WO2020206008A1 - Time-and data-efficient assurance of leak detection - Google Patents

Time-and data-efficient assurance of leak detection Download PDF

Info

Publication number
WO2020206008A1
WO2020206008A1 PCT/US2020/026232 US2020026232W WO2020206008A1 WO 2020206008 A1 WO2020206008 A1 WO 2020206008A1 US 2020026232 W US2020026232 W US 2020026232W WO 2020206008 A1 WO2020206008 A1 WO 2020206008A1
Authority
WO
WIPO (PCT)
Prior art keywords
gas
received
potential
processor
spatial location
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2020/026232
Other languages
French (fr)
Inventor
Victor Alexander MILLER II
Stuart Buckingham
Brendan James SMITH
Michael Price MCGUIRE
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Seekops Inc
Original Assignee
Seekops Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Seekops Inc filed Critical Seekops Inc
Priority to EP20783815.2A priority Critical patent/EP3948202A4/en
Priority to US17/601,559 priority patent/US12188847B2/en
Publication of WO2020206008A1 publication Critical patent/WO2020206008A1/en
Anticipated expiration legal-status Critical
Priority to US18/955,051 priority patent/US20250377256A1/en
Ceased legal-status Critical Current

Links

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01MTESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M3/00Investigating fluid-tightness of structures
    • G01M3/02Investigating fluid-tightness of structures by using fluid or vacuum
    • G01M3/04Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01MTESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M3/00Investigating fluid-tightness of structures
    • G01M3/02Investigating fluid-tightness of structures by using fluid or vacuum
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20Instruments for performing navigational calculations
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004Gaseous mixtures, e.g. polluted air
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01WMETEOROLOGY
    • G01W1/00Meteorology

