Systems and methods for likelihood-based detection of gas leaks using mobile survey equipment

• US 9,599,597 B1
• Filed: 12/23/2013
• Issued: 03/21/2017
• Est. Priority Date: 12/22/2012
• Status: Active Grant

• Current Assignee: Picarro, Inc.
• Original Assignee: Picarro, Inc.
• Inventors: Tan, Sze M., Steele, David, Rella, Chris W.
• Primary Examiner(s): Yi, Roy Y
• Application Number:
  US14/139,348
• Time in Patent Office: 1,184 Days
• US Class Current: 1/1
• CPC Class Codes: G01M 3/04 by detecting the presence of fluid at the leakage point
• CPC Class Codes: G01N 25/20 by investigating the development of heat, i.e. calorimetry, e.g. by measuring specific heat, by measuring thermal conductivity calorimeters per se G01K
• CPC Class Codes: G01N 33/0004 Gaseous mixtures, e.g. polluted air gaseous biological material G01N33/497; exhaust gas of internal combustion engines G01M15/102
• CPC Class Codes: G01N 33/0075 for multiple spatially distributed sensors, e.g. for environmental monitoring transmission systems for measured values G08C
• CPC Class Codes: G01W 1/02 Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed G01W1/10 takes precedence

Abstract

In some embodiments, vehicle-based natural gas leak detection methods are used to generate 2-D spatial distributions (heat maps) of gas emission source probabilities and surveyed area locations using measured gas concentrations and associated geospatial (e.g. GPS) locations, wind direction and wind speed, and atmospheric condition data. Bayesian updates are used to incorporate the results of one or more measurement runs into computed spatial distributions. Operating in gas-emission plume space rather than raw concentration data space allows reducing the computational complexity of updating gas emission source probability heat maps. Gas pipeline location data and other external data may be used to determine the heat map data.

Claims

1. A method comprising employing at least one processor to:

identify a first gas emission plume event according to a first set of gas emission data resulting from a first mobile measurement run performed by a mobile measurement device along a first measurement path, the first set of gas emission data reflecting a first set of gas concentration data acquired during the first mobile measurement run and a first set of associated atmospheric condition data characterizing the first mobile measurement run;

generate a prior 2-D surface map of gas emission source probabilities according to the first set of gas emission data;

receive a second set of gas emission data resulting from a second mobile measurement run, the second set of gas emission data reflecting a second set of gas concentration data acquired during the second mobile measurement run; and

update the prior 2-D surface map to generate a posterior 2-D surface map of gas emission source probabilities according to the second set of gas emission data, wherein generating the posterior 2-D surface map comprises determining an updated probability of a gas emission source being present at a given location according to a product of a prior probability of the gas emission source being present at the given location and a probability of observing the second set of gas emission data given the gas emission source being present at the given location.

2. The method of claim 1, wherein identifying the first gas emission plume event comprises identifying a concentration peak amplitude and associated spatial extent along the first measurement path.

3. The method of claim 1, wherein the prior 2-D surface map is generated according to an atmospheric transport model employing the first set of associated atmospheric condition data.

4. The method of claim 1, further comprising employing the at least one processor to generate display data for displaying the posterior 2-D surface map to a user.

5. The method of claim 4, wherein the display data defines a color-coded heat map to be displayed.

6. The method of claim 1, wherein at least one of the prior 2-D surface map and the posterior 2-D surface map is generated according to a set of geospatially-referenced locations of underground gas transmission pipelines representing potential gas emission sources.

7. The method of claim 6, wherein at least one of the prior 2-D surface map and the posterior 2-D surface map is generated according to pipeline condition data characterizing a condition of the underground gas transmission pipelines.

8. The method of claim 6, further comprising employing the at least one processor to generate display data for displaying to a user the posterior 2-D surface map overlaid on a map of the underground gas transmission pipelines.

9. The method of claim 1, wherein the first set of associated atmospheric condition comprises a plurality of wind speed and wind direction values acquired along the first measurement path.

10. The method of claim 1, wherein the second measurement run is performed along a second measurement path different from the first measurement path.

