Patent ID: 12216105

DETAILED DESCRIPTION

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.

The present system and method disclosed herein allows for determining a methane concentration and location of one or more emissions sources based on measurements from one or more sensors of an unmanned aerial vehicle (UAV), UAV data, and weather and/or atmospheric data from one or more sensors of a weather station. The UAV flies in a flight path about one or more methane sources. Data from the one or more UAV sensors, UAV data, and data from one or more sensors of the weather station are combined, stored, and filtered to generate a spatial map of methane concentration and the one or more emissions sources.

The goal of the natural gas production and supply chain is to deliver gas from source production areas to endpoint users without undue loss. Product loss in this context amounts to flaring or venting, intentional or otherwise, of natural gas to the atmosphere. Undue product loss results in uncaptured revenue, an increased environmental footprint, and possible safety hazards for vented emissions. There are many opportunities throughout the natural gas production and supply chain for gas to be released from containment and lost, e.g. pneumatic component venting, maintenance blowdowns, component failures, accidental release, etc. Natural gas production and distribution infrastructure is spatially distributed. Efficient, wide area survey methods are needed to identify, localize, and quantify natural gas releases throughout the system.

The disclosed UAS measures methane concentration along the chosen UAS flight path at high frequency to detect anomalies associated with natural gas releases. These data are reconciled with atmospheric conditions to identify and quantify the mass flow rate of natural gas sources within an inspection area.

Data Transfer Scheme

FIG.1depicts a data flow100in a single sensor and unmanned aerial vehicle (UAV)102configuration with a Ground Control Station (GCS)106as a point of interface between the UAV102and the Cloud Connected Processor, Local Server Processor, and/or Database108, according to one embodiment. The UAV102may be a small unmanned aerial vehicle (UAV), with the ability to fly in a three-dimensional flight path in the vicinity of (≤200 m from a ≥0.1 SCFH emissions point) potential methane source, and report GPS spatial position. The general flow of data is from one or more gas concentration sensors, i.e., payload104, affixed to one or more UAVs102and wirelessly transmitted to a centralized GCS106and transferred to a Cloud Connected Server Processer, Local Server Processor, and/or Database108. The payload104may be an ultra-lightweight, low power, Part per Billion (ppb) sensitivity, mid-Infrared (λ=3-8 μm), open path methane concentration sensor with sampling rate>0.1 Hz. A wireless radio or cellular connection may be used for remote data transfer between the UAV102and the base station106or a cloud server/processor108. A wireless interface or cellular connection may be used between the base station106and/or UAV102and a cloud server/processor108for performing advanced data analysis functions. Direct, bidirectional data transfer may occur between the UAV and the base station, between the UAV and the cloud processor, and/or between the base station and the cloud processor.

The UAV102flight path may be determined on a site-specific basis, using pilot experience and/or self-determined, remote commands. The purpose of the flight path is to measure atmospheric methane background concentration in the vicinity of a possible gas leak, as well as emissions signature (elevated ambient concentration) from all potential sources at the inspection site. High resolution (<0.1 m/s), high frequency measurements (>5 Hz) of wind speed and direction may be made using one or more wind sensors, and one or more additional weather/micro-meteorological sensors including, air temperature, humidity, atmospheric pressure, solar irradiance, ground surface temperature—from the ground, e.g., via a weather station110, and/or from the UAV102. The UAV flight path may be determined on a site-specific basis, using a human at the controls and/or self-determined, autonomous control. The purpose of the flight path is to measure gas concentration along a crosswind transect, and vertical profile in the vicinity of a possible gas emissions point. This flight plane is designed to capture the atmospheric methane background as well as emissions signature, i.e., elevated ambient concentration, from all potential sources at the inspection site. A stochastic, back-trajectory model to calculate the receptor sensitivity of the UAS concentration sensor payload and the source location probability. Source emissions data may be displayed on a map, satellite image, aerial image, two-dimensional color map, two-dimensional contour map, and/or three-dimensional topographical surface/mesh.

FIG.2depicts a data flow200in a single sensor and UAV configuration with the UAV102directly interfacing with the cloud connected processor, local server processor, and/or database108, according to one embodiment. In another embodiment, the payload(s)104, UAV(s)102, and/or weather station(s)110communicate directly with a cloud server, processor, local server, processor, and/or database108. In all cases, each subsystem, e.g., UAV102; payload104; GCS, as shown inFIG.1; cloud server processor, local server processor, and/or database108; and weather station110, may or may not have the ability to directly communicate with each other subsystem, which is represented inFIGS.1-2. At the GCS and/or cloud server processor, local server processor, and/or database108, the data from the payload104is coupled with local weather station110data through local private networks and/or publicly available over the internet. The data can then be post-processed on the GCS, as shown inFIG.1, and/or on a local server and/or on a cloud-hosted server108.

