CO2 non-point source leakage area diffusion simulation method

Abstract

The invention discloses a CO2 non-point source leakage area diffusion simulation method, and aims to solve the problems that a CO2 oil displacement storage project has the characteristics of multiple well sites, large range and long operation time, and non-point source soil leakage and multi-point source shaft leakage coexist in each well site. According to the method, for large-range diffusion of leakage of multiple well sites, real-time point source leakage source parameters are provided for CFD simulation through flux monitoring and leakage rate calculation of each well site, and therefore real-time distribution of the mass fraction of atmospheric CO2 in a diffusion area, support safety and environmental risk assessment are obtained.

Description

Technical Field

[0001] The invention relates to a diffusion simulation method, and in particular to a CO2 surface source leakage area diffusion simulation method. Background Art

[0002] Carbon capture, utilization and storage (CCUS) technology is a key CO2 emission reduction technology. According to the International Energy Agency (IEA), this technology will contribute 19% to global carbon neutrality by 2050. The integrity failure of CO2 flooding and storage injection and production wells and the activation of faults in geological structures make CO2 storage projects subject to the risk of leakage and escape [2-4]. Although CO2 is stored in deep strata in a supercritical form, when CO2 leaks and diffuses to shallow soil and the surface, it will diffuse into the atmosphere in the form of gas due to the decrease in pressure, which not only offsets the storage and emission reduction effect, but also affects the ecological safety and human health of the area around the leakage point due to surface atmospheric diffusion. Therefore, conducting surface leakage and diffusion research on CO2 flooding and storage projects is crucial to assessing regional ecological safety and human health.

[0003] Previous studies on CO2 storage leakage and diffusion have mainly focused on the application of CFD and heavy gas diffusion model simulation. In terms of CFD simulation, XING et al. conducted a CFD diffusion simulation study on the scenario of CO2 blowout. LIU et al. conducted a CFD diffusion simulation study on CO2 pipeline leakage. WANG et al. used CFD to simulate the continuous release and diffusion process of indoor CO2. MAZZOLDI et al. used CFD to simulate the leakage of high-pressure pipelines and compared it with the Gaussian model, concluding that CFD simulation is more accurate than the Gaussian model in calculating leakage distribution. Wen Hu et al. conducted a CFD numerical simulation of the leakage scenario of the CO2 buffer tank at the storage well site, and explored the diffusion law of CO2 leakage in the buffer tank and its impact on the CO2 mass fraction in the storage area. LI et al. established a CFD model for the scenario of large-scale and long-term leakage to study the diffusion behavior of CO2 and evaluated the human health risks under different leakage scenarios. HSIEH et al. conducted a CFD simulation of the CO2 storage tank leakage scenario near obstacles and studied the impact of buildings and complex terrain on the diffusion of dense gas clouds and plumes. In the simulation of heavy gas diffusion, the box model represented by the SLAB (heavy gas diffusion simulation software) model and the empirical model represented by the Gaussian model are currently widely used. The SLAB model is established by simplifying the one-dimensional or two-dimensional conservation equations to describe the flow field, and the Gaussian model gives the mass fraction distribution expression based on component conservation. Zhu Qianlin et al. used SLAB software to simulate the continuous leakage of CO2 injected into the oil and gas reservoir through the wellbore vertically spraying to the surface. CORTIS et al. established a two-dimensional mesoscale space CO2 diffusion steady-state model and studied the effect of CO2 density on leakage diffusion. Wang Lidong et al. used the Gaussian model to simulate the CO2 leakage scenario of an abandoned well, and studied the impact range of the leakage accident and the CO2 mass fraction distribution characteristics under different atmospheric conditions. Summary of the invention

[0004] The present invention provides a method for simulating the regional diffusion of CO2 non-point source leakage, aiming at the characteristics of CO2 flooding and oil storage projects, which have many well sites, a large range, and a long operation time, and each well site has the problem of non-point source soil leakage and multiple point source wellbore leakage. Aiming at the large-scale diffusion of leakage from multiple well sites, the present invention provides real-time point source leakage source parameters for CFD simulation through flux monitoring of each well site and calculation of leakage amount, thereby obtaining the real-time distribution of atmospheric CO2 mass fraction in the diffusion area, and supporting safety and environmental risk assessment.

