A method and system for real-time evaluation of gas extraction effect of a borehole

Abstract

The present application relates to the technical field of drilling production process, disclose a kind of drilling gas extraction effect real-time evaluation method and system, the method comprises: the fluid flow of target drilling hole, rock mass stress and regional geological structure data are synchronously collected;Based on fault azimuth and fracture density distribution diagram, the space weight distribution matrix of control fluid migration path is generated;Combining rock mass stress data, the permeability evolution model along the radial of drilling hole is constructed;Fluid flow data is input into the model, and the effective extraction coefficient is output by solving the time-varying integral equation of effective extraction radius;According to the matching relationship between the coefficient and the preset threshold, a real-time extraction effect grade signal is generated.The present application realizes the coupling modeling of geological structure heterogeneity and stress dynamic change, solves the problem of static assessment lag, and improves the efficiency of extraction effect feedback.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of drilling production, and particularly relates to a method and system for real-time evaluation of drilling gas extraction effect. BACKGROUND

[0002] In the field of oil and gas drilling engineering, especially in the process involving formation fluid extraction, accurate evaluation of the dynamic extraction effect of the fluid around the drilling hole is the key to optimizing production. The traditional method mainly relies on static geological models and empirical parameters to predict the fluid migration range, such as through core permeability testing or historical data fitting to establish a fixed permeability field model. Such models do not fully consider the real-time influence of spatial heterogeneity of geological structures (such as fault strike and fracture development direction) on fluid migration paths, and ignore the coupling effect of rock mass stress dynamic changes on permeability during drilling.

[0003] In addition, the existing technology usually relies on indirect means such as periodic well testing or pressure drop testing to calculate the extraction range, resulting in significant lag in the evaluation results, which cannot match the actual working conditions in real time. Especially in complex geological structure areas with developed fracture systems, static models are prone to overestimate or underestimate the effective extraction range due to the lack of effective quantification of the correlation between structural orientation and fluid dominant migration channels, thereby causing improper drilling deployment or reducing resource recovery rate.

[0004] Another core defect of the existing technology is the indirectness and simplicity of the extraction effect evaluation. Current methods mainly monitor wellhead flow and pressure data, and estimate extraction efficiency by combining simplified radial flow equations, but there is a key missing: key geological structure parameters (such as fault azimuth angle and fracture density field distribution) are not integrated into the dynamic evolution model in real time. This missing feature makes the model unable to dynamically reflect the spatio-temporal variation characteristics of permeability at different positions along the drilling radial direction, thereby significantly restricting the calculation accuracy of the effective extraction radius. At the same time, due to the lack of real-time analysis capability of the geomechanical coupling effect, the existing evaluation system cannot generate feedback signals representing the immediate extraction effect, which severely limits the dynamic optimization of drilling process parameters (such as adjusting the extraction negative pressure or optimizing the drilling densification deployment based on real-time effect). SUMMARY

[0005] The present application provides a method and system for real-time evaluation of drilling gas extraction effect, which mainly aims to solve the problem of evaluation lag caused by static models and empirical parameters, and the lack of quantification of spatial heterogeneity of geological structures and stress dynamic coupling.

[0006] To achieve the above-mentioned purpose, the present application provides a method for real-time evaluation of drilling gas extraction effect, which comprises:

[0007] S1. synchronously collecting gas flow data of a target borehole, rock mass stress data and geological structure data of a region where the borehole is located;

[0008] S2. generating a spatial weight distribution matrix for controlling a gas migration path based on a fault azimuth angle parameter and a fracture density distribution map in the geological structure data;

[0009] S3. constructing a permeability evolution model along a borehole radius according to the spatial weight distribution matrix and the rock mass stress data;

[0010] S4. inputting the gas flow data into the permeability evolution model, outputting an effective extraction coefficient of the borehole by solving a time-varying integral equation of the effective extraction radius;

[0011] S5. generating a real-time extraction effect level signal according to a comparison relationship between the effective extraction coefficient and a preset extraction efficiency threshold.

[0012] Optionally, the synchronously collecting gas flow data of a target borehole, rock mass stress data and geological structure data of a region where the borehole is located, comprises:

[0013] acquiring a real-time gas flow pulse signal as the gas flow data of the target borehole through a multi-parameter sensor array in the borehole;

[0014] measuring a stress fluctuation waveform of the surrounding rock of the borehole as the rock mass stress data by using a stress sensor;

[0015] obtaining a fault azimuth angle parameter and a fracture density distribution map of the region where the borehole is located as the geological structure data from a geological database.

[0016] Optionally, the generating a spatial weight distribution matrix for controlling a gas migration path based on a fault azimuth angle parameter and a fracture density distribution map in the geological structure data, comprises:

[0017] calculating a spatial included angle between a fault strike and a borehole axis based on the fault azimuth angle parameter in the geological structure data, wherein the spatial included angle is generated based on a fault azimuth angle and a grid point azimuth angle;

[0018] generating a fracture development intensity gradient field based on the fracture density distribution map in the geological structure data;

[0019] calculating a migration path weight value of each grid point in the borehole radius in combination with the spatial included angle and the intensity gradient field;

[0020] mapping the migration path weight value as a three-dimensional matrix element to generate the spatial weight distribution matrix.

[0021] Optionally, the migration path weight value of each grid point in the radial direction of the borehole is calculated by combining the spatial included angle and the intensity gradient field, comprising:

[0022] A polar coordinate system is established with the fault strike as the reference axis, and the intensity gradient field is projected into the polar coordinate system;

[0023] The azimuth alignment degree of the grid point and the fault is quantified by a cosine function, wherein the azimuth alignment degree is:

[0024]

[0025] wherein, is the azimuth alignment degree, is the fault azimuth angle, is the grid point azimuth angle;

[0026] An exponential decay function is used to associate the azimuth alignment degree with the fissure development density to obtain a migration path weight value representing the preferential migration path of gas, wherein the calculation formula of the migration path weight value is:

[0027]

[0028] wherein, is the migration path weight value, is the azimuth alignment degree, is the fissure development density, is the distance attenuation coefficient, is the radial distance extending outward from the center of the borehole, is the grid point azimuth angle.

[0029] Optionally, the permeability evolution model in the radial direction of the borehole is constructed according to the spatial weight distribution matrix and the rock mass stress data, comprising:

[0030] The migration path weight values in the spatial weight distribution matrix are normalized to obtain a geological structure influence factor;

[0031] A stress sensitivity coefficient is calculated according to the rock mass stress data;

[0032] A stress conversion operator between permeability and the rock mass stress data is derived based on the Coulomb failure criterion;

[0033] The geological structure influence factor and the stress conversion operator are fused to generate a permeability evolution model, and the permeability evolution model outputs a permeability evolution field varying with radial distance and time.