11. A non-transitory computer-readable medium encoding instructions which, when executed by a computer system comprising at least one processor, cause the at least one processor to:

identify a first gas emission plume event according to a first set of gas emission data resulting from a first mobile measurement run performed by a mobile measurement device along a first measurement path, the first set of gas emission data reflecting a first set of gas concentration data acquired during the first mobile measurement run and a first set of associated atmospheric condition data characterizing the first mobile measurement run;

generate a prior 2-D surface map of gas emission source probabilities according to the first set of gas emission data;

receive a second set of gas emission data resulting from a second mobile measurement run, the second set of gas emission data reflecting a second set of gas concentration data acquired during the second mobile measurement run; and

update the prior 2-D surface map to generate a posterior 2-D surface map of gas emission source probabilities according to the second set of gas emission data, wherein generating the posterior 2-D surface map comprises determining an updated probability of a gas emission source being present at a given location according to a product of a prior probability of the gas emission source being present at the given location and a probability of observing the second set of gas emission data given the gas emission source being present at the given location.

14. The computer-readable medium of claim 11, wherein identifying the first gas emission plume event comprises identifying a concentration peak amplitude and associated spatial extent along the first measurement path.

15. The computer-readable medium of claim 11, wherein the prior 2-D surface map is generated according to an atmospheric transport model employing the first set of associated atmospheric condition data.

16. The computer-readable medium of claim 11, wherein the instructions are further configured to cause the at least one processor to generate display data for displaying the posterior 2-D surface map to a user.

17. The computer-readable medium of claim 16, wherein the display data defines a color-coded heat map to be displayed.

18. The computer-readable medium of claim 11, wherein at least one of the prior 2-D surface map and the posterior 2-D surface map is generated according to a set of geospatially-referenced locations of underground gas transmission pipelines representing potential gas emission sources.

19. The computer-readable medium of claim 18, wherein at least one of the prior 2-D surface map and the posterior 2-D surface map is generated according to pipeline condition data characterizing a condition of the underground gas transmission pipelines.

20. The computer-readable medium of claim 18, wherein the instructions are further configured to cause the at least one processor to generate display data for displaying to a user the posterior 2-D surface map overlaid on a map of the underground gas transmission pipelines.

21. The computer-readable medium of claim 11, wherein the first set of associated atmospheric condition comprises a plurality of wind speed and wind direction values acquired along the first measurement path.

22. The computer-readable medium of claim 11, wherein the second measurement run is performed along a second measurement path different from the first measurement path.

12. A computer system comprising at least one processor configured to:

identify a first gas emission plume event according to a first set of gas emission data resulting from a first mobile measurement run performed by a mobile measurement device along a first measurement path, the first set of gas emission data reflecting a first set of gas concentration data acquired during the first mobile measurement run and a first set of associated atmospheric condition data characterizing the first mobile measurement run;

generate a prior 2-D surface map of gas emission source probabilities according to the first set of gas emission data;

receive a second set of gas emission data resulting from a second mobile measurement run, the second set of gas emission data reflecting a second set of gas concentration data acquired during the second mobile measurement run; and

update the prior 2-D surface map to generate a posterior 2-D surface map of gas emission source probabilities according to the second set of gas emission data, wherein generating the posterior 2-D surface map comprises determining an updated probability of a gas emission source being present at a given location according to a product of a prior probability of the gas emission source being present at the given location and a probability of observing the second set of gas emission data given the gas emission source being present at the given location.

13. A non-transitory computer-readable medium encoding instructions which, when executed by a computer system comprising at least one processor, cause the at least one processor to:

receive a prior 2-D surface map of gas emission source probabilities;

identify a first gas emission plume event according to a first set of gas emission data resulting from a first mobile measurement run performed by a mobile measurement device along a first measurement path, the first set of gas emission data reflecting a first set of gas concentration data acquired during the first mobile measurement run and a first set of associated atmospheric condition data characterizing the first mobile measurement run; and

selectively employ data characterizing the identified first gas emission plume event to update the prior 2-D surface map to generate a posterior 2-D surface map of gas emission source probabilities by determining an updated probability of a gas emission source being present at a given location according to a product of a prior probability of the gas emission source being present at the given location and a probability of observing the data characterizing the identified first gas emission plume event given the gas emission source being present at the given location.