Oilfield methane monitoring network arrangement method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention relates to an oil field methane monitoring network arrangement method and device, electronic equipment and a storage medium, and the method comprises the steps: compiling a gridding methane flux initial field based on methane emission facility identification information of a to-be-monitored oil field; calculating wind vector fields of multiple elevations based on a preset wind speed, and determining an initial feasible region of a monitoring station position through collaborative analysis with a methane emission source position based on the wind vector fields of the multiple elevations; based on the preset number of monitoring stations, adopting a Monte Carlo method to initialize the positions of the monitoring stations in the initial feasible region; calculating a quantitative footprint field of each monitoring station, and obtaining an evaluation result of the monitoring capability corresponding to each monitoring station based on the quantitative footprint field; and based on the evaluation result of the monitoring capability corresponding to each monitoring station and a preset optimization rule, obtaining optimal monitoring station position information. Therefore, the methane monitoring network can be designed and optimized for a specified to-be-monitored area, and technical support is provided for methane emission monitoring in the energy industry.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of environmental monitoring during energy production, and in particular to an oilfield methane monitoring network deployment method, device, electronic equipment and storage medium. Background Art

[0002] Methane is the second largest greenhouse gas after carbon dioxide, and its source and sink uncertainties far exceed those of carbon dioxide. Therefore, controlling methane emissions is a task of great significance.

[0003] The oil and gas extraction, refining, storage and transportation processes are all accompanied by certain methane emissions. However, there is currently no oil and gas related methane emission inventory, and there is a serious lack of monitoring of methane emissions. Therefore, the reasonable planning and construction of oilfield methane monitoring networks has great application prospects in the context of "carbon neutrality". At present, the design of methane monitoring networks lacks reference examples and rules, and the design of methane emission monitoring networks lacks constraints, and it is impossible to accurately and quantitatively estimate the effectiveness of different methane monitoring network schemes. Summary of the invention

[0004] In view of this, in order to solve the above-mentioned technical problems or part of the technical problems, the embodiments of the present invention provide an oilfield methane monitoring network deployment method, device, electronic device and storage medium.

[0005] In a first aspect, an embodiment of the present invention provides a method for deploying a network for oilfield methane monitoring, comprising: Based on the identification information of the methane emission facilities of the oil field to be monitored, compiling the gridded methane flux initial field of the oil field to be monitored; Calculating wind vector fields at multiple elevations based on preset wind speeds, and determining an initial feasible domain of monitoring site locations based on the wind vector fields at the multiple elevations through collaborative analysis with methane emission source locations; Based on the preset number of monitoring sites, the Monte Carlo method is used to initialize the positions of the monitoring sites within the initial feasible domain; Calculating the quantified footprint field of each monitoring site, and obtaining an evaluation result of the monitoring capability corresponding to each monitoring site based on the quantified footprint field; Based on the evaluation results of the monitoring capabilities corresponding to each monitoring site and the preset optimization rules, the optimal monitoring site location information is obtained.

[0006] In a possible implementation, the method further includes: Based on the methane emission facility identification information, a multi-scale template matching method is used to extract the oil tank information to obtain an initial oil tank information identification result; The initial oil tank information recognition result is optimized by calculating the roundness of the oil tank to obtain the target oil tank information recognition result; Based on the target oil tank information identification result, compiling a gridded methane flux initial field of the oil field to be monitored; The location of the methane emission source is determined based on the gridded methane flux initial field.

[0007] In a possible implementation, the method further includes: Obtain wind speed data corresponding to different distances in mesoscale weather forecasts to obtain preset wind speeds; Calculating the actual wind direction and wind speed of the oil field to be monitored based on the preset wind speed; Drawing wind vector fields at multiple elevations through a geographic information system based on the actual wind direction and wind speed; The location of the methane emission source is superimposed on the wind vector field, and the upwind frequent occurrence area is selected as the feasible domain of the background value monitoring station, and the downwind frequent occurrence area is selected as the feasible domain of the emission monitoring station.

[0008] In a possible implementation, the method further includes: The meteorological reanalysis data-driven system is used to generate high-resolution meteorological fields; A preset STILT model driven by the high resolution meteorological field; The initial monitoring site locations are input into the STILT model to calculate the quantized footprint field of each monitoring site.

[0009] In a possible implementation, the method further includes: The Lagrangian transport model is used to simulate the diffusion trajectory of methane molecules under the high-resolution meteorological field; The evaluation results of the monitoring capability of each monitoring site are calculated based on the simulated diffusion trajectory of methane molecules.

[0010] In a possible implementation, the method further includes: Based on the evaluation results of the monitoring capability corresponding to each monitoring station, the genetic algorithm is used to optimize the location of the monitoring stations to obtain the optimal location information of the monitoring stations.

[0011] In a possible implementation, the method further includes: Pre-design fitness functions, parent selection strategies, and hybridization methods; Update and iterate paternal monitoring sites based on pre-designed paternal selection strategies and hybridization patterns; The evaluation result of the monitoring capability corresponding to the parent monitoring site is input into the fitness function until the convergence threshold is met to obtain the optimal monitoring site location information.

