A method and system for predicting the diffusion range of CO2 storage coupled with the mechanical damage of coal body

Abstract

The application discloses a CO2 storage diffusion range prediction method and system coupled with coal body mechanical damage, and relates to the technical field of coalbed methane exploitation and carbon dioxide geological storage. The method comprises the following steps: arranging monitoring equipment in a target coal seam, synchronously collecting coal body mechanical response data and fluid seepage diffusion data in the process of carbon dioxide fracturing operation; calculating a coal body mechanical damage factor based on the mechanical response data before and after fracturing, and constructing a mapping relationship between the factor and the coal body fracture development degree; introducing the mechanical damage factor to correct the coal seam permeability, and constructing a bedding and through-seam diffusion radius prediction model integrating the mechanical damage effect; acquiring the diffusion radius data measured on site to perform error checking and parameter calibration on the model, and outputting the corrected diffusion radius prediction value. By introducing the mechanical damage effect into the diffusion model, the application realizes accurate quantification of the carbon dioxide migration law of low-permeability coal seams, and significantly improves the accuracy and reliability of the evaluation of the permeability enhancement effect.

Description

Technical Field

[0001] This invention relates to the field of coalbed methane extraction and carbon dioxide geological storage technology, specifically to a method and system for predicting the CO2 storage and diffusion range coupled with coal body mechanical damage. Background Technology

[0002] Low-permeability coal seams in my country (permeability generally ranges from 0.1 to 1.0 × 10⁻⁶) -6 The proportion of coal seams with a diameter in the μm² range (over 50%) is significant. Implementing CO2-ECBM technology in these seams is not only a key means of achieving efficient deep gas control but also an important pathway to promote the large-scale application of carbon sequestration in coal mines. During CO2 injection, liquid CO2 undergoes phase change pressurization and low-temperature fracturing, resulting in significant mechanical damage to the coal body, manifested as decreased compressive strength, deterioration of elastic modulus, and expansion of the fracture network. This damage directly alters the connectivity and aperture of the pore-fracture structure within the coal body, thereby affecting the adsorption-desorption and diffusion transport patterns of CO2, ultimately determining its sequestration range and sweep efficiency within the coal seam.

[0003] Existing technologies for monitoring and predicting the diffusion range of CO2 in coal seams have the following main defects and shortcomings:

[0004] 1) Existing methods separate mechanical damage from the diffusion process, ignoring the impact of crack development caused by mechanical damage in the coal body on the diffusion channel, resulting in large deviations between the predicted results and the actual results.

[0005] 2) Existing technologies rely on monitoring data after CO2 injection to determine the diffusion range, which cannot be predicted in advance during the engineering design stage, and is not conducive to the optimization of the layout of extraction boreholes.

[0006] 3) Although existing research focuses on coal body damage, no quantitative standard for the degree of mechanical damage to coal body has been established, and the impact of damage on fracture development cannot be accurately characterized.

[0007] 4) Widely used diffusion models are mostly based on the assumption of ideal homogeneous porous media, failing to fully consider the characteristics of low-permeability coal seams, such as denseness, high closure of primary fractures, and strong heterogeneity. These models often neglect key control factors such as dynamic changes in porosity and fracture connectivity, resulting in predictions that fail to meet the accuracy requirements of field engineering.

[0008] 5) Existing technologies lack a process for comparing and verifying the predicted results with the actual diffusion range, which cannot ensure the reliability of the predictions. Summary of the Invention

[0009] The technical problem to be solved by this invention is how to accurately predict the diffusion range of CO2 in low-permeability coal seams before the implementation of CO2 injection projects.

[0010] This invention solves the above-mentioned technical problems through the following technical means: a method for predicting the CO2 sequestration and diffusion range coupled with coal body mechanical damage, comprising: S1. Install monitoring equipment in the target coal seam to simultaneously collect coal mechanical response data and fluid seepage diffusion data during the carbon dioxide fracturing operation. S2. Based on the mechanical response data before and after fracturing, calculate the coal body mechanical damage factor and construct the mapping relationship between the mechanical damage factor and the degree of coal body fracture development. S3. The mechanical damage factor is introduced to correct the coal seam permeability, and a prediction model for the diffusion radius along and across the seam that integrates the mechanical damage effect is constructed. S4. Obtain the measured diffusion radius data on site, verify the error of the output of the prediction model, and calibrate the model parameters based on the verification results, and output the corrected diffusion radius prediction value.

[0011] Furthermore, S1 includes: Construct CO2 fracturing holes, in-seam monitoring holes, and cross-seam monitoring holes in the target coal seam; Add intrinsically safe acoustic emission instrument for mining, intrinsically safe borehole stress sensor for mining, and intrinsically safe gas flow meter for mining; The ringing count and energy changes of coal seam fracture development were collected using an intrinsically safe acoustic emission instrument for mining. The internal stress of the coal body before and after fracturing is monitored in real time using an intrinsically safe borehole stress sensor for mining. The instantaneous CO2 flow rate is collected in real time using an intrinsically safe gas flow meter for mining applications. Establish a communication connection between each acquisition device and the mine explosion-proof data acquisition instrument via an intrinsically safe data cable or wireless transmission module; and set the data acquisition frequency of each acquisition device.

[0012] Furthermore, S2 includes: The initial uniaxial compressive strength of the coal body before fracturing was obtained by monitoring the initial internal stress of the coal body using an intrinsically safe borehole stress sensor for mining, combined with uniaxial compression tests of standard coal samples from the same coal seam. and initial elastic modulus The initial porosity was obtained based on laboratory measurement data from borehole core sampling and acoustic emission data. ; After CO2 fracturing, the initial stress inside the coal seam after fracturing is monitored using an intrinsically safe borehole stress sensor. Combined with uniaxial compression tests of standard coal samples from the same coal seam, the uniaxial compressive strength after fracturing is indirectly obtained. and elastic modulus Based on laboratory measurements from core drilling and acoustic emission data, the porosity after fracturing was obtained. ; The weighting factor for uniaxial compressive strength was determined based on a combination of laboratory orthogonal experiments and field tests. and elastic modulus weighting factor Through formula Calculate mechanical damage factor ,in, ; By acquiring the ringing counts and energy changes of the coal body before and after fracturing using an acoustic emission instrument, the fracture connectivity before fracturing can be obtained through inversion. With post-fracture fissure connectivity Calculate the fissure connectivity increment Based on laboratory orthogonal test and field test data, the least squares linear regression method was used to fit and obtain the mechanical damage factor. and Relationship: .