FIG.3depicts a detailed data transfer300from a single sensor with a single UAV102, where this combination of devices comprise a UAS, according to one embodiment. Data from the payload104transfers to the UAV102and directly to the autopilot304via a serial connection. In some embodiments, the data transfer may be via any connection hardwire or wireless. Then, the data is fused with GPS location and time, barometric pressure altitude, relative altitude from LIDAR, Sonar, Radar, and/or UAV orientation, which forms a UAV Data Packet. The UAV Data Packet is transferred to the GCS and/or cloud processor302via a 500 mW 915 MHz Frequency Hopping Spread Spectrum (FHSS) transceiver. In some embodiments, the UAV Data Packet may be transferred via any wireless radio. In parallel, a Weather Station110having at least an anemometer and which may contain pressure sensors, pyranometers, i.e., solar irradiance, ground temperature sensors, air temperature sensors, and/or any sensors necessary for quantifying current atmospheric conditions forms a Meteorological Data Packet. The GCS and/or cloud processor302receives both the Meteorological Data Packet and UAV Data Packet at a frequency greater than 0.1 Hz. Each UAV Data Packet is fused with the nearest temporal or interpolated or extrapolated Meteorological Data Packet and saved on the GCS and/or a cloud processor302in an ASCII, binary, or any file necessary.

The data may be uploaded to a cloud server in real-time, near real-time, or at a later time. The data may include: time (GPS or other), latitude, longitude, altitude (barometric or GPS), relative altitude (from LIDAR, Sonar, and/or RADAR), gas concentration, wind vector (x, y, z or x, y), ambient temperature, and/or ambient pressure.

The payload104may include a concentration measurement instrument, a gas concentration analyzer, and/or an in situ gas concentration sensor. In some embodiments, the payload104may also include a pressure sensor, a temperature sensor, and/or an anemometer.

The weather station110may include at least an anemometer. In some embodiments, the weather station110may also include a pressure sensor, a pyranometer for solar irradiance, a ground temperature sensor, and an air temperature sensor.

Methodology

FIG.4depicts a plan view illustration400of a path for the disclosed UAS for natural gas release detection and localization, according to one embodiment One or more natural gas point sources402,404,406are located within a site boundary, and result in the downwind propagation of natural gas plumes408in an average downwind direction410, which is indicated by an arrow. The UAV102traverses a three-dimensional transect412and generates a spatial map of methane concentration, i.e., detection, and emissions sources, i.e., localization. This data is analyzed to determine source locations and quantify emission rates using non-parametric regression techniques.

Payload and Flight Path

The UAV payload may be an ultra-lightweight, low power, Part per Billion (ppb) sensitivity, mid-Infrared, open path methane concentration sensor with a sampling rate greater than 0.1 Hz. The UAV flight path is designed to measure the ambient methane concentration in the vicinity of possible source locations within the inspection area. The inspection area may include various natural gas infrastructure components, e.g., wells, valves, tanks, pipelines, compressors, condensers, flares, vents, and the like. The inspection area may also include other areas of possible methane emissions, such as compost facilities, manure collection facilities, livestock containment, landfills, sewer pipelines and vents, abandoned wells, and the like. The UAV flight path is designed based on pilot experience and/or automated input from a search algorithm commanded via autopilot software, as shown inFIG.3. The goal of the UAV flight path is to position the UAV in as many possible locations on the well pad as possible, both upwind and downwind of all potential and/or observed emission sources. Flight paths may maintain any specified intrinsically safe distance from infrastructure components.

The UAV records and transmits synchronized, CH4concentration data in volumetric concentration units, i.e., Parts Per Billion Volume (ppbv), and/or pressure and/or temperature and GPS coordinates (latitude, longitude and altitude) via wireless radio to a base station and/or cloud server, as shown inFIGS.1and3. The data is recorded in ASCII, binary, and/or database format on the base station, and synthesized with wind speed and direction data, as well as other Meteorological and/or weather data including air temperature and atmospheric pressure. The combined data is transmitted via wireless radio to a cloud processor for additional advanced analytics and reporting.

Background Calculation and Source Detection

FIG.5depicts a background gas concentration workflow500, according to one embodiment. The first step in the localization model is to calculate local background concentration. Typically, it is assumed that the background concentration measured on the upwind side of the source inspection area is a good representation of the local background concentration and provides an estimate of the upwind in-flow condition. Data may be selected for the appropriate time period (step502). The GPS coordinate, i.e., longitude and latitude, frame is converted to along a path distance (step504). A statistical filter is applied to the concentration data (step506).

FIG.6Adepicts a graph600of raw concentration data602, filtered/interpolated data604, and a background concentration estimation606, according to one embodiment.FIG.6Bdepicts a graph608of concentration enhancement data610resolved utilizing the sliding window median filter and spike detection algorithm612applied with a width filter614, according to one embodiment.