[0034] Optionally, the permeability evolution model is:

[0035]

[0036] wherein, is a radial distance and time at which the permeability, is a reference permeability, is the geological structure impact factor, is a reference stress, is a radial distance extending outward from the borehole center, is a time variable, is the rock mass stress data, is the stress sensitivity coefficient.

[0037] Optionally, the time-varying integral equation for calculating the effective drainage radius comprises:

[0038] establishing a radial flow integral equation with the permeability evolution model as a variable based on Darcy's law;

[0039] solving the effective drainage radius based on the radial flow integral equation, taking the gas flow data as an integral constraint condition;

[0040] calculating the ratio of the effective drainage radius to the designed drainage radius, and outputting an effective drainage coefficient.

[0041] Optionally, the radial flow integral equation is:

[0042]

[0043] wherein, is the gas flow data, is the coal seam height, is the pressure difference between the reservoir pressure and the borehole pressure, is the gas viscosity, is the borehole wall radius, is the effective drainage radius, is a radial distance and time at which the permeability, is a radial distance extending outward from the borehole center, is a time variable.

[0044] Optionally, the real-time drainage effect level signal is generated according to the comparison relationship between the effective drainage coefficient and a preset drainage efficiency threshold value, comprising:

[0045] presetting a multi-level drainage efficiency threshold interval;

[0046] The effective extraction coefficient is matched to a corresponding multi-stage extraction efficiency threshold interval, and a level identifier is activated according to a matching result, wherein the level identifier is encoded as a digital signal output.

[0047] To solve the above problems, the present application also provides a real-time evaluation system for gas extraction effect of a borehole, comprising:

[0048] A data acquisition module is configured to synchronously acquire gas flow data, rock mass stress data and geological structure data of a region where the borehole is located.

[0049] A weight distribution generation module is configured to generate a spatial weight distribution matrix for controlling a gas migration path based on a fault azimuth angle parameter and a fracture density distribution map in the geological structure data.

[0050] A permeability model construction module is configured to construct a permeability evolution model along a radial direction of the borehole according to the spatial weight distribution matrix and the rock mass stress data.

[0051] An effective extraction coefficient generation module is configured to input the gas flow data into the permeability evolution model, solve a time-varying integral equation of an effective extraction radius, and output an effective extraction coefficient of the borehole.

[0052] An extraction effect determination module is configured to generate a real-time extraction effect level signal according to a comparison relationship between the effective extraction coefficient and a preset extraction efficiency threshold.

[0053] The spatial weight distribution matrix constructed based on the fault azimuth angle and the fracture density distribution map quantifies the control effect of the geological structure on the gas migration path; the permeability evolution model derived in combination with real-time rock mass stress data dynamically fuses a stress sensitivity coefficient and a geological structure influence factor, accurately represents the spatiotemporal evolution of the permeability, and overcomes the defects of not considering the structural orientation correlation and stress fluctuation in the traditional static evaluation, so that the permeability field can respond to the rock mass stress change (such as crack closure caused by stress concentration) and the structural directivity (such as the guidance of the fault strike on the gas dominant migration path) in real time, effectively solves the evaluation lag problem, simultaneously, relies on real-time solving of the time-varying integral equation and a multi-stage threshold matching mechanism to realize dynamic feedback and hierarchical early warning of the extraction effect, inputs the gas flow data into the permeability evolution model, dynamically solves the effective extraction radius based on the radial flow integral equation constructed based on the Darcy law, and outputs a quantitative effective extraction coefficient; in combination with a preset multi-stage extraction efficiency threshold interval, a digital level signal is automatically generated to directly guide engineering regulation and control. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 A flowchart of a real-time evaluation method for gas extraction effect of a borehole provided by an embodiment of the present application is shown.

[0055] Figure 2 A functional module diagram of a drilling gas extraction effect real-time evaluation system provided by an embodiment of the present application is shown in the figure.

[0056] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0057] It should be understood that the specific embodiments described herein are merely intended to explain the present application and not to limit the present application.

[0058] Embodiments of the present application provide a drilling gas extraction effect real-time evaluation method. The execution subject of the drilling gas extraction effect real-time evaluation method includes but is not limited to at least one of electronic devices such as a server, a terminal, etc., which can be configured to execute the method provided by the embodiments of the present application. In other words, the drilling gas extraction effect real-time evaluation method can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be a stand-alone server, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks, and big data and artificial intelligence platforms, etc. basic cloud computing services.

[0059] Referring to Figure 1 The figure shows a flowchart of a drilling gas extraction effect real-time evaluation method provided by an embodiment of the present application. In the embodiment, the drilling gas extraction effect real-time evaluation method includes:

[0060] S1. Synchronously collecting gas flow data, rock mass stress data and geological structure data of the region where the target drilling hole is located.

[0061] In the embodiment of the present application, the synchronous collection of the gas flow data, the rock mass stress data and the geological structure data of the region where the target drilling hole is located includes:

[0062] The real-time gas flow pulse signal is obtained by a multi-parameter sensor array in the drilling hole as the gas flow data of the target drilling hole;

[0063] The stress sensor is used to measure the stress fluctuation waveform of the surrounding rock of the drilling hole as the rock mass stress data;

[0064] The fault azimuth parameter and the fracture density distribution map of the region where the drilling hole is located are retrieved from the geological database as the geological structure data.

[0065] In detail, the target borehole refers to a specific borehole that needs to be gas extracted, and is the object of the entire evaluation method.

[0066] In detail, the gas flow data refers to real-time gas flow pulse signals obtained through a multi-parameter sensor array in the borehole, which is used to represent the volume of gas extracted from the target borehole per unit time; the rock mass stress data refers to the stress fluctuation waveform of the surrounding rock of the borehole measured by a stress sensor, reflecting the stress state change of the rock mass around the borehole; the geological structure data refers to the fault azimuth parameters and fracture density distribution map of the area where the borehole is located, which is obtained from the geological database, and is used to describe the geological structure characteristics around the borehole.

[0067] In detail, the multi-parameter sensor array refers to a sensor combination device installed in the borehole, which can synchronously collect multiple parameters, and is used to obtain real-time gas flow pulse signals in this embodiment.