[0005] A method for simulating regional diffusion of CO2 non-point source leakage, characterized by comprising the following steps:

[0006] Step 1: The real terrain modeling process can use the digital elevation model (DEM) elevation data extracted from the map software GlobalMapper;

[0007] Step 2: Import the elevation data file into the 3D modeling software, and then export the created model file in the Rhino modeling software;

[0008] Step 3: CFD calculation domain setting process, including the definition of leakage source, air inlet and outlet surfaces, and wall surface definition

[0009] Step 4: The last step is to divide the simulation grid and import the divided grid file into the fluid calculation software.

[0010] In a possible implementation, step four also includes: importing into Fluent software for simulation calculation, and using a component transport model to calculate the diffusion behavior of CO2.

[0011] A possible implementation method simplifies the fluid flow based on the following assumptions: 1. The gas is a continuous medium; 2. The gas flow is an incompressible process; 3. The gas flow does not consider heat and mass transfer, phase change and chemical reaction; 4. The component transport model ignores other gases.

[0012] One possible implementation method is to monitor the CO2 leakage flux based on the eddy covariance method.

[0013] In one possible implementation, the flow conservation equation can be simplified as follows:

[0014] Continuity equation:

[0015]

[0016] Momentum conservation equation:

[0017]

[0018] Energy conservation equation:

[0019]

[0020] Component transport equation:

[0021]

[0022]

[0023] Where: ρ is density, unit: kg / m 3 ; t is time, unit s; v is velocity vector, unit m / s; p is pressure, unit Pa; τ is stress tensor; F is external force; E is total energy, unit J; kef f is the effective thermal conductivity, unit is w / (m 2 ·K); T is temperature, unit K; h i is the specific enthalpy of substance i, in J / Kg; Ji is the diffusion flux of substance i; R i is the net content of substance i in diffusion; S i is the source term of substance i; Y i is the mass fraction of substance i.

[0024] In the past, the application of CFD diffusion and heavy gas diffusion simulation mainly studied pipeline leakage during transportation. There was a lack of research based on well-site leakage, especially a lack of research based on multiple well-site leakage scenarios, which made it difficult to reflect the leakage risk of multiple well-sites in oil recovery and storage projects. Therefore, this study proposed a CO2 non-point source leakage regional diffusion simulation method that combines well-site flux monitoring and CFD simulation, and used the eddy covariance method to monitor the flux of the well-site. The proposed method was further applied to a CO2 recovery and storage project in an oil field in East China to verify the practicality of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 In order to calculate the CO2 flux using the eddy covariance method, 40 sets of data were selected and plotted with time as the abscissa and CO2 flux as the ordinate to obtain a CO2 flux scatter plot;

[0026] Figure 2 The 40 sets of CO2 flux data calculated from 18:00 on a certain day to 16:00 on the next day were compared with the field measured data of the case oil field;

[0027] Figure 3 To set up two monitoring planes perpendicular to the horizontal plane, a scatter plot of the CO2 mass fraction distribution was made;

[0028] Figure 4 The mass fraction distribution of CO2 in the z-axis direction (vertical direction) of the monitoring surface;

[0029] Figure 5 It is the CO2 mass fraction distribution cloud diagram of the leakage source in the plane parallel to the main wind direction;

[0030] Figure 6 The top view of the CO2 mass fraction distribution cloud map is made for the diffusion times 600, 1800, 3600, and 5400s respectively. DETAILED DESCRIPTION

[0031] The present invention provides a method for simulating the regional diffusion of CO2 non-point source leakage, aiming at the characteristics of CO2 flooding and oil storage projects, which have many well sites, a large range, and a long operation time, and each well site has the problem of non-point source soil leakage and multiple point source wellbore leakage. Aiming at the large-scale diffusion of leakage from multiple well sites, the present invention provides real-time point source leakage source parameters for CFD simulation through flux monitoring of each well site and calculation of leakage amount, thereby obtaining the real-time distribution of atmospheric CO2 mass fraction in the diffusion area, and supporting safety and environmental risk assessment.

