Measuring gas flow rate

Abstract

Methods and apparatus for detecting and measuring gas flow are disclosed, in which lidar range information acquired during detection of a gas using a lidar sensor is used together with prevailing wind data to determine local wind data for a leak location. This local wind data, which can include one or more vector or 3D wind models, is used to determine flow rates.

Description

Technical Field

[0001] This invention belongs to the field of detecting and measuring gas flow rate. It is particularly applicable, but not exclusively applicable, to measuring the flow rate of gas from sources such as leaks from containers. Background Technology

[0002] High-sensitivity, low-power, long-range gas detection and imaging systems based on novel semiconductor infrared lasers, single-photon detectors, and quantum technologies are being developed. An example application of this technology is the remote detection and quantification of leaks in natural gas wells and pipelines to locate, quantify, and map fugitive emissions.

[0003] For example, GB2586075A shows a gas lidar camera or sensor suitable for detecting gas leaks. Such a camera can be used to obtain gas concentration path length data. Gas concentration path length is a standard term in spectroscopy and is the product of the gas concentration and the laser path length from the sensor to the reflecting structure, both of which can be determined by the gas lidar camera. In the simplest case of a uniform concentration (e.g., 2 ppm), the gas concentration path length over a distance (e.g., 100 m) will be 200 ppm·m. More complex distributions require stepwise addition, but with the same units. For SI units, ppm can be converted to g / m³. 3 Therefore, the gas concentration path length is expressed in g / m 2 The unit of measurement is quantified. If the gas is hazardous, explosive, or a greenhouse gas, then gas leaks are a major safety and environmental concern. These issues are directly proportional to the size of the leak, and there is usually a critical threshold beyond which swift action must be taken. For these reasons, it is important not only to identify gas leaks but also to quantify them accurately.

[0004] Gas escaping from a pipe or container typically forms a local cloud quickly, which spreads from its origin to form a plume. The gas in the plume will move along the prevailing wind direction and at local wind speeds. If the wind speed is high, the gas will spread and disperse rapidly, and the measured gas concentration will be low. If the wind speed is low, the gas will remain concentrated for a longer period, and the measured concentration will be higher.

[0005] Therefore, it is desirable to accurately quantify the gas leakage in a manner that takes into account the wind speed.

[0006] In S. Yang et al.'s article, "Natural Gas Fugitive Leak Detection Using an Unmanned Aerial Vehicle: Measurement System Description and Mass Balance Approach," Atmosphere (Basel), Vol. 9, No. 10, p. 383, October 2018,... https: / / www.mdpi.com / 2073-4433 / 9 / 10 / 383 The paper discloses a method for quantifying gas leakage under the assumption of a fixed constant wind speed.

[0007] Several suggestions have been proposed to make measurements based on sampling of gases around a region using drones. An example is shown in: Sensitive Drone Mapping of Methane Emissions without the Need for Supplementary Ground-Based Measurements (Magnus). * Ni l sson and David Bastviken ACS EarthSpace Chem. 2021, 5, 2668-2676, https: / / www.diva-portal.org / smash / get / diva2 1613751 / FULLTEXT01.pdf And methods for quantifying methane emissions using unmanned aerial vehicles: a review, Jacob T. Shaw et al., https: / / royalsocietypublishing.org / doi / 10.1098 / rsta.2020.0450. Summary of the Invention

[0008] The following discloses methods and apparatus for detecting and measuring gas flow rates, wherein lidar distance information acquired during gas detection using a lidar sensor is used in conjunction with prevailing wind data to determine local wind data at the location of a leak. This local wind data, which may include one or more vector or 3D wind models, is used to determine the flow velocity. In other words, the determination relies not only on prevailing wind data. It should be understood that the determined local wind data may be inaccurate and may be an approximation of the actual local wind, but it can still improve the determination of the gas flow rate. The term "determine" as used herein should be interpreted as including approximations or estimates.

[0009] Therefore, in one aspect, a method for detecting and measuring gas flow rate is provided below, the method comprising: using a lidar sensor to detect the gas and obtain distance information relating to a solid structure in the sensor's field of view; determining the location of the detected gas; acquiring prevailing wind data for the location of the detected gas; determining local wind data for the location of the detected gas based on the prevailing wind data and the lidar distance information; and determining the gas velocity using the local wind data.

[0010] Some of the methods described herein can be performed using existing equipment configured to receive gas detection data and prevailing wind data to determine gas flow rate. Therefore, in another aspect, a method for detecting and measuring gas flow rate is provided, the method comprising: receiving gas detection data from a lidar sensor, wherein the data includes distance information relating to solid structures in the sensor's field of view; determining the location of the detected gas; receiving prevailing wind data for the location of the detected gas; determining local wind data for the location of a gas source based on the prevailing wind data and the lidar distance information; and determining the gas flow rate using the lidar gas detection data and the local wind data.

[0011] Determining the location of the detected gas may include determining the location of the gas source. The determined flow rate can then be the flow rate of the gas from the source. Prevailing wind data can be applied to an area that includes the location of the detected gas or its source. For example, it can be obtained from one or more sensors located near the gas or its source.

[0012] A computer-readable medium including instructions that, when implemented in a processor of a computing system, cause the system to perform as described herein.

[0013] It should be understood that operations such as determination and measurement can lead to the estimation or prediction of parameters or quantities; therefore, in the following text, the terms “determination” and “measurement” are intended to include “estimation” and “prediction”. Attached Figure Description

[0014] Embodiments of the invention will be described by way of example with reference to the following accompanying drawings, in which:

[0015] Figure 1 This is a flowchart illustrating a method for detecting and measuring gas flow rate according to some embodiments of the present invention.

[0016] Figure 2 This is a flowchart illustrating alternative methods for detecting and measuring gas flow rate according to some embodiments of the present invention.