[0068] In detail, the stress sensor refers to a sensor device for measuring the stress fluctuation of the surrounding rock of the borehole, which generates corresponding electrical signals or waveform data by sensing the stress change of the rock mass.

[0069] In detail, the geological database refers to a database system that stores geological exploration data of the area where the borehole is located, including fault azimuth, fracture density distribution and other geological structure information.

[0070] Further, the multi-parameter sensor array deployment includes implanting a multi-parameter sensor array inside the target borehole, which is usually installed at a specific depth position (such as 10-20m away from the hole) of the borehole to avoid interference from the hole section. The sensor adopts waterproof and pressure-resistant design (pressure resistance value ≥ 10MPa) to ensure stable work in the complex environment of the borehole.

[0071] In detail, the sensor array transmits the gas flow pulse signals in real time through a cable, and the sampling frequency is set to 10-100Hz (adjusted dynamically according to the gas emission amount), for example, when the gas flow fluctuation is large, the sampling frequency is automatically increased to 50Hz.

[0072] Further, the stress sensor installation and measurement includes arranging stress sensors at different positions (such as top, bottom, and side) of the surrounding rock of the borehole, and the sensor probe needs to be in close contact with the rock mass, and the stress fluctuation is sensed by strain gauges or piezoresistive effect.

[0073] Further, the stress sensor adopts wireless or wired transmission mode, and transmits the measured stress fluctuation waveform (such as sine wave, pulse wave) to the data processing unit in real time, and the waveform sampling accuracy is 16 bits, which ensures that the stress change details can be captured.

[0074] Further, the geological database interface includes: establishing a network connection with the geological database, which contains a three-dimensional model and attribute data of regional geological exploration. Through the borehole coordinates (longitude, latitude, depth) as an index, the fault azimuth parameters (such as the strike of a certain fault is north by east 30°) and the fracture density distribution map (represented by color gradient, such as the red area fracture density is 10 / m²) of the corresponding position are retrieved.

[0075] Further, the gas flow pulse signal processing includes: the original pulse signal obtained by the sensor is first filtered by a hardware filter circuit (low-pass filter, cutoff frequency 10Hz) to remove high-frequency noise, and then a software Kalman filter algorithm is used for dynamic optimization to eliminate environmental interference (such as false signals caused by drilling rig vibration), and finally smooth gas flow time series data Q(t) is obtained.

[0076] Further, the stress fluctuation waveform analysis includes: performing fast Fourier transform (FFT) on the waveform data output by the stress sensor, extracting the main frequency component and the amplitude of each harmonic, and identifying the characteristic parameters (such as peak stress, fluctuation period) of stress fluctuation. For example, when the rock mass is slightly fractured, high-frequency components will appear in the stress waveform, and the stress concentration area can be judged through frequency spectrum analysis.

[0077] Further, the geological data standardization processing includes: the fault azimuth parameters retrieved from the database need to be converted into the relative angle with the borehole axis, and the fracture density distribution map is generated by gridding interpolation (such as Kriging interpolation method) to generate regular grid data, which is convenient for subsequent calculation. For example, compare the fault azimuth θ (absolute azimuth) with the borehole axis direction to obtain the spatial included angle parameter.

[0078] Further, the clock synchronization technology includes: using GPS clock or Beidou clock to synchronize the time of multi-parameter sensor array, stress sensor and database system, ensuring that the timestamp accuracy of the three types of data is ≤10ms, and realizing true synchronization acquisition.

[0079] Further, the data synchronization triggering mechanism includes: setting data acquisition triggering conditions (such as gas flow mutation exceeding 10% or stress fluctuation exceeding 0.5MPa), when any condition is met, the three types of sensors start acquisition at the same time, avoiding data deviation caused by asynchronous acquisition.

[0080] In summary, through the above-mentioned synchronous acquisition method combining hardware and algorithm, the problems of traditional manual inspection data lag and low sampling frequency are solved, and real-time and high-precision acquisition of gas flow, rock mass stress and geological structure data is realized. This step provides a reliable data basis for subsequent permeability model construction, so that the evaluation results can timely reflect the real state of borehole extraction, and the efficiency and accuracy of gas extraction effect evaluation are improved from the data source.

[0081] In general, the gas flow data, rock mass stress data and geological structure data collected in S1 serve as the input basis for all subsequent steps: the geological structure data (fault azimuth angle, fracture density) are directly used in S2 to generate a spatial weight distribution matrix, quantifying the geological influence on the gas migration path; the rock mass stress data are combined with the spatial weight matrix in S3 to construct a permeability evolution model; the gas flow data are used as an integral constraint condition in S4 to solve the effective extraction radius and extraction coefficient. The synchronous collection of the three types of data ensures the spatio-temporal consistency of subsequent model calculations, forming a complete logical chain from data collection to effect evaluation.

[0082] S2. Generating a spatial weight distribution matrix controlling the gas migration path based on the fault azimuth angle parameter and the fracture density distribution map in the geological structure data.

[0083] In the embodiments of the present application, the generating of the spatial weight distribution matrix controlling the gas migration path based on the fault azimuth angle parameter and the fracture density distribution map in the geological structure data comprises:

[0084] calculating a spatial angle between the fault strike and the borehole axis based on the fault azimuth angle parameter in the geological structure data, wherein the spatial angle is generated based on the fault azimuth angle and the grid point azimuth angle;

[0085] generating a strength gradient field of fracture development based on the fracture density distribution map in the geological structure data;

[0086] calculating the migration path weight value of each grid point radially from the borehole in combination with the spatial angle and the strength gradient field;

[0087] mapping the migration path weight value into a three-dimensional matrix element to generate the spatial weight distribution matrix.

[0088] In detail, the calculating of the migration path weight value of each grid point radially from the borehole in combination with the spatial angle and the strength gradient field comprises:

[0089] establishing a polar coordinate system with the fault strike as the reference axis and projecting the strength gradient field into the polar coordinate system;

[0090] quantifying the azimuth alignment degree of the grid point and the fault by a cosine function, wherein the azimuth alignment degree is:

[0091]

[0092] wherein, is the azimuth alignment degree, is the fault azimuth angle, is the grid point azimuth angle;

[0093] An exponential decay function is used to associate the orientation alignment degree with the fissure development density to obtain a migration path weight value representing a gas preferential migration path, wherein a calculation formula of the migration path weight value is:

[0094]

[0095] wherein, is the migration path weight value, is the orientation alignment degree, is the fissure development density, is a distance decay coefficient, is a radial distance extending outward from a borehole center, is a grid point azimuth angle.