[0012] In a second aspect, an embodiment of the present invention provides an oilfield methane monitoring network device, comprising: A compilation module, used for compiling a gridded methane flux initial field of the oil field to be monitored based on the identification information of the methane emission facilities of the oil field to be monitored; A determination module, configured to calculate wind vector fields at multiple elevations based on a preset wind speed, and determine an initial feasible domain of a monitoring site location based on the wind vector fields at the multiple elevations through collaborative analysis with a location of a methane emission source; An initial module, used for initializing the monitoring site positions within the initial feasible domain by using a Monte Carlo method based on a preset number of monitoring sites; A calculation module, used to calculate the quantitative footprint field of each monitoring site, and obtain an evaluation result of the monitoring capability corresponding to each monitoring site based on the quantitative footprint field; The optimization module is used to obtain the optimal monitoring site location information based on the evaluation results of the monitoring capability corresponding to each monitoring site and the preset optimization rules.

[0013] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor and a memory, wherein the processor is used to execute an oilfield methane monitoring network deployment program stored in the memory to implement the oilfield methane monitoring network deployment method described in the first aspect above.

[0014] In a fourth aspect, an embodiment of the present invention provides a storage medium, including: the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the oilfield methane monitoring network deployment method described in the first aspect above.

[0015] The oilfield methane monitoring network layout scheme provided by the embodiment of the present invention compiles the gridded methane flux initial field of the oilfield to be monitored based on the identification information of the methane emission facilities of the oilfield to be monitored; calculates the wind vector fields of multiple elevations based on the preset wind speed, and determines the initial feasible domain of the monitoring site location based on the wind vector fields of the multiple elevations through collaborative analysis with the location of the methane emission source; initializes the monitoring site location within the initial feasible domain using the Monte Carlo method based on the preset number of monitoring sites; calculates the quantitative footprint field of each monitoring site, and obtains the evaluation result of the monitoring capability corresponding to each monitoring site based on the quantitative footprint field; obtains the optimal monitoring site location information based on the evaluation result of the monitoring capability corresponding to each monitoring site and the preset optimization rules. Compared with the current design of the methane monitoring network, which lacks reference examples and rules, the design of the methane emission monitoring network lacks constraints and cannot accurately and quantitatively estimate the effectiveness of different methane monitoring network schemes. This plan, through the comprehensive use of atmospheric transport models, Boltzmann-genetic optimization algorithms and remote sensing environmental monitoring data, designs and optimizes the methane monitoring network for designated monitored areas, providing technical support for methane emission monitoring in the energy industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of a method for deploying a network for monitoring methane in an oil field provided by an embodiment of the present invention; Figure 2 A technical flow chart of a method for deploying a network for monitoring methane in an oil field provided by an embodiment of the present invention; Figure 3 A schematic diagram of remote sensing interpretation results of oilfield facilities using Shengli Oilfield as an example provided by an embodiment of the present invention; Figure 4 A schematic diagram of the structure of an oilfield methane monitoring network device provided by an embodiment of the present invention; Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] The embodiment of the present invention designs and optimizes the methane monitoring network for the designated monitored area by comprehensively using the atmospheric transmission model, the Boltzmann-genetic optimization algorithm and remote sensing environmental monitoring data, thereby providing technical support for methane emission monitoring in the energy industry.

[0019] Embodiment 1: In order to obtain the best effect of methane monitoring in oilfield areas, accurately grasp the background field and dynamically monitor the emission sources, the embodiment of the present invention uses high-resolution optical satellite images, atmospheric methane column average concentration products, land use and land cover data multi-source remote sensing and geographic data, and adopts a hybrid method of target recognition and manual interpretation to obtain the spatial distribution of key methane emission settings in oilfields; uses the u, v wind speed data of 10 m and 100 m of the European Center for Mesoscale Weather Forecasts to draw wind vector diagrams, and screens out the feasible domain of monitoring points through an overlay analysis step; within this range, the number of sites to be deployed is set, and the positions of monitoring points are initialized using Monte Carlo simulation; the high-resolution WRF-STILT model is driven by meteorological reanalysis data to calculate the time-dependent footprint of each monitoring point, and the matrix product of the footprint and the emission inventory is used as a quantitative evaluation index for each monitoring point; the Boltzmann-GA model is used to adjust, iterate and optimize the position of the observation point, in this process, the σ stage strategy is used to linearly change the fitness value, and the Boltzmann selection is used to improve the parent retention strategy in the genetic iteration, and finally the optimized observation point position information is output through GA.

[0020] To facilitate understanding of the above-mentioned embodiments of the present invention, further explanation will be given below with reference to specific embodiments in conjunction with the accompanying drawings. The embodiments do not constitute a limitation on the embodiments of the present invention.