[0032] CO2 flooding and storage projects have the dual benefits of improving oil recovery and storing CO2, and are currently the most economically viable form of carbon storage. However, flooding and storage projects usually contain multiple well sites, which have a greater safety and environmental impact on the region due to the high risk of CO2 leakage in the wellbore. In view of the shortcomings of previous studies on atmospheric diffusion based on point source leakage from well sites, a method for studying the atmospheric CO2 diffusion of surface leakage in flooding and storage projects based on well site surface source flux monitoring was established. The case application of an oil field in East China based on scenario analysis shows that the CO2 leakage flux monitoring based on the eddy covariance method can obtain the surface source leakage flux of the entire well site, providing accurate data for large-scale CFD (computational fluid dynamics) simulation; the CFD diffusion simulation of multiple well sites can reflect the complex terrain of the region and the leakage of multiple well sites, supporting the regional safety and environmental risk management from well site leakage.

[0033] A method for simulating regional diffusion of CO2 non-point source leakage, characterized by comprising the following steps:

[0034] Step 1: The real terrain modeling process can use the digital elevation model (DEM) elevation data extracted from the map software GlobalMapper;

[0035] Step 2: Import the elevation data file into the 3D modeling software, and then export the created model file in the Rhino modeling software;

[0036] Step 3: CFD calculation domain setting process, including the definition of leakage source, air inlet and outlet surfaces, and wall surface definition

[0037] Step 4: The last step is to divide the simulation grid and import the divided grid file into the fluid calculation software.

[0038] CO2 flooding and storage projects are characterized by many well sites, large scope, and long operation time. Each well site also has the characteristics of non-point source soil leakage and multiple point source wellbore leakage. Usually, a flooding and storage project has dozens of well sites, and the scope can reach more than 10km2. Compared with the large-scale scene of 10km2, the point source leakage of hundreds of square meters of well site surface source and multiple wellbore can be simulated as a large point source leakage. The leakage of the entire well site surface source leakage is approximately equal to the average flux of the well site multiplied by the area of ​​the well site, and the non-point source flux of a single well site can be obtained using the atmospheric flux monitoring technology based on the eddy correlation method. Therefore, for the large-scale diffusion of leakage from multiple well sites in a certain storage project, the flux monitoring of each well site and the calculation of the leakage amount can provide real-time point source leakage source parameters for CFD simulation, thereby obtaining the real-time distribution of the atmospheric CO2 mass fraction in the diffusion area, supporting safety and environmental risk assessment.

[0039] The well field flux monitoring method based on eddy covariance method includes: eddy covariance method is a technology based on the combination of atmospheric turbulence theory and data analysis. This method calculates the turbulent flux through the covariance of atmospheric physical quantities (such as temperature, humidity, CO2 mass fraction) and wind speed to obtain the regional CO2 flux. At present, scholars at home and abroad have widely applied this method to the flux monitoring research of CO2 or other gases in ecosystems such as forests, lakes, and farmlands. For example, MIGLIAVACCA et al. used the eddy covariance method to study the net exchange of CO2 in poplar forests in northern Italy for 3 years, and analyzed the influence of seasons and climate on CO2 exchange through flux monitoring and data comparison. LONG et al. conducted eddy covariance measurements of CH4 net flux in northern swamps in Canada to study the daily average changes in flux, and compared the daytime and nighttime fluxes of CH4.