[0017] Figure 3 This is a schematic diagram illustrating the use of a gas lidar sensor that simultaneously measures both distance information and gas concentration data to obtain a 3D map of the target area.

[0018] Figures 4(a) and (b) show examples of 3D point clouds that can be generated using data obtained from a lidar distance sensor.

[0019] Figures 5(a) and 5(b) show 2D images of gas concentration data and 2D images of gas concentration data superimposed on signal level data from a gas lidar sensor, respectively.

[0020] Figure 6 A 2D image is shown, in which the gas plume has been isolated from the background data.

[0021] Figures 7(a) and (b) are schematic diagrams illustrating examples of the relative positions of gas leaks and sensors.

[0022] Figure 8 A 3D plot showing the predicted 3D location of the gas plume is displayed.

[0023] Figures 9(a) and (b) are schematic diagrams illustrating examples of determining local wind data from prevailing wind data.

[0024] Figure 10 This is a schematic diagram illustrating an example of determining local wind data from prevailing wind data.

[0025] Figures 11(a) and (b) are schematic diagrams of how gas leakage data is combined with local wind data.

[0026] Figures 12(a) and (b) show example 3D gas concentration images and a graph showing the calculation of gas leakage rate by integrating gas concentration data, respectively.

[0027] Figure 13 This is a schematic diagram of the basic architecture of a lidar system, including a gas lidar sensor.

[0028] Figure 14 This is a schematic diagram of a single-photon lidar system.

[0029] Figure 15 It is a schematic diagram of a computing system that can be used in any implementation of the methods described in this article.

[0030] The same reference numerals are used in all the accompanying drawings to indicate similar features. Detailed Implementation

[0031] Figure 1 This is a flowchart illustrating a method for detecting and measuring gas flow rate according to some embodiments of the present invention. The method begins by detecting gas in an environment (e.g., an open-air environment) using a lidar sensor. Figure 1 In this method, a tunable diode lidar sensor, known as a "TD lidar," is used, but other lidar sensors can be used. This sensor can be used to acquire gas detection data, such as obtaining the gas concentration path length in a manner known in the art. The presence of a gas is identified based on its absorption of emitted radiation. To determine the gas concentration path length, the sensor data includes distance information relating to a structure in the sensor's field of view from which lidar radiation has been reflected; this is typically a solid structure, such as a container or pipe from which gas is leaking. Since the interest lies with the gas cloud rather than the underlying solid structure, this distance information, obtainable from the TD lidar, is generally used only to determine the path length as described in the background section above. In the method described herein, this distance information must be acquired simultaneously with information about the gas concentration, which is used to determine local wind data, which in turn provides improved flow velocity measurements.

[0032] In the method described here, distance information obtained from a lidar system is used to acquire 3D structural information, such as in the form of a 3D lidar map. Then, computational fluid dynamics modeling or similar or simpler methods are used to determine the wind speed through the 3D structure, i.e., local wind data. This allows for more accurate calculation of local wind speeds, and therefore allows for more accurate velocity prediction. It should be noted that some improvements in velocity determination can be achieved without requiring complete 3D structural information or a 3D map.

[0033] Refer again Figure 1 After the gas has been located in Operation 1, determine the location of the gas, such as its source, like the location of a leak. Figure 1 In this method, this is achieved through a series of operations 2-5, which will be explained further below.

[0034] At operation 6, data related to prevailing winds are acquired for the location of the gas (e.g., but not necessarily the location of the source). This can be obtained, for example, from third-party sources of meteorological information (such as official weather reports), or from anemometers in the area, field metering data (such as that available at oil and gas facilities), or from any other suitable source. Therefore, prevailing wind data can be for an area including the location of the detected gas. This data will typically be in the form of a prevailing wind vector or used to generate a prevailing wind vector. It should be understood that prevailing wind data can be acquired before or after the location of the gas (e.g., the gas source) has been determined. Furthermore, prevailing wind data can be updated periodically, for example, anywhere between every second and every few minutes, depending on which information sources (e.g., which sensors) are available. In the method described below, the location of the gas source is determined. However, the determination of flow velocity can be used not only for determining the source location but also for any location of gas visible to lidar sensors. This is especially true when, for example, the source location is obscured by a solid structure.

[0035] Figure 3 The diagram schematically illustrates a TD lidar device 30 positioned on an aerial platform (not shown) to scan a target area A on the ground 36. A gas plume 32 is positioned above a portion of area A, escaping from a conduit 34. The lidar device can be located on a fixed platform to monitor the target area, or it can be carried on a drone or other suitable aerial platform. A typical range where a TD lidar sensor can successfully detect leaks will be anywhere from 1 meter (or closer) to 250 meters. The limitation is the range of the laser sensing system (currently ~250 meters). The methods described herein are not limited to TD lidar, and other sensing devices may be able to detect leaks at greater distances. TD lidar or other lidar gas detection devices can be used autonomously to locate leaks in large facilities. This can be achieved using drones. Additionally or alternatively, the sensor can be periodically rotated to monitor different areas, and this can be accomplished using standard translation / tilt stages and mechanisms.

[0036] An anemometer co-located with device 30 can be used to obtain prevailing wind data, which is assumed to apply to the area including both device 30 and the gas source. However, structures in the leak area (e.g., closer to the leak location than device 30) may cause local winds (i.e., the wind at the leak location) to differ from the prevailing winds.

[0037] Prevailing wind data and lidar distance information are used to determine local wind data for locating gas sources. This local wind data is then used to determine the flow velocity of the gas from the source. This is further described below. Figure 1 Operations 7-10 show one way to perform this operation.

[0038] Figure 2It shows something similar to Figure 1 A flowchart of a method for detecting and measuring gases. Figure 2 and Figure 1 The difference lies in the way local wind data is determined.