[0096] In detail, the fault azimuth angle parameter refers to an angle between a strike of a fault in a region where a borehole is located and a north direction, is used to represent an extension direction of the fault in space, and is one of core parameters of geological structure data.

[0097] In detail, the fissure density distribution map refers to a visual chart of fissure distribution density in a region around a borehole obtained through geological exploration, and usually represents a fissure development density degree in terms of a fissure number (strip / m²) in a unit area.

[0098] In detail, the spatial angle refers to an angle value calculated from the fault azimuth angle and the grid point azimuth angle, and is used to describe a spatial positional relationship between a fault strike and a radial grid point direction of the borehole.

[0099] In detail, the fissure development intensity gradient field refers to continuous field data generated based on the fissure density distribution map, represents a variation gradient of the fissure development intensity in space, and reflects a dominant path direction of gas migration.

[0100] In detail, the migration path weight value refers to a numerical value used to quantify an influence degree of each radial grid point of the borehole on gas migration, and a greater value indicates that the position is more likely to become a preferential path of gas migration.

[0101] In detail, the three-dimensional matrix element refers to a numerical value element mapping the migration path weight value of each grid point in space into a matrix to form a three-dimensional space weight distribution matrix, and is used for subsequent construction of a permeability model.

[0102] In detail, the polar coordinate system refers to a coordinate system established with a fault strike as a reference axis, facilitates conversion of an orientation relationship in space into radial distance and angle parameters, and simplifies quantitative analysis of a gas migration path.

[0103] Further, the calculation of the spatial angle between the fault strike and the borehole axis comprises: extracting the fault azimuth angle θ (such as θ = 30°, indicating that the fault strike is 30° north of east) from the geological structure data obtained in the S1 step, and determining the azimuth of the borehole axis (usually the absolute azimuth angle of the borehole extension direction, such as the borehole axis azimuth angle being 0°, i.e. the north direction); converting the fault azimuth angle and the borehole axis azimuth into a unified geographic coordinate system or a borehole local coordinate system, taking the borehole center as the origin and the axis direction as the z-axis to establish a three-dimensional space coordinate system; for each grid point in the radial direction of the borehole, obtaining the azimuth angle φ (the angle between the point and the borehole axis in the horizontal plane), and calculating the spatial angle between the fault strike and the grid point azimuth by a trigonometric function, i.e. using the (θ-φ) difference in the formula ζ = |cos(θ-φ)| to obtain the absolute value of the angle between the fault strike and the grid point direction. For example, when θ = 30° and φ = 45°, the spatial angle is 15°, and the absolute value of the cosine value is used to represent the azimuth alignment degree.

[0104] Further, the generation of the strength gradient field of the fracture development comprises: dividing the fracture density distribution map into regular spatial grids (such as 0.5m x 0.5m grids), each grid point corresponding to a fracture development density; using the Kriging interpolation method or the inverse distance weighted interpolation method to perform spatial interpolation on the discrete fracture density data to generate a continuous fracture density field. For example, given that the fracture densities of three points in a certain area are 8, 10 and 12 per square meter respectively, the fracture development density of any point in the area is obtained by interpolation; performing gradient operation (such as finite difference method) on the interpolated fracture density field to obtain the fracture development strength gradient (including gradient size and direction) of each grid point, forming the strength gradient field. The gradient direction represents the fastest direction of the increase of the fracture development, and the gradient size represents the degree of change in the fracture density.

[0105] Further, the calculation of the migration path weight value of each grid point in the radial direction of the borehole comprises: establishing a two-dimensional polar coordinate system with the fault strike as the polar axis (i.e. the 0° direction of the polar coordinate system) and the borehole center as the pole. In this coordinate system, the position of each grid point is determined by the radial distance r and the angle φ (the azimuth angle relative to the fault strike); projecting the three-dimensional strength gradient field into the polar coordinate system, so that the fracture development strength gradient of each grid point corresponds to the polar coordinate parameters (r, φ), facilitating subsequent coupling calculation with the azimuth alignment degree; calculating the azimuth alignment degree ζ of each grid point by the formula ζ = |cos(θ-φ)|. Wherein, θ is the fault azimuth angle, and φ is the azimuth angle of the grid point in the polar coordinate system. The closer the ζ value is to 1, the higher the consistency of the grid point direction with the fault strike, and the easier the gas migration along the direction; using an exponential decay function to calculate the migration path weight value.

[0106] In detail, p (unit: strip / m²) is the fracture development density of the grid point. The more concentrated the fractures are, the more gas migration channels there are, and the greater the weight value is; is the distance attenuation term, and γ is the distance attenuation coefficient (e.g., γ = 0.5 m -1 ), indicating that the gas migration capacity exponentially attenuates with the increase of distance r. The farther the distance from the borehole is, the smaller the weight value is; ζ is the azimuth alignment degree, reflecting the guiding effect of the fault strike on gas migration. For example, when a grid point r = 5 m, p = 10 strips / m², γ = 0.5 m -1 , and ζ = 0.966, the weight value w = 10 e^(-0.5 x 5) x 0.966 ≈ 0.792, indicating that the influence of this position on gas migration is weak.

[0107] Further, the distance attenuation coefficient is currently a constant, but the actual attenuation may be affected by the connectivity of the fracture network.

[0108] Further, generating the spatial weight distribution matrix comprises: according to the radial range of the borehole (e.g., 0 to 10 m), the azimuth angle range (0° to 360°), and the vertical height (e.g., the seam height of 3 m), the space is divided into a three-dimensional grid (e.g., radial interval of 0.5 m, azimuth angle interval of 5°, and vertical interval of 0.5 m), the row and column dimensions of the matrix are determined (e.g., 20 x 72 x 6); the migration path weight value w(r, φ) of each three-dimensional grid point is mapped to the element value of the corresponding position in the matrix to form a three-dimensional spatial weight distribution matrix W. The range of the matrix element value is 0 to 1, and the greater the value is, the stronger the control effect of the position on gas migration is; the matrix elements are normalized so that the sum of all elements is 1, ensuring the relativity of the weight value and the stability of the model calculation. For example, the weight value of a grid point is 0.792, and the normalized value is 0.792 / ∑w, where ∑w is the sum of the weight values of all grid points.

[0109] In general, by quantifying the influence of fault orientation and fracture development on gas migration paths, the problem of not considering the spatial difference of geological structure in traditional evaluation methods is solved. The spatial weight distribution matrix generated in this step can accurately depict the preferential migration path of gas under complex geological conditions, providing a quantitative basis for the construction of the permeability model at the geological structure level, thereby improving the accuracy and reliability of gas extraction effect evaluation and avoiding evaluation deviation caused by neglecting geological factors.