[0021] Embodiment 2 Figure 1 A flow chart of a method for deploying a network for monitoring methane in an oil field provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method specifically includes: S11. Based on the identification information of the methane emission facilities of the oil field to be monitored, compile a gridded methane flux initial field of the oil field to be monitored.

[0022] Embodiments of the present invention are combined Figure 2 The technical flow chart shown is used to illustrate that first, the methane emission inventory of the oil fields to be monitored and the surface observation data of atmospheric methane concentration are collected, and at the same time, the initial gridded methane flux field of the area to be monitored is compiled in combination with geographical national conditions data and high-resolution remote sensing satellite images.

[0023] It mainly includes the interpretation of oilfield methane emission facilities. Based on the identification information of methane emission facilities, the oil tank information is extracted by multi-scale template matching method to obtain the initial oil tank information identification result; the initial oil tank information identification result is optimized by calculating the circularity of the oil tank to obtain the target oil tank information identification result; based on the target oil tank information identification result, the gridded methane flux initial field of the oil field to be monitored is compiled; based on the gridded methane flux initial field, the location of the methane emission source is determined.

[0024] Specifically, the interpretation of oilfield methane emission facilities mainly involves interpreting the oil tank information of the oilfield. In the interpretation process, the multi-scale template matching method is used to extract the oil tank. First, the scale of the oil tank in the high-resolution remote sensing satellite image is analyzed, the size range of the oil tank is calculated, and the appropriate step size is determined, so that the template size changes continuously within the range according to the step size. Since the shape of the oil tank in the image is roughly the same as the shape generated by the Gaussian filter, the Gaussian filter can be used to generate a template of the target size to match the image. In addition, the individual volume of the oil tank is relatively large, and the shadow information of the oil tank can be clearly identified in the image. The recognition result can also be optimized by adding the corresponding shadow information to the matching template.

[0025] After adding shadow information, it is easy to cause the shadow of the high-rise building to be recognized as oil tank information. The matching result needs to be optimized. The calculation result is optimized by calculating the roundness of the oil tank. The calculation formula is shown in Formula 1: Formula 1 In formula 1, A is the area of ​​the target area, and S is the perimeter of the target area. The closer the R value is to 1, the closer the matching pattern is to a circle, and the matching area close to 0 is discarded.

[0026] Oil tanks are generally distributed in clusters in images and their distribution is relatively regular, so we can also calculate the oil tanks within a certain range, determine whether multiple oil tanks with similar distances are on the same straight line, and manually judge the oil tank clusters with constantly changing slopes, and combine the above methods to generate the final oil tank boundary. Figure 3 Shown is a schematic diagram of remote sensing interpretation results of oilfield facilities taking Shengli Oilfield as an example provided by an embodiment of the present invention.

[0027] S12. Calculating wind vector fields at multiple elevations based on preset wind speeds, and determining an initial feasible domain of monitoring site locations based on the wind vector fields at the multiple elevations through collaborative analysis with methane emission source locations.

[0028] Obtain wind speed data corresponding to different distances in mesoscale weather forecasts to obtain preset wind speeds; calculate the actual wind direction and wind speed of the oil field to be monitored based on the preset wind speed; draw wind vector fields at multiple elevations through a geographic information system based on the actual wind direction and wind speed; superimpose the location of the methane emission source on the wind vector field, select the upwind frequent area as the feasible domain of the background value monitoring station, and select the downwind frequent area as the feasible domain of the emission monitoring station.

[0029] Specifically, since the distribution of oilfield facilities is not centralized but widely distributed throughout the region, the feasible domain of the monitoring points is too large, and it is necessary to search for the best point on a scale of 10,000 square kilometers, which can easily cause the optimization to fall into a non-convergent situation. Therefore, it is necessary to reduce the feasible domain of the point before performing numerical optimization. The embodiment of the present invention uses the u, v wind speed data of 10 m and 100 m of the European Center for Mesoscale Weather Forecasts. The actual wind direction and wind speed calculated using u, v wind speed are shown in Formula 2 and Formula 3.

[0030] Formula 2 Formula 3 Where Φ is the wind direction expressed in degrees (°), is the wind speed in m / s, with u and v being the meridional and zonal wind speed components, respectively. u and v can use the 10 m and 100 m wind products in the monthly average data of ER5. 10 m is mainly used to reflect the surface wind field, while 100 m is more able to reflect the wind field in the lower atmosphere.

[0031] In order to achieve collaborative analysis with other geographic and remote sensing data products, it is necessary to draw the wind vector field in GIS software. Use arrow legends to represent wind direction and use their length to represent wind speed. Since the u, v wind data itself has longitude and latitude coordinates, the generated wind direction and wind speed data can directly use these coordinates. If you need to display the wind vector field in a high-density form, you can use the Kriging algorithm to interpolate the wind direction and wind speed data.

[0032] For the oil field area to be monitored, the average monthly wind field data for the past three years can be obtained, and wind vector diagrams can be drawn according to the above method. At the same time, the vector data of the selected methane emission facilities are superimposed on the wind vector layer. The principle for determining the feasible domain of methane monitoring stations is to find the upwind frequent area as the feasible domain of the background value monitoring station, and to find the downwind frequent area as the feasible domain of the emission monitoring station.