[0039] exist Figure 1 In this method, if the lidar distance information indicates that an object is obstructing airflow, local wind data is determined by modifying the prevailing wind data in the obstruction area to attenuate any wind component perpendicular to the obstruction direction. This local wind data is then used to determine the gas velocity. If no object is found obstructing airflow, it can be assumed that the local wind data is the same as the prevailing wind data.

[0040] Figure 2 A more sophisticated approach is shown in which lidar distance information is combined with prevailing wind data and a computational fluid dynamics model to generate local wind data in the form of a 3D wind model, which is then used to determine flow velocity.

[0041] In both approaches, if there are no structures obstructing the prevailing wind, the local wind data can be identical to the prevailing wind data. The prevailing and / or local wind data can be in the form of a single vector (amplitude and direction) or more complex data including a 3D vector model, both of which are examples of vector fields, or any other suitable data structure known to those skilled in the art.

[0042] Those skilled in the art will recognize from these two examples that other methods, such as those of moderate or greater complexity and potentially combined, are possible for determining local wind data and flow velocity using distance information obtained from lidar sensors. Figure 1 and Figure 2 The methods cover all aspects.

[0043] It should be understood that any method described herein can be implemented as an algorithm that can operate in a computer system included in a lidar detection device, or on a separate computing device or system located away from the device acquiring the gas detection data.

[0044] The following examples further illustrate this. Figure 1 and Figure 2 Details of operations 7-10 are shown.

[0045] Return to reference Figure 1 and Figure 2Operation 1, acquiring lidar data to determine the path length for gas concentration, is known in the art. For example, GB2586075A discloses a gas sensor that uses a combination of two laser technologies called single-photon lidar and tunable diode laser absorption spectroscopy (TDLAS), abbreviated as "TD lidar". Here, the output radiation from the laser device is modulated with binary code. The lidar scans over the target area. The received scattered radiation is correlated with the transmitted output radiation and fitted to one or more measured absorption spectra to detect the presence or concentration of a gas.

[0046] In the method shown in the figure, the time-of-flight information (in other words, lidar range information) obtained from a TD lidar device is used to determine the gas flow rate. Figure 1 and Figure 2 In the example, at operation 1, this distance information is used to obtain a 3D map of the target area. Figure 3 An example is schematically illustrated, in which a TD lidar device 30 is positioned on an airborne platform (not shown) to scan a target area A on the ground 36. For illustrative purposes, the scan is shown enlarged. In practice, device 30 is typically stationary, and a beam control mechanism within device 30 operates to scan the laser beam across area A. Thus, the scan defines the sensor's field of view. Known TD lidar sensors may have a field of view of approximately 25 degrees. This may be sufficient to obtain plume measurement data without requiring sensor movement. A gas plume 32 lies above a portion of area A, escaping from a conduit 34. Radiation output from the TD lidar device 30 is absorbed by the gas plume 32 as it travels toward the ground 36 and is reflected back to the TD lidar device 30. The distance information acquired by the TD lidar due to the reflection of output radiation from area A (from the ground or from structures surrounding the source of plume 32) is used to obtain a 3D map. Using such a device, accurate leakage flow estimates can be obtained using a single stationary measurement.

[0047] The TD LiDAR device 30 may include an onboard computer configured to create 3D maps. Alternatively, data acquired by the LiDAR device can be uploaded to an external device, such as a server, for data processing and 3D map creation. The same applies to the processing of other data acquired by device 30, which can be processed on-board or remotely.

[0048] The location of a gas source is determined using data obtained from a lidar sensor. The location can be determined based on gas concentration path length data, for example, by identifying areas where the gas concentration path length in the sensor's field of view exceeds a threshold, and then determining the leak location based on the sensor's location and the lidar range from the sensor to that area.

[0049] This can be achieved in many ways, and some of them will be described below.

[0050] Data obtained from lidar sensors can be used to generate so-called “3D point clouds” or 3D scatter plots, examples of which are shown in Figures 4(a) and (b).

[0051] Figures 4(a) and 4(b) show 3D point clouds in perspective and plan view, respectively. 3D point clouds illustrate the scattering of LiDAR from objects such as the ground or other solid objects or structures. Figure 3 As shown. The 3D point cloud also contains information about the gas that may be present between the lidar device and the object. It can be similar to the previously described 3D structural information or 3D map, which typically describes a solid structure, and additionally contains information about the gas between the solid structure and the sensor. The point cloud consists of many data points. In the example point cloud, each point is defined by three spatial coordinates (x, y, z). In addition, each point has information about the gas concentration path length (ppm.m) encountered by the laser beam across the distance between the sensor and the scattering object, which can be obtained in a manner known in the art, for example using a device as described in GB2586075A. Here we refer to the gas concentration path length as “c”. Furthermore, each point has information about the amount of laser light scattered from the object (i.e., the signal level). This parameter is referred to as “i”.

[0052] Therefore, in general, the points in a point cloud can be represented by 5 numbers.

[0053] x-meter

[0054] y-meter

[0055] z-meter

[0056] c - gas ppm.m

[0057] i - Signal strength (photons per second, cps).

[0058] Therefore, an example of a point cloud is a collection of many data points, each of which is in the form of (x, y, z, c, i).

[0059] In addition to observing 3D point cloud data, 2D gas concentration path length images can also be created, such as... Figure 1 and Figure 2 Operation 2 is shown in Figure 5(a) and Figure 5(b).

[0060] This is a useful way to visualize and process data. A 2D image of gas concentration in ppm.m can be created by taking the x, y, and c values ​​of a 3D point cloud and interpolating them into a 2D grid to form a gas concentration path length image. Similarly, a 2D image of signal level can be created by taking the x, y, and i values ​​of a 3D point cloud and interpolating them into a 2D grid. Gas ppm.m images can be overlaid on signal level images to help show where the gas leak originates structurally.

