Machine-learning based fracture-hit detection using low-frequency DAS signal
US11768307B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 11, 2020 |
| Grant date | Sep 26, 2023 |
| Priority date | — |
| Expiry date | Jan 9, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/044
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Various aspects described herein relate to a machine learning based detecting of fracture hits in offset monitoring wells when designing hydraulic fracturing processes for a particular well. In one example, a computer-implemented method includes receiving a set of features for a first well proximate to a second well, the second well undergoing a hydraulic fracturing process for extraction of natural resources from underground formations; inputting the set of features into a trained neural network; and providing, as output of the trained neural network, a probability of a fracture hit at a location associated with the set of features in the first well during a given completion stage of the hydraulic fracturing process in the second well.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.