir in accordance with time. The machine learning algorithm is trained using the reservoir saturation logs, the vertical production logs, the production performance, the injection performance, the reservoir pressure logs, and the petrophysical data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.