The raw concentration data as a function of distance, e.g., spatial coordinate, is filtered using a sliding window median filter. The filter window scale is determined based on the typical, or expected, gas plume width. For example, if the maximum plume width is expected to be on the order of 10 m, the filter scale would be set to three to five times the max plume width. The median filter also removes infrequent transients, or dropouts, in the concentration measurement caused by communication interference, or platform vibrations. The background concentration606is subtracted from the total concentration602to obtain the concentration enhancement610. The concentration enhancement signal contains the signature of an upwind emission source, and quantifies the emissions released by the local source.

A statistical filter is then applied to the concentration enhancement signal610to identify “spikes”612in the data that indicate methane plumes from nearby sources. The statistical filter determines the Cumulative Distribution Function (CDF) for the concentration enhancement, and targets extremum data points based on a prescribed percentile threshold. The selected points are then analyzed for contiguity and consolidated to form spatially continuous events. Each spike event may be further analyzed according to other metrics such as spatial extent, amplitude, magnitude, variance, and waveform shape. Individual spike events may be included or excluded through a selection process based on these derived quantities.

Wind and Weather Data Calculation

FIG.7depicts a graph700of typical wind values collected, according to one embodiment. Wind vector, i.e., a three-component magnitude and direction, is measured continuously, and concurrently over the duration of the UAV flight. Wind measurements may be performed using one or more stationary wind sensors connected to a ground station. Or the wind measurement may be made on-board the UAS during the flight. Additional weather sensors may be included with the ground station to quantify air temperature and pressure.

Spike events that were identified based on a statistical analysis of CH4concentration data are correlated with wind vector measurements and processed to obtain statistics of wind speed and direction during the detection of each plume.

FIG.8depicts a workflow800for spike identification and statistical analysis of atmospheric conditions, according to one embodiment. The wind statistics are then applied to determine the approximate location of a detected methane source using an inverse stochastic dispersion model. Meteorological and/or Weather data including air temperature, humidity, atmospheric pressure, solar irradiance, ground surface temperature may be applied to develop a model for local turbulence characteristics and quantify the spatial decorrelation of the wind. This approach is used to quantify the relationship between in situ wind measurements that are made some distance away from the probable source locations, and the actual winds and turbulence occurring at or near the source.

Candidate spike events are identified (step802). The time period of the spike event is correlated with weather data (step804). Qualitative statistics of weather data for each event are computed (step806). The computed qualitative statics (step806) determine an instantaneous wind vector808, an average wind vector810, a wind vector component magnitude variance812, and/or a wind vector component direction variance814.

Statistical Inverse Model for Source Location Footprint

FIG.9depicts a back trajectory workflow900taking account atmospheric statistics, Monte Carlo Markov Chain (MCMC) particle back trajectory simulation and localization in 3D space into account, according to one embodiment. An inverse stochastic dispersion model is applied to determine the probable location of a methane source or sources based on wind statistics measured during each plume event. An inverse dispersion model applies statistics of wind speed, direction, and turbulence to simulate upwind trajectories of massless particles, or air parcels, arriving at a specified downwind sensor location. After stochastically simulating many particle trajectories, the upwind distribution of particle positions provides an estimate of the sensor footprint. The footprint represents the spatial probability that a source of a given magnitude in any location within the model domain would have been detected by the sensor. When applied to individual plume events the inverse model predicts the most probable locations for the source(s) associated with the observed concentration enhancement. When data for many events are combined localization of sources to spatial regions on the order of 0.5-1000 m2is achieved through convergence of the ensemble particle trajectories.

Wind direction, variance, and turbulent kinetic energy for each event are calculated (step902). This calculation (step902) is iterated over M detected methane plumes. Then, the particle back trajectories are simulated using Markov Chain Monte Carlo (step904). This simulation (step904) is iterated over N trajectories. Then, the position of each particle in 3D space is tracked and stored (step906). The workflow900includes Eqs. 1-3, as discussed below. 3D space may include x, y positions used on a 3D map in some embodiments. In other embodiments, a 3D probability map, e.g., x, y, z, may be created for source location probability.