[0110] In summary, the output of step S2 (the spatial weight distribution matrix W) is one of the core inputs for constructing the permeability evolution model in step S3. Specifically, the spatial weight distribution matrix W, as a geological structural influence factor, is combined with the rock mass stress data σ(t) collected in step S1 to quantify the coupling effect of geological structure and stress on permeability through the permeability evolution model. The weight values ​​in the matrix reflect the degree of gas migration dominance at different spatial locations, directly affecting the distribution characteristics of permeability in the borehole radial direction, and thus affecting the calculation results of the effective extraction radius in step S4. This step establishes a quantitative bridge between geological data and the physical model, ensuring the logical coherence of the evaluation process.

[0111] S3. Construct a permeability evolution model along the borehole radial direction based on the spatial weight distribution matrix and the rock mass stress data.

[0112] In this embodiment of the invention, the step of constructing a permeability evolution model along the borehole radial direction based on the spatial weight distribution matrix and the rock mass stress data includes:

[0113] The migration path weight values ​​in the spatial weight distribution matrix are normalized to obtain the geological structure influence factor.

[0114] Calculate the stress sensitivity coefficient based on the rock mass stress data;

[0115] Based on the Coulomb fracture criterion, a stress conversion operator between permeability and the rock mass stress data is derived.

[0116] By integrating the geological structure influence factors and the stress transformation operator, a permeability evolution model is generated, and the permeability evolution model outputs a permeability evolution field that varies with radial distance and time.

[0117] In detail, the penetration rate evolution model is as follows:

[0118]

[0119] in, Radial distance and time Permeability at that location As the benchmark penetration rate, These are the geological structural influencing factors. For reference stress, It is the radial distance extending outward from the center of the borehole. For time variables, The rock mass stress data, It is the stress sensitivity coefficient.

[0120] In detail, the spatial weight distribution matrix refers to a three-dimensional matrix generated through the S2 step, used to represent the control effect of each position around the borehole on the gas migration path, and the greater the matrix element value, the easier the position becomes the preferred path of gas migration.

[0121] In detail, the migration path weight value refers to the element value in the spatial weight distribution matrix, quantifying the influence degree of each grid point in the radial direction of the borehole on gas migration, and the value is related to the fault orientation, fracture development density and distance from the borehole center.

[0122] In detail, the geological structure influence factor refers to a dimensionless parameter obtained after normalization processing of the spatial weight distribution matrix, used to represent the influence degree of geological structure (fault, fracture) on permeability, with a value range of 0 to 1.

[0123] In detail, the rock mass stress data refers to the borehole surrounding rock stress fluctuation waveform data collected through the S1 step, reflecting the change of rock mass internal stress with time, with the unit of Pascal (Pa).

[0124] In detail, the stress sensitivity coefficient refers to a parameter describing the sensitivity degree of permeability change with rock mass stress, obtained by fitting the curves of permeability and stress of different rock masses through experiments or theoretical derivation, dimensionless, used to represent the influence amplitude of stress on permeability.

[0125] In detail, the Coulomb fracture criterion refers to a theoretical criterion used to describe the rock mass fracture under the action of stress, based on the combination of normal stress and shear stress to judge whether the rock mass reaches the fracture state, which is the theoretical basis for deriving the relationship between permeability and stress.

[0126] In detail, the stress conversion operator refers to a mathematical expression derived based on the Coulomb fracture criterion, used to quantify the influence of rock mass stress change on permeability, and establish the conversion relationship between stress and permeability.

[0127] In detail, the permeability evolution model refers to a mathematical model generated after fusing the geological structure influence factor and the stress conversion operator, used to describe the change law of permeability at different positions in the radial direction of the borehole with time, and output the permeability evolution field.

[0128] Further, the normalization processing of the spatial weight matrix includes: accumulating all migration path weight values in the spatial weight distribution matrix generated by S2 to obtain the weight sum ∑w. For example, assuming that the matrix contains N grid points, and the weight value of each point is , then ; divide the weight value of each grid point by the weight sum to obtain the normalized weight value, i.e. the geological structure influence factor, and the formula is expressed as: This process limits the value range of the geological structure influence factor to between 0 and 1, facilitating coupling calculations with other parameters. Normalized weight values ​​in three-dimensional space are statistically aggregated along the borehole radial direction (r direction) to obtain the radial distribution of the geological structure influence factor. For example, the average value of the migration path weights of all circumferential and vertical grid points at the same radial distance r is taken to obtain the geological structure influence factor at that radial location.

[0129] Further, the calculation of the stress sensitivity coefficient includes: denoising and filtering the rock mass stress data σ(t) collected by S1, using moving average filtering or wavelet transform to remove high-frequency interference, and extracting effective stress change characteristics (such as static stress and dynamic disturbance stress); measuring core permeability under different stress conditions through indoor core experiments (such as triaxial compression permeability experiments) to establish a stress-permeability relationship curve. For example, gradually increasing confining pressure (1 MPa to 10 MPa) is applied to coal and rock samples, and permeability changes are measured simultaneously to obtain the functional relationship between permeability and stress; based on the experimental data, a power function fitting is used. Where n is the stress sensitivity coefficient. The value of n is determined by the least squares method to minimize the error between the fitted curve and the experimental data. For example, when the stress... As it increases, the permeability A decrease in n indicates that the permeability is more sensitive to stress changes.

[0130] Furthermore, the derivation of the stress transformation operator includes: the expression for the Coulomb fracture criterion is as follows: Where τ is the shear stress and c is the rock mass cohesion. Let be the internal friction angle. This criterion is correlated with the permeability-stress relationship, considering the influence of stress on the closure / opening of rock mass fractures. Based on rock fracture mechanics theory, when stress increases, rock mass fractures close, and permeability decreases; conversely, when stress decreases, fractures open, and permeability increases. The derivative relationship between permeability and stress is derived using differential calculus, establishing a stress transformation operator, i.e. This operator reflects the quantitative impact of stress changes on permeability.

[0131] Furthermore, reference stress The determination includes: The original geostress of the rock mass or the baseline stress value in the experiment (such as 10 MPa) is usually taken as the reference benchmark for stress conversion, so that the permeability calculation under different stress conditions is comparable.