Definitions

  • the invention relates to gas sensors, and more particularly to gas leak detection.
  • Trace gas sensors are used to detect and quantify leaks of toxic gases, e.g., hydrogen disulfide, or environmentally damaging gases, e.g., methane and sulfur dioxide, in a variety of industrial and environmental contexts. Detection and quantification of these leaks are of interest to a variety of industrial operations, e.g., oil and gas, chemical production, and painting, as well as environmental regulators for assessing compliance and mitigating environmental and safety risks.
  • toxic gases e.g., hydrogen disulfide
  • environmentally damaging gases e.g., methane and sulfur dioxide
  • a system embodiment may include: an aerial vehicle; at least one trace-gas sensor disposed on the aerial vehicle, the trace-gas sensor configured to generate gas data; a global positioning system disposed on the aerial vehicle to determine a location of the at least one trace-gas sensor; and a processor having addressable memory, the processor configured to: receive a spatial location having one or more potential gas sources; receive a spatial location of the one or more potential gas sources; receive a desired level of confidence for detecting gas leaks from the one or more potential gas sources; receive a wind data for the received spatial location; determine a flight envelope encompassing one or more potential plume envelopes based on the received spatial location, the received spatial location of the one or more potential gas sources, the received desired level of confidence, and the received wind data; determine a flight path for the aerial vehicle, where the flight path covers a portion of the determined flight envelope; receive the gas data from the one or more gas trace-gas sensors of the portion of the determined flight envelope; and determine based on the received gas data whether a gas leak may be present in the received
  • the wind data may include a wind direction and a wind speed.
  • the wind data may include at least one of: a predicted wind direction and a predicted wind speed.
  • the portion of the determined flight envelope excludes a restricted zone, where the restricted zone may be an area within a set distance of each of the one or more potential gas sources.
  • the at least one trace-gas sensor may be configured to detect hydrogen disulfide. In additional system embodiments, the at least one trace-gas sensor may be configured to detect methane. In additional system embodiments, the at least one trace-gas sensor may be configured to detect sulfur oxide. In additional system embodiments, the at least one trace-gas sensor may be configured to detect carbon dioxide. In additional system embodiments, the at least one trace-gas sensor may be configured to detect nitrogen oxide.
  • the aerial vehicle may be an unmanned aerial vehicle (UAV).
  • the determined flight plan may include one or more random points within the determined one or more potential plume envelopes.
  • the one or more random points may be connected into a flight pattern using a route planning algorithm.
  • the route planning algorithm may be a traveling salesman algorithm.
  • a method embodiment may include: receiving, by a processor having addressable memory, a spatial location having one or more potential gas sources; receiving, by the processor, a spatial location of the one or more potential gas sources; receiving, by the processor, a desired level of confidence for detecting gas leaks from the one or more potential gas sources; receiving, by the processor, a wind data for the received spatial location;
  • Additional method embodiments may include: receiving, by the processor, a state of the one or more potential gas sources.
  • the at least one trace-gas sensor may be configured to detect at least one of: hydrogen disulfide, methane, sulfur oxide, carbon dioxide, and nitrogen oxide.
  • An additional system embodiment may include: a portable device; at least one trace-gas sensor disposed on the portable device, the trace-gas sensor configured to generate gas data; a global positioning system disposed on the portable device to determine a location of the at least one trace-gas sensor; and a processor having addressable memory, the processor configured to: receive a spatial location having one or more potential gas sources; receive a spatial location of the one or more potential gas sources; receive a desired level of confidence for detecting gas leaks from the one or more potential gas sources; receive a wind data for the received spatial location; determine a flight envelope encompassing one or more potential plume envelopes based on the received spatial location, the received spatial location of the one or more potential gas sources, the received desired level of confidence, and the received wind data; determine a path for the portable device, where the path covers a portion of the determined flight envelope; receive the gas data from the one or more gas trace-sensors of the portion of the determined flight envelope; and determine based on the received gas data whether a gas leak may be present in the received
  • the wind data comprises at least one of: a wind direction, a wind speed, a predicted wind direction, and a predicted wind speed.
  • the at least one trace-gas sensor may be configured to detect at least one of: hydrogen disulfide, methane, sulfur oxide, carbon dioxide, and nitrogen oxide.
  • the portion of the determined flight envelope excludes a restricted zone, where the restricted zone may be an area within a set distance of each of the one or more potential gas sources.
  • FIG. 1 depicts a forward model potential plume envelopes generated using wind data, according to one embodiment
  • FIG. 2A depicts a flight envelope calculated from forward model plume mixing, according to one embodiment
  • FIG. 2B depicts a close-up view of a portion of the plume envelope of FIG.
  • FIG. 2C depicts a portion of the plume envelope of FIG. 2B to be sampled, according to one embodiment
  • FIG. 2D depicts waypoints in the portion of the plume envelope of FIG. 2C to be sampled, according to one embodiment
  • FIG. 2E depicts a flight path for the waypoints in the portion of the plume envelope of FIG. 2D, according to one embodiment
  • FIG. 3 depicts random waypoints with a traveling salesman route, according to one embodiment
  • FIG. 4A depicts an image of an area to be sampled, according to one embodiment
  • FIG. 4B depicts random samples of the image of FIG. 4A along the paths traversed in FIG. 2, according to one embodiment
  • FIG. 4C depicts a reconstructed image from the random samples of FIG. 4B, according to one embodiment
  • FIG. 5 shows a high-level block diagram and process of a computing system for implementing an embodiment of the system and process
  • FIG. 6 shows a block diagram and process of an exemplary system in which an embodiment may be implemented
  • FIG. 7 depicts a cloud computing environment for implementing an embodiment of the system and process disclosed herein;
  • FIG. 8A depicts a high-level block diagram of a gas leak detection system, according to one embodiment
  • FIG. 8B depicts a high-level block diagram of an alternate gas leak detection system, according to one embodiment.
  • FIG. 9 depicts a high-level flowchart of a method embodiment of determining a likelihood of gas leaks within a spatial location, according to one embodiment
  • the present system allows for the creation of a flight plan to ascertain whether any gas leaks are present within a set spatial location.
  • the spatial location may be a two- dimensional area, a three-dimensional area, a GPS location, and/or a geographical area.
  • the created flight plan accounts for wind and a likelihood of the presence of gas leaks. This created flight plan allows for the determination, within a desired confidence level, as to whether any gas leaks are present in the set spatial location.
  • This created flight plan may be accomplished by an aerial vehicle, such as an unmanned aerial vehicle, within a set time so as to provide time-efficient and data-efficient sampling of the set spatial location.
  • Trace gas sensors are used to detect and quantify leaks of toxic gases, e.g., hydrogen disulfide, or environmentally damaging gases, e.g., methane and sulfur dioxide, in a variety of industrial and environmental contexts. Detection and quantification of these leaks are of interest to a variety of industrial operations, e.g., oil and gas, chemical production, and painting, as well as environmental regulators for assessing compliance and mitigating environmental and safety risks.
  • toxic gases e.g., hydrogen disulfide
  • environmentally damaging gases e.g., methane and sulfur dioxide
  • leak localization and detection are made more challenging by the dynamic nature of wind and the limited flight duration of aerial platforms. For example, if no trace gas is detected downwind of a site, without taking into account the details of local weather patterns, there is no a prioi guarantee of no leaks at that site given that winds are constantly changing direction and velocity. For example, the trace gas may have been blowing in a direction that the survey did not capture.
  • site operators and regulators may require assurances that a site is leak free with a high confidence, i.e., site operators and regulators aim to minimize the likelihood of a false negative.
  • This advance in the art is achieved by fusing local wind measurements with flight planning and operation.
  • a flight envelope can be computed using a physics-based forward-computed fluid mixing model.
  • This forward-model takes in a time series of point measurements of wind speed, direction, and variance, and, based on conservation of fluid momentum and mass, computes the probability that the gas of interest will be present at any given time in each discretized location in the survey area.
  • flight trajectories may be computed to efficiently sample this space, by maximizing the flight space covered in the shortest amount of time, while simultaneously maximizing the confidence level of a leak false negative.
  • FIG. 1 depicts a forward model 100 potential plume envelopes 102 generated using wind 104 data, according to one embodiment.
  • Wind 104 creates potential plume envelopes 102 from potential gas sources 106.
  • Each potential gas source 106 may be a single potential gas source or a cluster of potential gas sources.
  • FIG. 2A depicts a flight envelope 200 calculated from forward model plume mixing 202, according to one embodiment.
  • the flight envelope 200 encompasses the potential plume envelopes 204, as shown in FIG. 1 as 102.
  • the plume envelopes 204 may be a two-dimensional location in some embodiments. In other embodiments, the plume envelopes 204 may be a three-dimensional area.
  • the plume envelopes 204 may account for rising or falling gases based on the wind direction, wind speed, type of gas from each potential gas source 210, and the like.
  • Each of the one or more potential gas sources 210 may each have an associated restricted zone 208.
  • the restricted zone may be a no-fly, or no entry, area within a set distance of a potential gas source 210.
  • the restricted zone 210 may be based on user preference, regulations, and/or type of potential gas source 210. For example, some potential gas sources 210 may have larger restricted zones 210 than other potential gas sources 210.
  • the optimal flight area 206 for each potential gas source 210 is based on the desired confidence level for detecting a gas leak. The optimal flight area 206 may be expanded for an increased desired confidence level. The optimal flight area 206 may be reduced for a decreased desired confidence level. In some embodiments, the optimal flight area 206 may be increased for higher winds and decreased for lower winds.
  • FIG. 2B depicts a close-up view of a portion of the plume envelope 204 of FIG. 2A, according to one embodiment. As shown in FIG. 2B, only a portion of the optimal flight area 206 surrounding each potential gas source 210 overlaps with the potential plume envelope 204 due to the wind direction and wind speed.
  • FIG. 2C depicts a portion 212 of the plume envelope of FIG. 2B to be sampled, according to one embodiment.
  • This portion 212 of the plume envelope includes the areas likely to include trace-gas if a gas leak is present while excluding any restricted zones 208, as shown in FIG. 2B, around each possible gas source 210.
  • FIG. 2D depicts waypoints 214 in the portion 212 of the plume envelope of FIG. 2C to be sampled, according to one embodiment.
  • One or more waypoints 214 may be added in the portion 212 of the plume envelope to be sampled.
  • the waypoints 214 may be distributed in a uniform, random, or other pattern.
  • waypoints 214 may be positioned in a greater density closer to each potential gas source 210.
  • the waypoints 214 may be positioned in a lower density farther away from each potential gas source 210.
  • the number and/or location of the waypoints 214 may be based on the desired level of confidence for detecting any gas leaks for the potential gas sources 210.
  • FIG. 2E depicts a flight path 216 for the waypoints 214 in the portion 212 of the plume envelope of FIG. 2D, according to one embodiment.
  • a raster pattern may be used as the flight path 216 to connect the waypoints 214.
  • a traveling salesman route as shown in FIG. 3, or other route may be used to connect the waypoints 214 within the portion 212 of the plume envelope.
  • the flight path may be contained within the portion 210 of the plume envelope.
  • the flight path may avoid going into the restricted zone 208, as shown in FIG. 2B.
  • the flight path may be a random walk to connect waypoints 214.
  • FIG. 3 depicts random waypoints 300 with a traveling salesman route, according to one embodiment.
  • the start point and finish point may be different.
  • One method for efficiently traversing the flight envelope is to randomly disperse waypoints across the plume, as shown in FIG. 1, and/or the flight envelope, as shown in FIG. 2, in densities proportional to likelihood of gas being present within the flight envelope.
  • This approach disclosed herein yields relatively narrow flight windows for low-variance wind conditions, and it yields larger flight envelopes for high-variance wind conditions.
  • the random distribution of flight waypoints forces the sensor to spend more time sampling regions with a high likelihood of gas and eliminates any sampling bias introduced by rastering.
  • a given site that has been deemed free of leaks after flying a known flight path can be simulated in a Monte Carlo fashion, using the same walk-forward model described above, testing the assumptions put in place regarding potential leak sources, and quantifying the level of confidence that a site is, indeed, free of any leaks.
  • a Monte Carlo simulation, or other simulation may be used to determine the one or more potential plume envelopes.
  • FIG. 4A depicts an image of an area to be sampled 402, according to one embodiment.
  • FIG. 4B depicts random samples 404 of the image of FIG. 4 A along the paths traversed in FIG. 2, according to one embodiment.
  • FIG. 4C depicts a reconstructed image 406 from the random samples of FIG. 4B, according to one embodiment.
  • FIG. 5 is a high-level block diagram 500 showing a computing system comprising a computer system useful for implementing an embodiment of the system and process, disclosed herein.
  • the computer system includes one or more processors 502, and can further include an electronic display device 504 (e.g., for displaying graphics, text, and other data), a main memory 506 (e.g., random access memory (RAM)), storage device 508, a removable storage device 510 (e.g., removable storage drive, a removable memory module, a magnetic tape drive, an optical disk drive, a computer readable medium having stored therein computer software and/or data), user interface device 511 (e.g., keyboard, touch screen, keypad, pointing device), and a communication interface 512 (e.g., modem, a network interface (such as an Ethernet card), a communications port, or a PCMCIA slot and card).
  • the communication interface 512 allows software and data to be transferred between the computer system and external devices.
  • Information transferred via communications interface 514 may be in the form of signals such as electronic, electromagnetic, optical, or other signals capable of being received by communications interface 514, via a communication link 516 that carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular/mobile phone link, a radio frequency (RF) link, and/or other communication channels.
  • Computer program instructions representing the block diagram and/or flowcharts herein may be loaded onto a computer, programmable data processing apparatus, or processing devices to cause a series of operations performed thereon to produce a computer implemented process.
  • Embodiments have been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments.
  • Each block of such illustrations/diagrams, or combinations thereof, can be implemented by computer program instructions.
  • the computer program instructions when provided to a processor produce a machine, such that the instructions, which execute via the processor, create means for implementing the functions/operations specified in the flowchart and/or block diagram.
  • Each block in the flowchart/block diagrams may represent a hardware and/or software module or logic, implementing embodiments. In alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures, concurrently, etc.
  • Computer programs are stored in main memory and/or secondary memory. Computer programs may also be received via a communications interface 512. Such computer programs, when executed, enable the computer system to perform the features of the embodiments as discussed herein. In particular, the computer programs, when executed, enable the processor and/or multi-core processor to perform the features of the computer system. Such computer programs represent controllers of the computer system.
  • FIG. 6 shows a block diagram of an example system 600 in which an embodiment may be implemented.
  • the system 600 includes one or more client devices 601 such as consumer electronics devices, connected to one or more server computing systems 630.
  • a server 630 includes a bus 602 or other communication mechanism for communicating information, and a processor (CPU) 604 coupled with the bus 602 for processing information.
  • the server 630 also includes a main memory 606, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus 602 for storing information and instructions to be executed by the processor 604.
  • the main memory 606 also may be used for storing temporary variables or other intermediate information during execution or instructions to be executed by the processor 604.
  • the server computer system 630 further includes a read only memory (ROM) 608 or other static storage device coupled to the bus 602 for storing static information and instructions for the processor 604.
  • ROM read only memory
  • a storage device 610 such as a magnetic disk or optical disk, is provided and coupled to the bus 602 for storing information and instructions.
  • the bus 602 may contain, for example, thirty -two address lines for addressing video memory or main memory 606.
  • the bus 602 can also include, for example, a 32-bit data bus for transferring data between and among the components, such as the CPU 604, the main memory 606, video memory and the storage 610. Alternatively, multiplex data/address lines may be used instead of separate data and address lines.
  • the server 630 may be coupled via the bus 602 to a display 612 for displaying information to a computer user.
  • An input device 614 is coupled to the bus 602 for communicating information and command selections to the processor 604.
  • cursor control 616 such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processor 604 and for controlling cursor movement on the display 612.
  • the functions are performed by the processor 604 executing one or more sequences of one or more instructions contained in the main memory 606. Such instructions may be read into the main memory 606 from another computer-readable medium, such as the storage device 610. Execution of the sequences of instructions contained in the main memory 606 causes the processor 604 to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in the main memory 606. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the embodiments. Thus, embodiments are not limited to any specific combination of hardware circuitry and software. [0056] The terms "computer program medium,” "computer usable medium,”
  • “computer readable medium”, and “computer program product,” are used to generally refer to media such as main memory, secondary memory, removable storage drive, a hard disk installed in hard disk drive, and signals. These computer program products are means for providing software to the computer system.
  • the computer readable medium allows the computer system to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium.
  • the computer readable medium may include non-volatile memory, such as a floppy disk, ROM, flash memory, disk drive memory, a CD-ROM, and other permanent storage. It is useful, for example, for transporting information, such as data and computer instructions, between computer systems.
  • the computer readable medium may comprise computer readable information in a transitory state medium such as a network link and/or a network interface, including a wired network or a wireless network that allow a computer to read such computer readable information.
  • Computer programs also called computer control logic
  • main memory and/or secondary memory Computer programs may also be received via a communications interface.
  • Such computer programs when executed, enable the computer system to perform the features of the embodiments as discussed herein.
  • the computer programs when executed, enable the processor multi-core processor to perform the features of the computer system. Accordingly, such computer programs represent controllers of the computer system.
  • Non-volatile media includes, for example, optical or magnetic disks, such as the storage device 610.
  • Volatile media includes dynamic memory, such as the main memory 606
  • Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise the bus 602 Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.
  • Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.
  • Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to the processor 604 for execution.
  • the instructions may initially be carried on a magnetic disk of a remote computer.
  • the remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem.
  • a modem local to the server 630 can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal.
  • An infrared detector coupled to the bus 602 can receive the data carried in the infrared signal and place the data on the bus 602.
  • the bus 602 carries the data to the main memory 606, from which the processor 604 retrieves and executes the instructions.
  • the instructions received from the main memory 606 may optionally be stored on the storage device 610 either before or after execution by the processor 604.
  • the server 630 also includes a communication interface 618 coupled to the bus 602.
  • the communication interface 618 provides a two-way data communication coupling to a network link 620 that is connected to the world wide packet data communication network now commonly referred to as the Internet 628.
  • the Internet 628 uses electrical,
  • the signals through the various networks and the signals on the network link 620 and through the communication interface 618, which carry the digital data to and from the server 630, are exemplary forms or carrier waves transporting the information.
  • interface 618 is connected to a network 622 via a communication link 620.
  • the communication interface 618 may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line, which can comprise part of the network link 620.
  • the communication interface 618 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented.
  • ISDN integrated services digital network
  • LAN local area network
  • communication interface 618 sends and receives electrical electromagnetic or optical signals that carry digital data streams representing various types of information.
  • the network link 620 typically provides data communication through one or more networks to other data devices.
  • the network link 620 may provide a connection through the local network 622 to a host computer 624 or to data equipment operated by an Internet Service Provider (ISP).
  • ISP Internet Service Provider
  • the ISP in turn provides data communication services through the Internet 628.
  • the local network 622 and the Internet 628 both use electrical, electromagnetic or optical signals that carry digital data streams.
  • the signals through the various networks and the signals on the network link 620 and through the communication interface 618, which carry the digital data to and from the server 630, are exemplary forms or carrier waves transporting the information.
  • the server 630 can send/receive messages and data, including e-mail, program code, through the network, the network link 620 and the communication interface 618.
  • the communication interface 618 can comprise a USB/Tuner and the network link 620 may be an antenna or cable for connecting the server 630 to a cable provider, satellite provider or other terrestrial transmission system for receiving messages, data and program code from another source.
  • the example versions of the embodiments described herein may be implemented as logical operations in a distributed processing system such as the system 600 including the servers 630.
  • the logical operations of the embodiments may be implemented as a sequence of steps executing in the server 630, and as interconnected machine modules within the system 600.
  • the implementation is a matter of choice and can depend on performance of the system 600 implementing the embodiments.
  • the logical operations constituting said example versions of the embodiments are referred to for e.g., as operations, steps or modules.
  • a client device 601 can include a processor, memory, storage device, display, input device and communication interface (e.g., e-mail interface) for connecting the client device to the Internet 628, the ISP, or LAN 622, for communication with the servers 630.
  • a processor e.g., a processor, memory, storage device, display, input device and communication interface (e.g., e-mail interface) for connecting the client device to the Internet 628, the ISP, or LAN 622, for communication with the servers 630.
  • communication interface e.g., e-mail interface
  • the system 600 can further include computers (e.g., personal computers, computing nodes) 605 operating in the same manner as client devices 601, wherein a user can utilize one or more computers 605 to manage data in the server 630.
  • computers e.g., personal computers, computing nodes
  • cloud computing environment 50 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA), smartphone, smart watch, set-top box, video game system, tablet, mobile computing device, or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or UAV 54N may communicate.
  • Nodes 10 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof.
  • cloud computing environment 50 to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices 54A-N shown in FIG. 7 are intended to be illustrative only and that computing nodes 10 and cloud computing environment 50 can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).
  • FIG. 8A depicts a high-level block diagram of a gas leak detection system 800, according to one embodiment.
  • the system includes a processor 802.
  • the processor 802 receives a spatial location 804, which may be an area containing one or more potential gas sources 806, 808, 810.
  • the processor 802 receives the spatial location of the one or more potential gas sources 806, 808, 810 within the spatial location 804.
  • the one or more potential gas sources 806, 808, 810 may be equipment and/or locations more likely to leak toxic gases, such as hydrogen disulfide, or environmentally damaging gases, such as methane and sulfur dioxide.
  • the at least one gas sensor 822 may be configured to detect carbon dioxide. In other embodiments, the at least one gas sensor 822 may be configured to detect nitrogen oxide.
  • the at least one gas sensor 822 may be configured to detect sulfur oxide, such as SO, SO 2 , SO 3 , S 7 O 2 , S 6 O 2 , S 2 O 2 , and the like.
  • the processor 802 may also receive a level of confidence 812 desired as to whether any gas leaks are present within the received spatial location 804. The higher the level of confidence 812, the longer it may take the system 800 to determine whether any gas leaks are present in the spatial location 804. A reasonably high level of confidence 812 may be achieved in a time- efficient manner using the system 800 disclosed herein.
  • the processor 802 may also receive wind data 814. Wind data 814 may include wind speed and/or wind direction for the spatial location 804. In some embodiments, wind data 814 may also include predictions as to changes in the wind speed and/or wind direction.
  • the processor 802 may determine one or more potential plume envelopes, such as shown in FIG. 2.
  • the potential plume envelopes cover potential plumes from gas leaks emanating from the one or more potential gas sources 806, 808, 810.
  • the one or more potential plume envelopes contain the area that will be tested by one or more gas leak sensors.
  • the processor determines a flight path for an aerial vehicle 816 having at least one gas sensor 822.
  • the flight path for the aerial vehicle 816 covers the one or more potential plume envelopes determined by the processor 802.
  • the aerial vehicle 816 may be an unmanned aerial vehicle (UAV) in some embodiments.
  • the aerial vehicle 816 may have a processor 818 in communication with addressable memory 820, a GPS 824, one or more motors 826, and a power supply 828.
  • the aerial vehicle 816 may receive the flight plan from the processor 802 and communicate gathered gas sensor 822 sensor to the processor 802.
  • the GPS 824 may record the location of the aerial vehicle 816 when each gas sensor 822 data is acquired.
  • the GPS 824 may also allow the aerial vehicle 816 to travel the flight path generated by the processor 802.
  • the location of the aerial vehicle 816 may be determined by an onboard avionics 834.
  • the onboard avionics 834 may include a triangulation system, a beacon, a spatial coordinate system, or the like.
  • the onboard avionics 834 may be used with the GPS 824 in some embodiments.
  • the aerial vehicle 816 may use only one of the GPS 824 and the onboard avionics 834.
  • the power supply 828 may be a battery in some embodiments.
  • the power supply 828 may limit the available flight time for the aerial vehicle 816 and so it is crucial that the potential plume envelopes are accurate to allow for data that can be used to make a determination as to whether there are any gas leaks within the desired level of confidence 812.
  • the flight plan may be split up into two or more flights based on a size of the potential plumes, a flight time of the aerial vehicle 816, weather conditions, and the like.
  • the processor 802 may be a part of the aerial vehicle 816, a cloud computing device, a ground control station (GCS) used to control the aerial vehicle 816, or the like.
  • GCS ground control station
  • the processor 802 may receive gas data from the one or more gas sensors 822 of the aerial vehicle 816. The processor may then determine, based on the received gas data, whether a gas leak is present in the received spatial location to the desired level of confidence. If a gas leak is not detected, no immediate action is needed and further tests may be accomplished in the future to ensure that no gas leaks develop. If a gas leak is detected, then corrective action may be taken to minimize and/or stop the gas leak.
  • the processor 802 may be in communication with addressable memory 830.
  • the memory may store the result of whether a gas leak was detected, historical gas data, the received spatial location 804, potential gas sources 806, 808, 810, level of confidence 812, wind data 814, and/or aerial vehicle 816 information.
  • the processor 802 may be in communication with an additional processor 832.
  • the additional processor 832 may be a part of the aerial vehicle 816, a cloud computing device, a GCS used to control the aerial vehicle 816, or the like.
  • FIG. 8B depicts a high-level block diagram of an alternate gas leak detection system 801, according to one embodiment.
  • the system includes a processor 802.
  • the processor 802 receives a spatial location 804, which may be an area containing one or more potential gas sources 806, 808, 810.
  • the processor 802 receives the spatial location of the one or more potential gas sources 806, 808, 810 within the spatial location 804.
  • the one or more potential gas sources 806, 808, 810 may be equipment and/or locations more likely to leak toxic gases, such as hydrogen disulfide, or environmentally damaging gases, such as methane and sulfur dioxide.
  • the processor 802 may also receive a level of confidence 812 desired as to whether any gas leaks are present within the received spatial location 804.
  • the processor 802 may also receive wind data 814.
  • Wind data 814 may include wind speed and/or wind direction for the spatial location 804. In some embodiments, wind data 814 may also include predictions as to changes in the wind speed and/or wind direction.
  • the processor 802 may determine one or more potential plume envelopes, such as shown in FIG. 2.
  • the potential plume envelopes cover potential plumes from gas leaks emanating from the one or more potential gas sources 806, 808, 810.
  • the one or more potential plume envelopes contain the area that will be tested by one or more gas leak sensors.
  • the processor determines a path for a portable device 836 having at least one gas sensor 822.
  • the portable device 836 may be a handheld device, a robot-mounted device, an aerial vehicle (AV), an unmanned aerial vehicle (UAV), or the like.
  • the path for the portable device 836 covers the one or more potential plume envelopes determined by the processor 802.
  • the portable device 836 may have a processor 818 in communication with addressable memory 820, a GPS 824, one or more motors 826, and a power supply 828.
  • the portable device 836 may receive the path from the processor 802 and communicate gathered gas sensor 822 sensor data to the processor 802.
  • the GPS 824 may record the location of the portable device 836 when each gas sensor 822 data is acquired.
  • the GPS 824 may also allow the portable device 836 to travel the path generated by the processor 802. In some
  • the location of the portable device 836 may be determined by a location service 838.
  • the location service 838 may include a triangulation system, a beacon, a spatial coordinate system, or the like.
  • the location service 838 may be used with the GPS 824 in some embodiments. In other embodiments, the portable device 836 may use only one of the GPS 824 and the location service 838.
  • the power supply 828 may be a battery in some embodiments. The power supply 828 may limit the available data gathering time for the portable device 836 and so it is crucial that the potential plume envelopes are accurate to allow for data that can be used to make a determination as to whether there are any gas leaks within the desired level of confidence 812.
  • the path may be split up into two or more paths based on a size of the potential plumes, a path time of the portable device 836, weather conditions, and the like.
  • the processor 802 may be a part of the portable device 836, a cloud computing device, a controller used to control the portable device 836, or the like.
  • the processor 802 may receive gas data from the one or more gas sensors 822 of the portable device 836. The processor may then determine, based on the received gas data, whether a gas leak is present in the received spatial location to the desired level of confidence. If a gas leak is not detected, no immediate action is needed and further tests may be accomplished in the future to ensure that no gas leaks develop. If a gas leak is detected, then corrective action may be taken to minimize and/or stop the gas leak.
  • the processor 802 may be in communication with addressable memory 830.
  • the memory may store the result of whether a gas leak was detected, historical gas data, the received spatial location 804, potential gas sources 806, 808, 810, level of confidence 812, wind data 814, and/or portable device 836 information.
  • the processor 802 may be in communication with an additional processor 832.
  • the additional processor 832 may be a part of the portable device 836, a cloud computing device, a controller used to control the portable device 836, or the like.
  • FIG. 9 depicts a high-level flowchart of a method embodiment 900 of determining a likelihood of gas leaks within a spatial location, according to one embodiment.
  • the method 900 may include receiving, by a processor having addressable memory, a spatial location having one or more potential gas sources (step 902).
  • the method 900 may then include receiving, by the processor, a spatial location and/or state of the one or more potential gas sources (step 904).
  • the state of the potential gas source i.e., on or off, may be communicated. For some potential gas sources, being in an off state could drop the potential for a gas leak to zero or close to zero.
  • the method 900 may then include receiving, by the processor, a desired level of confidence for detecting gas leaks from the one or more potential gas sources (step 906).
  • the method 900 may then include receiving, by the processor, a wind data for the received spatial location (step 908).
  • the wind data may include a wind speed and a wind direction.
  • the wind data may include multiple wind speeds and wind directions.
  • the wind data may include historical wind directions and wind speeds.
  • the wind data may include predicted future wind directions and wind speeds.
  • the method 900 may then include determining, by the processor, one or more potential plume envelopes based on the received spatial location, the received spatial location of the one or more potential gas sources, the received desired level of confidence, and/or the received wind direction and wind speed (step 910).
  • the method 900 may then include determining, by the processor, a flight path for an aerial vehicle having at least one gas sensor, where the flight path covers the determined one or more potential plume envelopes (step 912).
  • the aerial vehicle may not be able to cover an entire portion of the plume envelope due to flight restrictions, access constraints, or the like.
  • the flight path may be modified if the entire portion of the plume envelope cannot be covered.
  • the desired level of confidence may be lowered, the flight time may be increased, the flight path may be modified, or the like in response to a restriction on covering the entire portion of the plume envelope.
  • the gas sensor may be mounted on a robot, a handheld device, or the like.
  • the method 900 may then include receiving, by the processor, gas data from the one or more gas sensors of the determined one or more potential plume envelopes (step 914). The method 900 may then include determining, by the processor, based on the received gas data whether a gas leak is present in the received spatial location to the received desired level of confidence (step 916).