[0040] The theoretical basis of the eddy covariance method for monitoring CO2 flux is turbulence theory, which holds that turbulence is a random, unsteady, chaotic, irregular flow state with countless vortices of different shapes and sizes. During the movement of turbulent flow, due to the mixing of the upper and lower air layers, various substances and energy can be well transported in the vertical direction. The calculation of the eddy covariance method is based on the assumption that there is no vertical air transport, that is, the average vertical component of the wind speed is 0. When the vertically transported substances include CO2, the average vertical flux FC of CO2 can be expressed as:

[0041]

[0042] Where: F C is the average vertical flux of CO2, in mg / m 2 )·s -1 ; w is the vertical component of wind speed, unit: m / s; ρ c is the density of CO2, in mg / m 3 ; is w and ρ c The average value of a product over a period of time.

[0043] Reynolds decomposition is a basic method for studying turbulent motion. This method decomposes the average value of the product of various relevant physical quantities (such as velocity, temperature, momentum) over a period of time in turbulent motion into the sum of the average value of the product of regular physical quantities and the average value of the product of irregular pulsation values. Reynolds decomposition of the average value of the product of w and ρc in equation (1) yields:

[0044]

[0045] Where: is the average vertical component of wind speed, in m / s; is the average value of CO2 density, in mg / m 3 is w and ρ c The average value of the product of the pulsation values ​​over a period of time.

[0046] Since the eddy covariance method uses the premise that the average vertical component of wind speed is 0, the average vertical flux of CO2 is 0, that is, formula (1) can be transformed as follows:

[0047] In order to meet the basic measurement assumptions and eliminate the influence of side wind on flux calculation, tilt correction is required. The commonly used method for tilt correction is to use the coordinate system transformation method, namely the secondary coordinate rotation (DR). The study adopted the secondary coordinate rotation method. After two rotation corrections, the average lateral wind speed and the average vertical wind speed were equal to zero, which basically eliminated the influence of the average vertical wind speed not being zero on the flux results. On this basis, the CO2 flux is corrected for water vapor flux and sensible heat flux, which can further eliminate the influence of inaccurate density caused by gas expansion or compression caused by heat flux and water vapor flux in the atmosphere. The study uses the Webb-Pearman-Leuning algorithm to correct the flux observation error caused by density changes.

[0048] In the field of atmospheric diffusion simulation, CFD overcomes the shortcomings of other heavy gas diffusion models such as box models and empirical models in heavy gas identification and simulation of physical effects such as gravity sinking, air entrainment, and gas cloud heating under complex terrain conditions, and can better describe the physical phenomena of heavy gas in atmospheric turbulent motion under complex terrain conditions. By treating the CO2 flux values ​​of multiple well-field source leakage in the CCUS storage area as the amount of CO2 leaked into the atmosphere from multiple point sources, a CFD model of the storage project area is established, which can effectively solve the diffusion simulation problem of multiple well-field source leakage areas in CCUS storage projects.

[0049] For CO2 emitted from wellbore sources in the storage area, the flow situation can be appropriately simplified based on the following assumptions: ① The gas is a continuous medium; ② The gas flow is an incompressible process; ③ The gas flow does not consider heat and mass transfer, phase change and chemical reaction; ④ The component transport model ignores other gases. The flow conservation equation can be simplified as follows:

[0050] Continuity equation:

[0051]

[0052] Momentum conservation equation:

[0053]

[0054] Energy conservation equation:

[0055]

[0056] Component transport equation:

[0057]

[0058]

[0059] In formula (4) to formula (8): ρ is density, unit: kg / m 3 ; t is time, unit s; v is velocity vector, unit m / s; p is pressure, unit Pa; τ is stress tensor; F is external force term; E is total energy, unit J; k eff is the effective thermal conductivity, unit is w / (m 2 ·K); T is temperature, unit K; h is the specific enthalpy of substance i, unit J / Kg; J i is the diffusion flux of substance i: R i is the net content of substance i in diffusion; S i is the source term of substance i; Y i is the mass fraction of substance i.

