Optimized multi-stage intermittent fugitive emission detection

Patent metadata

Field	Value
Publication number	US-2021255157-A1
Application number	US-201917261785-A
Country	US
Kind code	A1
Filing date	Jul 19, 2019
Priority date	Jul 20, 2018
Publication date	Aug 19, 2021
Grant date	—

Abstract

A method is provided for mitigating fugitive methane emission, which includes scanning a plurality of facilities for fugitive methane emission using an airborne sensor, and classifying the plurality of facilities based on results of the scanning. Optionally, further inspection of at least one facility of the plurality of facilities can be performed to detect and locate fugitive methane emission based on the classifying. Optionally, at least one facility can be selectively repaired based on the further inspection in order to mitigate fugitive methane emission. In another aspect, a planning workflow is provided that employs a clustering method to define cluster data representing a set of facility clusters in a geographical region that are associated with a particular base. The cluster data can be processed to determine flight path data representing flight path segments or route that form a trip, wherein the trip originates at the particular base, travels to a sequence of facility clusters and scans each facility in each facility cluster, and returns back to the particular base, wherein the sequence of facility clusters of the trip corresponds to the set of facility clusters represented by the cluster data.

First claim

What is claimed is:

1 . A method of mitigating fugitive methane emission comprising:
scanning a plurality of facilities for fugitive methane emission using an airborne sensor; and classifying the plurality of facilities based on results of the scanning.

2 . The method of claim 1 , further comprising:
selectively performing further inspection of at least one facility of the plurality of facilities for fugitive methane emission based on the classifying; and/or selectively repairing at least one facility of the plurality of facilities based on the further inspection in order to mitigate fugitive methane emission.

3 . The method of claim 1 , further comprising:
building a map of the plurality of facilities.

4 . The method of claim 1 , further comprising:
determining a flight path for the scanning.

5 . The method of claim 4 , wherein:
the flight path is determined by minimizing flight time costs for the scanning.

6 . The method of claim 4 , wherein:
the flight path covers a set of facility clusters that are serviced by a respective base.

7 . The method of claim 6 , further comprising:
using a computer-implemented clustering method to identify the set of facility clusters that are serviced by the respective base; and using a computer-implemented vehicle routing problem (VRP) solver to determine flight path data that represents the flight path that covers the set of facility clusters that are serviced by the respective base as output by the clustering method.

8 . The method of claim 7 , wherein:
the flight path data represents a trip that originates from the respective base and travels to a sequence of facility clusters that corresponds to the set of facility clusters and scans each facility in each facility cluster and returns back to the respective base.

9 . The method of claim 1 , wherein:
the airborne sensor comprises a laser-based sensor.

10 . The method of claim 1 , wherein:
the plurality of facilities are selected from the group consisting of well sites, compressor stations, and other upstream facilities.

11 . The method of claim 1 , wherein:
the airborne sensor is mounted to an aircraft selected from the group consisting of a drone, a helicopter, a fixed-winged airplane, or other aircraft or flight vehicle.

12 . A method for planning aerial inspection of a plurality of facilities in a geographical region, the method comprising:
a) storing data that represents the plurality of facilities in the geographical region and data that represents at least one base in the geographical region, wherein the at least one base supports aerial inspection of the plurality of facilities in the geographical region; b) selecting a particular base in the geographical region; c) performing a clustering method on the data of a) to define cluster data representing a set of facility clusters in the geographical region that are associated with the particular base of b); and d) processing the cluster data of c) to determine flight path data representing flight path segments that form a trip, wherein the trip originates at the particular base, travels to a sequence of facility clusters and scans each facility in each facility cluster, and returns back to the particular base, wherein the sequence of facility clusters of the trip corresponds to the set of facility clusters represented by the cluster data of c).

13 . The method of claim 12 , wherein:
the data of a) is stored in computer memory; and the operations of c) and d) are performed by at least one processor.

14 . The method of claim 12 , wherein:
in d), the flight path data representing the flight segments of the trip is determined by minimizing flight time costs for the trip.

15 . The method of claim 14 , further comprising:
storing flight vehicle data that represents operational parameters for at least one flight vehicle, and storing sensor data that represents operational parameters for at least one airborne sensor; wherein, in d) the flight time costs for the trip are based on the flight vehicle data and the sensor data.

16 . The method of claim 12 , further comprising:
repeating the operations of c) and d) for at least one additional base in the geographic region.

17 . The method of claim 12 , further comprising:
repeating the operations of c) and d) for different combinations of flight vehicle and airborne sensor that could be used for the aerial inspection.

18 . The method of claim 17 , wherein:
the different combinations of flight vehicle and airborne sensor have different flight vehicles.

19 . The method of claim 17 , wherein
the different combinations of flight vehicle and airborne sensor have different airborne sensors.

20 . The method of claim 17 , wherein
the different combinations of flight vehicle and airborne sensor have both different flight vehicles and different airborne sensors.

21 . The method of claim 20 , further comprising:
using the flight path data of d) to determine overall costs for the different combinations of flight vehicle and airborne sensor; and evaluating the overall costs for the different combinations of flight vehicle and airborne sensor in order to select a particular combination of flight vehicle and airborne sensor that will be used for the aerial inspection.

22 . The method of claim 21 , wherein:
the overall costs for the different combinations of flight vehicle and airborne sensor are based on financial parameters for the different combinations of flight vehicle and airborne sensor.

23 . The method of claim 21 , further comprising:
using the particular combination of flight vehicle and airborne sensor and the flight path data of d) for the particular combination of flight vehicle and airborne sensor to perform the aerial inspection of the facilities in the geographical region.

24 . The method of claim 12 , wherein:
the clustering method of c) is a hierarchical multilevel clustering method.

25 . The method of claim 12 , wherein:
the clustering method of c) is applied to a filtered set of facilities that are associated with the particular base.

26 . The method of claim 12 , wherein:
the processing of d) uses a computer-implemented vehicle routing problem (VRP) solver to determine the flight path data.

27 . The method of claim 26 , wherein:
the VRP solver employs a graph with the facility clusters defined as vertices of the graph, time to travel between clusters at flight vehicle cruising speed defined as edge costs in the graph, scan times for scanning each facility in the clusters embedded as vertex costs in the graph, and vehicle range limits imposed as capacity constraints.

28 . The method of claim 27 , wherein:
no-fly zone restrictions and possibly other limitations are defined by a set of constraints that are added as penalties on non-compliant edges of the graph.

29 . The method of claim 14 , further comprising:
storing data representing a template scan pattern which is intended to be used in scanning one or more facilities in a respective cluster; wherein the flight time costs include scanning costs for scanning the respective cluster which is based on the data representing the template scan pattern.

30 . The method of claim 29 , wherein:
the scanning costs for scanning the respective cluster is fu

Assignees

• Schlumberger Technology Corp

Inventors

• Rashid Kashif
• Speck Andrew J
• Perroni Dominic
• Osedach Timothy Paul
• Meade Christian
• Manson Ronald
• Pomerantz Andrew E

Classifications

• B64D47/00 Primary

  Equipment not otherwise provided for · CPC title

• G01N33/0062

  concerning the measuring method or the display, e.g. intermittent measurement or digital display · CPC title

• G06Q10/047

  Optimisation of routes or paths, e.g. travelling salesman problem · CPC title

• G01C21/005

  with correlation of navigation data from several sources, e.g. map or contour matching (G01C21/30 takes precedence) · CPC title

• G01N33/0075

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