FIG.10depicts a graph1000showing a UAV trajectory according to measured gas concentration enhancements and projected to the (x,y) plane, according to one embodiment. Particle trajectories1002are shown as back-trajectories simulated using MCMC and the Langevin Equation. Each particle trajectory1002is created by a stochastic particle trajectory model or stochastic particle back trajectory model, such as shown in Eq. 1. Upwind trajectories are modeled according to a Langevin Equation stochastic differential equation using a MCMC method. In this model, the upwind position of the particle at each timestamp depends on the current position of the particle, the average wind speed and direction, and a random component which is parameterized in terms of the turbulent kinetic energy. Equation 1 shows a form of the Langevin Equation used to compute the particle back trajectory. In Eq. 1 xiis the vector representing the position of the back trajectory at time t, v is the advective velocity vector, η is a stochastic random variable, κ is the turbulent kinetic energy, and A is scaling parameter which depends on the position of the particle at any given time and other aspects of the turbulent velocity field. v and η are vector quantities of 3-dimensional space.

d⁢x⇀i(t)d⁢t=v⇀(x,y,z,t)+A⁡(x,y,z)⁢κ⁢η⇀(t)Eq.1

The path of each parcel back trajectory is determined by solving Equation 1 iteratively using an Euler method and substituting measured values of v, η, κ and A during each plume event. Several hundred particle back-trajectories are derived from independent realizations of Eq. 1 for each plume event and tracked backward in space over a specified time interval. Because each individual particle trajectory is independent of the others, the solution to Eq. 1 is readily distributed in a shared CPU architecture across many processors. When very large simulations are completed near 1:1 speed up can be achieved by distributing the calculation of individual particle trajectories in across many processors. An additional computational advantage of Eq. 1 is that it does not rely on a spatially regular grid, and solutions to particle trajectories are solved on an unstructured grid. This substantially reduces memory usage of the algorithm. Stochastic particle trajectory models in place of Eq. 1 are possible and contemplated.

FIG.11depicts a workflow1100for a localization clustering, according to one embodiment. After particle trajectories are determined the spatial footprint of the sensor, weighted over all the methane plumes that were detected, is calculated on a regular grid. A spatial grid is defined across a range/extent of simulated particles (step1102). Each kernel function is defined for each particle position in grid variables (step1104). The normalized sum of all kernel functions is computed to yield a normalized footprint (step1106). Thresholds are applied to the footprint function for probable source location (step1108). The workflow1100includes Eqs. 4-6, as discussed below.

The grid is defined in terms of a fixed coordinate system, which may be Cartesian, spherical, or following a geodesic approximation. The position of each particle at time t is represented on the grid as a kernel. An example of a typical Gaussian kernel p(x,y,z) is shown in Eq. 2, where μ and σ are parameters in the model and x0, y0, z0a define the location of the maximum value of the Gaussian. Eq. 2-3 show a Monte Carlo simulation using Gaussian kernel. Other simulations are possible and contemplated.

p⁡(x,y,z)=12⁢π⁢σx⁢σy⁢σz⁢exp⁢{-[(x-μx)22⁢σx2]-[(y-μy)22⁢σy2]-[(z-μz)22⁢σz2]}-x0-y0-z0Eq.2p⁡(X⇀,t)i=12⁢π⁢σ⇀⁢exp⁢{-[(X⇀-μ⇀)22⁢σ⇀2]}-x⇀i(t)Eq.3

The kernel function is calculated for each independent trajectory and at each timestep (Eq. 3), then summed to generate the cumulative footprint function (Eq. 4). Eqs. 4-6 relate to generating the probability map. The cumulative footprint function describes the probability that the source is in a given location within the simulation domain based on all the methane plume events identified by the disclosed UAS system.

f⁡(X⇀)=1N⁢M⁢∑N*M⁢p⁡(X⇀,t)iEq.4

After the footprint is calculated, the source location area is determined by applying a threshold τ to the source location probability (Eq. 5). The threshold may be set based on a determined value. In some embodiments, the threshold may be tuned manually. For example, the entire grid shown inFIG.12may have a non-zero probability. Applying the threshold may constrain the probability down to the overlay1302shown inFIG.13. The source location probability function may be further modified using a power parameter β to enhance the probability gradient in the predicted source area. The power parameter β may be used to scale the gradient to increase the rate of change of the gradient. The power parameter β may be used to create a larger difference between the minimum probability and the maximum probability. Smaller variations may be enhanced and larger changes may be lessened. The power parameter β may be tunable in some embodiments, such as based on wind conditions, environmental factors, or the like. The perimeter of the source location area can also be calculated to provide a spatially uniform source location prediction (Eq. 6).

l⁡(X⇀)={f⁡(X⇀)β;f>τ0;f≤TEq.5∂l⁡(X⇀)Eq.6

The result is a three-dimensional probability map, shown inFIG.12.FIG.12depicts a relative probability map1200of emissions source location (x,y,P), according to one embodiment. Each particle trajectory is created by a stochastic particle trajectory model and mapped to a cell on the grid. The density of each cell in the grid, e.g., counting the number of particles in each grid, is used to create the relative probability map1200.