Landscapes

  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Automation & Control Theory (AREA)
  • Environmental & Geological Engineering (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Medicinal Chemistry (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biochemistry (AREA)
  • Analytical Chemistry (AREA)
  • Food Science & Technology (AREA)
  • Combustion & Propulsion (AREA)
  • Atmospheric Sciences (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Ecology (AREA)
  • Environmental Sciences (AREA)
  • Examining Or Testing Airtightness (AREA)

Abstract

Systems, devices, and methods including an aerial vehicle (816) having a global positioning system (GPS) (824) and at least one trace-gas sensor (822) configured to generate gas data; and a processor (802) having addressable memory (830), the processor configured to: determine a flight envelope (200) based on a received spatial location (804), a received spatial location of the one or more potential gas sources (806, 808, 810), a received desired level of confidence (812), and a received wind data (814); determine a flight path (300) for the aerial vehicle, where the flight path covers a portion (212) of the determined flight envelope; and determine based on a received gas data whether a gas leak is present in the received spatial location to the received desired level of confidence.

Description

Time- and Data-Efficient Assurance Of Leak Detection
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims the priority benefit of U.S. Provisional Patent
Application Serial Number 62/829,752 filed April 5, 2019, which is incorporated herein by reference in its entirety
FIELD OF ENDEAVOR
[0002] The invention relates to gas sensors, and more particularly to gas leak detection.
BACKGROUND
[0003] Trace gas sensors are used to detect and quantify leaks of toxic gases, e.g., hydrogen disulfide, or environmentally damaging gases, e.g., methane and sulfur dioxide, in a variety of industrial and environmental contexts. Detection and quantification of these leaks are of interest to a variety of industrial operations, e.g., oil and gas, chemical production, and painting, as well as environmental regulators for assessing compliance and mitigating environmental and safety risks.
SUMMARY
[0004] A system embodiment may include: an aerial vehicle; at least one trace-gas sensor disposed on the aerial vehicle, the trace-gas sensor configured to generate gas data; a global positioning system disposed on the aerial vehicle to determine a location of the at least one trace-gas sensor; and a processor having addressable memory, the processor configured to: receive a spatial location having one or more potential gas sources; receive a spatial location of the one or more potential gas sources; receive a desired level of confidence for detecting gas leaks from the one or more potential gas sources; receive a wind data for the received spatial location; determine a flight envelope encompassing one or more potential plume envelopes based on the received spatial location, the received spatial location of the one or more potential gas sources, the received desired level of confidence, and the received wind data; determine a flight path for the aerial vehicle, where the flight path covers a portion of the determined flight envelope; receive the gas data from the one or more gas trace-gas sensors of the portion of the determined flight envelope; and determine based on the received gas data whether a gas leak may be present in the received spatial location to the received desired level of confidence.
[0005] In additional system embodiments, the wind data may include a wind direction and a wind speed. In additional system embodiments, the wind data may include at least one of: a predicted wind direction and a predicted wind speed. In additional system embodiments, the portion of the determined flight envelope excludes a restricted zone, where the restricted zone may be an area within a set distance of each of the one or more potential gas sources.
[0006] In additional system embodiments, the at least one trace-gas sensor may be configured to detect hydrogen disulfide. In additional system embodiments, the at least one trace-gas sensor may be configured to detect methane. In additional system embodiments, the at least one trace-gas sensor may be configured to detect sulfur oxide. In additional system embodiments, the at least one trace-gas sensor may be configured to detect carbon dioxide. In additional system embodiments, the at least one trace-gas sensor may be configured to detect nitrogen oxide.
[0007] In additional system embodiments, the aerial vehicle may be an unmanned aerial vehicle (UAV). In additional system embodiments, the determined flight plan may include one or more random points within the determined one or more potential plume envelopes. In additional system embodiments, the one or more random points may be connected into a flight pattern using a route planning algorithm. In additional system embodiments, the route planning algorithm may be a traveling salesman algorithm.
[0008] A method embodiment may include: receiving, by a processor having addressable memory, a spatial location having one or more potential gas sources; receiving, by the processor, a spatial location of the one or more potential gas sources; receiving, by the processor, a desired level of confidence for detecting gas leaks from the one or more potential gas sources; receiving, by the processor, a wind data for the received spatial location;
determining, by the processor, a flight envelope encompassing one or more potential plume envelopes based on the received spatial location, the received spatial location of the one or more potential gas sources, the received desired level of confidence, and the received wind data; determining, by the processor, a flight path for an aerial vehicle having at least one trace-gas sensor, where the flight path covers a portion of the determined flight envelope; receiving, by the processor, gas data from the one or more trace-gas sensors of the portion of the determined flight envelope; and determining, by the processor, based on the received gas data whether a gas leak may be present in the received spatial location to the received desired level of confidence. [0009] Additional method embodiments may include: receiving, by the processor, a state of the one or more potential gas sources. In additional method embodiments, the at least one trace-gas sensor may be configured to detect at least one of: hydrogen disulfide, methane, sulfur oxide, carbon dioxide, and nitrogen oxide.
[0010] An additional system embodiment may include: a portable device; at least one trace-gas sensor disposed on the portable device, the trace-gas sensor configured to generate gas data; a global positioning system disposed on the portable device to determine a location of the at least one trace-gas sensor; and a processor having addressable memory, the processor configured to: receive a spatial location having one or more potential gas sources; receive a spatial location of the one or more potential gas sources; receive a desired level of confidence for detecting gas leaks from the one or more potential gas sources; receive a wind data for the received spatial location; determine a flight envelope encompassing one or more potential plume envelopes based on the received spatial location, the received spatial location of the one or more potential gas sources, the received desired level of confidence, and the received wind data; determine a path for the portable device, where the path covers a portion of the determined flight envelope; receive the gas data from the one or more gas trace-sensors of the portion of the determined flight envelope; and determine based on the received gas data whether a gas leak may be present in the received spatial location to the received desired level of confidence.
[0011] In additional system embodiments, the wind data comprises at least one of: a wind direction, a wind speed, a predicted wind direction, and a predicted wind speed. In additional system embodiments, the at least one trace-gas sensor may be configured to detect at least one of: hydrogen disulfide, methane, sulfur oxide, carbon dioxide, and nitrogen oxide. In additional system embodiments, the portion of the determined flight envelope excludes a restricted zone, where the restricted zone may be an area within a set distance of each of the one or more potential gas sources.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principals of the invention. Like reference numerals designate corresponding parts throughout the different views. Embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which:
[0013] FIG. 1 depicts a forward model potential plume envelopes generated using wind data, according to one embodiment; [0014] FIG. 2A depicts a flight envelope calculated from forward model plume mixing, according to one embodiment;
[0015] FIG. 2B depicts a close-up view of a portion of the plume envelope of FIG.
2A, according to one embodiment;
[0016] FIG. 2C depicts a portion of the plume envelope of FIG. 2B to be sampled, according to one embodiment;
[0017] FIG. 2D depicts waypoints in the portion of the plume envelope of FIG. 2C to be sampled, according to one embodiment;
[0018] FIG. 2E depicts a flight path for the waypoints in the portion of the plume envelope of FIG. 2D, according to one embodiment;
[0019] FIG. 3 depicts random waypoints with a traveling salesman route, according to one embodiment;
[0020] FIG. 4A depicts an image of an area to be sampled, according to one embodiment;
[0021] FIG. 4B depicts random samples of the image of FIG. 4A along the paths traversed in FIG. 2, according to one embodiment;
[0022] FIG. 4C depicts a reconstructed image from the random samples of FIG. 4B, according to one embodiment;
[0023] FIG. 5 shows a high-level block diagram and process of a computing system for implementing an embodiment of the system and process;
[0024] FIG. 6 shows a block diagram and process of an exemplary system in which an embodiment may be implemented;
[0025] FIG. 7 depicts a cloud computing environment for implementing an embodiment of the system and process disclosed herein;
[0026] FIG. 8A depicts a high-level block diagram of a gas leak detection system, according to one embodiment;
[0027] FIG. 8B depicts a high-level block diagram of an alternate gas leak detection system, according to one embodiment; and
[0028] FIG. 9 depicts a high-level flowchart of a method embodiment of determining a likelihood of gas leaks within a spatial location, according to one embodiment
DETAILED DESCRIPTION
[0029] The following description is made for the purpose of illustrating the general principles of the embodiments discloses herein and is not meant to limit the concepts disclosed herein. Further, particular features described herein can be used in combination with other described features in each of the various possible combinations and permutations. Unless otherwise specifically defined herein, all terms are to be given their broadest possible interpretation including meanings implied from the description as well as meanings understood by those skilled in the art and/or as defined in dictionaries, treatises, etc.
[0030] The present system allows for the creation of a flight plan to ascertain whether any gas leaks are present within a set spatial location. The spatial location may be a two- dimensional area, a three-dimensional area, a GPS location, and/or a geographical area. The created flight plan accounts for wind and a likelihood of the presence of gas leaks. This created flight plan allows for the determination, within a desired confidence level, as to whether any gas leaks are present in the set spatial location. This created flight plan may be accomplished by an aerial vehicle, such as an unmanned aerial vehicle, within a set time so as to provide time-efficient and data-efficient sampling of the set spatial location.
[0031] Trace gas sensors are used to detect and quantify leaks of toxic gases, e.g., hydrogen disulfide, or environmentally damaging gases, e.g., methane and sulfur dioxide, in a variety of industrial and environmental contexts. Detection and quantification of these leaks are of interest to a variety of industrial operations, e.g., oil and gas, chemical production, and painting, as well as environmental regulators for assessing compliance and mitigating environmental and safety risks.
[0032] The recent availability of small, highly maneuverable, remotely piloted airborne platforms presents an opportunity to detect, localize, and quantify leaks at industrial sites. The presence of a leak can be ascertained by flying downwind of a site and surveying for the gas of interest. If the gas of interest is detected, the leak location and quantification can be determined by subsequent surveys, each moving upstream until the source of the leak is determined.
[0033] In practice, leak localization and detection are made more challenging by the dynamic nature of wind and the limited flight duration of aerial platforms. For example, if no trace gas is detected downwind of a site, without taking into account the details of local weather patterns, there is no a prioi guarantee of no leaks at that site given that winds are constantly changing direction and velocity. For example, the trace gas may have been blowing in a direction that the survey did not capture. For a leak detection method to be effective, site operators and regulators may require assurances that a site is leak free with a high confidence, i.e., site operators and regulators aim to minimize the likelihood of a false negative. [0034] While it is possible to fly a route that reduces the likelihood of missing the discovery of a leak within a survey area, flying such a flight pattern downwind of an industrial site requires expert knowledge of dynamic jet propagation and mixing. It would be an advance in the art to provide flight platform operators an envelope and route to follow that time- and space-efficiently surveys a site and can determine, with a computed level of confidence, whether the site is leak free.
[0035] This advance in the art is achieved by fusing local wind measurements with flight planning and operation. By measuring and recording local wind data and making intelligent assumptions about leak sources based on equipment located on site, a flight envelope can be computed using a physics-based forward-computed fluid mixing model. This forward-model takes in a time series of point measurements of wind speed, direction, and variance, and, based on conservation of fluid momentum and mass, computes the probability that the gas of interest will be present at any given time in each discretized location in the survey area.
[0036] Then, once flight envelopes are computed, flight trajectories may be computed to efficiently sample this space, by maximizing the flight space covered in the shortest amount of time, while simultaneously maximizing the confidence level of a leak false negative.
[0037] FIG. 1 depicts a forward model 100 potential plume envelopes 102 generated using wind 104 data, according to one embodiment. Wind 104 creates potential plume envelopes 102 from potential gas sources 106. Each potential gas source 106 may be a single potential gas source or a cluster of potential gas sources.
[0038] FIG. 2A depicts a flight envelope 200 calculated from forward model plume mixing 202, according to one embodiment. The flight envelope 200 encompasses the potential plume envelopes 204, as shown in FIG. 1 as 102. The plume envelopes 204 may be a two-dimensional location in some embodiments. In other embodiments, the plume envelopes 204 may be a three-dimensional area. The plume envelopes 204 may account for rising or falling gases based on the wind direction, wind speed, type of gas from each potential gas source 210, and the like. Each of the one or more potential gas sources 210 may each have an associated restricted zone 208. The restricted zone may be a no-fly, or no entry, area within a set distance of a potential gas source 210. The restricted zone 210 may be based on user preference, regulations, and/or type of potential gas source 210. For example, some potential gas sources 210 may have larger restricted zones 210 than other potential gas sources 210. The optimal flight area 206 for each potential gas source 210 is based on the desired confidence level for detecting a gas leak. The optimal flight area 206 may be expanded for an increased desired confidence level. The optimal flight area 206 may be reduced for a decreased desired confidence level. In some embodiments, the optimal flight area 206 may be increased for higher winds and decreased for lower winds.
[0039] FIG. 2B depicts a close-up view of a portion of the plume envelope 204 of FIG. 2A, according to one embodiment. As shown in FIG. 2B, only a portion of the optimal flight area 206 surrounding each potential gas source 210 overlaps with the potential plume envelope 204 due to the wind direction and wind speed.
[0040] FIG. 2C depicts a portion 212 of the plume envelope of FIG. 2B to be sampled, according to one embodiment. The overlapping area between the optimal flight area 206, as shown in FIG. 2B, and the potential plume envelope 204, as shown in FIG. 2B, is the portion 212 of the plume envelope to be sampled. This portion 212 of the plume envelope includes the areas likely to include trace-gas if a gas leak is present while excluding any restricted zones 208, as shown in FIG. 2B, around each possible gas source 210.
[0041] FIG. 2D depicts waypoints 214 in the portion 212 of the plume envelope of FIG. 2C to be sampled, according to one embodiment. One or more waypoints 214 may be added in the portion 212 of the plume envelope to be sampled. The waypoints 214 may be distributed in a uniform, random, or other pattern. In some embodiments, waypoints 214 may be positioned in a greater density closer to each potential gas source 210. The waypoints 214 may be positioned in a lower density farther away from each potential gas source 210. The number and/or location of the waypoints 214 may be based on the desired level of confidence for detecting any gas leaks for the potential gas sources 210.
[0042] FIG. 2E depicts a flight path 216 for the waypoints 214 in the portion 212 of the plume envelope of FIG. 2D, according to one embodiment. A raster pattern may be used as the flight path 216 to connect the waypoints 214. In other embodiments, a traveling salesman route, as shown in FIG. 3, or other route may be used to connect the waypoints 214 within the portion 212 of the plume envelope. The flight path may be contained within the portion 210 of the plume envelope. In other embodiments, the flight path may avoid going into the restricted zone 208, as shown in FIG. 2B. In one embodiment, the flight path may be a random walk to connect waypoints 214. While waypoints are shown, the flight path 216 may be generated by the system and method disclosed herein so as to cover the portion 212 of the plume envelope to be sampled at the desired level of confidence for detecting trace-gas leaks from the potential gas sources 210. [0043] FIG. 3 depicts random waypoints 300 with a traveling salesman route, according to one embodiment. In some embodiments, the start point and finish point may be different. One method for efficiently traversing the flight envelope is to randomly disperse waypoints across the plume, as shown in FIG. 1, and/or the flight envelope, as shown in FIG. 2, in densities proportional to likelihood of gas being present within the flight envelope.
These random points are then connected into a flight pattern using a route planning algorithm, such as the traveling salesman algorithm. The resulting data is reconstructed into a 3D representation of the space using an LI -norm regression and an appropriate basis set, e.g., a wavelet or discrete cosine transform.
[0044] This approach disclosed herein yields relatively narrow flight windows for low-variance wind conditions, and it yields larger flight envelopes for high-variance wind conditions. The random distribution of flight waypoints forces the sensor to spend more time sampling regions with a high likelihood of gas and eliminates any sampling bias introduced by rastering.
[0045] Furthermore, in a post-processing step, a given site that has been deemed free of leaks after flying a known flight path can be simulated in a Monte Carlo fashion, using the same walk-forward model described above, testing the assumptions put in place regarding potential leak sources, and quantifying the level of confidence that a site is, indeed, free of any leaks. In some embodiments, a Monte Carlo simulation, or other simulation, may be used to determine the one or more potential plume envelopes.
[0046] FIG. 4A depicts an image of an area to be sampled 402, according to one embodiment.
[0047] FIG. 4B depicts random samples 404 of the image of FIG. 4 A along the paths traversed in FIG. 2, according to one embodiment.
[0048] FIG. 4C depicts a reconstructed image 406 from the random samples of FIG. 4B, according to one embodiment.
[0049] FIG. 5 is a high-level block diagram 500 showing a computing system comprising a computer system useful for implementing an embodiment of the system and process, disclosed herein. Embodiments of the system may be implemented in different computing environments. The computer system includes one or more processors 502, and can further include an electronic display device 504 (e.g., for displaying graphics, text, and other data), a main memory 506 (e.g., random access memory (RAM)), storage device 508, a removable storage device 510 (e.g., removable storage drive, a removable memory module, a magnetic tape drive, an optical disk drive, a computer readable medium having stored therein computer software and/or data), user interface device 511 (e.g., keyboard, touch screen, keypad, pointing device), and a communication interface 512 (e.g., modem, a network interface (such as an Ethernet card), a communications port, or a PCMCIA slot and card). The communication interface 512 allows software and data to be transferred between the computer system and external devices. The system further includes a communications infrastructure 514 (e.g., a communications bus, cross-over bar, or network) to which the aforementioned devices and modules are connected as shown.
[0050] Information transferred via communications interface 514 may be in the form of signals such as electronic, electromagnetic, optical, or other signals capable of being received by communications interface 514, via a communication link 516 that carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular/mobile phone link, a radio frequency (RF) link, and/or other communication channels. Computer program instructions representing the block diagram and/or flowcharts herein may be loaded onto a computer, programmable data processing apparatus, or processing devices to cause a series of operations performed thereon to produce a computer implemented process.
[0051] Embodiments have been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments. Each block of such illustrations/diagrams, or combinations thereof, can be implemented by computer program instructions. The computer program instructions when provided to a processor produce a machine, such that the instructions, which execute via the processor, create means for implementing the functions/operations specified in the flowchart and/or block diagram. Each block in the flowchart/block diagrams may represent a hardware and/or software module or logic, implementing embodiments. In alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures, concurrently, etc.