[0060] The application of CFD to construct a regional atmospheric CO2 diffusion model includes the following steps: first, the modeling process of the real terrain, which can be done by extracting the digital elevation model (DEM) elevation data from the map software GlobalMapper, then importing the data file into the 3D modeling software, and then exporting the established model file in the Rhino modeling software. The second is the setting process of the CFD calculation domain, including the definition of the leakage source, the definition of the air inlet and outlet surfaces, and the definition of the wall surface. Finally, the simulation grid is divided, and the divided grid file is imported into the fluid calculation software, such as the Fluent software for simulation calculation, and the diffusion behavior of CO2 is calculated using the component transport model.

[0061] In some possible implementation examples, a large-scale CO2 flooding and storage project demonstration is being carried out in an oil field in East China, involving more than 200 gas injection wells and production wells. The study selected this demonstration project as a case study object. First, the accuracy of the eddy covariance method was verified using the flux monitoring data carried out in the oil field. Secondly, on this basis, four well site leakage scenarios were designed, and a regional atmospheric diffusion model was constructed using CFD technology. Finally, the scenario-based CFD simulation analyzed the CO2 diffusion under various leakage scenarios.

[0062] The eddy covariance method was used to select 40 sets of data to calculate the CO2 flux, and the CO2 flux scatter plot was obtained with time as the horizontal axis and CO2 flux as the vertical axis. Figure 1). Statistics show that the maximum CO2 flux from 18:00 on a certain day to 16:00 on the next day is 1.2028 (mg / m2)·s-1, the minimum is -0.2934 (mg / m2)·s-1, the median is 0.1086 (mg / m2)·s-1, the average is 0.1866 (mg / m2)·s-1, and the variance is 0.1264 (mg / m2)·s-1. Comparison of 40 sets of CO2 flux data calculated from 18:00 on a certain day to 16:00 on the next day with the field measured data of the case oil field ( Figure 2 ), the fitting formula of CO2 flux and measured value is y=1.0107x+0.0137, the R2 value is 0.9519, the mean absolute error is 0.058806 (mg / m2)·s-1, and the root mean square error is 0.075958 (mg / m2)·s-1, indicating that the calculated value corresponds well to the measured value.

[0063] The changes in CO2 flux in the oil field storage area show that there is an obvious diurnal variation trend in the CO2 flux of the storage site. During the day, since the photosynthesis of plants is greater than the respiration, CO2 is absorbed and the flux has more negative values, indicating that no obvious leakage occurs; and the maximum flux value of 1.2 (mg / m2)·s-1 occurs at night, reflecting the release of CO2 by the respiration of animals and plants in the area at night.

[0064] In some possible embodiments, in order to study the impact of simultaneous leakage from multiple leakage sources on the region, the above-mentioned regional atmospheric CO2 diffusion simulation method is applied to the multi-well field leakage scenario, and a CFD regional diffusion model for the multi-well field leakage scenario is established to simulate the distribution of leaked CO2 in the well field area. The research simulation area includes 4 storage well fields, with a range of 5km×5km. The specific steps of the simulation are: use the online database ASTER GDEM in the GlobalMapper map software to extract the elevation model elevation data of 5km near the storage point. The highest altitude in the area is 14m. Import the elevation data into Sketchup and Rhino modeling software to complete the modeling. Export the established model file in the Rhino software, import it into ICEM (grid division software), and perform grid division. The entire model is a cubic area with a length and width of 5km and a height of 100m.

[0065] The positive direction of the x-axis is the north side; the positive direction of the y-axis is the east side, which is the main wind direction. The air enters from the west and flows out from the east. The z-axis is the vertical direction. The bottom and the remaining two sides are set as walls, and the top is set as an outlet for free outflow conditions. The meshing uses unstructured meshes to generate tetrahedral meshes, the bottom is set as the boundary layer, and the boundary layer encryption operation is performed. The final mesh number is 235×10 4 indivual.