Map of Source Location Probability

FIG.13depicts an aerial map1300with a relative probability overlay1302, according to one embodiment.FIG.14depicts a three-dimensional illustration1400of the disclosed UAS system and process for methane source detection and localization, according to one embodiment. The UAV102flight trajectory1304is projected onto overlay1302and illustration1400. The UAV102measures point source gas concentration measurements as it flies the flight path1304. Each measured gas concentration along the flight path1304has a stochastic particle trajectory model applied to determine a potential source for elevated gas concentrations. The potential sources are combined in a grid to create the overlay1302showing the probability of the gas source location. The overlay1302may have an area of highest probability surrounded by areas of lower probability. The flight path1304may be any flight path that is downwind of the potential gas source.

The source footprint, localization probability, source location boundary areas are geo-referenced and displayed visually on a map for data reporting purposes. The base map may include a variety of styles including basic street maps, satellite images, and high resolution aerial images, as shown inFIGS.13-14.FIG.14depicts a three-dimensional view of the overlay1302. In addition to displaying the source location areas, the UAS vehicle path is also shown to indicate the flight path with in the inspection area and identify areas where no sources were detected. The map may also include other features including information about the mass flow rate of the source, wind direction indicators, a distance scale, and a compass rose.

FIG.15depicts a high-level block diagram of a UAS system1500and process for methane source detection and localization, according to one embodiment. The system1500may include a UAV1504. In some embodiments, the UAV1504may be a quadcopter-style aerial vehicle capable of hovering and flying a flight path. In other embodiments, the UAV1504may be a winged aerial vehicle. The UAV1504may have any number of rotors, motors1528, wings, or the like to sustain flight and fly the determined UAV flight path. The UAV1504may have the ability to fly in a three-dimensional flight path in the vicinity of a potential methane or other gas source.

Embodiments of the unmanned aerial vehicle1504may include any number of sensors shown inFIG.15based on the desired data. Embodiments of the weather station1554may include any number of sensors shown inFIG.15based on the desired data. In some embodiments, the weather station1554may only include an anemometer1560. In other embodiments, the weather station1554may be integrated into the unmanned aerial vehicle1504. For example, the anemometer1560may be integrated on the unmanned aerial vehicle1504. In another embodiment, the weather station1554may be located on another aerial vehicle or unmanned aerial vehicle. For example, the system may include two or more unmanned aerial vehicles where at least one unmanned aerial vehicle is recording methane gas concentrations and at least one unmanned aerial vehicle is recording meteorological data. The weather station1554may be stationary or mobile. The weather station1554may be in relatively close proximity to the unmanned aerial vehicle1504. In some embodiments, the weather station1554may record meteorological data. In some embodiments, the weather station1554may be from a third-party source, such as a third-party sensor. In some embodiments, the weather station1554may predict future meteorological measurements. The nearest temporal Meteorological (MET) data packet1574may be combined with the UAV data packet1552. The frequency of each of the MET data packet1574and the UAV data packet1552may be different but close in some embodiments. The frequency of each of the MET data packet1574and the UAV data packet1552may be substantially the same in some embodiments.

The UAV1504may have a global positioning system1514, an onboard avionics1517, and/or a location sensor1518to track a spatial position of the UAV1504as it travels along the flight path. The UAV1504may track its spatial position as it measures gas concentrations along the flight path such that each gas measurement of the UAV1504corresponds to a spatial position where that gas measurement was taken. The global positioning system1514, onboard avionics1517, and/or location sensor1518may be in communication with a UAV processor1516having addressable memory1518. In some embodiments, the location of the UAV1504may be determined by the onboard avionics1517. The onboard avionics1517may include a triangulation system, a beacon, a spatial coordinate system, or the like. The onboard avionics1517may be used with the GPS1514and/or location sensor1518in some embodiments. In other embodiments, the UAV1504may use only one of the GPS1514, the onboard avionics1517, and/or the location sensor1518.

The UAV1504may include a payload1520in communication with the UAV processor1516. The payload1520may include one or more gas concentration sensors. The payload1520may be detachably attached to the UAV1504. In other embodiments, the payload1520may be fixedly attached to the UAV1504. The payload1520may be in communication with the UAV processor1516. In one embodiment, the payload1520may be an ultra-lightweight, low power, Part per Billion (ppb) sensitivity, mid-Infrared (λ=3-8 μm), open path methane concentration sensor with sampling rate>0.1 Hz. The payload1520may record point source gas concentration measurements.

The UAV processor1516may also be in communication with an orientation sensor1528, an inertial measurement unit (IMU)1530, an altitude sensor1532, a radar1534, a LIDAR1536, and/or a Sonar1538for generating additional information on the spatial position of the UAV1504during each gas measurement by the payload1520. The orientation sensor1528may be used to determine an orientation of the UAV1504relative to ground. The IMU1530may be used to determine attitude, velocity and/or position of the UAV1504. The altitude sensor1532may be used to determine an altitude of the UAV1504. The LIDAR1536, Sonar1538, and/or radar1534may be used to determine a relative altitude of the UAV1504.