[0061] Figure 5(a) shows a 2D image of the ppm.m data. This was calculated from (x,y,c) data from 3D point clouds of the type shown in Figures 4(a) and 4(b). Colors are used to display c (ppm.m) information, represented in grayscale in the figure, while x and y give the horizontal and vertical positions. The location of the plume source can be automatically detected, as explained further below, and is highlighted in box 500.

[0062] Figure 5(b) shows a 2D image of ppm.m data superimposed on the signal level data. The signal level data is the amount of light (laser signal) returning to the sensor from each point in the scene. It does not contain distance information and is therefore not a 3D image. The signal level image (greyscale image) is generated from the (x,y,i) data from the point cloud. In this image, for visualization purposes, the ppm.m image is made transparent for ppm.m values ​​below a threshold (2000 ppm.m in this example). This is done to visualize where the plume is located and is not necessarily part of any algorithm used to measure gas flow rate.

[0063] Gas "images," such as those shown in Figures 5(a) and 5(b), can be generated and displayed by sensors and are 2D. In a sense, they can be used to measure the total amount of gas along the laser beam path, but cannot discern the extent of the gas along the laser path. The location of the gas can be estimated by finding the distance between the laser radar and the nearest object, for example, identified from distance information or a 3D structural map, to the gas emission source.

[0064] The methods described here use distance information obtained from lidar sensors to determine local wind data. Some methods described below utilize gas concentration path length images. However, in such methods, the image itself is not necessary, and some methods can use image data without generating a visual image.

[0065] It should be noted that the time (e.g., scan duration) required to acquire gas concentration path length images using current TD LiDAR technology is in the range of 100 seconds or longer, which is relatively long in the general field of image acquisition. Acquiring a gas concentration path length image may include acquiring multiple gas concentration measurements at each point on the image during each scan, and optionally, acquiring path length measurements. These multiple measurements can then be processed to provide a single measurement for each point, for example, through a suitable averaging process.

[0066] In the methods described here, the lidar distance information used to determine gas concentration is also used to determine local wind data. It can be used to identify objects obstructing plume flow. In some implementations, a 3D map of the objects in the lidar sensor's field of view can be created for this purpose. Since each gas measurement (e.g., pmm.m) and each lidar distance measurement are acquired from the same light signal, these methods benefit from a very good understanding of exactly how far the light travels through the gas. The methods provide information about exactly how much ambient gas the laser should encounter and can identify any excess above the ambient gas level.

[0067] If separate sensors are used for lidar and gas measurement, the certainty about how far the light used for gas measurement has traveled will be less. This can be estimated by superimposing the two datasets, but it will be less accurate.

[0068] To measure gas flow rate and determine the location of the gas source or leak. Figure 1 and Figure 2 In the process, at point 3, the possible leak location for measurement purposes is found in a 2D image, such as those shown in Figures 5(a) and 5(b), which map ppm.m measurements. Other methods can be used to determine the source or leak location. Furthermore, as mentioned above, if the source is not visible, another location can be used to perform the measurement. One possible method for determining a possible location from an image includes:

[0069] • The expected noise level in the ppm.m reading is calculated by using the signal level of each ppm.m reading.

[0070] • Identify data points where the ppm.m reading exceeds the noise threshold. (Select the ppm.m threshold for the noise level, such as 2-σ or 3-σ)

[0071] • Identify regions of 2D data where ppm.m consistently exceeds a threshold (i.e., for example, >10 points exceeding the threshold).

[0072] If found, the area in the 2D data is classified as a possible leak location at Operation 3 and used for gas flow measurement.

[0073] At Operation 4, identify and isolate the area with the highest ppm.m among potential leak locations, assuming it corresponds to a gas plume. This is in Figure 6 As shown in the figure, Figure 6 A 2D image is shown in which the plume has been separated from the ppm.m background data. The plume can be identified by thresholding, for example, the ppm.m image shown in Figure 5(a), by removing all data below a threshold (such as 400 ppm.m), and then using standard algorithms to find the largest contiguous area of ​​gas containing previously identified possible leak locations, such as the area of ​​2D data where the ppm.m always exceeds the threshold.

[0074] In operation 5, this contiguous region (now isolated) is used to determine the 3D location of the source or leak origin from the 2D image information. This can be accomplished as follows:

[0075] • Locate the lidar range of all data points in the plume area (i.e., the distance to the reflector, such as the surface of the structure, other surfaces, other reflectors, corresponding to each image data point).

[0076] • It is assumed that the leakage range (z) is the shortest in the lidar range within the plume region.

[0077] The x and y locations of the leak (from the 2D image) and the z extent of the leak have now been determined to provide an estimate or determination of the leak location in 3D.

[0078] The location of the leak is further illustrated in Figures 7(a) and 7(b). Figure 7(a) schematically shows the gas leak as seen from the sensor's viewpoint. Figure 7(b) shows the same leak as seen from a bird's-eye view (i.e., aerial view), including the sensor location. The sensor laser beam path is shown as a black arrow. The leak location can be inferred by finding the shortest beam path in the region of rising gas ppm.m. The inferred leak location is shown as a black circle. Figure 7(b) shows that the laser beam (black arrow) only reflects away from the object. It will pass directly through the plume.

[0079] At Operation 6, prevailing wind data is obtained to model the gas plume and for velocity determination. As mentioned above, this can be obtained, for example, from a third-party meteorological source, from an anemometer in the area, or from any other suitable source. The source of the prevailing wind data can be one or more sensors that are, for example, close to or adjacent to the detected gas or a structure containing the gas, optionally not so close that the wind will be affected by solid structures from which the gas might leak. Prevailing wind data is typically, but not necessarily, applicable to areas including the detected location and can simply include velocity and direction. Alternatively, in Operation 6, local wind data discussed in conjunction with Operation 9 can be used to model the gas plume. This may make the modeling more complex because the wind direction may be curved.