[0013] Furthermore, the correction of coal seam permeability based on the aforementioned mechanical damage factor includes: The dynamic viscosity of CO2 is obtained using standard physical properties under corresponding downhole temperature and pressure conditions. The initial permeability of the coal seam was determined through in-situ gas injection tests. The wellbore pressure is determined based on a comprehensive analysis of coal seam stress, coal mechanical parameters, and laboratory fracture pressure test results. ; Introducing the mechanical damage factor Establish a penetration rate correction model ,in, This represents the damage sensitivity coefficient.

[0014] Furthermore, the construction of the prediction model for the diffusion radius along and across layers of mechanical damage includes: Formula for predicting the diffusion radius of bedding planes: ,in, To predict the time, For low-permeability coal seams, the correction factor is... The coefficient of volume expansion of CO2; Formula for predicting the diffusion radius across layers: ,in, This is the cross-layer diffusion attenuation coefficient; Introducing a temperature correction factor The formulas for predicting diffusion radius along bedding planes and across bedding planes are modified, and the modified formulas are as follows:

[0015] .

[0016] Furthermore, in S4, the prediction time is set. Consistent with the engineering design monitoring cycle, the input parameters are used to calculate the predicted value of the bedding diffusion radius. and predicted cross-layer diffusion radius The actual diffusion radius along the bedding plane was obtained by multi-parameter monitoring of temperature, pressure, and concentration. and the actual diffusion radius of the layer Calculate the prediction error of the diffusion radius along the layer and the prediction error of the diffusion radius across the layer. When the prediction error exceeds the preset threshold, adjust the low permeability coal seam correction coefficient, the diffusion attenuation coefficient across the layer, or the temperature correction coefficient in S3 until the error is not higher than the preset threshold.

[0017] Furthermore, it also includes: associating and archiving the collected data and verification results to generate an associated data table containing mechanical damage factors, predicted radius and actual radius; and using a two-plug-one-injection process to permanently seal the monitoring holes.

[0018] This invention also provides a CO2 sequestration and diffusion range prediction system coupled with coal mechanical damage, comprising: The acquisition module is used to deploy monitoring equipment in the target coal seam to simultaneously acquire coal mechanical response data and fluid seepage diffusion data during the carbon dioxide fracturing operation. The damage quantification module is used to calculate the coal body mechanical damage factor based on the mechanical response data before and after fracturing, and to construct a mapping relationship between the mechanical damage factor and the degree of coal body fracture development. The model building module is used to introduce the mechanical damage factor to correct the coal seam permeability and build a prediction model for the diffusion radius along and across the seam that integrates the mechanical damage effect. The prediction and calibration module is used to acquire the diffusion radius data measured on-site, verify the error of the prediction model's output, and calibrate the model parameters based on the verification results, outputting the corrected predicted diffusion radius value.

[0019] The present invention also provides a processing device, including at least one processor and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor can execute the above-described method steps by calling the program instructions.

[0020] The present invention also provides a computer-readable storage medium storing computer instructions that cause the computer to perform the above-described method steps.

[0021] The advantages of this invention are: This invention overcomes the limitation of existing technologies that can only measure diffusion range after the fact by incorporating coal body mechanical damage into diffusion range prediction, providing advance support for the layout of gas drainage boreholes in the engineering design stage. Through damage factor quantification and multi-parameter coupling models, the prediction error is controlled within a manageable range, adapting to the characteristics of low-permeability coal seams. Based on the existing monitoring borehole system in coal mines, only a few additional dedicated sensors are needed, and it is fully compatible with CO2 fracturing and gas drainage equipment, without the need to restructure the engineering process, thus reducing construction costs. A clear quantitative relationship is established between mechanical damage, fracture development, and permeability, providing a reusable calculation standard for similar projects. Advance prediction of diffusion range can optimize the density and location of gas drainage boreholes, reduce ineffective borehole construction, and improve drainage efficiency, taking into account both carbon sequestration safety and coal mine economic benefits. Attached Figure Description

[0022] Figure 1 This is a flowchart of a method for predicting the CO2 sequestration and diffusion range coupled with mechanical damage to coal, according to Embodiment 1 of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1 like Figure 1 As shown, a method for predicting the CO2 sequestration and diffusion range coupled with coal body mechanical damage is presented. Taking the B6 coal seam in a certain mining area as the test object, the specific implementation steps are as follows: S1. Install monitoring equipment in the target coal seam to simultaneously collect coal mechanical response data and fluid seepage diffusion data during the carbon dioxide fracturing operation.

[0025] Specifically, the B6 coal seam in a certain mining area will be selected as the test area. The permeability of this coal seam is 0.3~0.8×10⁻⁶. -6 μm 2 The dip angle is 0°, the occurrence is stable, there is no mining impact within 50m of the test area, and no regional gas control measures have been taken.

[0026] The mechanical damage-diffusion parameter collaborative acquisition system is based on one CO2 fracturing hole (fracturing 3#), one set of in-seam monitoring holes (inspection 1#~inspection 16#), and one set of cross-seam monitoring holes (K1#~K8#). It adds an intrinsically safe acoustic emission instrument for mining, an intrinsically safe borehole stress sensor for mining, and an intrinsically safe gas flow meter for mining to adapt to the characteristics of long in-seam diffusion and difficult cross-seam diffusion in this coal seam.

[0027] CO2 fracturing hole parameters: hole diameter 113mm, hole depth 140m, adopting "bag-type" sealing technology, with an effective sealing depth of 26m.

[0028] The bedding monitoring wells are symmetrically arranged on both sides of the fracturing well, with a total of 16 wells. For specific parameters, please refer to Table 1.

[0029] Eight cross-layer monitoring holes are vertically arranged above and below the fracturing holes. For details, please refer to Table 2.

[0030] Table 1. Detailed Parameter Table of In-Bedding Monitoring Wells (Inspection 1#~Inspection 16#)

[0031] Table 2 Detailed Parameter Table of Through-Layer Monitoring Holes (K1#~K8#)

[0032] The drilling operation uses a common anchor drilling machine used in coal mines, and the drilling accuracy meets the following requirements: hole diameter error ≤ ±1.5mm, hole depth deviation ≤ ±0.3m.

[0033] After the sealing operation is completed, the area is left to stand for 24 hours. An airtightness test is then conducted to confirm that there is no gas leakage, ensuring the accuracy of the monitoring.