[0052] Computer programs (i.e., computer control logic) are stored in main memory and/or secondary memory. Computer programs may also be received via a communications interface 512. Such computer programs, when executed, enable the computer system to perform the features of the embodiments as discussed herein. In particular, the computer programs, when executed, enable the processor and/or multi-core processor to perform the features of the computer system. Such computer programs represent controllers of the computer system.
[0053] FIG. 6 shows a block diagram of an example system 600 in which an embodiment may be implemented. The system 600 includes one or more client devices 601 such as consumer electronics devices, connected to one or more server computing systems 630. A server 630 includes a bus 602 or other communication mechanism for communicating information, and a processor (CPU) 604 coupled with the bus 602 for processing information. The server 630 also includes a main memory 606, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus 602 for storing information and instructions to be executed by the processor 604. The main memory 606 also may be used for storing temporary variables or other intermediate information during execution or instructions to be executed by the processor 604. The server computer system 630 further includes a read only memory (ROM) 608 or other static storage device coupled to the bus 602 for storing static information and instructions for the processor 604. A storage device 610, such as a magnetic disk or optical disk, is provided and coupled to the bus 602 for storing information and instructions. The bus 602 may contain, for example, thirty -two address lines for addressing video memory or main memory 606. The bus 602 can also include, for example, a 32-bit data bus for transferring data between and among the components, such as the CPU 604, the main memory 606, video memory and the storage 610. Alternatively, multiplex data/address lines may be used instead of separate data and address lines.
[0054] The server 630 may be coupled via the bus 602 to a display 612 for displaying information to a computer user. An input device 614, including alphanumeric and other keys, is coupled to the bus 602 for communicating information and command selections to the processor 604. Another type or user input device comprises cursor control 616, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processor 604 and for controlling cursor movement on the display 612.
[0055] According to one embodiment, the functions are performed by the processor 604 executing one or more sequences of one or more instructions contained in the main memory 606. Such instructions may be read into the main memory 606 from another computer-readable medium, such as the storage device 610. Execution of the sequences of instructions contained in the main memory 606 causes the processor 604 to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in the main memory 606. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the embodiments. Thus, embodiments are not limited to any specific combination of hardware circuitry and software. [0056] The terms "computer program medium," "computer usable medium,"
"computer readable medium", and "computer program product," are used to generally refer to media such as main memory, secondary memory, removable storage drive, a hard disk installed in hard disk drive, and signals. These computer program products are means for providing software to the computer system. The computer readable medium allows the computer system to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium. The computer readable medium, for example, may include non-volatile memory, such as a floppy disk, ROM, flash memory, disk drive memory, a CD-ROM, and other permanent storage. It is useful, for example, for transporting information, such as data and computer instructions, between computer systems. Furthermore, the computer readable medium may comprise computer readable information in a transitory state medium such as a network link and/or a network interface, including a wired network or a wireless network that allow a computer to read such computer readable information. Computer programs (also called computer control logic) are stored in main memory and/or secondary memory. Computer programs may also be received via a communications interface. Such computer programs, when executed, enable the computer system to perform the features of the embodiments as discussed herein. In particular, the computer programs, when executed, enable the processor multi-core processor to perform the features of the computer system. Accordingly, such computer programs represent controllers of the computer system.
[0057] Generally, the term "computer-readable medium" as used herein refers to any medium that participated in providing instructions to the processor 604 for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical or magnetic disks, such as the storage device 610. Volatile media includes dynamic memory, such as the main memory 606 Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise the bus 602 Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.
[0058] Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.
[0059] Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to the processor 604 for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the server 630 can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to the bus 602 can receive the data carried in the infrared signal and place the data on the bus 602. The bus 602 carries the data to the main memory 606, from which the processor 604 retrieves and executes the instructions. The instructions received from the main memory 606 may optionally be stored on the storage device 610 either before or after execution by the processor 604.
[0060] The server 630 also includes a communication interface 618 coupled to the bus 602. The communication interface 618 provides a two-way data communication coupling to a network link 620 that is connected to the world wide packet data communication network now commonly referred to as the Internet 628. The Internet 628 uses electrical,
electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 620 and through the communication interface 618, which carry the digital data to and from the server 630, are exemplary forms or carrier waves transporting the information.
[0061] In another embodiment of the server 630, interface 618 is connected to a network 622 via a communication link 620. For example, the communication interface 618 may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line, which can comprise part of the network link 620. As another example, the communication interface 618 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, the
communication interface 618 sends and receives electrical electromagnetic or optical signals that carry digital data streams representing various types of information.
[0062] The network link 620 typically provides data communication through one or more networks to other data devices. For example, the network link 620 may provide a connection through the local network 622 to a host computer 624 or to data equipment operated by an Internet Service Provider (ISP). The ISP in turn provides data communication services through the Internet 628. The local network 622 and the Internet 628 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 620 and through the communication interface 618, which carry the digital data to and from the server 630, are exemplary forms or carrier waves transporting the information.
[0063] The server 630 can send/receive messages and data, including e-mail, program code, through the network, the network link 620 and the communication interface 618.
Further, the communication interface 618 can comprise a USB/Tuner and the network link 620 may be an antenna or cable for connecting the server 630 to a cable provider, satellite provider or other terrestrial transmission system for receiving messages, data and program code from another source.
[0064] The example versions of the embodiments described herein may be implemented as logical operations in a distributed processing system such as the system 600 including the servers 630. The logical operations of the embodiments may be implemented as a sequence of steps executing in the server 630, and as interconnected machine modules within the system 600. The implementation is a matter of choice and can depend on performance of the system 600 implementing the embodiments. As such, the logical operations constituting said example versions of the embodiments are referred to for e.g., as operations, steps or modules.
[0065] Similar to a server 630 described above, a client device 601 can include a processor, memory, storage device, display, input device and communication interface (e.g., e-mail interface) for connecting the client device to the Internet 628, the ISP, or LAN 622, for communication with the servers 630.
[0066] The system 600 can further include computers (e.g., personal computers, computing nodes) 605 operating in the same manner as client devices 601, wherein a user can utilize one or more computers 605 to manage data in the server 630.
[0067] Referring now to FIG. 7, illustrative cloud computing environment 50 is depicted. As shown, cloud computing environment 50 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA), smartphone, smart watch, set-top box, video game system, tablet, mobile computing device, or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or UAV 54N may communicate. Nodes 10 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment 50 to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices 54A-N shown in FIG. 7 are intended to be illustrative only and that computing nodes 10 and cloud computing environment 50 can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).
[0068] FIG. 8A depicts a high-level block diagram of a gas leak detection system 800, according to one embodiment. The system includes a processor 802. The processor 802 receives a spatial location 804, which may be an area containing one or more potential gas sources 806, 808, 810. The processor 802 receives the spatial location of the one or more potential gas sources 806, 808, 810 within the spatial location 804. The one or more potential gas sources 806, 808, 810 may be equipment and/or locations more likely to leak toxic gases, such as hydrogen disulfide, or environmentally damaging gases, such as methane and sulfur dioxide. In some embodiments, the at least one gas sensor 822 may be configured to detect carbon dioxide. In other embodiments, the at least one gas sensor 822 may be configured to detect nitrogen oxide. In other embodiments, the at least one gas sensor 822 may be configured to detect sulfur oxide, such as SO, SO2, SO3, S7O2, S6O2, S2O2, and the like. The processor 802 may also receive a level of confidence 812 desired as to whether any gas leaks are present within the received spatial location 804. The higher the level of confidence 812, the longer it may take the system 800 to determine whether any gas leaks are present in the spatial location 804. A reasonably high level of confidence 812 may be achieved in a time- efficient manner using the system 800 disclosed herein. The processor 802 may also receive wind data 814. Wind data 814 may include wind speed and/or wind direction for the spatial location 804. In some embodiments, wind data 814 may also include predictions as to changes in the wind speed and/or wind direction.
[0069] The processor 802 may determine one or more potential plume envelopes, such as shown in FIG. 2. The potential plume envelopes cover potential plumes from gas leaks emanating from the one or more potential gas sources 806, 808, 810. The one or more potential plume envelopes contain the area that will be tested by one or more gas leak sensors.
[0070] The processor then determines a flight path for an aerial vehicle 816 having at least one gas sensor 822. The flight path for the aerial vehicle 816 covers the one or more potential plume envelopes determined by the processor 802. The aerial vehicle 816 may be an unmanned aerial vehicle (UAV) in some embodiments. The aerial vehicle 816 may have a processor 818 in communication with addressable memory 820, a GPS 824, one or more motors 826, and a power supply 828. The aerial vehicle 816 may receive the flight plan from the processor 802 and communicate gathered gas sensor 822 sensor to the processor 802. The GPS 824 may record the location of the aerial vehicle 816 when each gas sensor 822 data is acquired. The GPS 824 may also allow the aerial vehicle 816 to travel the flight path generated by the processor 802. In some embodiments, the location of the aerial vehicle 816 may be determined by an onboard avionics 834. The onboard avionics 834 may include a triangulation system, a beacon, a spatial coordinate system, or the like. The onboard avionics 834 may be used with the GPS 824 in some embodiments. In other embodiments, the aerial vehicle 816 may use only one of the GPS 824 and the onboard avionics 834.
[0071] The power supply 828 may be a battery in some embodiments. The power supply 828 may limit the available flight time for the aerial vehicle 816 and so it is crucial that the potential plume envelopes are accurate to allow for data that can be used to make a determination as to whether there are any gas leaks within the desired level of confidence 812. In some embodiments, the flight plan may be split up into two or more flights based on a size of the potential plumes, a flight time of the aerial vehicle 816, weather conditions, and the like. In some embodiments, the processor 802 may be a part of the aerial vehicle 816, a cloud computing device, a ground control station (GCS) used to control the aerial vehicle 816, or the like.
[0072] The processor 802 may receive gas data from the one or more gas sensors 822 of the aerial vehicle 816. The processor may then determine, based on the received gas data, whether a gas leak is present in the received spatial location to the desired level of confidence. If a gas leak is not detected, no immediate action is needed and further tests may be accomplished in the future to ensure that no gas leaks develop. If a gas leak is detected, then corrective action may be taken to minimize and/or stop the gas leak.
[0073] In some embodiments, the processor 802 may be in communication with addressable memory 830. The memory may store the result of whether a gas leak was detected, historical gas data, the received spatial location 804, potential gas sources 806, 808, 810, level of confidence 812, wind data 814, and/or aerial vehicle 816 information. In some embodiments, the processor 802 may be in communication with an additional processor 832. The additional processor 832 may be a part of the aerial vehicle 816, a cloud computing device, a GCS used to control the aerial vehicle 816, or the like.
[0074] FIG. 8B depicts a high-level block diagram of an alternate gas leak detection system 801, according to one embodiment. The system includes a processor 802. The processor 802 receives a spatial location 804, which may be an area containing one or more potential gas sources 806, 808, 810. The processor 802 receives the spatial location of the one or more potential gas sources 806, 808, 810 within the spatial location 804. The one or more potential gas sources 806, 808, 810 may be equipment and/or locations more likely to leak toxic gases, such as hydrogen disulfide, or environmentally damaging gases, such as methane and sulfur dioxide. The processor 802 may also receive a level of confidence 812 desired as to whether any gas leaks are present within the received spatial location 804. The higher the level of confidence 812, the longer it may take the system 800 to determine whether any gas leaks are present in the spatial location 804. A reasonably high level of confidence 812 may be achieved in a time-efficient manner using the system 800 disclosed herein. The processor 802 may also receive wind data 814. Wind data 814 may include wind speed and/or wind direction for the spatial location 804. In some embodiments, wind data 814 may also include predictions as to changes in the wind speed and/or wind direction.
[0075] The processor 802 may determine one or more potential plume envelopes, such as shown in FIG. 2. The potential plume envelopes cover potential plumes from gas leaks emanating from the one or more potential gas sources 806, 808, 810. The one or more potential plume envelopes contain the area that will be tested by one or more gas leak sensors.
[0076] The processor then determines a path for a portable device 836 having at least one gas sensor 822. The portable device 836 may be a handheld device, a robot-mounted device, an aerial vehicle (AV), an unmanned aerial vehicle (UAV), or the like. The path for the portable device 836 covers the one or more potential plume envelopes determined by the processor 802. The portable device 836 may have a processor 818 in communication with addressable memory 820, a GPS 824, one or more motors 826, and a power supply 828. The portable device 836 may receive the path from the processor 802 and communicate gathered gas sensor 822 sensor data to the processor 802. The GPS 824 may record the location of the portable device 836 when each gas sensor 822 data is acquired. The GPS 824 may also allow the portable device 836 to travel the path generated by the processor 802. In some
embodiments, the location of the portable device 836 may be determined by a location service 838. The location service 838 may include a triangulation system, a beacon, a spatial coordinate system, or the like. The location service 838 may be used with the GPS 824 in some embodiments. In other embodiments, the portable device 836 may use only one of the GPS 824 and the location service 838. [0077] The power supply 828 may be a battery in some embodiments. The power supply 828 may limit the available data gathering time for the portable device 836 and so it is crucial that the potential plume envelopes are accurate to allow for data that can be used to make a determination as to whether there are any gas leaks within the desired level of confidence 812. In some embodiments, the path may be split up into two or more paths based on a size of the potential plumes, a path time of the portable device 836, weather conditions, and the like. In some embodiments, the processor 802 may be a part of the portable device 836, a cloud computing device, a controller used to control the portable device 836, or the like.
[0078] The processor 802 may receive gas data from the one or more gas sensors 822 of the portable device 836. The processor may then determine, based on the received gas data, whether a gas leak is present in the received spatial location to the desired level of confidence. If a gas leak is not detected, no immediate action is needed and further tests may be accomplished in the future to ensure that no gas leaks develop. If a gas leak is detected, then corrective action may be taken to minimize and/or stop the gas leak.
[0079] In some embodiments, the processor 802 may be in communication with addressable memory 830. The memory may store the result of whether a gas leak was detected, historical gas data, the received spatial location 804, potential gas sources 806, 808, 810, level of confidence 812, wind data 814, and/or portable device 836 information. In some embodiments, the processor 802 may be in communication with an additional processor 832. The additional processor 832 may be a part of the portable device 836, a cloud computing device, a controller used to control the portable device 836, or the like.
[0080] FIG. 9 depicts a high-level flowchart of a method embodiment 900 of determining a likelihood of gas leaks within a spatial location, according to one embodiment. The method 900 may include receiving, by a processor having addressable memory, a spatial location having one or more potential gas sources (step 902). The method 900 may then include receiving, by the processor, a spatial location and/or state of the one or more potential gas sources (step 904). In some embodiments, the state of the potential gas source, i.e., on or off, may be communicated. For some potential gas sources, being in an off state could drop the potential for a gas leak to zero or close to zero. For example, a potential gas source in an off position could have no potential plume enveloped associated with the off potential gas source. The method 900 may then include receiving, by the processor, a desired level of confidence for detecting gas leaks from the one or more potential gas sources (step 906). [0081] The method 900 may then include receiving, by the processor, a wind data for the received spatial location (step 908). The wind data may include a wind speed and a wind direction. In some embodiments, the wind data may include multiple wind speeds and wind directions. In some embodiments, the wind data may include historical wind directions and wind speeds. In some embodiments, the wind data may include predicted future wind directions and wind speeds. These four inputs (902, 904, 906, 908) may be received in any order. In some embodiments, the received spatial location (step 902) may be inferred from the received spatial location and/or state of the one or more potential gas sources (step 904).
[0082] The method 900 may then include determining, by the processor, one or more potential plume envelopes based on the received spatial location, the received spatial location of the one or more potential gas sources, the received desired level of confidence, and/or the received wind direction and wind speed (step 910). The method 900 may then include determining, by the processor, a flight path for an aerial vehicle having at least one gas sensor, where the flight path covers the determined one or more potential plume envelopes (step 912). In some embodiments, the aerial vehicle may not be able to cover an entire portion of the plume envelope due to flight restrictions, access constraints, or the like. In some embodiments, the flight path may be modified if the entire portion of the plume envelope cannot be covered. For example, the desired level of confidence may be lowered, the flight time may be increased, the flight path may be modified, or the like in response to a restriction on covering the entire portion of the plume envelope. While an aerial vehicle is described, in some embodiments, the gas sensor may be mounted on a robot, a handheld device, or the like.
[0083] The method 900 may then include receiving, by the processor, gas data from the one or more gas sensors of the determined one or more potential plume envelopes (step 914). The method 900 may then include determining, by the processor, based on the received gas data whether a gas leak is present in the received spatial location to the received desired level of confidence (step 916).
[0084] It is contemplated that various combinations and/or sub-combinations of the specific features and aspects of the above embodiments may be made and still fall within the scope of the invention. Accordingly, it should be understood that various features and aspects of the disclosed embodiments may be combined with or substituted for one another in order to form varying modes of the disclosed invention. Further, it is intended that the scope of the present invention herein disclosed by way of examples should not be limited by the particular disclosed embodiments described above.