[0080] At operation point 7, the prevailing wind direction and the 3D leak origin are used to estimate the size and trajectory of the plume in 3D. For this, it can be assumed that the plume is a thin planar sheet extending from the leak origin in the direction of the prevailing wind. This will refer to... Figure 8 Further description.

[0081] For accurate velocity estimation, a reasonable 3D model of the plume is desired. This is because the size of the plume is proportional to the velocity.

[0082] It is important to note that laser radiation is not scattered from the plume. However, the 3D position of the plume can be inferred from the object's 3D lidar map, as shown in Figures 4(a) and 4(b), for example. The gas in the plume absorbs some wavelengths from the laser, creating a "spectral dip," which is measured to infer the amount of gas. In other words, some wavelengths in the beam are attenuated due to the presence of gas, and this can be used to infer the 3D position of the gas plume.

[0083] exist Figure 8 In the figure, the predicted 3D location of the plume is indicated by reference numeral 801, and the 3D point cloud data is indicated by reference numeral 803. The plume is modeled as a 2D plane parallel to the wind. That is, the plume has no "thickness" because it cannot be measured using TD lidar. However, this is not important for the calculation.

[0084] The predicted 3D location of the plume can be generated as follows:

[0085] • Assume the plume trajectory begins at the leak 3D location and follows the prevailing wind. Local winds defined elsewhere can be used to predict the plume's 3D location, but this would be more complex, and it has been found that using the prevailing wind is sufficient at this stage.

[0086] • The 2D plane is defined as parallel to the plume trajectory and perpendicular to the ground.

[0087] • The ppm.m data from the isolated plume (Operation 4) is a rasterized 2D plane to create Figure 8 The plume model is shown below. For example, ppm.m data can be received as a point cloud (i.e., a 3D scatter plot), which is common for LiDAR systems. To form an image from the scatter plot, a process called rasterization is used, in which the points are "binded" and averaged into a square 2D pixel grid.

[0088] At operation 8, data obtained from lidar device 30 is used to create a 3D structural map of objects within the device's field of view. This can include areas of the ground, pipes, containers, and other structures that may reflect the transmitted laser radiation, such as, for example, reference... Figure 3 The explanation given.

[0089] At operation point 9, local wind data at gas sources or other locations are determined based on prevailing wind data and lidar distance information. Figure 1 and Figure 2 Two different methods are shown, through which this can be performed, which will be referred to Figure 9(a), Figure 9(b) and Figure 10 Further description. These methods are not mutually exclusive, and combinations of these methods can be used to determine local wind data, or local wind data can be determined in other ways.

[0090] The lidar range information used to determine local wind data can include a 3D map created at operation 8. Therefore, in Figure 1 and Figure 2 In operation 9, the 3D map is used to determine local wind data.

[0091] To determine local wind data, prevailing wind data can be modified to account for objects detected by lidar that may obstruct the prevailing wind and / or plume. For example, such as Figure 1 As shown in Operation 9, if an object is blocking a plume, the prevailing wind can be altered to attenuate the wind component perpendicular to the obstruction. This alteration can be specifically applied to the area obstructing the object.

[0092] It should be noted that local wind data can correspond to prevailing wind data. In other words, in any of the methods described herein, it can be found that there are no structures that would affect the prevailing wind and / or feather path, and therefore it is not necessary to modify the prevailing wind data to determine the local wind data.

[0093] For reference Figure 2 and Figure 10 As stated, changes in wind data (in the case of execution) can be modeled using computational fluid dynamics (CFD) to depict the wind around the structure. However, as referenced... Figure 1As shown in Figures 9(a) and 9(b), an improved estimate of fluid flow rate can be obtained by taking into account simple measures of the structure.

[0094] Figures 9(a) and 9(b) show some schematic diagrams illustrating simple examples of how prevailing wind data can be modified. These demonstrate how to calculate wind vector fields using highly simplified models. Figure 9(a) is a bird's-eye view showing wind flowing around a simple object. The prevailing wind data consists of a single vector in the direction from left to right, as shown. In this case, near the object, the wind vector field component perpendicular to the object's surface can be attenuated, as indicated by the different dashed slanted arrows in Figure 9(a). For curved surfaces, the wind vector field component perpendicular to the tangent of the curved surface can be attenuated. Therefore, local wind data can be determined from the prevailing wind data by attenuating the component of the prevailing wind perpendicular to the structure's surface.

[0095] Figure 9(b) shows that the prevailing wind vector can be divided into two components, perpendicular and parallel to the nearest surface of the object. The wind vector is then attenuated by scaling the normal component n of the prevailing wind. The normal component n can be scaled, for example, by a function depending on the distance d to the surface (e.g., 1 - exp(-σ.d)), where σ is the attenuation rate of the vertical wind component closer to the structure. Thus, for d = 0, the normal component is 0, while for d -> larger, the normal component is 1. In the simplest example, this modification of the prevailing wind ensures that the local wind vector does not include wind emanating from flat walls.

[0096] In this example, complex CFD calculations are not required to estimate local wind data, and therefore the gas velocity can be estimated more accurately than if this structure were not considered. In practical implementations, several structures and / or structures with complex surfaces may exist. The extent to which the prevailing wind data is modified to determine the local wind data can then be determined based on the required measurement accuracy. For example, in some cases, selecting a primary surface of the detected structure may be sufficient, and the modification of the prevailing wind data may be based on that primary surface. In all cases, the extent to which the prevailing wind data is modified to determine the local wind data can depend on the prevailing wind velocity.