[0034] New equipment deployment: Two intrinsically safe acoustic emission meters (sampling frequency 1MHz) for mining are deployed within a 20m radius around the fracturing hole; an intrinsically safe borehole stress sensor for mining (range 0~100MPa, accuracy ±0.05MPa) is installed 4m from the sealing section of the designated monitoring hole.

[0035] The collaborative monitoring components include a temperature sensor, a pressure transmitter, a gas chromatograph, a seepage parameter tester, an intrinsically safe acoustic emission instrument for mining, an intrinsically safe borehole stress sensor for mining, and an intrinsically safe gas flow meter for mining. All components meet the explosion-proof requirements for underground coal mines.

[0036] Temperature sensor installation: Measurement range -50~60℃, accuracy ±0.1℃, installed 4m from the sealing section of each monitoring hole, and secured with fixing clamps.

[0037] Pressure transmitter installation: measuring range 0~40MPa, accuracy ±0.01MPa, installed inside the monitoring port, close to the sealing joint.

[0038] Gas chromatograph installation: detection accuracy ±0.01%, gas is taken through the reserved sampling interface at the monitoring orifice, and equipped with an intrinsically safe sampling pump for mining.

[0039] Installation of seepage parameter tester: Fixed at 5 m beside the fracturing hole to measure CO2 seepage velocity.

[0040] Installation of intrinsically safe borehole stress sensor for mining: Measurement range 0~100MPa, accuracy ±0.05MPa, installed at 4m of the sealed section of the designated monitoring hole to monitor the internal stress of the coal body before and after fracturing in real time.

[0041] Installation of intrinsically safe acoustic emission instrument for mining: Deployed within 20m around the fracturing hole, with a sampling frequency of 1MHz, to capture the ringing count and energy changes of coal seam fracture development.

[0042] Installation of intrinsically safe gas flow meter for mining: Installed in series in the extraction pipeline at the orifice of each in-seam monitoring hole and cross-seam monitoring hole to collect the instantaneous CO2 flow rate in real time.

[0043] After component installation, each component was tested individually: the data transmission delay of each monitoring device was ≤1s, the interface was sealed without leakage, the device was in stable working condition, and the data acquisition was continuous and reliable.

[0044] The data acquisition and processing module includes a mining explosion-proof data acquisition instrument and an industrial computer.

[0045] The industrial computer has built-in dedicated predictive analysis software, which pre-stores quantitative formulas for coal mechanical damage factors, permeability correction models, and diffusion radius prediction models.

[0046] Signal connection: The temperature sensor, the intrinsically safe borehole stress sensor, and the intrinsically safe gas flow meter are connected in series with the data acquisition instrument via an explosion-proof data cable. The data acquisition instrument communicates with the industrial computer via an RS485 interface.

[0047] Wireless transmission: Gas chromatograph, acoustic emission instrument, and seepage parameter tester transmit data through an intrinsically safe wireless transmission module for mining applications; Power supply connection: The mine uses a 660V to 220V explosion-proof power supply, which shares the power supply line with the CO2 fracturing equipment; System testing: Data acquisition intervals meet requirements (temperature, pressure, and mechanical parameters 1s / time; concentration 20min / time; acoustic emission data 1MHz sampling); data storage function is normal, with no data loss.

[0048] S2. Based on the mechanical response data before and after fracturing, calculate the mechanical damage factor of the coal body and construct the mapping relationship between the mechanical damage factor and the degree of coal body fracture development.

[0049] Specifically, based on the theory of thick-walled cylinders in elasticity, the laboratory calibration method for coal mechanics, and the evolution of acoustic emission energy, the uniaxial compressive strength, elastic modulus, porosity, fracture connectivity, CO2 dynamic viscosity, and initial permeability of the coal seam are obtained. The specific implementation process is as follows: (1) Uniaxial compressive strength and elastic modulus were obtained by inversion based on the thick-walled cylinder theory of elasticity and the laboratory calibration method of coal mechanics. First, a laboratory calibration process was conducted to obtain the inherent property parameters of the coal body. Standard coal samples from the same coal seam in the test area were taken, and multiple sets of uniaxial compression tests were carried out. In the experimental data processing, the stress-strain constitutive relationship of the coal body during the elastic deformation stage was determined: During the elastic deformation stage of coal, stress and strain exhibit a strictly linear relationship, and the calculation formula is as follows:

[0050] in, This represents the axial stress of the coal body (MPa). The elastic modulus of coal (GPa). This represents the axial strain of the coal body.

[0051] The elastic modulus of standard coal samples was obtained through multiple sets of experiments. Uniaxial compressive strength (Peak stress at coal sample failure), and the strength-modulus correlation coefficient with the coal seam was obtained by fitting. :

[0052] This coefficient As an inherent property of the coal body in the same coal seam, it is not affected by the on-site stress environment and is the core calibration parameter for on-site inversion.

[0053] The effectiveness of borehole stress monitoring was verified and inversion was performed based on the thick-walled cylinder theory of elasticity. The coal body surrounding the underground borehole can be equivalently represented as a thick-walled cylinder. The formula for axial stress concentration at the borehole wall is as follows:

[0054] in, This represents the axial stress at the borehole wall (i.e., the measured value). This refers to the stress in the original coal seam rock.

[0055] During on-site construction, boreholes were drilled in the test area and intrinsically safe borehole stress sensors for mining were installed. These intrinsically safe borehole stress sensors are integrated stress-strain monitoring devices. After the data stabilized, the real-time axial stress of the coal at the borehole wall was measured. and the axial strain of the coal body at the corresponding location The initial in-situ stress of the coal seam is calculated using the above formula. Assuming the coal body is in the elastic deformation stage under the original rock stress, Substituting the stress-strain constitutive relation calibrated in the laboratory, the elastic modulus of the in-situ coal body is obtained by inversion. : Finally, the strength-modulus correlation coefficient determined in the laboratory was used. The elastic modulus of the in-situ coal body was obtained by combining the inversion. The uniaxial compressive strength of the in-situ coal body was calculated. : .

[0056] (2) In terms of porosity inversion, porosity was accurately obtained through laboratory core sampling benchmark determination and in-situ dynamic inversion of acoustic emission parameters. In accordance with GB / T23561.4-2009, core samples were taken from the same coal seam in the test area, and laboratory porosity determination was carried out to obtain the initial benchmark porosity of the coal body. The calculation formula is as follows:

[0057] in, The laboratory reference porosity of the coal sample is expressed in % (%). This is the dry density of the coal sample, in g / cm³. 3 ; This is the true density of the coal sample, expressed in g / cm³. 3 .

