Leak detection event aggregation and ranking systems and methods

Patent metadata

Field	Value
Publication number	US-11525819-B1
Application number	US-202117249795-A
Country	US
Kind code	B1
Filing date	Mar 12, 2021
Priority date	Jun 1, 2017
Publication date	Dec 13, 2022
Grant date	Dec 13, 2022

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.

First claim

What is claimed is:

1. A system comprising at least one hardware processor and a memory storing instructions which, when executed, cause the at least one hardware processor to:
receive collected data including gas concentration and location values collected by a vehicle-borne gas concentration measurement device configured to perform a sequence of geospatially-referenced mobile gas concentration measurements along one or more survey paths;
automatically analyze the collected data to identify a plurality of natural gas leaks, each leak corresponding to a cluster of leak identifications in the collected data; and
generate a list of leaks sorted by hazard level by applying a risk model to the collected data.

2. The system of claim 1 , wherein analyzing the collected data comprises applying a filter to a set of leak detections to filter out a subset of leak detection, the filter including at least one filter selected from a filter based on a background level of natural gas, and a filter based on an inter-peak distance for leak detections.

3. The system of claim 1 , wherein analyzing the collected data comprises spatially aggregating leak indications of the collected data into a plurality of clusters, each cluster comprising a plurality of leak indications, each cluster representing a corresponding leak.

4. The system of claim 3 , wherein generating the plurality of clusters comprises performing density-based spatial clustering using a maximum distance scale and a minimum number of points per cluster as input parameters to distinguish between members and non-members of each cluster.

5. The system of claim 3 , wherein generating the plurality of clusters comprises carrying out Markov clustering by performing a random walk through points representing leak indications to distinguish between members and non-members of each cluster.

6. The system of claim 3 , wherein generating the plurality of clusters comprises employing wind directions values corresponding to leak indications to distinguish between members and non-members of each cluster.

7. The system of claim 1 , wherein the risk model embodies training data characterizing known leaks and associated hazard levels.

8. The system of claim 1 , wherein applying the risk model comprises determining values for a plurality of hazard-predictive features for a leak according to the collected data, and determining a hazard level for the leak according to the determined values.

9. The system of claim 8 , wherein the plurality of hazard-predictive features includes a maximum concentration, a minimum concentration, and a representative concentration for the leak, determined from a cluster of leak indications assigned to the leak.

10. The system of claim 8 , wherein the plurality of hazard-predictive features includes an estimated leak flow rate for the leak.

11. The system of claim 8 , wherein the plurality of hazard-predictive features includes an estimated likelihood of the leak being a natural gas leak, the estimated likelihood determined according to a relationship between ethane and methane concentration values in the collected data.

12. The system of claim 8 , wherein the plurality of hazard-predictive features includes an estimated distance between a leak indication and a corresponding leak source.

13. The system of claim 8 , wherein the risk model is selected from a linear regression model yielding continuous values representing hazard levels, a logistic regression model yielding probabilities that leaks fit within predefined categories of hazard, and a random forest model.

14. The system of claim 1 , wherein the hazard level for a leak represents an estimated probability that the leak fits within a predefined category of hazard.

15. A method comprising employing a system comprising at least one hardware processor and a memory storing instructions which, when executed, cause the at least one hardware processor to:
receive collected data including gas concentration and location values collected by a vehicle-borne gas concentration measurement device configured to perform a sequence of geospatially-referenced mobile gas concentration measurements along one or more survey paths;
automatically analyze the collected data to identify a plurality of natural gas leaks, each leak corresponding to a cluster of leak identifications in the collected data; and
generate a list of leaks sorted by hazard level by applying a risk model to the collected data.

Assignees

• Picarro Inc

Inventors

• Macmullin Sean
• Rella Chris W
• Van Pelt Aaron
• Balkanski Alex
• He Yonggang
• Tan Sze M
• Steele David
• Clark Tim

Classifications

• G01N33/0075 Primary

  for multiple spatially distributed sensors, e.g. for environmental monitoring · CPC title

• G01N33/0047 Primary

  Organic compounds · CPC title

• G01M3/18

  for pipes, cables or tubes; for pipe joints or seals; for valves; {for welds; for containers, e.g. radiators} · CPC title

• G01M3/04

  by detecting the presence of fluid at the leakage point · CPC title