[0033] S13. Based on the preset number of monitoring sites, the Monte Carlo method is used to initialize the positions of the monitoring sites within the initial feasible domain.

[0034] S14, calculating the quantified footprint field of each monitoring site, and obtaining an evaluation result of the monitoring capability corresponding to each monitoring site based on the quantified footprint field.

[0035] The meteorological reanalysis data-driven system is used to generate high-resolution meteorological fields. At the same time, the Monte Carlo method is used to initialize the locations of monitoring stations within the feasible domain of methane monitoring stations given above according to the given number of monitoring stations.

[0036] Calculating the quantitative footprint field of each monitoring site includes: using meteorological reanalysis data to drive the system to generate a high-resolution meteorological field; driving the preset STILT model based on the high-resolution meteorological field; and inputting the initialized monitoring site location into the STILT model to calculate the quantitative footprint field of each monitoring site.

[0037] Specifically, the STILT model has been shown to be useful for studying the transport of trace gases. As an atmospheric back-transport model, it can directly obtain the emission contribution of different regions in the study area from measurements at fixed locations and quantify the contribution using footprints. In order to drive the model, a high-resolution meteorological field is required. This step focuses on describing how to generate a high-resolution meteorological field.

[0038] The embodiment of the present invention uses meteorological reanalysis data to drive WRF to generate a high-resolution meteorological field. WRF has a wide range of applications and a large application group. It is easier to obtain some basic related knowledge in practical applications. The initial and boundary meteorological conditions of WRF are established based on the final analysis data of the National Center for Environmental Prediction (NCEP). The simulation domain is 900 km × 900 km. In order to obtain a meteorological field with a resolution of 1km or higher and take into account the computational efficiency, it is necessary to set the simulation area in a multi-layer nested manner. The preferred different levels of nested resolution setting schemes are 1km, 3km, 9km and 27km. Another more efficient scheme is 1km, 4km and 16km. The user should choose one of the two schemes according to the regional scale to be analyzed, and this parameter cannot be set arbitrarily. The preferred WRF main physical module settings are as follows. The long-wave radiation uses the rapid radiation transfer model scheme, and the short-wave radiation uses the Goddard radiation transfer model scheme; the boundary layer uses the Ethan University program; the microphysics module uses the Purdue Lin module; and the convection uses the Grell-Devenyi ensemble mass flux scheme. For the height setting, the release height of the observation station is 10m to represent the receptor location of methane measurement in the study area. In order to accurately characterize the emission characteristics within a certain range, it is required that there are no local methane emission sources within about 0.5 km nearby. If the release height is set higher, the above restrictions can be relaxed.

[0039] Monitoring site footprint calculation: The key to this step is to use an intelligent optimization algorithm to optimize the location of the monitoring site, and the core of implementing this step is to construct a quantitative indicator to evaluate the effectiveness of each monitoring site. To this end, the embodiment of the present invention introduces the concept of footprint to characterize the monitoring site's ability to measure the methane emission characteristics of the oil field area to be measured. The embodiment of the present invention uses the Lagrangian transmission model to describe the diffusion trajectory of methane molecules. Compared with the common Gaussian diffusion model, the Lagrangian transmission model can characterize the random walk process of gas molecules, thereby accurately simulating the diffusion of atmospheric molecules at high resolution. Formula 4 can be used to describe the relationship between the surface flux S and the monitoring site concentration C.

[0040] Formula 4 In formula 4, x r represents the location of the observation point, t r represents a certain observation time, t0 is the initial moment when the gas molecules begin to diffuse (i.e. the time when they are emitted into the atmosphere). I(x r , t r │x,t) is the influence function, which is used to quantitatively describe the effect of flux S(x,t) on concentration observation C(x r , t r ) is the contribution of . U represents the maximum effective range of atmospheric transmission. According to the theoretical derivation of Lagrangian random walk, I(x r , t r │x,t) can be expressed by Formula 5.

[0041] Formula 5 N in formula 5 tot is the total number of atmospheric molecules released. It can be seen that only by using simulation calculation to obtain the position of each molecule at time t, I can be solved. In reality, the forward simulation method can be used, using the high-resolution meteorological field given above as the driving force, and setting x at a given monitoring station r Release N tot molecules, and the position of each molecule at each moment is obtained through numerical simulation, and then I is obtained by reverse calculation. When calculating formula 5, it is necessary to set N tot And the length of the backtracking time. In the embodiment of the present invention, 1000 particles are released and traced back for at least 3 days, and the time resolution is selected as 1 hour. According to the actual situation, if there are higher computing resources, the traceback time can be set to 7 days.