[0132] Furthermore, the physical meaning of each parameter in the permeability evolution model is as follows: Baseline penetration rate (e.g.) ), representing the initial permeability without stress or geological structure influence; Reflecting the enhancement or inhibition effect of geological structure (fault, fracture) on permeability at radial distance r; Reflecting the adjustment effect of real-time stress change relative to reference stress on permeability.

[0133] Further, the spatio-temporal distribution calculation includes: for each position r (such as 0.5 m to 10 m) and each time point t of the borehole radial direction, the corresponding geological structure influence factor and rock mass stress are substituted to obtain , forming a permeability evolution field varying with radial distance and time. For example, at a certain time t1, the geological structure influence factor at radial distance r = 5 m is 0.6, the stress is 8 MPa, the reference stress is 10 MPa, and the stress sensitivity coefficient is 0.8, then k (5, t1) ≈ 0.738 , that is, the permeability is increased by 23.8% compared with the reference value.

[0134] Further, the model verification and calibration includes: comparing the permeability results calculated by the model with the measured permeability data (such as obtained by pressure drop test), adjusting the model parameters (such as n, γ) to make the error between the calculated value and the measured value less than 10%, and ensuring the accuracy and reliability of the model.

[0135] In summary, by fusing the geological structure influence factor and the stress conversion operator, the problem that the traditional permeability model does not consider the spatial difference of geological structure and the dynamic change of stress is solved. The permeability evolution model constructed in this step can reflect the change law of permeability at different positions around the borehole with the change of geological structure and stress in real time, providing a physical basis for accurately evaluating the gas extraction effect, avoiding the evaluation deviation caused by ignoring the geological-stress coupling effect, and improving the applicability and accuracy of the model.

[0136] In summary, the output (permeability evolution model) of S3 step is the core input of S4 step for solving the effective extraction coefficient: the permeability evolution model is used as the variable of the radial flow integral equation in S4, combined with the gas flow data, to calculate the dynamic effective extraction radius by Darcy's law; the geological structure influence factor in the model is derived from the spatial weight matrix generated in S2, and the stress data is derived from the synchronous acquisition in S1, forming a complete data chain from geological data to physical model; the spatio-temporal distribution of the permeability evolution field directly affects the calculation result of the effective extraction radius, and then determines the extraction effect level evaluation in S5 step, ensuring the logical coherence and data closed loop of each step.

[0137] S4. Inputting the gas flow data into the permeability evolution model, solving the time-varying integral equation of the effective extraction radius, and outputting the effective extraction coefficient of the borehole.

[0138] In the embodiment of the present application, the time-varying integral equation for solving the effective extraction radius includes:

[0139] A radial flow integral equation with a permeability evolution model as the variable is established based on Darcy's law;

[0140] Based on the radial flow integral equation, the gas flow data is used as an integral constraint to solve for the effective extraction radius;

[0141] Calculate the ratio of the effective extraction radius to the designed extraction radius, and output the effective extraction coefficient.

[0142] In detail, the radial flow integral equation is:

[0143]

[0144] in, This refers to the gas flow data. It is the height of the coal seam. It is the pressure difference between reservoir pressure and borehole pressure. It's the viscosity of the gas. It is the borehole wall radius. It is the effective extraction radius. It is the radial distance of the borehole. and time Permeability at that location It is the radial distance extending outward from the center of the borehole. It is a time variable.

[0145] In detail, the effective sampling coefficient is:

[0146]

[0147] in, It is the effective extraction radius. It is the design extraction radius (m). It is the effective sampling coefficient (dimensionless).

[0148] In detail, Darcy's law is a fundamental law describing the seepage law of fluids in porous media. It states that the fluid flow rate is directly proportional to the permeability and pressure difference, and inversely proportional to the fluid viscosity and flow distance. It is the theoretical basis for establishing the radial flow integral equation.

[0149] In detail, the radial flow integral equation is a mathematical equation based on Darcy's law, used to describe the flow law of gas in the radial direction of the borehole. It uses the permeability evolution model as a variable and combines gas flow data to solve for the effective extraction radius.

[0150] In detail, the effective extraction radius refers to the maximum radial distance at which a borehole can effectively extract gas within a certain period of time, measured in meters (m), reflecting the influence range of borehole gas extraction.

[0151] In detail, the designed extraction radius refers to the preset extraction influence range radius in the drilling design stage, in meters (m), determined based on the geological conditions and the extraction technology, and used to measure the actual extraction effect.

[0152] In detail, the effective extraction coefficient refers to the ratio of the effective extraction radius to the designed extraction radius, dimensionless, used to quantify the matching degree between the actual extraction effect and the design target of the drilling, and is a core index for evaluating the extraction efficiency.

[0153] Further, the derivation of the radial flow of Darcy's law includes: the basic expression of Darcy's law is , wherein Q is the flow rate, k is the permeability, A is the seepage cross-sectional area, ΔP is the pressure difference, μ is the fluid viscosity, and L is the flow distance. For the drilling radial flow field, the seepage cross-sectional area is (r is the radial distance, and h is the seam height), and the flow distance element is dr, so the radial flow differential equation is derived as .

[0154] Further, the construction of the radial flow integral equation includes: integrating from the wellbore radius to the effective extraction radius, the radial flow integral equation is obtained:

[0155]

[0156] , wherein Q(t) is the real-time gas flow data collected by S1, k(r, t) is the permeability evolution model constructed by S3, h is the seam height (such as 3 m), ΔP is the pressure difference between the reservoir and the drilling (such as 0.5 MPa), μ is the gas viscosity (such as ), is the drilling radius (such as 0.1 m).

[0157] Further, the parameter preprocessing includes: obtaining the time series data of the gas flow data from S1, and performing smoothing processing (such as moving average filtering) to eliminate high-frequency noise; obtaining the numerical distribution of k(r, t) at different radial positions r and time t from S3, and forming a permeability evolution field database.

[0158] Further, the numericalization of the integral equation includes: using a numerical integration method (such as the trapezoidal method or the Simpson method) to discretely calculate the integral term in the denominator. The radial interval is divided into N equidistant subintervals (such as N = 100), and each subinterval has a width , the value of at the midpoint of each subinterval is calculated and accumulated to obtain the integral approximation value: , wherein is the midpoint radial distance of the i-th subinterval.

[0159] Further, the iterative solution of Includes: settings Initial guess values ​​(such as design extraction radius) (50%); Substitute into the integral equation to calculate the theoretical value of Q(t) on the left side, and compare it with the measured value; Adjust using Newton's iteration method. This continues until the error between the theoretical Q(t) and the measured value is less than a set threshold (e.g., 5%). The iterative formula is: ,in, for right The derivative of It represents the number of iterations.