Claims

WHAT IS CLAIMED IS:
1. A system comprising:
an aerial vehicle (816);
at least one trace-gas sensor (822) disposed on the aerial vehicle, the trace-gas sensor configured to generate gas data;
a global positioning system (GPS) (824) disposed on the aerial vehicle to determine a location of the at least one trace-gas sensor; and
a processor (802) having addressable memory (830), the processor configured to: receive a spatial location (804) having one or more potential gas sources (806, 808, 810);
receive a spatial location of the one or more potential gas sources;
receive a desired level of confidence (812) for detecting gas leaks from the one or more potential gas sources;
receive a wind data (814) for the received spatial location;
determine a flight envelope (200) encompassing one or more potential plume envelopes (204) based on the received spatial location, the received spatial location of the one or more potential gas sources, the received desired level of confidence, and the received wind data;
determine a flight path (216, 300) for the aerial vehicle, wherein the flight path covers a portion (212) of the determined flight envelope; receive the gas data from the one or more gas trace-gas sensors of the portion of the determined flight envelope; and
determine based on the received gas data whether a gas leak is present in the received spatial location to the received desired level of confidence.
2. The system of claim 1, wherein the wind data comprises a wind direction and a wind speed.
3. The system of claim 1, wherein the wind data comprises at least one of: a predicted wind direction and a predicted wind speed.
4. The system of claim 1, wherein the at least one trace-gas sensor is configured to
detect hydrogen disulfide.
5. The system of claim 1, wherein the at least one trace-gas sensor is configured to detect methane.
6. The system of claim 1, wherein the at least one trace-gas sensor is configured to
detect sulfur oxide.
7. The system of claim 1, wherein the aerial vehicle is an unmanned aerial vehicle
(UAV).
8. The system of claim 1, wherein the determined flight plan comprises one or more random points within the determined one or more potential plume envelopes.
9. The system of claim 8, wherein the one or more random points are connected into a flight pattern using a route planning algorithm.
10. The system of claim 9, wherein the route planning algorithm is a traveling salesman algorithm.
11. The system of claim 1, wherein the at least one trace-gas sensor is configured to
detect carbon dioxide.
12. The system of claim 1, wherein the at least one trace-gas sensor is configured to
detect nitrogen oxide.
13. The system of claim 1, wherein the portion of the determined flight envelope excludes a restricted zone (208), wherein the restricted zone is an area within a set distance of each of the one or more potential gas sources.
14. A method comprising:
receiving, by a processor (802) having addressable memory (830), a spatial location (804) having one or more potential gas sources (806, 808, 810);
receiving, by the processor, a spatial location of the one or more potential gas sources; receiving, by the processor, a desired level of confidence (812) for detecting gas leaks from the one or more potential gas sources; receiving, by the processor, a wind data (814) for the received spatial location;
determining, by the processor, a flight envelope (200) encompassing one or more potential plume envelopes (204) based on the received spatial location, the received spatial location of the one or more potential gas sources, the received desired level of confidence, and the received wind data;
determining, by the processor, a flight path (216, 300) for an aerial vehicle (816) having at least one trace-gas sensor (822), wherein the flight path covers a portion (212) of the determined flight envelope;
receiving, by the processor, gas data from the one or more trace-gas sensors of the portion of the determined flight envelope; and
determining, by the processor, based on the received gas data whether a gas leak is present in the received spatial location to the received desired level of confidence.
15. The method of claim 14 further comprising:
receiving, by the processor, a state of the one or more potential gas sources.
16. The method of claim 14, wherein the at least one trace-gas sensor is configured to detect at least one of: hydrogen disulfide, methane, sulfur oxide, carbon dioxide, and nitrogen oxide.
17. A system comprising:
a portable device (836);
at least one trace-gas sensor (822) disposed on the portable device, the trace-gas
sensor configured to generate gas data;
a global positioning system (GPS) (824) disposed on the portable device to determine a location of the at least one trace-gas sensor; and
a processor (802) having addressable memory (830), the processor configured to: receive a spatial location (804) having one or more potential gas sources (806, 808, 810);
receive a spatial location of the one or more potential gas sources; receive a desired level of confidence (812) for detecting gas leaks from the one or more potential gas sources;
receive a wind data (814) for the received spatial location; determine a flight envelope (200) encompassing one or more potential plume envelopes (204) based on the received spatial location, the received spatial location of the one or more potential gas sources, the received desired level of confidence, and the received wind data;
determine a path for the portable device, wherein the path covers a portion (212) of the determined flight envelope;
receive the gas data from the one or more gas trace-sensors of the portion of the determined flight envelope; and
determine based on the received gas data whether a gas leak is present in the received spatial location to the received desired level of confidence.
18. The system of claim 17, wherein the wind data comprises at least one of: a wind
direction, a wind speed, a predicted wind direction, and a predicted wind speed.
19. The system of claim 17, wherein the at least one trace-gas sensor is configured to detect at least one of: hydrogen disulfide, methane, sulfur oxide, carbon dioxide, and nitrogen oxide.
20. The system of claim 17, wherein the portion of the determined flight envelope
excludes a restricted zone (208), wherein the restricted zone is an area within a set distance of each of the one or more potential gas sources.
PCT/US2020/026232 2019-04-05 2020-04-01 Time-and data-efficient assurance of leak detection Ceased WO2020206008A1 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
EP20783815.2A EP3948202A4 (en) 2019-04-05 2020-04-01 TIME AND DATA EFFICIENT LEAK DETECTION ASSURANCE
US17/601,559 US12188847B2 (en) 2019-04-05 2020-04-01 Time-and data-efficient assurance of leak detection
US18/955,051 US20250377256A1 (en) 2019-04-05 2024-11-21 Time-and data-efficient assurance of leak detection