In some embodiments, the UAV processor1516may also be in communication with an anemometer1542, one or more weather sensors1544, and/or a barometric pressure sensor1546. The anemometer1542may be used to measure the speed of the wind. The anemometer1542may be attached to the UAV1504at a point so as to ensure an accurate wind measurement without interfering with the propulsion from the motors1528or sensors of the payload1520. The weather sensor1544may measure weather and/or atmospheric conditions. The barometric pressure sensor1546may measure a barometric pressure. The anemometer1542, weather sensor1544, and/or barometric pressure sensor1546may be used to record data at each gas measurement from the payload1520.

In some embodiments, the UAV processor1516may also be in communication with a time measurement device1540. The time measurement device1540may be used to record the time for each gas measurement measured by the payload1520of the UAV1504. Each gas measurement, position measurement, orientation measurement, weather measurement, and/or relative altitude measurement may be ‘time-stamped’ so as to be combined by the processor1522and/or the UAV processor1516.

The UAV processor1516may also be in communication with a transceiver1548and/or a wireless radio1550. The transceiver1548and/or wireless radio1550may be used to communicate between the UAV1504and the processor1522, the ground control station (GCS)1526, and/or a cloud server1524.

The processor1522, the cloud server1524, the ground control station (GCS)1526, and/or the UAV processor1516may determine a flight path for the UAV1504having the payload1520. In some embodiments, the flight path may be determined on a site-specific basis. In other embodiments, the flight path may be determined and/or flown via a user of the GCS1526. In other embodiments, the flight path may be self-determined, autonomous control. The flight path is used to measure gas concentration along a crosswind transect, and vertical profile, in the vicinity of a possible gas emissions point. This flight plane of the flight path is designed to capture the atmospheric methane background as well as emissions signature, i.e., elevated ambient concentration, from all potential sources at an inspection site.

The UAV1504may have the UAV processor1516in communication with addressable memory1518, a GPS1514, one or more motors1528, and a power supply1530. The UAV1504may communicate gathered payload1520data to the UAV processor1516. The power supply1530may be a battery in some embodiments. In some embodiments, the processor1522may be a part of the UAV1504, the cloud server1524, the GCS1526used to control the UAV1504, or the like.

The UAV processor1516may receive gas data from the one or more gas sensors of the payload1520. The UAV processor1516may also receive spatial position data from the GPS1514, altitude sensor1532, location sensor1518, radar1534, LIDAR1536, Sonar1538, orientation sensor1528, IMU1530, and/or onboard avionics1517. In some embodiments, the UAV processor1516may also receive weather data from the weather sensor1544, the barometric pressure sensor1546, and/or the anemometer. The UAV processor1516may also receive the time from the time measurement device1540. The UAV processor1516may fuse the gas data from the payload1520with the UAV1504spatial position data, weather data, and/or time to form a UAV Data Packet1552.

The UAV data packet1552may be sent to the processor1522, ground control station1526, and/or cloud server1524via the transceiver1548and/or wireless radio1550. In some embodiments, the wireless radio1550or cellular connection may be used for remote data transfer between the UAV1504, the GCS1526, the processor1522, and/or the cloud server1524. The wireless interface or cellular connection between the UAV1504, the GCS1526, the processor1522, and/or the cloud server1524may be used to performing advanced data analysis functions. Direct, bidirectional data transfer may occur between the UAV1504and the GCS1526, between the UAV1504and the cloud server1524, and/or between the GCS1524and the cloud server1524.

The processor1522may be a part of the UAV1504, the GCS1526, the cloud server1524, and/or the weather station1554in some embodiments. While multiple sensors and devices are depicted for the UAV1504, any number of sensors and/or devices may be used based on the system1500, desired accuracy, time limitations, weight limitations, and the like.

One or more weather stations1554,1556,1558may provide local weather information to the UAV1504, payload1520, GCS1526, and/or cloud server1524. The weather stations1554,1556,1558may also receive information from the UAV1504, payload1520, GCS1526, and/or cloud server1524.

The first weather station1554may include one or more anemometers1560, one or more pressure sensors1562, one or more pyranometers1564, one or more ground temperature sensors1566, one or more air temperature sensors1568, one or more atmospheric condition sensors1570, and one or more location sensors1572. The anemometer may be used to measure wind speed. The pressure sensor1562may measure a pressure. The pyranometer may be used to measure solar irradiance. The ground temperature sensor1566may be used to measure a temperature of the ground. The air temperature sensor1568may be used to measure a temperature of the air. An atmospheric condition sensor1570may be used to measure data relating to the atmosphere. The location sensor1572may be used to measure the location of the weather station1554. Each weather station1554,1556,1558may include any number of sensors and/or devices based on the system1500, desired accuracy, number of weather stations over a geographical area, and the like.