[0160] Furthermore, when Q(t) collected by S1 or k(r,t) of S3 changes, the integral equation is triggered to be solved again, ensuring... It reflects the current extraction status in real time. For example, when a change in rock mass stress σ(t) leads to an increase in k(r,t), It will automatically update to a larger value.

[0161] Furthermore, obtaining the design extraction radius includes retrieving the design extraction radius (e.g., 8m) from the borehole design document. This value is pre-set based on parameters such as coal seam permeability and extraction time, and serves as a benchmark for evaluating the extraction effect.

[0162] Furthermore, the ratio calculation and output include: according to the formula Calculate the effective extraction coefficient and output a dimensionless value. For example, when the effective extraction radius is 6.8m and the designed extraction radius is 8m, the effective extraction coefficient is 0.85, indicating that the current extraction influence range has reached 85% of the design value.

[0163] Furthermore, accuracy calibration includes: periodic verification through field measurements (such as borehole gas content testing). To ensure accuracy, adjust the solution parameters of the integral equation (such as the numerical integration step size and iteration error threshold) to ensure that the calculation error of η(t) does not exceed 10%.

[0164] In summary, by solving the effective extraction radius using the radial flow integral equation based on Darcy's law, the problem of relying on static empirical parameters to evaluate extraction effectiveness in traditional methods is solved. This step enables dynamic calculation of the extraction influence range, allowing the effective extraction coefficient to reflect the extraction efficiency of the borehole under different geological conditions and stress states in real time. This provides data support for optimizing extraction processes (such as adjusting extraction negative pressure and arranging more dense boreholes), and avoids waste of extraction resources due to evaluation lag.

[0165] In summary, the input and output of the S4 step constitute the key data chain of the evaluation process: the input data come from the gas flow Q(t) of S1 and the permeability evolution model k(r,t) of S3, wherein k(r,t) fuses the geological structure influencing factor of S2 and the rock mass stress data σ(t) of S1; the output effective extraction coefficient η(t) is used as the input of S5 step to generate the extraction effect level signal by comparing with the preset threshold value; this step establishes a quantitative relationship between the physical model (permeability) and the engineering index (extraction radius), ensuring the logical coherence from geological data to application evaluation.

[0166] S5. Generating a real-time extraction effect level signal according to the comparison relationship between the effective extraction coefficient and the preset extraction efficiency threshold value.

[0167] In the embodiments of the present application, generating a real-time extraction effect level signal according to the comparison relationship between the effective extraction coefficient and the preset extraction efficiency threshold value comprises:

[0168] presetting a multi-level extraction efficiency threshold value interval;

[0169] matching the effective extraction coefficient to the corresponding multi-level extraction efficiency threshold value interval, and activating a level identifier according to the matching result, wherein the level identifier is encoded as a digital signal output.

[0170] In detail, matching the effective extraction coefficient to the corresponding multi-level extraction efficiency threshold value interval comprises:

[0171] activating a low-efficiency identifier when the coefficient is lower than the first threshold value;

[0172] activating a medium-efficiency identifier when the coefficient is between the first and second threshold values;

[0173] activating a high-efficiency identifier when the coefficient is between the second and third threshold values;

[0174] activating a super-high-efficiency identifier when the coefficient exceeds the third threshold value.

[0175] In detail, the effective extraction coefficient refers to a dimensionless parameter calculated by the S4 step, which is the ratio of the effective extraction radius to the designed extraction radius, and is used to quantify the matching degree of the actual extraction effect of the borehole to the design target.

[0176] In detail, the preset extraction efficiency threshold value refers to the extraction efficiency evaluation standard value preset in the system initialization stage, which is used as the critical value for dividing different extraction effect levels, and is usually determined based on the geological conditions and extraction process requirements.

[0177] In detail, the multi-stage extraction efficiency threshold interval refers to a plurality of efficiency intervals divided by a plurality of preset thresholds, each interval corresponding to a different extraction effect level, such as low, medium, high, and super-high efficiency intervals.

[0178] In detail, the level identifier refers to a symbol or code used to represent the extraction effect level, which is activated according to the effective extraction coefficient matching result, such as a text identifier, an icon, or a numerical code.

[0179] In detail, the digital signal refers to the conversion of the level identifier into a binary or multi-bit electrical signal that can be recognized by the monitoring system, facilitating remote transmission and automated control.

[0180] In detail, the low-efficiency identifier is a level identifier activated when the effective extraction coefficient is below the first threshold, indicating that the extraction effect is not as expected and the extraction process needs to be adjusted; the medium-efficiency identifier is a level identifier activated when the effective extraction coefficient is between the first and second thresholds, indicating that the extraction effect is at a medium level and the current process can be maintained; the high-efficiency identifier is a level identifier activated when the effective extraction coefficient is between the second and third thresholds, indicating that the extraction effect is good and meets the design requirements; and the super-high-efficiency identifier is a level identifier activated when the effective extraction coefficient exceeds the third threshold, indicating that the extraction effect is better than expected and resource allocation can be optimized.

[0181] Further, the determination of the preset multi-stage extraction efficiency threshold interval includes setting the thresholds based on industry standards (such as the "Interim Provisions for Coal Mine Gas Extraction Standards") and field experience, combined with coal seam permeability, extraction time, and other parameters. For example, for high-permeability coal seams, the first threshold can be set to 0.6, the second threshold to 0.8, and the third threshold to 0.95.

[0182] Further, using statistical analysis, the effective extraction coefficients of different effect levels in historical extraction data are clustered to determine the boundary values of each interval, ensuring that the threshold division meets the actual production needs.

[0183] Further, the interval division implementation includes dividing the extraction efficiency from low to high into four intervals, corresponding to four levels of effect levels:

[0184] Low-efficiency interval: η(t) < first threshold (e.g., η(t) < 0.6)

[0185] Medium-efficiency interval: first threshold ≤ η(t) < second threshold (e.g., 0.6 ≤ η(t) < 0.8)

[0186] High-efficiency interval: second threshold ≤ η(t) < third threshold (e.g., 0.8 ≤ η(t) < 0.95)

[0187] Super-high-efficiency interval: η(t) ≥ third threshold (e.g., η(t) ≥ 0.95)

[0188] The threshold parameter is stored in a system configuration file, supporting users to dynamically adjust according to actual conditions.