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201962829752P 2019-04-05 2019-04-05
US62/829,752 2019-04-05

Related Child Applications (2)

Application Number Title Priority Date Filing Date
US17/601,559 A-371-Of-International US12188847B2 (en) 2019-04-05 2020-04-01 Time-and data-efficient assurance of leak detection
US18/955,051 Continuation US20250377256A1 (en) 2019-04-05 2024-11-21 Time-and data-efficient assurance of leak detection

Publications (1)

Publication Number Publication Date
WO2020206008A1 true WO2020206008A1 (en) 2020-10-08

Family

ID=72666955

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2020/026232 Ceased WO2020206008A1 (en) 2019-04-05 2020-04-01 Time-and data-efficient assurance of leak detection

Country Status (3)

Country Link
US (2) US12188847B2 (en)
EP (1) EP3948202A4 (en)
WO (1) WO2020206008A1 (en)

Cited By (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP4086602A1 (en) * 2021-05-06 2022-11-09 Abb Schweiz Ag Technologies for improved visualization of gas leak detection data
WO2023133345A1 (en) * 2022-01-10 2023-07-13 Cameron International Corporation Method and apparatus for greenhouse gas emission management
US12188912B2 (en) 2019-12-19 2025-01-07 Seekops Inc. Concurrent in-situ measurement of wind speed and trace gases on mobile platforms for localization and qualification of emissions
US12188847B2 (en) 2019-04-05 2025-01-07 Seekops Inc. Time-and data-efficient assurance of leak detection
EP4237741A4 (en) * 2020-10-27 2025-01-08 SeekOps Inc. METHODS AND APPARATUS FOR MEASURING METHANE EMISSIONS WITHIN A MESH SENSOR NETWORK
US12197233B2 (en) 2019-10-04 2025-01-14 Seekops Inc. Closed surface flight pattern generation for unmanned aerial vehicle (UAV) flux plane assessment of large facilities
US12216105B2 (en) 2018-06-19 2025-02-04 Seekops Inc. Localization analytics algorithms and methods
US12217412B2 (en) 2020-07-17 2025-02-04 Seekops Inc. Systems and methods of automated detection of gas plumes using optical imaging
US12254622B2 (en) 2023-06-16 2025-03-18 Schlumberger Technology Corporation Computing emission rate from gas density images
US12276597B2 (en) 2020-02-05 2025-04-15 Seekops Inc. Multiple path length optical cell for trace gas measurement
US12281983B2 (en) 2018-10-22 2025-04-22 Seekops Inc. UAV-borne, high-bandwidth, lightweight point sensor for quantifying greenhouse gases in atmospheric strata
US12292310B2 (en) 2022-12-15 2025-05-06 Schlumberger Technology Corporation Machine learning based methane emissions monitoring
US12392680B2 (en) 2019-09-20 2025-08-19 Seekops Inc. Spectral fitting of compact laser-based trace gas sensor measurements for high dynamic range (HDR)
US12399164B2 (en) 2018-06-19 2025-08-26 Seekops Inc. Emissions estimate model algorithms and methods
US12449409B2 (en) 2018-06-19 2025-10-21 Seekops Inc. Emissions estimate model algorithms and methods
US12449354B2 (en) 2020-02-05 2025-10-21 Seekops Inc. Multispecies measurement platform using absorption spectroscopy for measurement of co-emitted trace gases
US12475798B2 (en) 2020-07-17 2025-11-18 Seekops Inc. UAS work practice
US12480922B2 (en) 2022-12-09 2025-11-25 Schlumberger Technology Corporation Methods and systems for characterizing methane emission employing mobile methane emission detection
US12480867B2 (en) 2020-10-27 2025-11-25 Seekops Inc. Methods and apparatus for measuring methane emissions with an optical open-cavity methane sensor
US12480924B2 (en) 2022-08-03 2025-11-25 Schlumberger Technology Corporation Automated record quality determination and processing for pollutant emission quantification
US12555125B2 (en) 2023-06-09 2026-02-17 Schlumberger Technology Corporation Emission detecting camera placement planning using 3D models
US12577871B2 (en) 2022-08-03 2026-03-17 Schlumberger Technology Corporation Linear cut generation method for sensor inversion constraint imposition

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11761590B2 (en) * 2020-10-06 2023-09-19 Abb Schweiz Ag Technologies for producing efficient investigation routes for identifying gas leak locations
FR3142556A1 (en) * 2022-11-30 2024-05-31 Totalenergies Onetech Method for measuring the contents of at least one gas emitted by a source in a plume propagating in the atmosphere from the source, method, and associated drone
CN116109104A (en) * 2023-03-03 2023-05-12 成都秦川物联网科技股份有限公司 Gas repair management method for smart gas, Internet of Things system, device and medium

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140236390A1 (en) * 2013-02-20 2014-08-21 Farrokh Mohamadi Vertical takeoff and landing (vtol) small unmanned aerial system for monitoring oil and gas pipelines
US20160214715A1 (en) * 2014-11-21 2016-07-28 Greg Meffert Systems, Methods and Devices for Collecting Data at Remote Oil and Natural Gas Sites
US9599529B1 (en) 2012-12-22 2017-03-21 Picarro, Inc. Systems and methods for likelihood-based mapping of areas surveyed for gas leaks using mobile survey equipment
US20180292374A1 (en) 2017-04-05 2018-10-11 International Business Machines Corporation Detecting gas leaks using unmanned aerial vehicles
CN109780452A (en) * 2019-01-24 2019-05-21 天津中科飞航技术有限公司 Gas based on laser telemetry technology leaks unmanned plane inspection retrieving concentration method
WO2019246280A1 (en) * 2018-06-19 2019-12-26 Seekops Inc. Emissions estimate model algorithms and methods