[0042] According to the physical definition, we know that I can express the monitoring site x rThe quantitative correlation degree of any flux S(x,t0) within U of the oil field area to be measured. If the flux S in the default area is completely unknown, S can be set to a matrix of all 1s. The embodiment of the present invention uses the emission facility location given in step S11 and combines the public emission inventory data to set S, so as to obtain a more accurate measurement site effectiveness value. After setting S, the observation point x can be obtained by matrix multiplication with I. r The measurement capability of methane emissions within the entire observation area U. Through this step, the measurement capabilities of all initialized measurement points are calculated. At the same time, in step S15, after each round of optimization, the measurement capabilities of new measurement points are also calculated.

[0043] S15. Based on the evaluation results of the monitoring capability corresponding to each monitoring site and the preset optimization rules, the optimal monitoring site location information is obtained.

[0044] There are three key links in optimizing the location of the emission point using a genetic algorithm. The first is to design a fitness function, which is a key parameter to guide the iteration of the genetic algorithm. The embodiment of the present invention uses I calculated in step S14 as a basis and uses σ truncation to improve the common linear transformation deficiencies of the fitness function. The improved fitness function I' is represented by Formula 6.

[0045] Formula 6 Where c is a small integer with any value between 1 and 7, which can be adjusted according to the experiment. is the average fitness of all individuals in all genetic algorithm populations, and σ is its corresponding standard deviation.

[0046] Secondly, the parent selection strategy also has an important influence on the optimization results. The embodiment of the present invention uses the Boltzmann strategy to complete the parent selection in each iteration. In the Boltzmann strategy, the probability of each individual being selected as the parent of the next generation can be described by Formula 7.

[0047] Formula 7 Among them, i is the i-th feasible solution, N is the total number of feasible solutions, It represents the fitness value of individual i, and T is a control parameter that needs to be set by the user. When T takes a larger value, the relative proportion of the fitness value becomes smaller, with a smaller selection pressure; when T takes a smaller value, the relative proportion of the fitness value becomes larger, with a larger selection pressure. Therefore, by adjusting the size of T, the convergence speed can be controlled, and the search range can also be controlled.

[0048] Finally, the design of the hybridization method also plays an important role in the genetic algorithm. For the site selection of methane monitoring stations, two parameters need to be determined, namely longitude and latitude. These two variables to be optimized are both numerical, so the embodiment of the present invention requires the use of partial arithmetic hybridization to interact chromosomes. Generally speaking, the footprints of observation points have a strong spatial correlation, so once a point with strong observation capabilities is searched, the probability of the optimal observation position being near it is relatively high. Traditional genetic algorithms are too random during hybridization, which is conducive to solving the problem of falling into local optimality, but it also leads to a slower optimization process. For the site selection of observation points, the most critical observation point effectiveness calculation requires the use of high-resolution meteorological data and Lagrangian random walk models, and its computational complexity is very large. Therefore, if random point hybridization is simply used, it is easy to cause the calculation time to be unaffordable. Partial arithmetic crossover first selects a part of the components in the parent vector, for example, the nk components after the kth component, and then generates nk random numbers αk+1, αk+2,... , αn on [0, 1]. After the crossover operator, the two offspring obtained are x'=(x1,…,xk, αk+1 xk+1+(1-αk+1)yk+1,…,αnxn+(1-αn) yn) and y'=(y1,…,yk, αk+1 yk+1+(1-αk+1)xk+1,…,αnyn+(1-αn) xn).

[0049] The above-mentioned oilfield methane monitoring network layout method solves the problem that the current oilfield methane monitoring network design lacks quantitative basis and cannot evaluate the effectiveness of the network construction plan. It can not only provide the optimal network construction plan, but also predict the effective range and accuracy of the plan in monitoring methane emissions in actual applications.

[0050] The oilfield methane monitoring network layout method provided by the embodiment of the present invention compiles the gridded methane flux initial field of the oilfield to be monitored based on the identification information of the methane emission facilities of the oilfield to be monitored; calculates the wind vector fields of multiple elevations based on the preset wind speed, and determines the initial feasible domain of the monitoring site location based on the wind vector fields of the multiple elevations through collaborative analysis with the location of the methane emission source; initializes the monitoring site location within the initial feasible domain using the Monte Carlo method based on the preset number of monitoring sites; calculates the quantitative footprint field of each monitoring site, and obtains the evaluation result of the monitoring capability corresponding to each monitoring site based on the quantitative footprint field; obtains the optimal monitoring site location information based on the evaluation result of the monitoring capability corresponding to each monitoring site and the preset optimization rules. Compared with the current design of methane monitoring network, which lacks reference examples and rules, the design of methane emission monitoring network lacks constraints and cannot accurately and quantitatively estimate the effectiveness of different methane monitoring network schemes. This method, through the comprehensive use of atmospheric transport models, Boltzmann-genetic optimization algorithms and remote sensing environmental monitoring data, designs and optimizes the methane monitoring network for the designated monitored areas, providing technical support for methane emission monitoring in the energy industry.