[0058] Based on in-situ dynamic inversion of acoustic emission parameters, the porosity change of coal under stress is directly related to the degree of internal fracture development. The cumulative acoustic emission energy is the core indicator for quantifying the scale of coal fracture development, and there is a clear linear correlation between the two. This correlation model is calibrated by laboratory acoustic emission tests on the same coal seam.

[0059] Calculate the in-situ damage variable of coal body based on the cumulative energy of acoustic emission. :

[0060] in, The cumulative energy of acoustic emission monitored on-site. The total acoustic emission energy is the cumulative energy of the coal sample from the same coal seam during the entire uniaxial compression experiment.

[0061] Based on coal and rock damage mechanics, porosity and damage variables have a positive linear correlation, and the inversion formula is as follows:

[0062] in, The porosity of the coal seam in situ is expressed as % (%). The baseline porosity is measured in the laboratory, and the unit is % . The porosity-damage correlation coefficient was obtained through synchronous acoustic emission-porosity tests in the same coal seam laboratory. For coal body damage variables.

[0063] (3) Since the dynamic viscosity of CO2 is sensitive to changes in temperature and pressure, especially under high pressure conditions, the standard physical property values ​​under the corresponding downhole temperature and pressure conditions are used for calculation.

[0064] The following two methods are generally used to calculate the dynamic viscosity of CO2: Method 1: Utilize the REFPROP property calculation software officially released by the National Institute of Standards and Technology (NIST). Using the software's built-in high-precision multi-parameter equation of state, input the downhole temperature and pressure parameters obtained through real-time monitoring, and directly output the CO2 dynamic viscosity value under that operating condition. This method offers high accuracy. Method 2: The industry-standard Arnold gas viscosity formula is used, combined with a high-pressure correction term for calculation. First, the basic viscosity at normal pressure is calculated using the Arnold formula. Then, a correction factor is introduced based on the high-pressure parameters measured downhole, and the dynamic viscosity of CO2 is directly calculated using temperature and pressure parameters.

[0065] (4) In terms of fracture connectivity inversion, standard coal samples from the same coal seam in the test area were taken and uniaxial compression tests were carried out. The cumulative acoustic emission energy of the coal sample during the entire fracture process was collected simultaneously. After the experiment, the fracture connectivity under the corresponding conditions was determined by industrial CT scanning. Linear regression fitting was performed on the collected data to establish a baseline correlation model between cumulative acoustic emission energy and fracture connectivity.

[0066] in, The coal seam fracture connectivity rate. Accumulated energy for acoustic emission; , These are the fitting coefficients for calibrating coal seam coal samples. Values , Values And the model's coefficient of determination .

[0067] Before fracturing operations, acoustic emission instruments are used to collect signals from the original coal seam to obtain the initial accumulated energy. And ring count as baseline data. Substituting into the baseline correlation model, the fracture connectivity before fracturing is obtained by inversion. CO2 phase change fracturing was implemented, and acoustic emission was used for continuous monitoring, recording the total cumulative energy after fracturing was completed. .Will Substituting into the model, the fracture connectivity after fracturing is obtained by inversion. .

[0068] In this embodiment, initial data acquisition involves measuring the initial internal stress and corresponding strain values ​​of the coal body using an intrinsically safe borehole stress sensor. This data is then combined with the stress-strain constitutive relationship and strength-modulus correlation coefficient calibrated from uniaxial compression tests of standard coal samples from the same coal seam to invert and obtain the initial uniaxial compressive strength. Initial elastic modulus Based on laboratory measurements from borehole core samples and acoustic emission data, the initial porosity was obtained through inversion. By using an acoustic emission instrument to obtain the ringing counts and energy changes of the coal body before fracturing in a low-temperature region, the fracturing connectivity rate before fracturing can be obtained through inversion. The dynamic viscosity of CO2 was obtained using standard physical property values ​​under corresponding downhole temperature and pressure conditions. The initial permeability of the coal seam was determined using standard coal samples from the same coal seam. .

[0069] CO2 fracturing operation parameters: Liquid CO2 is injected into the fracturing hole, with a cumulative injection volume of 3.51 m³. 3 The wellbore pressure is 3.83 MPa.

[0070] The entire fracturing process was monitored: there were no abnormal leaks, the orifice pressure rose steadily, and the injection valve was closed after fracturing was completed.

[0071] Monitoring start-up timing: The data acquisition instrument and industrial computer are started immediately after fracturing is completed, and the monitoring cycle is 60 days.

[0072] Mechanical damage parameter acquisition: The internal stress of the coal body after fracturing was measured by an intrinsically safe borehole stress sensor used in mining. Combined with the stress-strain constitutive relationship and strength-modulus correlation coefficient calibrated by uniaxial compression tests of standard coal samples from the same coal seam, the uniaxial compressive strength of the coal body after fracturing was obtained by inversion. Post-fracture elastic modulus The acoustic emission instrument recorded a 32% increase in cumulative ring counts. By acquiring the ring counts and energy changes after coal fracturing in a low-temperature region using the acoustic emission instrument, the fracture connectivity rate after fracturing was obtained through inversion. .

[0073] Coal body and seepage parameter acquisition: Based on laboratory measurement data from borehole core sampling and acoustic emission data acquisition, the porosity after fracturing was obtained by inversion. The seepage parameter tester records the changes in CO2 seepage velocity in real time, with an average seepage velocity of 0.008 m / d.

[0074] Auxiliary monitoring requirements: Scan the coal wall 50m around the fracturing hole with an infrared thermal imager at 9:00 every day and record the temperature distribution cloud map.

[0075] Monitoring and quality control: Check the component connection status once a week, remove 2 sets of sensor faulty and abnormal data, and the integrity of effective data reaches 97%.

[0076] Data recording: The industrial computer automatically generates a five-dimensional data table of "time-mechanical damage parameters-porosity-fracture connectivity-seepage velocity" and backs it up daily.

[0077] Quantifying Coal Mechanical Damage Factors and Constructing Predictive Models The raw data within the effective monitoring period are screened, and extreme values ​​and abnormal fluctuations are removed, while continuous and stable monitoring sequences are retained.

[0078] Based on the joint calibration of laboratory orthogonal experiments and field engineering tests, the uniaxial compressive strength weighting factor was determined using the least squares error fitting method. and elastic modulus weighting factor Mechanical damage factor Used to quantify the degree of mechanical property degradation after coal fracturing, including uniaxial compressive strength Elastic modulus These are two core characterization parameters of coal body mechanical damage, and their weighting factors. , These parameters represent the respective contributions of each to the overall damage level of the coal body. The core objective of the calibration is to obtain the optimal weight combination through fitting experimental data, ensuring the highest degree of matching between the calculated damage factor and the actual damage level of the coal body, while minimizing calculation errors.