[0097] exist Figure 2 In operation 9, the 3D structural form of lidar range information obtained at operation 8 is combined with prevailing wind and CFD models to predict local wind data in the form of a local wind vector field. This local wind vector field can then be used at operation 10 to determine the flow velocity. This is in... Figure 10 As shown schematically in Figures 11(a) and 11(b).

[0098] As with the examples in Figures 9(a) and 9(b), prevailing winds can be measured by an anemometer or provided by local meteorological services or other suitable sources. The dashed arrows indicate local wind vectors (direction and magnitude) that can be calculated using hydrodynamic models. Figure 10 The diagram shows the wind vectors surrounding the entire structure. The plume path can be predicted based on the estimated plume location and prevailing wind direction. The local wind vector at each point along the plume direction can be used to determine the flow velocity.

[0099] In principle, prevailing wind data can be acquired at any suitable frequency. As previously mentioned, the acquisition period for gas concentration path length data can be 100 seconds or longer, thus potentially suitable for acquiring local wind data and determining local wind data at least once for each data acquisition period. During this period, the prevailing wind data may change. Therefore, in some implementations, local wind data can be determined at multiple instances during the data acquisition period by acquiring the prevailing wind data for each instance. Additionally or alternatively, the gas velocity can be determined using only the gas concentration path length measurement obtained when the local wind vector meets predetermined criteria. For example, local wind data can be determined at multiple instances during the acquisition period and used to determine constant local wind data, such as a constant wind vector, for the acquisition period. Measurements taken when the local wind data exceeds a preset range for the constant wind data can then be discarded. The constant wind data can be based, for example, on the average of the measured data.

[0100] At Operation 10, the simple 3D model of the gas plume in the plane along the prevailing wind direction is modified to follow the direction of the local wind field calculated and modeled in Operation 9. Figure 12(a) shows a schematic diagram of how this is achieved. The gas plume model is still sheet-like, but it now follows the local wind direction instead of the prevailing wind direction, and therefore no longer enters or passes through solid objects identified by the lidar.

[0101] At operation 11, the flow velocity is determined by multiplying the local gas concentration path length at each point in the plume by the local wind speed at that point and integrating the combined gas concentration × wind speed determined in operation 10 with respect to a plane perpendicular to the local wind direction.

[0102] One possible method for determining the flow rate will now be described. Other methods are familiar to those skilled in the art. This method uses the following inputs to output the leak location (e.g., GPS) and the gas leak flow rate (g / s or other suitable units):

[0103] • Gas image, in units of concentration path length (ppm.m), a two-dimensional field spanning the imager angle (degrees).

[0104] • An image of the distance (m) of a laser radar reaching a solid object, spanning the same two-dimensional field across the imager angle (degrees).

[0105] • TD LiDAR device imager field of view (FOV) diameter (degrees) (Note: FOV will depend on scanner zoom)

[0106] • The horizontal imager is pointed (east of North latitude or some other standard).

[0107] • The vertical orientation of the imager (the angle from horizontal to downward) - This information can be obtained, for example, from the tilting stage supporting the imager or internal sensors.

[0108] Prevailing wind speed (m / s)

[0109] • Winds are approaching in the main horizontal direction (east of North latitude or some other standard) - as mentioned above, this information can be obtained from a third-party source (such as a weather bureau or anemometer) in the area that includes the location of the gas source.

[0110] Methods for determining gas flow rate may include the following steps:

[0111] The location of the leak can be identified by the area of ​​highest methane concentration in the gas image, as shown in Figure 5(a).

[0112] Determine the distance (D) of the leak location from the lidar image (e.g., as shown in Figure 7(b)).

[0113] In 3D, the gas is modeled as a thin 2D vertical plane, where the region of highest concentration identified above is located at the leak distance (D), and this plane extends from there in the prevailing wind direction. The size of the gas field is calibrated from the imager angle (degrees) to the vertical dimension (m) by multiplying by the distance from the imager to the gas plume plane.

[0114] Local wind speed and direction are modeled in 3D around solid objects near the modeled gas plane, identified by lidar.

[0115] The 3D gas plume model was modified so that the gas is a thin sheet anchored at the leak location as defined above, but now follows the local wind direction instead of the prevailing wind direction.

[0116] Multiply the local gas concentration path length (ppm.m) at each point in the model by the gas density (g / ppm / m³). 3 The local wind speed (m / s) is calculated and multiplied by the sine of the angle between the laser imager beam and the local wind direction at each point (and thus by a factor of 1 if they are perpendicular) to derive a two-dimensional field with units of g / m / s.

[0117] Integrating the field along a vertical line through the plume yields a series of measurements of the gas leakage velocity within the plume, where the downward velocity along the direction of the local wind is expressed in g / s, such as... Figure 12b As shown.

[0118] Take the maximum value among these as the optimal measurement of the gas leak flow rate.

[0119] Now refer to Figure 13 and Figure 14 A brief description of a gas lidar camera or sensor suitable for measuring gas leak flow rates in the methods described herein. Further details on the operation of such a camera for gas detection can be found in GB2586075A.

[0120] Figure 13 The basic architecture of a lidar system is described, where TX represents the laser emitter, RX represents the optical receiver, PC is a personal computer, and FPGA is a field-programmable gate array. The laser emitter (TX) operates in continuous wave (CW), pulsed, or modulated conditions, and the beam is emitted through a lens system, beam expander, or telescope. The reflected signal is detected by the receiver (RX) and electronically processed to derive the distance to the target and other information. Depending on the system, the RX may also use a portion of the emitted light as a reference or as a local oscillator to compare with the signal returned on the detector. In other possible implementations, a local oscillator is not required. The laser can be a distributed feedback (DFB) laser, as is known in the art.

[0121] The original lidar distance measurement has been extended to measure many new parameters, including the velocity of remote objects, the amount and type of gas through which the laser passes, and the velocity of the air.

