Leak detection event aggregation and ranking systems and methods
US10948471B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 1, 2018 |
| Grant date | Mar 16, 2021 |
| Priority date | — |
| Expiry date | Apr 28, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01N33/0047
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
In some embodiments, data from multiple vehicle-based natural gas leak detection survey runs are used by computer-implemented machine learning systems to generate a list of natural gas leaks ranked by hazard level. A risk model embodies training data having known hazard levels, and is used to classify newly-discovered leaks. Hazard levels may be expressed by continuous variables, and/or probabilities that a given leak fits within a predefined category of hazard (e.g. Grades 1-3). Each leak is represented by a cluster of leak indications (peaks) originating from a common leak sources. Hazard-predictive features may include maximum, minimum, mean, and/or median CH4/amplitude of aggregated leak indications; estimated leak flow rate, determined from an average of leak indications in a cluster; likelihood of leak being natural gas based on other indicator data (e.g. ethane concentration); probability the leak was detected on a given pass; and estimated distance to leak source.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.