[0066] The study simulated the most common natural conditions. The boundary condition set the CO2 inlet mass flow rate to 0.5 kg / s. In order to simulate the actual environmental wind speed conditions, the velocity distribution of the wind speed inlet boundary condition was set to follow the power law wind profile

[23] :

[0067]

[0068] Where: z is the height, unit is m; u is the wind speed at height z, unit is m / s; z r is the reference height, in m; u r For reference height z r The reference wind speed measured at the location, in m / s; α is the wind shear index, which is related to factors such as atmospheric stability and geographical environment.

[0069] The study adopted the parameter values ​​measured in the experiment, namely zr=5m, ur=5m / s, α=0.2.

[0070] Since the research is aimed at the diffusion of CO2 in the atmospheric environment, the outlet is set to a free outflow condition to ensure the free development of the heavy gas diffusion process. Since the calculation domain is large, it will not affect the flow field on the top and sides, so the top and sides can also be set to free outflow conditions. In the calculation, the pressure-based coupling solver is used to solve the time-averaged NS equations, the continuity equation and the turbulence additional equation using the second-order upwind scheme, and the coupled calculation of the velocity field and the pressure field uses the improved SIMPLE (pressure-based solution algorithm) algorithm.

[0071] Since no leakage has been found in the demonstration area of ​​the project, the study uses the scenario analysis method to set up multiple leakage scenarios, and uses the above-constructed model to simulate the regional atmospheric CO2 diffusion distribution of multiple well field leakage sources under different scenarios. In the basic scenario, it is assumed that leakage is detected in 4 well fields using the well field flux monitoring method. The 4 well fields are fitted to 4 cylindrical leakage points with a diameter of 5m at coordinates (750m, 850m), (2354m, 3564m), (3982m, 1942m), and (4573m, 3864m), which are numbered as leakage sources 1, 2, 3, and 4 respectively. The ambient temperature is set to 291K according to the field conditions, and the leakage rate is characterized by the mass flow rate of the CO2 inlet, which is set to 0.5kg / s. The wind direction along the positive direction of the y-axis represents the westerly wind. Since the CO2 diffusion scenario of the well field is different from the hazardous gas pipeline leakage scenario, it belongs to normal pressure leakage, the leakage rate is slow, but the leakage time is continuous, so the diffusion time is set to 5400s.

[0072] In some possible embodiments, two monitoring planes perpendicular to the horizontal plane are set to make a scatter plot of the CO2 mass fraction distribution ( Figure 3). The horizontal axis is the distance along the main wind direction, and the vertical axis is the CO2 mass fraction. It can be seen that when the height is 5m and the wind speed is 5m / s under the power law wind speed condition, the position of the monitoring surface 1 is x=850m plane ( Figure 3 a), the maximum CO2 mass fraction is only 0.0000019, indicating that the CO2 diffused to this plane has limited effect on the mass fraction. The reason for this is that it is far away from the diffusion source. On the monitoring surface 2, which is closer to the diffusion source, that is, the x=3564m plane ( Figure 3 b), the maximum value of CO2 mass fraction reached 0.000094, indicating that the distance from the diffusion source has a direct impact on the mass fraction of CO2. Figure 4 To monitor the mass fraction distribution of CO2 in the z-axis direction (vertical direction), that is, the diffusion of CO2 in the vertical direction, it can be seen that the mass fraction points in the z-axis direction are sparser than those in the y-axis direction, indicating that CO2 is difficult to diffuse over a large range in the vertical direction. The reason for this is that the density of CO2 is greater than that of air, and its diffusion in the vertical direction is more affected by gravity.