[0079] The specific implementation process is as follows: The basic weighting interval is obtained based on orthogonal experiments in the laboratory. This step strictly follows the relevant national standards for the determination of the physical and mechanical properties of coal and rock. First, raw coal from the B6 coal seam in the same test area is taken and prepared... Twenty-five standard cylindrical coal samples (50 mm × 100 mm) were used to cover different degrees of primary fracture development, ensuring sample representativeness. An orthogonal experiment was designed: "uniaxial compressive strength degradation rate" and "elastic modulus degradation rate" were selected as two core factors, with five levels for each factor (0, 5%, 10%, 20%, 30%, covering the full range of coal body damage in actual engineering). The L-shaped test was designed... 25 (5 2 Orthogonal experimental design.

[0080] Uniaxial compression tests were conducted on each group of coal samples, and acoustic emission data were collected simultaneously. The cumulative acoustic emission energy was used as the benchmark value for the actual damage degree of the coal body. The compressive strength degradation rate, elastic modulus degradation rate, and corresponding actual damage value were recorded for each group of coal samples. With the goal of minimizing the average relative error between the calculated damage value and the measured acoustic emission damage value, a multiple linear regression fitting was used to obtain... The basic range is 0.55~0.65. The baseline range is 0.35~0.45, which clarifies the contribution range of the two parameters to coal body damage.

[0081] The final optimal weights were determined based on joint calibration from field engineering tests. After determining the basic weighting interval in the laboratory, the final calibration needs to be performed in conjunction with on-site engineering data.

[0082] Field test design: Three parallel test borehole groups were set up in the same coal seam in the test area. Each group included one fracturing hole and eight monitoring holes. The engineering geological conditions and fracturing parameters were completely consistent with those in this embodiment. On-site data acquisition: CO2 fracturing tests were conducted for each group of experiments, and three types of core data were collected simultaneously: uniaxial compressive strength of the coal body before and after fracturing (laboratory test by borehole coring); elastic modulus (in-situ inversion by borehole stress sensor); and actual damage verification data of the coal body (actual damage range inverted by borehole coring porosity test, acoustic emission monitoring data, and gas drainage effect inversion). Optimal weight fitting: Based on the weight intervals obtained in the laboratory, different weights are substituted... , Combination (step size 0.05, satisfying) The theoretical damage value is calculated; then compared with the actual damage value measured on-site. The objective function is to minimize the average relative error between the theoretical and measured damage values. The least squares method is used for fitting, and the optimal weight combination is finally determined. , .

[0083] The weighting factor for uniaxial compressive strength was determined based on a combination of laboratory orthogonal experiments and field tests. and elastic modulus weighting factor Using the measured damage degree of coal body acoustic emission as the benchmark, and taking the minimum average relative error between the theoretical damage value and the measured damage value as the objective function, the optimal weight combination is obtained by fitting using the least squares method; through the formula Calculate mechanical damage factor ,in, Mechanical damage factor The value range is 0~1. The larger the value, the more severe the coal seam damage. Substituting the data yields... .

[0084] Establishing a correlation between damage factor and fracture connectivity: Initial values ​​of fracture connectivity were obtained using an acoustic emission instrument. and the value after cracking Calculate the fissure connectivity increment Based on laboratory orthogonal tests and field tests conducted under the same coal seam and engineering conditions as in this embodiment, the least squares linear regression method was used to fit and obtain the mechanical damage factor D and The correlation is as follows. The specific implementation conditions and process are as follows: The test samples were taken from the B6 coal seam of the same coal seam as in this embodiment, and 25 groups were prepared. A standard raw coal sample of 50 mm × 100 mm was used; the test environment was constant temperature and humidity at 20±2℃, and an RMT-150C rock mechanics testing machine was employed. The results were compared with those obtained using a similar acoustic emission instrument in the field. 25 (5 2 An orthogonal experiment was conducted, setting two factors—compressive strength and elastic modulus degradation rate—at five levels (0–30%). Stress-strain and acoustic emission data were collected synchronously throughout the experiment, and the increase in fracture connectivity under corresponding damage was retrieved. The field engineering experiment was conducted in the same coal seam within the test area. The test location was the same working face, B6 coal seam, as in this embodiment. A test borehole group was set up, and the borehole parameters, CO2 fracturing process, monitoring equipment model, data acquisition frequency, and 60-day monitoring cycle were completely consistent with this embodiment. The geological conditions, geostress environment, and construction parameters of the test area were unified, ensuring the uniqueness of the test variables and the complete comparability of the data with this embodiment.

[0085] Based on the standardized laboratory and field tests described above, multiple sets of corresponding data were collected, including the increase in fracture connectivity measured in the laboratory and the mechanical damage factor obtained from field inversion. The mechanical damage factor was obtained through fitting. and Relationship: The coefficient 0.8 is a fitting coefficient obtained by least squares linear regression based on laboratory orthogonal experiments and multiple sets of field experimental data, reflecting the rate of change of coal fracture connectivity with mechanical damage factor.

[0086] Determine basic diffusion parameters: Obtain CO2 dynamic viscosity using standard physical properties under corresponding downhole temperature and pressure conditions; directly determine initial coal seam permeability through in-situ gas injection tests. The wellbore pressure is determined based on a comprehensive analysis of coal seam stress, coal mechanical parameters, and laboratory fracture pressure test results. The value is taken between the critical fracturing pressure of the coal seam and the safe breaking pressure of the roof and floor. In practice, the critical fracturing pressure (lower limit of fracturing initiation) of the coal seam and the safe breaking pressure of the roof and floor (lower limit of fracturing initiation) are obtained through in-situ stress testing and laboratory fracturing pressure tests. Based on the above theoretical calculations and experimental data, combined with engineering practice experience, the final wellbore pressure is determined. This pressure value is strictly limited to the critical fracturing pressure of the coal seam and the safe fracturing pressure of the roof and floor. This ensures that the fracturing operation can effectively overcome the strength of the coal seam and form a complex fracture network to achieve the fracturing and permeability enhancement effect, while strictly avoiding the safety risks caused by damage to the roof and floor due to excessive pressure. In this embodiment, through the above comprehensive analysis method, the optimal wellbore pressure was finally determined to be... MPa.

[0087] S3. Mechanical damage factors are introduced to correct the coal seam permeability, and a prediction model for the diffusion radius along and across layers that integrates mechanical damage effects is constructed.