In some embodiments, sensors and/or devices of the weather station1554may be located and/or duplicated on the UAV1504. High resolution (<0.1 m/s), high-frequency measurements (>5 Hz) of wind speed and direction may be recorded using one or more wind sensors, and one or more additional weather/micro-meteorological sensors including, air temperature, humidity, atmospheric pressure, solar irradiance, ground surface temperature—from the ground via a weather station1554,1556,1558and/or from the UAV1504as disclosed herein. For example, both the weather station1554and the UAV1504may include respective anemometers1560,1542, which may be used to generate wind speed data. The weather station data may be associated with a time the data was collected and/or generated. The weather station data may be used to generate a Meteorological (MET) data packet1574. The Meteorological data packet1574may be sent to the processor1522, ground control station1526, cloud server1524, and/or UAV1504. The Meteorological data packet1574may include measurements and/or predictions of the atmosphere, weather, temperature, wind patterns, or the like.

Each UAV Data Packet1552may be combined with the nearest temporal Meteorological Data Packet1574by the processor1522and saved on the GCS1526and/or cloud server1524. The data may be uploaded to the cloud server1524in real-time, near real-time, or at a later time. The combined UAV data packet1552and Meteorological data packet1574may be used to determine an elevated ambient emission of the methane source by the processor1522, GCS1526, and/or cloud server1524. The elevated ambient emission may be determined based on a control volume model that combines concentration measurements from the UAV flight plane with measured wind speed, direction and spatial gradient to determine the mass flow rate emissions from sources in the inspection area.

This determined elevated ambient emission may used to generate a back trajectory, store the back trajectories in a grid, determine a gas source location probability, and/or generate an overlay of the probability of the gas source location by the processor1522, GCS1526, and/or cloud server1524. The display1576may show the overlay on a map, satellite image, aerial image, two-dimensional color map, two-dimensional contour map, and/or three-dimensional topographical surface/mesh.

FIG.16depicts a high-level flowchart of a method1600embodiment of detecting and localizing methane sources via point source gas measurements, according to one embodiment. The method1600may include generating a UAV data packet including a point source gas concentration measurement and UAV information (step1602). The UAV data packet may be generated by one or more UAVs. The method1600may also include generating, by a weather station, a Meteorological data packet including data measured by the weather station. One or more weather stations may be used. The weather station may be located on the UAV or another UAV in some embodiments. The weather station may include third-party data in some embodiments. The method1600then includes receiving, by the processor, the UAV data packet and the Meteorological data packet (step1606). The UAV data packet may be joined with the nearest temporal Meteorological data packet.

The method1600may then include determining if each point source gas concentration measurement is an elevated ambient gas concentration (step1608). Levels of methane, or other gasses, may be present in the atmosphere at certain levels. The processor determines if an elevated level of methane, or another gas, is detected which could indicate a gas leak from a gas source. The method1600may then include generating a back trajectory for each elevated ambient gas concentration (step1610). The point source gas concentration reading location, and meteorological data allow the system and method1600to use a stochastic particle back trajectory model to determine a back trajectory of each elevated ambient gas concentration. The back trajectory model allows the system and method1600to determine a probable location of a gas source by combining the cumulative calculated back trajectories.

The method1600may then include storing the position of each generated back trajectory in a grid (step1612). In some embodiments, the grid may be two-dimensional (2D) having x, y coordinates for each cell in the grid. In other embodiments, the grid may be three-dimensional (3D) having x, y, z coordinates for each cell in the grid. Each cell in the grid may be summed to find a density of each cell in the grid. The method1600may then include normalizing the stored position of each generated back trajectory in the grid (step1614). Normalizing may be used to clean up the results of the system and method1600. For example, an entire area may have a non-zero probability of containing a gas source causing the detected elevated ambient gas concentration. By normalizing the results, the probability may be confined to an area with a higher likelihood of containing the gas source.

The method1600may then include determining a probability of a gas source location corresponding to the stored positions in the grid (step1616). The method1600may then include generating an overlay showing the probability of the gas source location (step1618). The method1600may then include displaying the generated overlay on a map (step1612). The map may be 2D map or a 3D map. The displayed overlay and map may be used to identify the most likely sources of gas leaks, which may then be used to take corrective action to repair equipment, minimize or eliminate gas leaks, or the like. The processor having addressable memory may be a part of a ground control system (GCS), a cloud server, a remote server, and/or one or more UAVs.

FIG.17is a high-level block diagram1700showing 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 processors1702, and can further include an electronic display device1704(e.g., for displaying graphics, text, and other data), a main memory1706(e.g., random access memory (RAM)), storage device1708, a removable storage device1710(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 device1711(e.g., keyboard, touch screen, keypad, pointing device), and a communication interface1712(e.g., modem, a network interface (such as an Ethernet card), a communications port, or a PCMCIA slot and card). The communication interface1712allows software and data to be transferred between the computer system and external devices. The system further includes a communications infrastructure1714(e.g., a communications bus, cross-over bar, or network) to which the aforementioned devices/modules are connected as shown.