[0073] The CO2 mass fraction distribution diagram on the vertical plane can only show the distribution of the CO2 mass fraction on the monitoring surface, and it is difficult to distinguish the diffusion movement law of CO2 from each leakage source. Therefore, the cloud map of the CO2 leakage source is further analyzed. The cross-sectional cloud map of the diffusion source shows that during the initial period of escaping from the diffusion source to the surface, the diffusion trend of CO2 is uniformly diffused in all directions, but under the influence of wind speed, it changes to a downwind trend, and then maintains this trend to move downwind, and the mass fraction of CO2 gradually decreases from the diffusion source to the downwind direction.

[0074] Figure 5 is the CO2 mass fraction distribution cloud diagram of the leakage source in the plane parallel to the main wind direction, Figure 5 c and Figure 5d shows that CO2 has a tendency to diffuse to both sides of the diffusion source near the diffusion source, and the CO2 mass fraction stratification phenomenon only occurs in local locations. By comparing the CO2 mass fraction in the vertical direction, it can be seen that the closer to the ground, the higher the CO2 mass fraction. This is because CO2 is a heavy gas with a density greater than that of air. The CO2 mass fraction decreases with the increase of the downwind distance, which also reflects the transport effect of downwind atmospheric turbulence on CO2. Because CO2 is a heavy gas, the diffusion process of heavy gas can generally be divided into four stages

[17] : gas cloud formation stage, gravity sedimentation stage, air entrainment stage, and passive diffusion stage. The diffusion behavior of CO2 in the initial stage is mainly affected by inertia and initial momentum. When the initial momentum is exhausted, gravity becomes the main influencing factor, with two main effects: first, it forces the gas to settle to the ground and form accumulation on the ground; second, it causes the movement to be mainly along the surface. Thereafter, the converged gas cloud and the surrounding air undergo entrainment, resulting in a decrease in the overall gas density, and finally gradually transforming into a steady movement. From the cross-sectional view of the leakage source 1 ( Figure 5 a) It can be seen that CO2 is not continuously and evenly distributed. This is because in real terrain scenes, the terrain is not a space without obstacles. Since there is a hillside in front that is higher than the leakage source area, the diffusion trajectory of CO2 has shifted. Combining the distribution maps of each monitoring surface, it can be concluded that the coordinate of the farthest distance of CO2 diffusion in the main direction is 4581m, and the maximum value of CO2 mass fraction is 0.0001.

[0075] By viewing the distribution cloud map of the CO2 mass fraction in the entire well site area, we can intuitively analyze the area where the diffused CO2 has an impact in the well site. The top view of the CO2 mass fraction distribution cloud map is made for the diffusion time 600, 1800, 3600, and 5400s respectively ( Figure 6 ). Figure 6 The graph depicts the area where the CO2 mass fraction is as high as 0.001 and as low as 0 in the top view. The area with high CO2 mass fraction in the figure is mainly distributed along the y-axis direction. The mass fraction in the downwind direction is significantly higher than that in the lateral direction. This also shows that the diffusion trend of CO2 is directly affected by the main wind direction, and there is obvious deposition near the diffusion source. No cross-diffusion phenomenon was observed. This is because the distance between different diffusion sources exceeds the maximum diffusion distance of a single leakage source. Multi-source diffusion shows a similar movement law to single-source diffusion. The mass fraction distribution in the same horizontal or vertical plane is uneven. CO2 deviates to the left and right in the x-axis direction during diffusion. This is because the flow direction is changed by the terrain during diffusion

[24] . Figure 6 c It can be seen that at 3600s, the area with CO2 mass fraction higher than 0.001 is larger than that at other times. At 3600s, the area with CO2 mass fraction higher than 0.001 is the largest in the whole area, and the whole top view shows that the leakage source is 3 ( Figure 6c) The area downwind where the CO2 mass fraction is less than 0.001 reaches 800m. The reason for this is that it is in the entrainment stage of heavy gas diffusion, and the heavy gas entrains the surrounding gas, causing CO2 to accumulate locally. At 5400s, the range of CO2 mass fraction of 0.001 reached 200m, while the area with a mass fraction higher than 0.1 was only within 10m of the leakage source. It can be seen that in the current scenario, the leakage and diffusion of CO2 will not cause a large area of ​​dangerous areas, and the storage site can be considered safe. However, leakage for a certain period of time may promote the accumulation of CO2, and terrain conditions will promote the accumulation of CO2, which needs to be paid attention to. Since the surface leakage diffusion is a relatively long-term diffusion, the diffusion distribution of CO2 will change with time.