[0051] Embodiment three: Figure 4 A schematic diagram of the structure of an oilfield methane monitoring network device provided by an embodiment of the present invention specifically includes: The compilation module 401 is used to compile the gridded methane flux initial field of the oil field to be monitored based on the identification information of the methane emission facilities of the oil field to be monitored. For detailed description, please refer to the relevant description corresponding to the above method embodiment, which will not be repeated here.

[0052] The determination module 402 is used to calculate the wind vector fields at multiple elevations based on the preset wind speed, and determine the initial feasible domain of the monitoring site location based on the wind vector fields at the multiple elevations through collaborative analysis with the location of the methane emission source. For detailed description, please refer to the relevant description corresponding to the above method embodiment, which will not be repeated here.

[0053] The initial module 403 is used to initialize the monitoring site positions in the initial feasible region by using the Monte Carlo method based on the preset number of monitoring sites. Detailed descriptions refer to the relevant descriptions corresponding to the above method embodiments, which will not be repeated here.

[0054] The calculation module 404 is used to calculate the quantified footprint field of each monitoring site, and obtain the evaluation result of the monitoring capability corresponding to each monitoring site based on the quantified footprint field. For detailed description, please refer to the relevant description corresponding to the above method embodiment, which will not be repeated here.

[0055] The optimization module 405 is used to obtain the optimal monitoring site location information based on the evaluation results of the monitoring capability corresponding to each monitoring site and the preset optimization rules. For detailed description, please refer to the relevant description corresponding to the above method embodiment, which will not be repeated here.

[0056] The oilfield methane monitoring network device provided in this embodiment can be as follows Figure 4 The oilfield methane monitoring network device shown in FIG. can be used as follows: Figure 1 All steps of the network layout method for methane monitoring in oil fields, thus achieving Figure 1 The technical effect of the oilfield methane monitoring network layout method shown in the figure is as follows. For details, please refer to Figure 1 For the sake of brevity, the relevant description is not repeated here.

[0057] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, Figure 5 The electronic device 500 shown includes: at least one processor 501, a memory 502, at least one network interface 504 and other user interfaces 503. The various components in the electronic device 500 are coupled together via a bus system 505. It is understood that the bus system 505 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 505 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, the bus system 505 is not shown in FIG. Figure 5 Various buses are labeled as bus system 505 .

[0058] The user interface 503 may include a display, a keyboard, or a pointing device (eg, a mouse, a trackball, a touch pad, or a touch screen).

[0059] It can be understood that the memory 502 in the embodiment of the present invention can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DRRAM). The memory 502 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0060] In some implementations, the memory 502 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system 5021 and an application program 5022 .

[0061] The operating system 5021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application 5022 includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present invention can be included in the application 5022.

[0062] In the embodiment of the present invention, by calling the program or instruction stored in the memory 502, specifically, the program or instruction stored in the application 5022, the processor 501 is used to execute the method steps provided by each method embodiment, for example, including: Based on the identification information of the methane emission facilities of the oil field to be monitored, a gridded methane flux initial field of the oil field to be monitored is compiled; based on the preset wind speed, wind vector fields at multiple elevations are calculated, and based on the wind vector fields at multiple elevations, an initial feasible domain of the monitoring site locations is determined through collaborative analysis with the locations of methane emission sources; based on the preset number of monitoring sites, the Monte Carlo method is used to initialize the monitoring site locations within the initial feasible domain; the quantitative footprint field of each monitoring site is calculated, and an evaluation result of the monitoring capability corresponding to each monitoring site is obtained based on the quantitative footprint field; based on the evaluation result of the monitoring capability corresponding to each monitoring site and the preset optimization rules, the optimal monitoring site location information is obtained.

[0063] In one possible implementation, based on the methane emission facility identification information, a multi-scale template matching method is used to extract oil tank information to obtain an initial oil tank information identification result; the initial oil tank information identification result is optimized by calculating the circularity of the oil tank to obtain a target oil tank information identification result; based on the target oil tank information identification result, a gridded methane flux initial field of the oil field to be monitored is compiled; and the location of the methane emission source is determined based on the gridded methane flux initial field.

[0064] In one possible implementation, wind speed data corresponding to different distances in a mesoscale weather forecast are obtained to obtain a preset wind speed; the actual wind direction and wind speed of the oil field to be monitored are calculated based on the preset wind speed; wind vector fields at multiple elevations are drawn through a geographic information system based on the actual wind direction and wind speed; the location of the methane emission source is superimposed on the wind vector field, and an upwind frequent area is selected as a feasible domain for background value monitoring sites, and a downwind frequent area is selected as a feasible domain for emission monitoring sites.

[0065] In one possible implementation, a meteorological reanalysis data-driven system is used to generate a high-resolution meteorological field; a preset STILT model is driven based on the high-resolution meteorological field; and the locations of the initialized monitoring sites are input into the STILT model to calculate a quantitative footprint field for each monitoring site.