[0088] Specifically, permeability correction: Considering the effect of mechanical damage on permeability, a mechanical damage factor is introduced to establish a permeability correction model: ,in, The initial permeability of the coal seam was determined through in-situ gas injection tests. The damage sensitivity coefficient was determined based on laboratory orthogonal experiments. The baseline range is 2.0~3.0. Combined with field tests and calibration, the optimal value was obtained by fitting a model with the goal of minimizing the error between the predicted and measured diffusion radius. Substituting the data yields... .

[0089] Based on Darcy's law of seepage and the mass conservation equation, a prediction model for the diffusion radius along and across bedding planes is derived. To address the actual differences between along-bedding and cross-bedding seepage in low-permeability coal seams, a correction coefficient is introduced for optimization. (1) Darcy's Law Volumetric Flow Rate Darcy's law and volumetric flow rate formula are known. ,in, Injecting flow into CO2 For coal permeability, The cross-sectional area of ​​the seepage flow. For seepage pressure difference, CO2 dynamic viscosity, Let be the seepage path length. Consider radial seepage in cylindrical coordinates. Assume the seepage region is cylindrical, and the gas flows from a radius of... Flow towards the wellbore (wellbore radius is) ), in radial distance Above, the pressure difference is Darcy's law can then be written as For cylindrical seepage, the cross-sectional area ,in, Where is the thickness of the seepage layer; then ; after sorting, we can obtain .

[0090] (2) Integral operations Integrating both sides of the above equation, the integration interval starts from the wellbore radius. To radial diffusion radius (Pressure from) arrive ),Right now =- Integral on the left: Integral on the right: - Assuming For wellbore pressure, For boundary pressure, .

[0091] Based on the porosity of coal after fracturing and fracture connectivity after fracturing According to the law of conservation of mass, the volume of CO2 injected is then... ,in, To inject traffic, t To predict the time, The volume expansion coefficient of CO2 (obtainable from a table) is used in this embodiment. .Will use Substitute the expressions into the integral: = .Will = Substitution The results are as follows: Further simplified to .

[0092] Assuming the boundary pressure extending to the furthest point is the original formation pressure ( =0), that is = ,but Solving for it yields... Because of the well radius (0.0565m) is much smaller than the diffusion radius. (Project range 20~50 m) The value of is in the range of 5.8 to 6.6, and it is an approximate constant. Let , with coefficient 2 and By combining and calibrating through field tests, the correction coefficient for low-permeability coal seams was obtained as follows: Because low-permeability coal seams have a significant bedding structure, the resistance to cross-seam seepage is much greater than the resistance to in-seam seepage. Therefore, a method is introduced... Correction coefficient for low permeability coal seams After applying attenuation correction, the translaminar diffusion attenuation coefficient is: ,in, It is a constant, calculated based on field test data.

[0093] Diffusion range prediction: Set prediction time sky.

[0094] Formula for predicting the diffusion radius of bedding planes: .in, This is a correction factor for low-permeability coal seams. .

[0095] Formula for predicting the diffusion radius across layers: .in, is the diffusion attenuation coefficient across layers. = .

[0096] To address the impact of throttling and cooling during CO2 injection on seepage characteristics, a temperature correction factor is introduced. The formula for predicting diffusion radius along / across bedding planes is revised overall. The revised formula is as follows:

[0097]

[0098] Among them, temperature correction coefficient Based on CO2 thermobaric property data and field test results, interval calibration was performed when the temperature... hour, ;when and hour, =1.0; when hour, =0.98; The corrected model can effectively adapt to the low-temperature environment of CO2 phase change.

[0099] Final bedding prediction radius Cross-layer prediction radius .

[0100] S4. Obtain the measured diffusion radius data on site, verify the error of the output of the prediction model, and calibrate the model parameters based on the verification results, and output the corrected diffusion radius prediction value.

[0101] Specifically, on-site verification involved a gradient monitoring system deployed around the fracturing borehole. Monitoring boreholes with a spacing of 5 m were arranged along the bedding plane and across the formation, and temperature sensors, pressure transmitters, and gas chromatographs were installed inside the boreholes to ensure complete coverage of the potential diffusion area. Based on the typical physical characteristics of downhole CO2 diffusion, the following thresholds were set: temperature threshold ≤0℃ (low-temperature influence zone) or a decrease in temperature ≥2℃ from the background temperature; pressure threshold ≥0.05 MPa increase from the original formation pressure; and gas concentration threshold ≥0.05% CO2 volume fraction. When determining the diffusion radius, the criterion was that "any monitoring parameter exceeds the set threshold and the data remains stable for 3 consecutive days." Based on this criterion, the location of the furthest compliant monitoring borehole in the bedding plane and across the formation was determined. The precise radius value of the diffusion boundary, i.e., the actual diffusion radius along the bedding plane, was obtained by fitting the data using linear interpolation. and the actual diffusion radius of the layer In this embodiment, the actual diffusion radius along the bedding plane is... Actual diffusion radius across layers .

[0102] Error Calculation: Calculate the prediction error of the bedding diffusion radius. Error in predicting the diffusion radius across layers If the prediction error Adjusting the model , , Adjust the correction factor until the error is ≤8%.

[0103] bedding prediction error Trans-layer prediction error The prediction errors are all ≤8%.

[0104] Engineering application: Based on the prediction results, the layout of gas drainage boreholes was optimized. Drainage holes were arranged at 30m intervals along the stratum and at 20m intervals across the stratum, reducing the number of invalid boreholes by 3.

[0105] Output results: Generate a diffusion range prediction report, specifying the predicted radius, error range, and optimization suggestions for extraction boreholes for both in-strata and cross-strata diffusion.

[0106] The collected data and verification results are linked and archived, and the sealing operation of the monitoring borehole is completed.

[0107] Stop monitoring: After the prediction verification is completed, stop the operation of the data acquisition system according to the procedure.

[0108] Equipment recycling: Recycling is carried out in the following order: "acoustic emission instrument → intrinsically safe borehole stress sensor for mining → temperature / pressure transmitter → gas flow meter → data acquisition instrument → industrial computer". After cleaning, the components are stored separately.

[0109] Borehole sealing: All monitoring boreholes are permanently sealed using a "two-plug-one-injection" process to prevent gas leakage or CO2 escape.

[0110] Data archiving: Organize and archive initial parameters, monitoring data, damage factor calculations, prediction results, and measured data to provide technical support for subsequent engineering projects.