[0122] In the method described herein, a lidar system is used to measure the distance to structures in an environment where a specific gas leak may occur, as well as to detect the gas itself. Data acquired by the lidar system can be processed in-system or remotely and combined with wind vector measurements to more accurately calculate the total gas mass flow rate of the leak.

[0123] Single-photon lidar is a very active field, with multiple research groups dedicated to long-range measurements. Geiger-mode single-photon lidar systems, initially developed by MIT Lincoln Laboratory, are already commercially available and used for satellite observation of the Earth's surface. For example, Zheng-Ping Li et al., *Single Photon Imaging Over 200km*, *Optica*, Vol. 8, No. 3, p. 344, March 2021, proposed a single-photon lidar system using "optimized compact coaxial transceiver optics." A transceiver is an optical system that emits a lidar beam from a laser source into the environment, then receives the scattered light returning from the environment and guides it to a single-photon detector.

[0124] It should be noted that although light is mentioned here, the methods and systems described are not limited to visible light and are applicable to radiation of other wavelengths.

[0125] Figure 14 This is a schematic diagram of a single-photon lidar system, which is further described in detail in GB2586075A as an optical gas detection device. The gas detection device is configured to detect the presence or concentration of at least one gas 2.

[0126] exist Figure 14 In the system, laser device 4 is operable to output a first output radiation 6 having a continuous wave output. Control element 8 is operable to continuously tune a first emission wavelength 9 of the first output radiation 6 within a first wavelength spectrum 10 to allow for rapid scanning of the environment while reducing the spectral coherence of the transmitted radiation from optical device 1. The laser device may include a TDLAS device, but other laser devices may be used in the method described herein.

[0127] As shown, device 1 includes a modulator 14 operable to apply a first output modulation 16 to the first output radiation 6. Furthermore, device 1 includes an optical transceiver system 26 operable to transmit the first output radiation 6 toward a first target location or region 18 and collect / receive scattered radiation 20, which has been at least partially altered by gas 2 present in the first target location 18. A detector 22 is configured to receive the scattered radiation 20, and a processing element 24 operable to process the received scattered radiation 20. Detector 22 may include a single-photon lidar sensor known in the art.

[0128] The control element 8 and the processing element 24 may be included in a computer or computing system that may be part of the device 1. The processing element may include an FPGA. Alternatively, components of the device may be controlled from a computer or computing system located remotely from the device itself.

[0129] GB2586075A discloses a gas sensor that uses a combination of two laser technologies, known as single-photon lidar and tunable diode laser absorption spectroscopy (TDLAS), designed to provide a fast and accurate leak identification, quantification, and mapping system to meet the commercial needs of oil and gas producers, enabling high-speed sensing and large survey coverage at a fraction of the operating cost of existing solutions. This type of sensor can use a fast-tuned (>100 kHz) diode laser utilizing DC modulation. In other words, the frequency across the gas spectrum is greater than 100 kHz. In the currently implemented system, one pixel is acquired every 10 μs, thus requiring 10,000 wavelength-tuning scans to obtain a single pixel. The modulation scheme does not necessarily require tuning the light wavelength, as different light sources can be used to emit light of different wavelengths. For example, a DFB laser can be set to emit a custom wavelength. Therefore, an array of different DFB lasers can be used to generate the desired wavelength range. Furthermore, in the case of using a tuning source, a specific tuning frequency is not required.

[0130] Some operations of the methods described herein can be performed by software in a machine-readable form, such as a computer program including computer program code. Therefore, some aspects of the invention provide a computer-readable medium that, when implemented in a computing system, causes the system to perform some or all of the operations of any of the methods described herein. The computer-readable medium can be in a temporary or tangible (or non-temporary) form, such as storage media including disks, thumb drives, memory cards, etc. The software can be adapted to execute on a parallel or serial processor, such that the method steps can be performed in any suitable order or simultaneously.

[0131] exist Figure 15 The diagram illustrates a computational system that can be used in the implementation of any of the methods described herein.

[0132] The computing system 1400 may include a single computing device or not, such as a laptop computer, tablet computer, desktop computer, or other computing device. Alternatively, the functionality of the system 1400 may be distributed across multiple computing devices. Some or all of the computing system components may be integrated into the system. Figure 13 In the system.

[0133] The computing system 1400 may include one or more controllers (such as controller 1405, which may be, for example, a central processing unit processor (CPU), a chip, or any suitable processor or computing or arithmetic device, such as the FPGA described above), an operating system 1415, a memory 1420 storing executable code 1425, a storage device 1430 that may be external to the system or embedded in the memory 1420, one or more input devices 1435, and one or more output devices 1440.

[0134] One or more processors in one or more controllers, such as controller 1405, can be configured to perform any of the methods described herein. For example, one or more processors within controller 1405 can be connected to memory 1420 storing software or instructions that, when executed by one or more processors, cause the one or more processors to perform methods according to some embodiments of the invention. Controller 1405 or the central processing unit within controller 1405 can be configured, for example, using instructions stored in memory 1425 to perform... Figure 1 and Figure 2 Some of the operations shown are illustrated.

[0135] exist Figure 1 and Figure 2 The LiDAR sensor data received at operation 1 can be received at a processor included in controller 1405, which then controls the system according to one or more algorithms that can be stored as part of executable code 1425. Figure 1 and Figure 2 The subsequent operations.

[0136] Input device 1435 may be or may include a mouse, keyboard, touchscreen, or pad, or any suitable input device. It will be appreciated that any suitable number of input devices may be operatively connected to computing system 1400, as shown in box 1435. Output device 1440 may include one or more displays, speakers, and / or any other suitable output devices. It will be appreciated that any suitable number of output devices may be operatively connected to computing system 1400, as shown in box 1440. Input and output devices may be used, for example, to enable a user to select information to be displayed, such as images and graphics shown herein.