[0076] in conclusion:

[0077] 1) The CO2 leakage flux monitoring based on the eddy covariance method can reflect the surface source leakage flux of the entire well site and provide accurate data for large-scale CFD simulation; CFD simulation can reflect the complex terrain of the region and leakage in multiple well sites, thereby accurately reflecting the regional safety and environmental risks from well site leakage.

[0078] 2) The application results of the case study of an oil field in East China based on scenario analysis show that when the four leakage source wells are far apart, the wind speed is 5m / s, the ambient temperature is 291K, and the outlet is free flow, the diffusion danger range of CO2 is mainly concentrated within the radius of 10m from the leakage source, so the impact is small. In the case, the range of CO2 mass fraction of 0.001 at 5400s reaches 200m.

[0079] 3) In scenarios where the distance to the leak source is closer, the leak intensity is greater, the wind speed is higher, and the leak duration is longer, the regional diffusion of such multi-well field leaks may be longer and the CO2 mass fraction is higher, thus leading to certain safety and environmental risks.

[0080] It should be noted that, for the aforementioned product embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present invention is not limited by the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention. In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0081] In the several embodiments provided by the present invention, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the modules, which is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical or other forms.

[0082] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of software functional modules.

[0084] The embodiments of the present invention are described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the product of the present invention and its core idea. At the same time, for those skilled in the art, according to the product of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for simulating regional diffusion of CO2 non-point source leakage, characterized in that: The following steps are involved: Step 1: The real terrain modeling process can use the digital elevation model (DEM) elevation data extracted from the map software GlobalMapper; Step 2: Import the elevation data file into the 3D modeling software, and then export the created model file in the Rhino modeling software; Step 3: CFD calculation domain setting process, including the definition of leakage source, air inlet and outlet surfaces, and wall surface definition Step 4: The last step is to divide the simulation grid and import the divided grid file into the fluid calculation software.

2. A CO2 non-point source leakage regional diffusion simulation method according to claim 1, characterized in that: Step 4 also includes: importing into Fluent software for simulation calculation, and using the component transport model to calculate the diffusion behavior of CO2.

3. A method for simulating regional diffusion of CO2 non-point source leakage according to any one of claims 1-2, characterized in that: The fluid flow is simplified based on the following assumptions: the gas is a continuous medium; the gas flow is an incompressible process; the gas flow does not consider heat and mass transfer, phase change and chemical reaction; and the component transport model ignores other gases.

4. A method for simulating regional diffusion of CO2 non-point source leakage according to any one of claims 1-2, characterized in that: The CO2 leakage flux is monitored based on the eddy covariance method.

5. A method for simulating regional diffusion of CO2 non-point source leakage according to any one of claims 1-2, characterized in that: The flow conservation equation can be simplified as follows: Continuity equation: Momentum conservation equation: Energy conservation equation: Component transport equation: Where: ρ is density, unit: kg / m 3 ; t is time, unit s; v is velocity vector, unit m / s; p is pressure, unit Pa; τ is stress tensor; F is external force; E is total energy, unit J; k eff is the effective thermal conductivity, unit is w / (m 2 ·K); T is temperature, unit K; h i is the specific enthalpy of substance i, in J / Kg; J i is the diffusion flux of substance i; R i is the net content of substance i in diffusion; S i is the source term of substance i; Y i is the mass fraction of substance i.

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