[0111] Experimental results: After optimizing the layout of gas drainage boreholes based on the prediction results, the cost of ineffective borehole construction was reduced by 18.2%, and the average pure gas extraction volume per borehole in the fracturing zone remained stable at [value missing]. The prediction error was controlled within 5.2%, which verified the accuracy, engineering adaptability and economy of this method. It can provide early technical support for the layout of extraction boreholes in the engineering design stage of CO2-ECBM for low-permeability coal seams.

[0112] This method has been applied to the B6 coal seam (permeability) in a certain mining area. The system was successfully applied, and based on the existing multi-parameter monitoring system, two new acoustic emission instruments and three mechanical sensors were added to complete the prediction of CO2 diffusion range in advance.

[0113] Example 2 Based on Example 1, Example 2 of the present invention also provides a CO2 sequestration and diffusion range prediction system coupled with coal mechanical damage, including: The acquisition module is used to deploy monitoring equipment in the target coal seam to simultaneously acquire coal mechanical response data and fluid seepage diffusion data during the carbon dioxide fracturing operation.

[0114] Specifically, the data acquisition unit is used to construct CO2 fracturing holes, in-seam monitoring holes, and cross-seam monitoring holes in the target coal seam; it adds an intrinsically safe acoustic emission instrument, an intrinsically safe borehole stress sensor, and an intrinsically safe gas flow meter; it collects the ringing count and energy changes of coal fracture development through the intrinsically safe acoustic emission instrument; it monitors the internal stress of the coal body before and after fracturing in real time through the intrinsically safe borehole stress sensor; it collects the instantaneous CO2 flow rate in real time through the intrinsically safe gas flow meter; it establishes a communication connection between each acquisition device and the explosion-proof data acquisition instrument through an intrinsically safe data cable or wireless transmission module; and it sets the data acquisition frequency of each acquisition device.

[0115] The damage quantification module is used to calculate the mechanical damage factor of coal body based on the mechanical response data before and after cracking, and to construct the mapping relationship between the mechanical damage factor and the degree of coal body fracture development.

[0116] Specifically, the pre-fracture initial parameter acquisition unit is used to monitor the initial internal stress of the coal body before fracturing using an intrinsically safe borehole stress sensor for mining. Combined with the uniaxial compression test calibration of the same coal seam laboratory standard coal sample, the initial uniaxial compressive strength of the initial coal body is obtained. and initial elastic modulus The initial porosity was obtained by laboratory measurements using core drilling combined with acoustic emission parameter inversion. .

[0117] The post-fracture parameter acquisition unit is used to monitor the initial stress inside the coal body after CO2 fracturing using an intrinsically safe borehole stress sensor. Combined with uniaxial compression test calibration of standard coal samples from the same coal seam, the uniaxial compressive strength after fracturing is indirectly obtained through inversion. and elastic modulus Based on laboratory measurements from core drilling and acoustic emission data, the porosity after fracturing was obtained. .

[0118] The mechanical damage factor calculation unit is used to determine the uniaxial compressive strength weighting factor based on a combination of laboratory orthogonal tests and field tests. and elastic modulus weighting factor Through formula Calculate mechanical damage factor ,in, .

[0119] The joint equation fitting unit is used to obtain the ringing count and energy change of the coal body before and after fracturing using an acoustic emission instrument, and to invert the fracture connectivity before fracturing. With post-fracture fissure connectivity Calculate the fissure connectivity increment Based on laboratory orthogonal test and field test data, the least squares linear regression method was used to fit and obtain the mechanical damage factor. and Relationship: .

[0120] The model building module is used to introduce mechanical damage factors to correct coal seam permeability and build a prediction model for in-seam and cross-seam diffusion radius that integrates mechanical damage effects.

[0121] Specifically, the permeability correction model building unit is used to introduce mechanical damage factors to correct the permeability of coal seams, including: The dynamic viscosity of CO2 is obtained using standard physical properties under corresponding downhole temperature and pressure conditions. The initial permeability of the coal seam was directly determined through in-situ gas injection tests. The wellbore pressure is determined based on a comprehensive analysis of coal seam stress, coal mechanical parameters, and laboratory fracture pressure test results. .

[0122] Introducing mechanical damage factors Establish a penetration rate correction model ,in, This represents the damage sensitivity coefficient.

[0123] The prediction model building unit is used to construct prediction models for in-laminar and translaminar diffusion radius that integrate mechanical damage effects, including: Formula for predicting the diffusion radius of bedding planes: ,in, To predict the time, For low-permeability coal seams, the correction factor is... is the coefficient of volume expansion of CO2.

[0124] Formula for predicting the diffusion radius across layers: ,in, is the diffusion attenuation coefficient across layers.

[0125] Introducing a temperature correction factor The formulas for predicting diffusion radius along bedding planes and across bedding planes are modified, and the modified formulas are as follows:

[0126]

[0127] The prediction and calibration module is used to acquire the diffusion radius data measured on-site, verify the error of the prediction model's output, and calibrate the model parameters based on the verification results, outputting the corrected predicted diffusion radius value.

[0128] Specifically, the error correction unit is used to set the prediction time. Consistent with the engineering design monitoring cycle, the input parameters are used to calculate the predicted value of the bedding diffusion radius. and predicted cross-layer diffusion radius The actual diffusion radius along the bedding plane was obtained by multi-parameter monitoring of temperature, pressure, and concentration. and the actual diffusion radius of the layer Calculate the prediction error of the diffusion radius along the layer and the prediction error of the diffusion radius across the layer. When the prediction error exceeds the preset threshold, adjust the low permeability coal seam correction coefficient, the diffusion attenuation coefficient across the layer, or the temperature correction coefficient in the model construction module until the error is not higher than the preset threshold.

[0129] The archiving and plugging module is used to associate and archive the collected data and verification results, and to complete the plugging operation of the monitoring borehole.

[0130] Specifically, the archiving unit is used to link and archive the collected data and verification results, generating a linked data table containing mechanical damage factors, predicted radius, and actual radius; the monitoring hole is permanently sealed using a two-plug-one-injection process.

[0131] Example 3 Based on Embodiment 1, Embodiment 3 of the present invention also provides a processing device, including at least one processor and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor can execute the method steps of Embodiment 1 by calling the program instructions.

[0132] Example 4 Based on Embodiment 1, Embodiment 4 of the present invention also provides a computer-readable storage medium storing computer instructions that cause the computer to perform the steps of the method described in Embodiment 1.