Information transferred via communications interface1714may be in the form of signals such as electronic, electromagnetic, optical, or other signals capable of being received by communications interface1714, via a communication link1716that carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular/mobile phone link, an 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 (i.e., computer control logic) are stored in main memory and/or secondary memory. Computer programs may also be received via a communications interface1712. 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.18shows a block diagram of an example system1800in which an embodiment may be implemented. The system1800includes one or more client devices1801such as consumer electronics devices, connected to one or more server computing systems1830. A server1830includes a bus1802or other communication mechanism for communicating information, and a processor (CPU)1804coupled with the bus1802for processing information. The server1830also includes a main memory1806, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus1802for storing information and instructions to be executed by the processor1804. The main memory1806also may be used for storing temporary variables or other intermediate information during execution or instructions to be executed by the processor1804. The server computer system1830further includes a read only memory (ROM)1808or other static storage device coupled to the bus1802for storing static information and instructions for the processor1804. A storage device1810, such as a magnetic disk or optical disk, is provided and coupled to the bus1802for storing information and instructions. The bus1802may contain, for example, thirty-two address lines for addressing video memory or main memory1806. The bus1802can also include, for example, a 32-bit data bus for transferring data between and among the components, such as the CPU1804, the main memory1806, video memory and the storage1810. Alternatively, multiplex data/address lines may be used instead of separate data and address lines.

The server1830may be coupled via the bus1802to a display1812for displaying information to a computer user. An input device1814, including alphanumeric and other keys, is coupled to the bus1802for communicating information and command selections to the processor1804. Another type or user input device comprises cursor control1816, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processor1804and for controlling cursor movement on the display1812.

According to one embodiment, the functions are performed by the processor1804executing one or more sequences of one or more instructions contained in the main memory1806. Such instructions may be read into the main memory1806from another computer-readable medium, such as the storage device1810. Execution of the sequences of instructions contained in the main memory1806causes the processor1804to 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 memory1806. 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.

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.

Generally, the term “computer-readable medium” as used herein refers to any medium that participated in providing instructions to the processor1804for 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 device1810. Volatile media includes dynamic memory, such as the main memory1806. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise the bus1802. 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 processor1804for 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 server1830can 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 bus1802can receive the data carried in the infrared signal and place the data on the bus1802. The bus1802carries the data to the main memory1806, from which the processor1804retrieves and executes the instructions. The instructions received from the main memory1806may optionally be stored on the storage device1810either before or after execution by the processor1804.

The server1830also includes a communication interface1818coupled to the bus1802. The communication interface1818provides a two-way data communication coupling to a network link1820that is connected to the world wide packet data communication network now commonly referred to as the Internet1828. The Internet1828uses electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link1820and through the communication interface1818, which carry the digital data to and from the server1830, are exemplary forms or carrier waves transporting the information.

In another embodiment of the server1830, interface1818is connected to a network1822via a communication link1820. For example, the communication interface1818may 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 link1820. As another example, the communication interface1818may 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 interface1818sends and receives electrical electromagnetic or optical signals that carry digital data streams representing various types of information.

The network link1820typically provides data communication through one or more networks to other data devices. For example, the network link1820may provide a connection through the local network1822to a host computer1824or to data equipment operated by an Internet Service Provider (ISP). The ISP in turn provides data communication services through the Internet1828. The local network1822and the Internet1828both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link1820and through the communication interface1818, which carry the digital data to and from the server1830, are exemplary forms or carrier waves transporting the information.

The server1830can send/receive messages and data, including e-mail, program code, through the network, the network link1820and the communication interface1818. Further, the communication interface1818can comprise a USB/Tuner and the network link1820may be an antenna or cable for connecting the server1830to 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 system1800including the servers1830. The logical operations of the embodiments may be implemented as a sequence of steps executing in the server1830, and as interconnected machine modules within the system1800. The implementation is a matter of choice and can depend on performance of the system1800implementing 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.

Similar to a server1830described above, a client device1801can include a processor, memory, storage device, display, input device and communication interface (e.g., e-mail interface) for connecting the client device to the Internet1828, the ISP, or LAN1822, for communication with the servers1830.

The system1800can further include computers (e.g., personal computers, computing nodes)1805operating in the same manner as client devices1801, wherein a user can utilize one or more computers1805to manage data in the server1830.

Referring now toFIG.19, illustrative cloud computing environment50is depicted. As shown, cloud computing environment50comprises one or more cloud computing nodes10with 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 telephone54A, desktop computer54B, laptop computer54C, and/or automobile computer system54N may communicate. Nodes10may 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 environment50to 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 devices54A-N shown inFIG.19are intended to be illustrative only and that computing nodes10and cloud computing environment50can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

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.