[0066] In a possible implementation, a Lagrangian transport model is used to simulate the diffusion trajectory of methane molecules under the high-resolution meteorological field; and an evaluation result of the monitoring capability corresponding to each monitoring site is calculated based on the simulated diffusion trajectory of methane molecules.

[0067] In a possible implementation, based on the evaluation result of the monitoring capability corresponding to each monitoring site, a genetic algorithm is used to optimize the location of the monitoring site to obtain optimal location information of the monitoring site.

[0068] In one possible implementation, a fitness function, a parent selection strategy, and a hybridization method are pre-designed; the parent monitoring site is iteratively updated based on the pre-designed parent selection strategy and hybridization method; and the evaluation result of the monitoring capability corresponding to the parent monitoring site is input into the fitness function until the convergence threshold is met to obtain the optimal monitoring site location information.

[0069] The method disclosed in the above embodiment of the present invention can be applied to the processor 501, or implemented by the processor 501. The processor 501 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 501 or the instruction in the form of software. The above processor 501 can be a general processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor to execute, or the hardware and software units in the decoding processor can be executed. The software unit can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 502, and the processor 501 reads the information in the memory 502 and completes the steps of the above method in combination with its hardware.

[0070] It is understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit may be implemented in one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPDevice, DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application, or a combination thereof.

[0071] For software implementation, the technology described herein can be implemented by a unit that performs the functions described herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0072] The electronic device provided in this embodiment may be Figure 5 The electronic device shown in FIG. 1 may perform the following steps: Figure 1 All steps of the network layout method for methane monitoring in oil fields, thus achieving Figure 1 The technical effect of the oilfield methane monitoring network layout method shown in the figure is as follows. For details, please refer to Figure 1 For the sake of brevity, the relevant description is not repeated here.

[0073] The embodiment of the present invention further provides a storage medium (computer-readable storage medium). The storage medium here stores one or more programs. The storage medium may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk or a solid-state drive; the memory may also include a combination of the above-mentioned types of memory.

[0074] When one or more programs in the storage medium can be executed by one or more processors, the above-mentioned oilfield methane monitoring network deployment method executed on the electronic device side can be implemented.

[0075] The processor is used to execute the oilfield methane monitoring network layout program stored in the memory to implement the following steps of the oilfield methane monitoring network layout method executed on the electronic device side: Based on the identification information of the methane emission facilities of the oil field to be monitored, a gridded methane flux initial field of the oil field to be monitored is compiled; based on the preset wind speed, wind vector fields at multiple elevations are calculated, and based on the wind vector fields at multiple elevations, an initial feasible domain of the monitoring site locations is determined through collaborative analysis with the locations of methane emission sources; based on the preset number of monitoring sites, the Monte Carlo method is used to initialize the monitoring site locations within the initial feasible domain; the quantitative footprint field of each monitoring site is calculated, and an evaluation result of the monitoring capability corresponding to each monitoring site is obtained based on the quantitative footprint field; based on the evaluation result of the monitoring capability corresponding to each monitoring site and the preset optimization rules, the optimal monitoring site location information is obtained.

[0076] In one possible implementation, based on the methane emission facility identification information, a multi-scale template matching method is used to extract oil tank information to obtain an initial oil tank information identification result; the initial oil tank information identification result is optimized by calculating the circularity of the oil tank to obtain a target oil tank information identification result; based on the target oil tank information identification result, a gridded methane flux initial field of the oil field to be monitored is compiled; and the location of the methane emission source is determined based on the gridded methane flux initial field.

[0077] In one possible implementation, wind speed data corresponding to different distances in a mesoscale weather forecast are obtained to obtain a preset wind speed; the actual wind direction and wind speed of the oil field to be monitored are calculated based on the preset wind speed; wind vector fields at multiple elevations are drawn through a geographic information system based on the actual wind direction and wind speed; the location of the methane emission source is superimposed on the wind vector field, and an upwind frequent area is selected as a feasible domain for background value monitoring sites, and a downwind frequent area is selected as a feasible domain for emission monitoring sites.

[0078] In one possible implementation, a meteorological reanalysis data-driven system is used to generate a high-resolution meteorological field; a preset STILT model is driven based on the high-resolution meteorological field; and the locations of the initialized monitoring sites are input into the STILT model to calculate a quantitative footprint field for each monitoring site.

[0079] In a possible implementation, a Lagrangian transport model is used to simulate the diffusion trajectory of methane molecules under the high-resolution meteorological field; and an evaluation result of the monitoring capability corresponding to each monitoring site is calculated based on the simulated diffusion trajectory of methane molecules.

[0080] In a possible implementation, based on the evaluation result of the monitoring capability corresponding to each monitoring site, a genetic algorithm is used to optimize the location of the monitoring site to obtain optimal location information of the monitoring site.

[0081] In one possible implementation, a fitness function, a parent selection strategy, and a hybridization method are pre-designed; the parent monitoring site is iteratively updated based on the pre-designed parent selection strategy and hybridization method; and the evaluation result of the monitoring capability corresponding to the parent monitoring site is input into the fitness function until the convergence threshold is met to obtain the optimal monitoring site location information.