[0133] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the CO2 sequestration and diffusion range coupled with coal body mechanical damage, characterized in that, include: S1. Install monitoring equipment in the target coal seam to simultaneously collect coal mechanical response data and fluid seepage diffusion data during the carbon dioxide fracturing operation. S2. Based on the mechanical response data before and after fracturing, calculate the coal body mechanical damage factor and construct the mapping relationship between the mechanical damage factor and the degree of coal body fracture development. S3. The mechanical damage factor is introduced to correct the coal seam permeability, and a prediction model for the diffusion radius along and across the seam that integrates the mechanical damage effect is constructed. S4. Obtain the measured diffusion radius data on site, verify the error of the output of the prediction model, and calibrate the model parameters based on the verification results, and output the corrected diffusion radius prediction value.

2. The method for predicting the CO2 sequestration and diffusion range coupled with coal body mechanical damage according to claim 1, characterized in that, S1 includes: Construct CO2 fracturing holes, in-seam monitoring holes, and cross-seam monitoring holes in the target coal seam; Add intrinsically safe acoustic emission instrument for mining, intrinsically safe borehole stress sensor for mining, and intrinsically safe gas flow meter for mining; The ringing count and energy changes of coal seam fracture development were collected using an intrinsically safe acoustic emission instrument for mining. The internal stress of the coal body before and after fracturing is monitored in real time using an intrinsically safe borehole stress sensor for mining. The instantaneous CO2 flow rate is collected in real time using an intrinsically safe gas flow meter for mining applications. Establish a communication connection between each acquisition device and the mine explosion-proof data acquisition instrument via an intrinsically safe data cable or wireless transmission module; and set the data acquisition frequency of each acquisition device.

3. The method for predicting the CO2 sequestration and diffusion range coupled with coal body mechanical damage according to claim 1, characterized in that, S2 includes: The initial uniaxial compressive strength of the coal body before fracturing was obtained by monitoring the initial internal stress of the coal body using an intrinsically safe borehole stress sensor for mining, combined with uniaxial compression tests of standard coal samples from the same coal seam. and initial elastic modulus The initial porosity was obtained based on laboratory measurement data from borehole core sampling and acoustic emission data. ; After CO2 fracturing, the initial stress inside the coal seam after fracturing is monitored using an intrinsically safe borehole stress sensor. Combined with uniaxial compression tests of standard coal samples from the same coal seam, the uniaxial compressive strength after fracturing is obtained. and elastic modulus Based on laboratory measurements from core drilling and acoustic emission data, the porosity after fracturing was obtained. ; The weighting factor for uniaxial compressive strength was determined based on a combination of laboratory orthogonal experiments and field tests. and elastic modulus weighting factor Through formula Calculate mechanical damage factor ,in, ; By acquiring the ringing counts and energy changes of the coal body before and after fracturing using an acoustic emission instrument, the fracture connectivity before fracturing can be obtained through inversion. With post-fracture fissure connectivity Calculate the fissure connectivity increment Based on laboratory orthogonal test and field test data, the least squares linear regression method was used to fit and obtain the mechanical damage factor. and Relationship: .

4. The method for predicting the CO2 sequestration and diffusion range coupled with coal body mechanical damage according to claim 1, characterized in that, Correcting coal seam permeability based on the aforementioned mechanical damage factor includes: The dynamic viscosity of CO2 is obtained using standard physical properties under corresponding downhole temperature and pressure conditions. The initial permeability of the coal seam was determined through in-situ gas injection tests. The wellbore pressure is determined based on a comprehensive analysis of coal seam stress, coal mechanical parameters, and laboratory fracture pressure test results. ; Introducing the mechanical damage factor Establish a penetration rate correction model ,in, This represents the damage sensitivity coefficient.

5. The method for predicting the CO2 sequestration and diffusion range coupled with coal body mechanical damage according to claim 1, characterized in that, The construction of the prediction model for the diffusion radius of both in-laminar and translaminar damage effects, which integrates mechanical damage effects, includes: Formula for predicting the diffusion radius of bedding planes: ,in, To predict the time, For low-permeability coal seams, the correction factor is... The coefficient of volume expansion of CO2; Formula for predicting the diffusion radius across layers: ,in, This is the cross-layer diffusion attenuation coefficient; Introducing a temperature correction factor The formulas for predicting diffusion radius along bedding planes and across bedding planes are modified, and the modified formulas are as follows: 。 6. The method for predicting the CO2 sequestration and diffusion range coupled with coal body mechanical damage according to claim 1, characterized in that, In S4, the prediction time is set. Consistent with the engineering design monitoring cycle, the input parameters are used to calculate the predicted value of the bedding diffusion radius. and predicted cross-layer diffusion radius The actual diffusion radius along the bedding plane was obtained by multi-parameter monitoring of temperature, pressure, and concentration. and the actual diffusion radius of the layer ; Calculate the prediction error of the diffusion radius along the layer and the prediction error of the diffusion radius across the layer. When the prediction error exceeds the preset threshold, adjust the low permeability coal seam correction coefficient, the diffusion attenuation coefficient across the layer, or the temperature correction coefficient in S3 until the error is not higher than the preset threshold.

7. The method for predicting the CO2 sequestration and diffusion range coupled with coal body mechanical damage according to claim 1, characterized in that, It also includes: associating and archiving the collected data and verification results to generate an associated data table containing mechanical damage factors, predicted radius and actual radius; and using a two-plug-one-injection process to permanently seal the monitoring holes.

8. A CO2 sequestration and diffusion range prediction system coupled with coal body mechanical damage, characterized in that, include: The acquisition module is used to deploy monitoring equipment in the target coal seam to simultaneously acquire coal mechanical response data and fluid seepage diffusion data during the carbon dioxide fracturing operation. The damage quantification module is used to calculate the coal body mechanical damage factor based on the mechanical response data before and after fracturing, and to construct a mapping relationship between the mechanical damage factor and the degree of coal body fracture development. The model building module is used to introduce the mechanical damage factor to correct the coal seam permeability and build a prediction model for the diffusion radius along and across the seam that integrates the mechanical damage effect. The prediction and calibration module is used to acquire the diffusion radius data measured on-site, verify the error of the prediction model's output, and calibrate the model parameters based on the verification results, outputting the corrected predicted diffusion radius value.

9. A processing device, characterized in that, The method includes at least one processor and at least one memory communicatively connected to the processor, wherein the memory stores program instructions executable by the processor, and the processor can execute the method as described in any one of claims 1 to 7 by invoking the program instructions.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause the computer to perform the method as described in any one of claims 1 to 7.

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