Family Cites Families (203)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3780566A (en) 1972-03-07 1973-12-25 Argus Chem Apparatus for continuously monitoring air-pollution
US4135092A (en) 1978-04-12 1979-01-16 Geomet Exploration, Inc. Method of quantifying fugitive emission rates from pollution sources
US4233564A (en) 1978-09-05 1980-11-11 Hazeltine Corporation Apparatus for changing the scale of a logarithmic signal
US4507558A (en) 1983-02-22 1985-03-26 Honeywell Inc. Selective leak-detector for natural gas
SE8802536D0 (en) 1988-07-07 1988-07-07 Altoptronic Ab METHOD AND APPARATUS FOR SPECTROSCOPIC MEASUREMENT OF THE CONCENTRATION OF A GAS IN A SAMPLE
US4988833A (en) 1989-08-29 1991-01-29 W. L. Gore & Associates, Inc. Retractable coiled electrical cable
US5047639A (en) 1989-12-22 1991-09-10 Wong Jacob Y Concentration detector
US5075619A (en) 1990-04-06 1991-12-24 Tektronix, Inc. Method and apparatus for measuring the frequency of a spectral line
US5317156A (en) 1992-01-29 1994-05-31 Sri International Diagnostic tests using near-infrared laser absorption spectroscopy
US5291265A (en) 1992-06-03 1994-03-01 Aerodyne Research, Inc. Off-axis cavity absorption cell
ATE241315T1 (en) 1993-08-12 2003-06-15 Kurashiki Boseki Kk NON-INVASIVE METHOD AND INSTRUMENT FOR MEASURING BLOOD SUGAR LEVELS
US5767780A (en) 1993-09-22 1998-06-16 Lockheed Martin Energy Research Corporation Detector for flow abnormalities in gaseous diffusion plant compressors
DE29500705U1 (en) 1995-01-18 1995-03-02 Future Technics Medizin und Technik GmbH, 96515 Sonneberg Device for determining a certain proportion of gas in the air
JPH08247939A (en) 1995-03-15 1996-09-27 Anritsu Corp Gas concentration measuring device
US5822058A (en) 1997-01-21 1998-10-13 Spectral Sciences, Inc. Systems and methods for optically measuring properties of hydrocarbon fuel gases
US6295859B1 (en) 1997-04-18 2001-10-02 The B. F. Goodrich Co. Method and system for remotely determining column density of trace gases
US6064488A (en) 1997-06-06 2000-05-16 Monitor Labs, Inc. Method and apparatus for in situ gas concentration measurement
AU3401499A (en) 1998-04-20 1999-11-08 Horace Rekunyk Infrared remote monitoring system for leak
US6356350B1 (en) 1998-07-30 2002-03-12 Southwest Sciences Incorporated Wavelength modulation spectroscopy with multiple harmonic detection
US6549630B1 (en) 2000-02-04 2003-04-15 Plantronics, Inc. Signal expander with discrimination between close and distant acoustic source
US6597462B2 (en) 2000-03-01 2003-07-22 Lambda Physik Ag Laser wavelength and bandwidth monitor
US6509566B1 (en) 2000-06-22 2003-01-21 Ophir Corporation Oil and gas exploration system and method for detecting trace amounts of hydrocarbon gases in the atmosphere
WO2002004903A1 (en) 2000-07-12 2002-01-17 Macquarie Research Ltd Optical heterodyne detection in optical cavity ringdown spectroscopy
SE516843C2 (en) 2000-07-12 2002-03-12 Bo Galle Method for measuring gaseous emissions and / or flux
US6632402B2 (en) 2001-01-24 2003-10-14 Ntc Technology Inc. Oxygen monitoring apparatus
TW522226B (en) 2001-02-20 2003-03-01 Tlv Co Ltd Portable leak detector
US7022992B2 (en) 2002-01-17 2006-04-04 American Air Liquide, Inc. Method and apparatus for real-time monitoring of furnace flue gases
US20030230716A1 (en) 2002-04-12 2003-12-18 Infrared Industries, Inc. Multi-gas analyzer
US10633093B2 (en) 2017-05-05 2020-04-28 General Electric Company Three-dimensional robotic inspection system
US10739770B2 (en) 2018-01-16 2020-08-11 General Electric Company Autonomously-controlled inspection platform with model-based active adaptive data collection
US10521960B2 (en) 2017-05-03 2019-12-31 General Electric Company System and method for generating three-dimensional robotic inspection plan
JP3091618U (en) 2002-07-24 2003-02-07 船井電機株式会社 Optical pickup
ATE434757T1 (en) 2002-09-06 2009-07-15 Tdw Delaware Inc METHOD FOR DETECTING GASES USING ABSORPTION SPECTROSCOPY
US6949734B2 (en) 2003-04-22 2005-09-27 Itt Manufacturing Enterprises, Inc. Active remote sensing using a spectral lock-in technique
US7966155B2 (en) 2004-06-04 2011-06-21 William K. Warburton Method and apparatus for improving detection limits in x-ray and nuclear spectroscopy systems
WO2006005014A2 (en) 2004-06-30 2006-01-12 Valtronics, Inc. Gas sample conditioning system
US7477377B2 (en) 2004-07-21 2009-01-13 Southwest Sciences Incorporated Dense pattern optical multipass cell
US20060044562A1 (en) 2004-08-25 2006-03-02 Norsk Elektro Optikk As Gas monitor
US7469183B2 (en) 2005-01-24 2008-12-23 International Business Machines Corporation Navigating UAVs in formation
US20100004798A1 (en) 2005-01-25 2010-01-07 William Kress Bodin Navigating a UAV to a next waypoint
US8147302B2 (en) 2005-03-10 2012-04-03 Aircuity, Inc. Multipoint air sampling system having common sensors to provide blended air quality parameter information for monitoring and building control
US8001841B2 (en) 2005-10-14 2011-08-23 Olympus Ndt Ultrasonic fault detection system using a high dynamic range analog to digital conversion system
US7421911B2 (en) 2005-12-20 2008-09-09 Desrochers Eric M Duct probe assembly system for multipoint air sampling
US7800751B1 (en) 2006-02-27 2010-09-21 Southwest Sciences Incorporated Dense pattern multiple pass cells
US20080169934A1 (en) 2006-08-11 2008-07-17 Steve Lang Portable gas detector
US7999232B2 (en) 2006-12-22 2011-08-16 Photonic Innovations Limited Gas detector
US20090326792A1 (en) 2007-05-06 2009-12-31 Mcgrath Alan Thomas Method and system for increasing the degree of autonomy of an unmanned aircraft by utilizing meteorological data received from GPS dropsondes released from an unmanned aircraft to determine course and altitude corrections and an automated data management and decision support navigational system to make these navigational calculations and to correct the unmanned aircraft's flight path
JP2009075823A (en) 2007-09-20 2009-04-09 Toho Gas Co Ltd Gas leak detection communication system and gas leak detection communication apparatus
EP2072979B1 (en) 2007-12-21 2012-02-29 Siemens Aktiengesellschaft Method for measuring the concentration of a gas component in a measuring gas
US8060270B2 (en) 2008-02-29 2011-11-15 The Boeing Company System and method for inspection of structures and objects by swarm of remote unmanned vehicles
JP2009276339A (en) 2008-04-16 2009-11-26 Ngk Spark Plug Co Ltd Sensor
DE112008003880T5 (en) 2008-05-27 2011-04-14 Sadik Hafizovic Device for lock-in amplification of an input signal and method for generating a reference signal for a lock-in amplifier
WO2009156437A2 (en) 2008-06-25 2009-12-30 Shell Internationale Research Maatschappij B.V. Method and system for screening an area of the atmosphere for sources of emissions
US9267881B2 (en) 2008-11-06 2016-02-23 Li-Cor, Inc. Hybrid gas analyzer with thermally insulated flow cell
US8121798B2 (en) 2008-11-24 2012-02-21 Itt Manufacturing Enterprises, Inc. Gas flux determination using airborne DIAL LIDAR and airborne wind measurement
EP2199790A1 (en) 2008-12-19 2010-06-23 Duvas Technologies Limited System and apparatus for measurement and mapping of pollutants
CA2751209C (en) 2009-02-02 2018-08-21 Planetary Emissions Management System of systems for monitoring greenhouse gas fluxes
US8515609B2 (en) 2009-07-06 2013-08-20 Honeywell International Inc. Flight technical control management for an unmanned aerial vehicle
US8451120B2 (en) 2009-08-14 2013-05-28 Accenture Global Services Limited System for relative positioning of access points in a real time locating system
CA2681681A1 (en) 2009-10-06 2010-06-08 Colin Irvin Wong Mapping concentrations of airborne matter
US8811439B2 (en) 2009-11-23 2014-08-19 Seminex Corporation Semiconductor laser assembly and packaging system
US20110150035A1 (en) 2009-12-17 2011-06-23 Hanson Ronald K Non-intrusive method for sensing gas temperature and species concentration in gaseous environments
US8508740B2 (en) 2010-01-04 2013-08-13 University Corporation For Atmospheric Research Optical multi-pass cell
US8731889B2 (en) 2010-03-05 2014-05-20 Schlumberger Technology Corporation Modeling hydraulic fracturing induced fracture networks as a dual porosity system
IT1401884B1 (en) 2010-10-06 2013-08-28 Tea Sistemi S P A METHOD FOR QUANTIFYING A FLOW OF GAS FUGITIVE BY MEANS OF VERTICAL CONCENTRATION MEASUREMENTS
US8665442B2 (en) 2011-08-18 2014-03-04 Li-Cor, Inc. Cavity enhanced laser based isotopic gas analyzer
US9329365B2 (en) 2011-09-23 2016-05-03 Goodrich Corporation Wide field of view monocentric lens system for infrared aerial reconnaissance camera systems
EP2776673B1 (en) 2011-11-03 2022-01-19 Fastcap Systems Corporation A logging apparatus
WO2013096396A1 (en) 2011-12-20 2013-06-27 The Board Of Trustees Of The Leland Stanford Junior University A method for calibration-free scanned-wavelength modulation spectroscopy for gas sensing
ITTO20120894A1 (en) 2012-10-12 2014-04-13 Sea Marconi Technologies Di Vander Tumiatti S A S CO-PRODUCTION PROCEDURE OF BIOENERGY AND INTEGRATED CONVERSION OF BIOMASS AND URBAN WASTE
TWI665437B (en) 2012-10-16 2019-07-11 巴哈馬商愛克斯崔里斯科技有限公司 Method and apparatus for determining at least one point of entry of smoke into a smoke detection system, and smoke detector
EP2948761B1 (en) 2013-01-23 2023-06-28 California Institute of Technology Miniature tunable laser spectrometer for detection of a trace gas
CA2937917A1 (en) 2013-03-14 2014-09-18 Total S.A. Systems and methods for monitoring and controlled capture of air samples for analysis
US9183371B2 (en) 2013-03-15 2015-11-10 Tyfone, Inc. Personal digital identity device with microphone
US9263787B2 (en) 2013-03-15 2016-02-16 Dockon Ag Power combiner and fixed/adjustable CPL antennas
US9266611B2 (en) 2013-06-20 2016-02-23 University Of Florida Research Foundation, Inc. Flight path development for remote sensing vehicles in a moving reference frame
US9947051B1 (en) 2013-08-16 2018-04-17 United Services Automobile Association Identifying and recommending insurance policy products/services using informatic sensor data
GB2518010A (en) 2013-09-09 2015-03-11 Crfs Ltd Frequency discriminator
WO2015073687A1 (en) 2013-11-13 2015-05-21 Schlumberger Canada Limited Unmanned aerial vehicles for well monitoring and control
US10250821B2 (en) 2013-11-27 2019-04-02 Honeywell International Inc. Generating a three-dimensional model of an industrial plant using an unmanned aerial vehicle
WO2015113962A1 (en) * 2014-01-28 2015-08-06 Explicit I/S A method and an unmanned aerial vehicle for determining emissions of a vessel
US9756263B2 (en) 2014-05-01 2017-09-05 Rebellion Photonics, Inc. Mobile gas and chemical imaging camera
US11290662B2 (en) 2014-05-01 2022-03-29 Rebellion Photonics, Inc. Mobile gas and chemical imaging camera
WO2015172056A1 (en) 2014-05-09 2015-11-12 Kairos Aerospace Inc. Systems and methods for detecting gas leaks
US9183731B1 (en) 2014-05-15 2015-11-10 Umm Al-Qura University Emergency detection and alert device and system utilizing a mobile communication device
US9783293B2 (en) 2014-05-20 2017-10-10 Verizon Patent And Licensing Inc. Unmanned aerial vehicle platform
EP3186605B1 (en) 2014-08-25 2019-12-18 Isis Geomatics Inc. Apparatus and method for detecting a gas using an unmanned aerial vehicle
EP3855276A1 (en) 2014-09-05 2021-07-28 SZ DJI Technology Co., Ltd. Multi-sensor environmental mapping
DE102014013822B4 (en) 2014-09-23 2024-04-25 Schütz GmbH Meßtechnik Operating procedure for a gas detector
US10598562B2 (en) 2014-11-21 2020-03-24 Picarro Inc. Gas detection systems and methods using measurement position uncertainty representations
CA2872783A1 (en) 2014-12-01 2016-06-01 David Andrew Risk Gas emission detection device, system and method
US9250175B1 (en) 2014-12-16 2016-02-02 Aerodyne Research, Inc. Optical multi-pass cell for long path-length spectroscopy
CN104458588B (en) 2014-12-24 2017-02-22 威特龙消防安全集团股份公司 Bidirectional self-cleaning type optical fiber gas sensor probe
US20160202225A1 (en) 2015-01-09 2016-07-14 Case Western Reserve University System for Detecting a Gas and Method Therefor
US10365646B1 (en) 2015-01-27 2019-07-30 United Services Automobile Association (Usaa) Systems and methods for unmanned vehicle management
US9983126B2 (en) 2015-02-06 2018-05-29 Block Engineering, Llc Quantum cascade laser (QCL) based gas sensing system and method
WO2016130994A1 (en) 2015-02-13 2016-08-18 Unmanned Innovation, Inc. Unmanned aerial vehicle remote flight planning system
US11768508B2 (en) 2015-02-13 2023-09-26 Skydio, Inc. Unmanned aerial vehicle sensor activation and correlation system
WO2016162673A1 (en) 2015-04-10 2016-10-13 Bae Systems Plc Long range sensor apparatus and method of providing a long range sensor apparatus
US10023323B1 (en) 2015-04-29 2018-07-17 X Development Llc Estimating wind from an airborne vehicle
US10240998B2 (en) 2015-05-12 2019-03-26 The United States Of America, As Represented By The Secretary Of Commerce Determining a location and size of a gas source with a spectrometer gas monitor
GB2538563B (en) 2015-05-22 2017-08-02 Optosci Ltd Gas sensing apparatus
US9715235B2 (en) 2015-06-05 2017-07-25 The Boeing Company Autonomous unmanned aerial vehicle decision-making
US20170158353A1 (en) 2015-08-07 2017-06-08 Mark Schmick Remote Aerodrome for UAVs
DE102015216272A1 (en) 2015-08-26 2017-03-02 Airbus Operations Gmbh Modular robot kit, swarm of modularized robots, and task accomplishment by a swarm of modularized robots
US10662765B2 (en) 2015-09-18 2020-05-26 Schlumberger Technology Corporation Wellsite emissions monitoring and control
WO2017058901A1 (en) 2015-09-28 2017-04-06 Ball Aerospace & Technologies Corp. Differential absorption lidar
US9970756B2 (en) 2015-10-06 2018-05-15 Bridger Photonics, Inc. High-sensitivity gas-mapping 3D imager and method of operation
US11287407B2 (en) 2015-10-19 2022-03-29 University Of North Texas Dynamic reverse gas stack model for portable chemical detection devices to locate threat and point-of-source from effluent streams
US9810627B2 (en) 2015-10-27 2017-11-07 Nec Corporation Flexible three-dimensional long-path gas sensing by unmanned vehicles
NL2017595A (en) 2015-11-10 2017-05-26 Asml Netherlands Bv Proximity sensor, lithographic apparatus and device manufacturing method
US10069918B2 (en) 2015-11-11 2018-09-04 Ut-Battelle, Llc Global communication and control
AU2016359163A1 (en) 2015-11-23 2018-07-05 Kespry Inc. Autonomous mission action alteration
KR101770254B1 (en) 2015-11-30 2017-08-22 엘케이테크넷(주) Gas leak detection system using smart-phone and hydrocarbon detection device
US20170168487A1 (en) 2015-12-11 2017-06-15 International Business Machines Corporation System and method for tracking pollution
US10083616B2 (en) 2015-12-31 2018-09-25 Unmanned Innovation, Inc. Unmanned aerial vehicle rooftop inspection system
CA3001023A1 (en) 2016-01-08 2017-07-13 Pictometry International Corp. Systems and methods for taking, processing, retrieving, and displaying images from unmanned aerial vehicles
WO2017127711A1 (en) 2016-01-20 2017-07-27 Ez3D, Llc System and method for structural inspection and construction estimation using an unmanned aerial vehicle
FR3047073B1 (en) 2016-01-21 2019-08-02 Pfeiffer Vacuum REMOTE CONTROL FOR LEAK DETECTOR AND LEAK DETECTION MODULE
CN109073544B (en) 2016-02-11 2021-06-15 汤姆·鲁宾 Long optical path absorption cell
JP6691721B2 (en) 2016-02-15 2020-05-13 株式会社トプコン Flight planning method and flight guidance system
US10429546B1 (en) 2016-02-25 2019-10-01 Intellisense Systems, Inc. Weather sensor including vertically stacked multi-power modules
CN113342038B (en) 2016-02-29 2024-08-20 星克跃尔株式会社 Method and system for generating maps for unmanned aerial vehicle flight
US10023311B2 (en) 2016-03-10 2018-07-17 International Business Machines Corporation Automatic painting system with drone, user interface and computer vision
US10533965B2 (en) 2016-04-19 2020-01-14 Industrial Scientific Corporation Combustible gas sensing element with cantilever support
US10180393B2 (en) 2016-04-20 2019-01-15 Cascade Technologies Holdings Limited Sample cell
AU2017268056B2 (en) 2016-05-18 2021-08-05 Lineriders Inc. Apparatus and methodologies for leak detection using gas and infrared thermography
WO2017201194A1 (en) 2016-05-18 2017-11-23 MultiSensor Scientific, Inc. Hydrocarbon leak imaging and quantification sensor
US10448555B2 (en) 2016-05-27 2019-10-22 Cnh Industrial America Llc System and method for scouting vehicle mapping
CN205749271U (en) 2016-06-07 2016-11-30 绍兴国正安全技术检测有限公司 A kind of intelligent portable infrared gas analyser
US11391669B2 (en) 2016-07-07 2022-07-19 Nec Corporation Gas detection system
US9915562B2 (en) 2016-08-12 2018-03-13 Abb, Inc. Method of increasing power within an optical cavity with long path lengths
US10703474B2 (en) 2016-08-20 2020-07-07 The Hi-Tech Robotic Systemz Ltd Tethered unmanned aerial vehicle
US10775297B2 (en) 2016-08-24 2020-09-15 Ecotec Solutions, Inc. Laser absorption spectroscopy system and method for discrimination of a first and a second gas
WO2018045377A1 (en) 2016-09-05 2018-03-08 Brewer Science Inc. Energetic pulse clearing of environmentally sensitive thin-film devices
US11299268B2 (en) 2016-11-02 2022-04-12 California Institute Of Technology Positioning of in-situ methane sensor on a vertical take-off and landing (VTOL) unmanned aerial system (UAS)
CN106568516A (en) 2016-11-08 2017-04-19 广西大学 Thermometer
IL249780B (en) 2016-12-26 2020-08-31 Wolfson Noah System and method for predicting presence of hazardous airborne materials in a region to be protected
CN108271118B (en) 2016-12-30 2020-09-25 华为技术有限公司 High-altitude communication system, method and device
CN106769977A (en) 2016-12-30 2017-05-31 武汉市欧睿科技有限公司 A kind of hand-held high-precision gas quantitative leak detector
US11175202B2 (en) 2018-01-02 2021-11-16 Arthur W Mohr, Jr. Apparatus and method for collecting environmental samples
US10704981B2 (en) 2017-01-04 2020-07-07 General Electric Company Remote leak detection system
GB201700905D0 (en) 2017-01-19 2017-03-08 Cascade Tech Holdings Ltd Close-Coupled Analyser
US20200065433A1 (en) 2017-02-22 2020-02-27 Middle Chart, LLC Method and apparatus for construction and operation of connected infrastructure
US10329017B2 (en) 2017-03-13 2019-06-25 General Electric Company System and method for integrating flight path and site operating data
US10031040B1 (en) 2017-03-28 2018-07-24 Palo Alto Research Center Incorporated Method and system for analyzing gas leak based on machine learning
WO2018193578A1 (en) 2017-04-20 2018-10-25 エスゼット ディージェイアイ テクノロジー カンパニー リミテッド Flight path establishment method, information processing device, program and recording medium
WO2018227153A1 (en) 2017-06-09 2018-12-13 Resnick Blake Drone implemented border patrol
US10962437B1 (en) 2017-06-27 2021-03-30 Picarro, Inc. Aggregate leak indicator display systems and methods
US20190011920A1 (en) 2017-07-07 2019-01-10 Sharper Shape Oy Method and system for generating flight plan of unmanned aerial vehicle for aerial inspection
US10330592B2 (en) 2017-07-21 2019-06-25 Serguei Koulikov Laser absorption spectroscopy isotopic gas analyzer
US10365199B2 (en) 2017-07-26 2019-07-30 Met One Instruments, Inc. Twin-spot light absorbing particulate monitoring instrument
US10427786B2 (en) 2017-09-14 2019-10-01 At&T Intellectual Property I, L.P. Drone authentication system
US10489649B2 (en) 2017-09-28 2019-11-26 At&T Intellectual Property I, L.P. Drone data locker system
CN107703075A (en) 2017-10-10 2018-02-16 黑龙江聚晶科技有限公司 Distributed concentration of methane gas detection means based on Fibre Optical Sensor
US11275068B2 (en) 2017-10-31 2022-03-15 Honeywell International Inc. Remote gas sensing using UAVs
WO2019099567A1 (en) 2017-11-14 2019-05-23 Bridger Photonics, Inc. Apparatuses and methods for anomalous gas concentration detection
US10928549B2 (en) 2017-11-21 2021-02-23 United States Of America As Represented By The Administrator Of Nasa High altitude UAV for monitoring meteorological parameters
US11557212B2 (en) 2017-12-21 2023-01-17 Beijing Xiaomi Mobile Software Co., Ltd. Method and device for determining flight path of unmanned aerial vehicle
US10466106B2 (en) 2017-12-22 2019-11-05 Nec Corporation Gas concentration measurement by 2F signal trough distance
WO2019135494A1 (en) 2018-01-08 2019-07-11 주식회사 에스오에스랩 Lidar device
US10607406B2 (en) 2018-01-25 2020-03-31 General Electric Company Automated and adaptive three-dimensional robotic site surveying
US12066353B2 (en) 2018-02-01 2024-08-20 Bridger Photonics, Inc. Apparatuses and methods for gas flux measurements
US11226323B2 (en) 2018-04-27 2022-01-18 International Business Machines Corporation Air-pollution emission source monitoring
CN112327274B (en) 2018-06-08 2022-11-22 上海禾赛科技有限公司 Laser radar
US12399164B2 (en) 2018-06-19 2025-08-26 Seekops Inc. Emissions estimate model algorithms and methods
EP3811172B1 (en) 2018-06-19 2025-08-20 SeekOps Inc. Method and system for determining a gas source location
EP3591379B1 (en) 2018-07-04 2022-01-26 Q.E.D. Environmental Systems Limited Portable optical spectroscopy device for analyzing gas samples
US11891192B2 (en) 2018-07-23 2024-02-06 Shanghai Autoflight Co., Ltd. Landing platform for unmanned aerial vehicle
WO2020028353A1 (en) 2018-07-30 2020-02-06 Seekops Inc. Ultra-lightweight, handheld gas leak detection device
CN109030374A (en) 2018-08-16 2018-12-18 上海禾赛光电科技有限公司 Data managing method and data management terminal for laser gas detector
US10325485B1 (en) 2018-09-11 2019-06-18 Rockwell Automation Technologies, Inc. System or process to detect, discriminate, aggregate, track, and rank safety related information in a collaborative workspace
US11062614B2 (en) 2018-09-12 2021-07-13 Alliance Solutions Group, Inc. Systems and methods for collecting and analyzing hazardous materials information using an unmanned aerial vehicle
US12281983B2 (en) 2018-10-22 2025-04-22 Seekops Inc. UAV-borne, high-bandwidth, lightweight point sensor for quantifying greenhouse gases in atmospheric strata
US10753864B2 (en) 2018-12-10 2020-08-25 General Electric Company Gas analysis system
US10816458B2 (en) 2018-12-10 2020-10-27 General Electric Company Gas analysis system
EP3911571A4 (en) 2019-01-15 2022-10-19 Bridger Photonics, Inc. DEVICES, SYSTEMS AND METHODS FOR MEASUREMENT OF GAS FLOW WITH MOBILE PLATFORMS
US10955294B2 (en) 2019-02-04 2021-03-23 Honeywell International Inc. Optical sensor for trace-gas measurement
EP3948202A4 (en) 2019-04-05 2023-01-04 SeekOps Inc. TIME AND DATA EFFICIENT LEAK DETECTION ASSURANCE
WO2020206006A1 (en) 2019-04-05 2020-10-08 Seekops Inc. Analog signal processing for a lightweight and compact laser-based trace gas sensor
US20220165162A1 (en) 2019-04-05 2022-05-26 Seekops Inc. Route optimization for energy industry infrastructure inspection
JP7334063B2 (en) 2019-05-24 2023-08-28 株式会社ディスコ Manufacturing method of mold chip
US11761938B2 (en) 2019-06-21 2023-09-19 General Electric Company Sensing system and method
US11649782B2 (en) 2019-07-16 2023-05-16 Baker Hughes Oilfield Operations Llc Gas emission monitoring and detection
GB2600323B (en) 2019-08-05 2024-04-03 Teledyne Flir Detection Inc Radiation source localization systems and methods
WO2021055902A1 (en) 2019-09-20 2021-03-25 Seekops Inc. Spectral fitting of compact laser-based trace gas sensor measurements for high dynamic range (hdr)
US11105784B2 (en) 2019-10-04 2021-08-31 Sensors Unlimited, Inc. System and method of sensing for petroleum, oil, and gas leaks using optical detection
US20210109074A1 (en) 2019-10-14 2021-04-15 Seekops Inc. Gas measurement instrument on unmanned vehicle
US11614430B2 (en) 2019-12-19 2023-03-28 Seekops Inc. Concurrent in-situ measurement of wind speed and trace gases on mobile platforms for localization and qualification of emissions
US11988598B2 (en) 2019-12-31 2024-05-21 Seekops Inc. Optical cell cleaner
US12055485B2 (en) 2020-02-05 2024-08-06 Seekops Inc. Multispecies measurement platform using absorption spectroscopy for measurement of co-emitted trace gases
WO2021158916A1 (en) 2020-02-05 2021-08-12 Seekops Inc. Multiple path length optical cell for trace gas measurement
CN211508182U (en) 2020-04-15 2020-09-15 深圳市利拓光电有限公司 Power semiconductor laser device with constant temperature control function
US11710411B2 (en) 2020-05-08 2023-07-25 The Travelers Indemnity Company Systems and methods for autonomous hazardous area data collection
CN112213443B (en) 2020-05-25 2021-05-14 南京大学环境规划设计研究院集团股份公司 Method for correcting deviation of atmospheric pollutant concentration monitoring value of rotor unmanned aerial vehicle
US12475798B2 (en) 2020-07-17 2025-11-18 Seekops Inc. UAS work practice
US11748866B2 (en) 2020-07-17 2023-09-05 Seekops Inc. Systems and methods of automated detection of gas plumes using optical imaging
US11619562B2 (en) 2020-10-06 2023-04-04 Abb Schweiz Ag Systems and methods for efficiently identifying gas leak locations
WO2022093864A1 (en) 2020-10-27 2022-05-05 Seekops Inc. Methods and apparatus for measuring methane emissions with an optical open-cavity methane sensor
WO2022211837A1 (en) 2021-04-02 2022-10-06 Seekops Inc. Multispecies measurement platform using absorption spectroscopy for measurement of co-emitted trace gases
US20220357231A1 (en) 2021-05-06 2022-11-10 Abb Schweiz Ag Technologies for improved visualization of gas leak detection data
US20230213413A1 (en) 2021-10-01 2023-07-06 Arthur W. Mohr, JR. Apparatus and method for collecting environmental samples
WO2023239351A1 (en) 2022-06-06 2023-12-14 Landmark Graphics Corporation Emissions estimations at a hydrocarbon operation location using a data-driven approach