[0082] The professionals should further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0083] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0084] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for deploying a network for oilfield methane monitoring, characterized in that: include: Based on the identification information of the methane emission facilities of the oil field to be monitored, compiling the gridded methane flux initial field of the oil field to be monitored; Calculating wind vector fields at multiple elevations based on preset wind speeds, and determining an initial feasible domain of monitoring site locations based on the wind vector fields at the multiple elevations through collaborative analysis with methane emission source locations; Based on the preset number of monitoring sites, the Monte Carlo method is used to initialize the positions of the monitoring sites within the initial feasible domain; Calculating the quantified footprint field of each monitoring site, and obtaining an evaluation result of the monitoring capability corresponding to each monitoring site based on the quantified footprint field; Based on the evaluation results of the monitoring capabilities corresponding to each monitoring site and the preset optimization rules, the optimal monitoring site location information is obtained.

2. The method according to claim 1, characterized in that The method of compiling a gridded methane flux initial field of the oil field to be monitored based on the identification information of the methane emission facilities of the oil field to be monitored includes: Based on the methane emission facility identification information, a multi-scale template matching method is used to extract the oil tank information to obtain an initial oil tank information identification result; The initial oil tank information recognition result is optimized by calculating the roundness of the oil tank to obtain the target oil tank information recognition result; Based on the target oil tank information identification result, compiling a gridded methane flux initial field of the oil field to be monitored; The location of the methane emission source is determined based on the gridded methane flux initial field.

3. The method according to claim 2, characterized in that The method of calculating wind vector fields at multiple elevations based on a preset wind speed, and determining an initial feasible region of a monitoring site location based on the wind vector fields at multiple elevations through collaborative analysis with a location of a methane emission source, includes: Obtain wind speed data corresponding to different distances in mesoscale weather forecasts to obtain preset wind speeds; Calculating the actual wind direction and wind speed of the oil field to be monitored based on the preset wind speed; Drawing wind vector fields at multiple elevations through a geographic information system based on the actual wind direction and wind speed; The location of the methane emission source is superimposed on the wind vector field, and the upwind frequent occurrence area is selected as the feasible domain of the background value monitoring station, and the downwind frequent occurrence area is selected as the feasible domain of the emission monitoring station.

4. The method according to claim 3, characterized in that The calculation of the quantified footprint field of each monitoring site includes: The meteorological reanalysis data-driven system is used to generate high-resolution meteorological fields; A preset STILT model driven based on the high resolution meteorological field; The initial monitoring site locations are input into the STILT model to calculate the quantized footprint field of each monitoring site.

5. The method according to claim 4, characterized in that The evaluation result of the monitoring capability corresponding to each monitoring site is obtained based on the quantified footprint field, including: The Lagrangian transport model is used to simulate the diffusion trajectory of methane molecules under the high-resolution meteorological field; The evaluation results of the monitoring capability of each monitoring site are calculated based on the simulated diffusion trajectory of methane molecules.

6. The method according to claim 5, characterized in that The optimal monitoring site location information is obtained based on the evaluation results of the monitoring capability corresponding to each monitoring site and the preset optimization rules, including: Based on the evaluation results of the monitoring capability corresponding to each monitoring station, the genetic algorithm is used to optimize the location of the monitoring stations to obtain the optimal location information of the monitoring stations.

7. The method according to claim 6, characterized in that The method of optimizing the monitoring site location using a genetic algorithm based on the evaluation result of the monitoring capability corresponding to each monitoring site to obtain the optimal monitoring site location information includes: Pre-design fitness functions, parent selection strategies, and hybridization methods; Update iterative paternal monitoring sites based on pre-designed paternal selection strategies and hybridization patterns; The evaluation result of the monitoring capability corresponding to the parent monitoring site is input into the fitness function until the convergence threshold is met to obtain the optimal monitoring site location information.

8. An oilfield methane monitoring network device, characterized in that: include: A compilation module, used for compiling a gridded methane flux initial field of the oil field to be monitored based on the identification information of the methane emission facilities of the oil field to be monitored; A determination module, configured to calculate wind vector fields at multiple elevations based on a preset wind speed, and determine an initial feasible domain of a monitoring site location based on the wind vector fields at the multiple elevations through collaborative analysis with a location of a methane emission source; An initial module, used for initializing the monitoring site positions within the initial feasible domain by using a Monte Carlo method based on a preset number of monitoring sites; A calculation module, used to calculate the quantitative footprint field of each monitoring site, and obtain an evaluation result of the monitoring capability corresponding to each monitoring site based on the quantitative footprint field; The optimization module is used to obtain the optimal monitoring site location information based on the evaluation results of the monitoring capability corresponding to each monitoring site and the preset optimization rules.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the processor is used to execute the oilfield methane monitoring network deployment program stored in the memory to implement the oilfield methane monitoring network deployment method described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the oilfield methane monitoring network deployment method described in any one of claims 1 to 7.

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