[0137] This application confirms that firmware and software can be valuable, separately tradable commodities. It is intended to include software that runs on or controls “dumb” or standard hardware to perform desired functions. It is also intended to include software that “describes” or defines the configuration of hardware, such as HDL (Hardware Description Language) software, for example, for designing silicon chips or configuring general-purpose programmable chips to perform desired functions.

[0138] The above embodiments are largely automated. In some examples, the user or operator of the system may manually instruct some steps of the method to be performed.

[0139] In the described embodiments of the invention, the system can be implemented as any form of computing and / or electronic system as described elsewhere. Such a device may include one or more processors, which may be a microprocessor, a controller, or any other suitable type of processor for processing computer-executable instructions to control the operation of the device in order to collect and record routing information.

[0140] As used herein, the term "computing system" refers to any device with processing power that enables it to execute instructions. Those skilled in the art will recognize that such processing power can be incorporated into many different devices; therefore, the term "computing system" includes PCs, servers, smartphones, personal digital assistants, and many other devices.

[0141] It should be understood that the above benefits and advantages may apply to one embodiment or several embodiments. The embodiments are not limited to embodiments that solve any or all of the described problems or that have any or all of the described benefits and advantages.

[0142] Unless otherwise stated, any reference to “a” item or “an item” means one or more of those items. The term “comprising” is used herein to mean including identified method steps or elements, but not an exclusive list of such steps or elements, and a method or apparatus may include additional steps or elements.

[0143] Furthermore, in terms of the extent to which the term “comprising” is used in the detailed description or claims, such terms are intended to be inclusive in a similar manner to the term “including,” since “comprising” is interpreted when used as a transitional word in the claims.

[0144] These accompanying figures illustrate exemplary methods. While these methods are shown and described as a series of actions performed in a specific order, it should be understood and appreciated that these methods are not limited to the order of the sequence. For example, some actions may occur in a different order than that described herein. Furthermore, one action may occur simultaneously with another. Moreover, in some cases, not all actions may be required to implement the methods described herein.

[0145] The order of steps in the methods described herein is exemplary, but these steps may be performed in any suitable order, or simultaneously where appropriate. Furthermore, steps may be added to or substituted in any method, or individual steps may be removed from any method, without departing from the scope of the subject matter herein. Aspects of any of the examples above may be combined with aspects of any other examples described to form other examples.

[0146] It should be understood that the above description of preferred embodiments is given by way of example only, and various modifications can be made by those skilled in the art. The above description includes examples of one or more embodiments. Of course, it is impossible to describe every conceivable modification and alteration of the above-described apparatus or method in order to describe the above aspects, but those skilled in the art will recognize that many further modifications and substitutions of the various aspects are possible. Therefore, the described aspects are intended to encompass all such changes, modifications, and variations that fall within the scope of the appended claims.

Claims

1. A method for detecting and measuring gas flow rate, the method comprising: Receive gas detection data from the field of view of the lidar sensor; Receive distance information related to solid structures in the field of view; The distance information is used to obtain three-dimensional structural information related to the solid structure; Determine the location of the detected gas; Receive prevailing wind data for the location of the detected gas; Local wind data that determines the location of the detected gas based on the prevailing wind data and the distance information; The gas flow rate is determined using the lidar gas detection data and the local wind data; and Based on the gas detection data and the distance information, the gas concentration path length of a point in the field of view is determined, wherein the gas concentration path length represents the path integral obtained by integrating the gas concentration along the laser beam propagation path from the lidar sensor to the scattering object. Determining the location of the detected gas includes determining the location of the gas source, and the location of the gas source is determined by the gas concentration path length; and The location of the detected gas source is determined based on the sensor location and the lidar distance from the sensor to the field of view where the gas concentration path length exceeds a threshold.

2. The method according to claim 1, comprising: The gas detection data and distance information are obtained using the lidar sensor.

3. The method according to claim 1, comprising: The distance information and the prevailing wind data are used to identify objects in the field of view that obstruct the flow of the gas. Determining the local wind data includes modifying the prevailing wind data to take into account the identified objects.

4. The method according to claim 3, wherein, The distance information is combined with the prevailing wind data and the computational flow dynamics model to predict local wind data used to determine the velocity of the gas.

5. The method according to claim 1, wherein, Local wind data is determined by attenuating one or more components of the prevailing wind data perpendicular to the structure surface.

6. The method according to claim 1, wherein, The acquisition of the gas detection data and the acquisition of the distance information are repeated in a continuous data acquisition period, wherein the local wind data is determined based on data collected at multiple time points during the data acquisition period, and wherein the local wind data determined based on data collected at multiple time points during the data acquisition period is used to determine the constant local wind data for the acquisition period.

7. The method according to claim 1, comprising: The path length of the gas concentration at a point in the field of view is determined based on the gas detection data and the distance information. Determining the gas flow rate includes multiplying the gas concentration path length by the local wind data, and Specifically, the gas flow rate is determined using only the gas concentration path length measurement obtained when the local wind data meets a predetermined standard.

8. The method according to claim 1, wherein, During the data acquisition period, the lidar sensor is scanned across the field of view, and multiple gas concentration measurements are obtained for different points in the field of view.

9. The method according to claim 1, wherein, Determining the location of the source of the detected gas involves overlaying a gas concentration path length image onto a signal level image to indicate the location of the gas leak.

10. The method according to claim 1, wherein, The distance information is used to create a 3D map of the structure in the field of view, and the 3D map is used to determine the local wind data.

11. The method according to claim 1, wherein, The prevailing wind data is obtained using an anemometer, wherein the anemometer and the lidar sensor are used for localization.

Images