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9599529B1 (en) 2012-12-22 2017-03-21 Picarro, Inc. Systems and methods for likelihood-based mapping of areas surveyed for gas leaks using mobile survey equipment
US10126200B1 (en) * 2012-12-22 2018-11-13 Picarro, Inc. Systems and methods for likelihood-based mapping of areas surveyed for gas leaks using mobile survey equipment
US20140236390A1 (en) * 2013-02-20 2014-08-21 Farrokh Mohamadi Vertical takeoff and landing (vtol) small unmanned aerial system for monitoring oil and gas pipelines
US20160214715A1 (en) * 2014-11-21 2016-07-28 Greg Meffert Systems, Methods and Devices for Collecting Data at Remote Oil and Natural Gas Sites
US20180292374A1 (en) 2017-04-05 2018-10-11 International Business Machines Corporation Detecting gas leaks using unmanned aerial vehicles
WO2019246280A1 (en) * 2018-06-19 2019-12-26 Seekops Inc. Emissions estimate model algorithms and methods
CN109780452A (en) * 2019-01-24 2019-05-21 天津中科飞航技术有限公司 Gas based on laser telemetry technology leaks unmanned plane inspection retrieving concentration method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3948202A4

Cited By (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12216105B2 (en) 2018-06-19 2025-02-04 Seekops Inc. Localization analytics algorithms and methods
US12449409B2 (en) 2018-06-19 2025-10-21 Seekops Inc. Emissions estimate model algorithms and methods
US12399164B2 (en) 2018-06-19 2025-08-26 Seekops Inc. Emissions estimate model algorithms and methods
US12281983B2 (en) 2018-10-22 2025-04-22 Seekops Inc. UAV-borne, high-bandwidth, lightweight point sensor for quantifying greenhouse gases in atmospheric strata
US12188847B2 (en) 2019-04-05 2025-01-07 Seekops Inc. Time-and data-efficient assurance of leak detection
US12392680B2 (en) 2019-09-20 2025-08-19 Seekops Inc. Spectral fitting of compact laser-based trace gas sensor measurements for high dynamic range (HDR)
US12197233B2 (en) 2019-10-04 2025-01-14 Seekops Inc. Closed surface flight pattern generation for unmanned aerial vehicle (UAV) flux plane assessment of large facilities
US12188912B2 (en) 2019-12-19 2025-01-07 Seekops Inc. Concurrent in-situ measurement of wind speed and trace gases on mobile platforms for localization and qualification of emissions
US12449354B2 (en) 2020-02-05 2025-10-21 Seekops Inc. Multispecies measurement platform using absorption spectroscopy for measurement of co-emitted trace gases
US12276597B2 (en) 2020-02-05 2025-04-15 Seekops Inc. Multiple path length optical cell for trace gas measurement
US12475798B2 (en) 2020-07-17 2025-11-18 Seekops Inc. UAS work practice
US12217412B2 (en) 2020-07-17 2025-02-04 Seekops Inc. Systems and methods of automated detection of gas plumes using optical imaging
EP4237741A4 (en) * 2020-10-27 2025-01-08 SeekOps Inc. METHODS AND APPARATUS FOR MEASURING METHANE EMISSIONS WITHIN A MESH SENSOR NETWORK
US12480867B2 (en) 2020-10-27 2025-11-25 Seekops Inc. Methods and apparatus for measuring methane emissions with an optical open-cavity methane sensor
EP4086602A1 (en) * 2021-05-06 2022-11-09 Abb Schweiz Ag Technologies for improved visualization of gas leak detection data
WO2023133345A1 (en) * 2022-01-10 2023-07-13 Cameron International Corporation Method and apparatus for greenhouse gas emission management
US12480924B2 (en) 2022-08-03 2025-11-25 Schlumberger Technology Corporation Automated record quality determination and processing for pollutant emission quantification
US12577871B2 (en) 2022-08-03 2026-03-17 Schlumberger Technology Corporation Linear cut generation method for sensor inversion constraint imposition
US12480922B2 (en) 2022-12-09 2025-11-25 Schlumberger Technology Corporation Methods and systems for characterizing methane emission employing mobile methane emission detection
US12292310B2 (en) 2022-12-15 2025-05-06 Schlumberger Technology Corporation Machine learning based methane emissions monitoring
US12555125B2 (en) 2023-06-09 2026-02-17 Schlumberger Technology Corporation Emission detecting camera placement planning using 3D models
US12254622B2 (en) 2023-06-16 2025-03-18 Schlumberger Technology Corporation Computing emission rate from gas density images

Also Published As

Publication number Publication date
US20250377256A1 (en) 2025-12-11
US12188847B2 (en) 2025-01-07
EP3948202A4 (en) 2023-01-04
EP3948202A1 (en) 2022-02-09
US20220170810A1 (en) 2022-06-02

Similar Documents

Publication Publication Date Title
US20250377256A1 (en) Time-and data-efficient assurance of leak detection
US12475798B2 (en) UAS work practice
US20230194487A1 (en) Concurrent in-situ measurement of wind speed and trace gases on mobile platforms for localization and qualification of emissions
US20220165162A1 (en) Route optimization for energy industry infrastructure inspection
EP3811171B1 (en) Emissions estimate model algorithms and methods
US20250164994A1 (en) Closed surface flight pattern generation for unmanned aerial vehicle (uav) flux plane assessment of large facilities
US20250314629A1 (en) Emissions Estimate Model Algorithms and Methods
US20250389702A1 (en) Localization analytics algorithms and methods
US20210109074A1 (en) Gas measurement instrument on unmanned vehicle
US20240339021A1 (en) Scale triggering of emissions detection, localization, quantification and repair
US20230393013A1 (en) Methods and apparatus for measuring methane emissions within a mesh sensor network
Uyanik et al. Next generation gas emission monitoring system
WO2024258311A1 (en) Group of unmanned aerial vehicles mission control system for geophysical exploration
US20250052677A1 (en) Emissions localization and quantification using combined unmanned aerial systems and continuous monitoring
Wang et al. Tracking Drones with Manual Operation Representation
Oida et al. Blindfolded flight: A novel approach for secure drone flight
Lu et al. Research on optimal deep learning‐based formation flight positioning of UAV
Kopanon On the Deployment Optimization of Sensors and Coverage Path Planning for Drone Inspections
Nasiri et al. Environmental Monitoring in the Oil and Gas Industry Using
WO2023164185A1 (en) Measurement-corrected wind profile for increased accuracy of wind flow field

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20783815

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2020783815

Country of ref document: EP

Effective date: 20211105