[0189] Further, matching the effective extraction coefficient to the threshold interval includes: reading the effective extraction coefficient η(t) from the output result of S4 step in real time, updating the frequency consistent with the S4 solving frequency (such as updating once every 10 seconds), ensuring the real-time of the matching process; using multiple conditional branching structures (such as if-else statements) for interval matching, for the precision error that may be caused by floating point operation, using tolerance processing (such as allowing an error range of ±0.01), avoiding misjudgment of the level caused by calculation error.

[0190] Further, when η(t) is equal to the threshold value, it is matched to a higher level interval according to the "high but not low" principle (such as η=0.6 being classified into the medium efficiency interval), ensuring that the evaluation result is safe-oriented.

[0191] Further, the identifier mapping rule includes: establishing a mapping table of level name and identifier

[0192] Further, the hardware activation mechanism includes: outputting a level signal through the IO interface of PLC (Programmable Logic Controller) or MCU (Micro Control Unit), driving the indicator light or buzzer:

[0193] Low efficiency: red indicator light always on, buzzer low-frequency alarm;

[0194] Medium efficiency: yellow indicator light flickering, buzzer intermittent prompt;

[0195] High efficiency: green indicator light always on, no alarm;

[0196] Super high efficiency: green indicator light flashes quickly, accompanied by prompt sound.

[0197] Further, the digital signal encoding and transmission includes: encoding the level identifier into a binary digital signal (such as two-bit binary number 00-11), and transmitting it to the central monitoring system through RS485, Modbus, etc. Industrial communication protocol:

[0198] Further, the signal visualization processing includes: dynamically displaying the extraction effect level on the monitoring software interface, presenting it in the form of color blocks and text annotations, for example:

[0199] Low efficiency: red block displays "low efficiency (η(t)=X.XX)";

[0200] Super high efficiency: green block displays "super high efficiency (η(t)=X.XX)".

[0201] Overall, by converting the abstract effective extraction coefficient into an intuitive grade signal, the problem of fuzzy effect judgment and inconvenience for real-time control in traditional evaluation methods is solved. This step realizes the visualization and digital expression of the extraction effect, enabling field operators to quickly grasp the running state of the extraction system and take timely optimization measures (such as adjusting the extraction negative pressure and drilling density), avoiding substandard gas extraction or resource waste due to evaluation lag, thereby improving the automation and intelligent level of gas extraction management.

[0202] Overall, the S5 step is the final link of the entire evaluation process, with the input directly from the effective extraction coefficient η(t) calculated in the S4 step, and the calculation of η(t) relying on the gas flow data of S1, the spatial weight matrix of S2, and the permeability model of S3. The specific connection is as follows: η(t) output by S4 is the only input of S5, determining the generation logic of the grade signal; the generated grade signal can be fed back to the sensor collection frequency of S1 or the model parameter calibration of S3, forming a closed-loop control (such as automatically increasing the sensor sampling frequency to obtain more accurate data when low efficiency is detected); this step converts the physical model calculation result (η(t)) into an engineering application signal, realizing the key conversion from data processing to production decision-making, and ensuring the engineering practicability of the evaluation result.

[0203] As shown in Figure 2 , it is a functional module diagram of the borehole gas extraction effect real-time evaluation system provided by an embodiment of the present application.

[0204] The borehole gas extraction effect real-time evaluation system 100 described in the present application can be installed in an electronic device. According to the realized functions, the borehole gas extraction effect real-time evaluation system 100 can include a data acquisition module 101, a weight distribution generation module 102, a permeability model construction module 103, an effective extraction coefficient generation module 104, and an extraction effect determination module 105. The modules described in the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, stored in the memory of the electronic device.

[0205] In the present embodiment, the functions of each module / unit are as follows:

[0206] The data acquisition module 101 is used to synchronously acquire the gas flow data, rock mass stress data, and geological structure data of the region where the borehole is located.

[0207] The weight distribution generation module 102 is used to generate a spatial weight distribution matrix that controls the gas migration path based on the fault azimuth angle parameter in the geological structure data and the fracture density distribution map.

[0208] The permeability model construction module 103 is configured to construct a permeability evolution model along a radial direction of the borehole according to the spatial weight distribution matrix and the rock mass stress data;

[0209] The effective extraction coefficient generation module 104 is configured to input the gas flow data into the permeability evolution model, solve a time-varying integral equation of an effective extraction radius, and output an effective extraction coefficient of the borehole.

[0210] The extraction effect determination module 105 is configured to generate a real-time extraction effect level signal according to a comparison relationship between the effective extraction coefficient and a preset extraction efficiency threshold.

[0211] In several embodiments provided in the present application, it should be understood that the disclosed method and system can be implemented in other ways. For example, the system embodiments described above are merely illustrative, for example, the division of the modules is only a logical function division, and another division mode can be used in actual implementation.

[0212] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs.

[0213] In addition, the function modules in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software function modules.

[0214] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0215] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology and application system for simulating, extending and expanding human intelligence by using digital computers or computer-controlled machines, perceiving environment, acquiring knowledge and using knowledge to obtain optimal results.

[0216] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for real-time evaluation of borehole gas extraction effectiveness, characterized in that, The method includes: S1. Simultaneously collect gas flow data, rock stress data, and geological structure data of the target borehole area; S2. Based on the fault azimuth parameters and fracture density distribution map in the geological structural data, generate a spatial weight distribution matrix controlling the gas migration path, including: The spatial angle between the fault strike and the borehole axis is calculated based on the fault azimuth parameters in the geological structure data, wherein the spatial angle is generated based on the fault azimuth and the grid point azimuth. An intensity gradient field for fracture development is generated based on the fracture density distribution map in the geological structure data. The migration path weights of each grid point in the borehole radial direction are calculated by combining the spatial angle and the intensity gradient field. The weight values ​​of the transport path are mapped to three-dimensional matrix elements to generate a spatial weight distribution matrix; S3. Construct a permeability evolution model along the borehole radial direction based on the spatial weight distribution matrix and the rock mass stress data, including: The migration path weight values ​​in the spatial weight distribution matrix are normalized to obtain the geological structure influence factor. Calculate the stress sensitivity coefficient based on the rock mass stress data; Based on the Coulomb fracture criterion, a stress conversion operator between permeability and the rock mass stress data is derived. By integrating the geological structure influencing factors and the stress transformation operator, a permeability evolution model is generated, and the permeability evolution model outputs a permeability evolution field that varies with radial distance and time; S4. Input the gas flow data into the permeability evolution model, and output the effective extraction coefficient of the borehole by solving the time-varying integral equation of the effective extraction radius; S5. Based on the comparison between the effective extraction coefficient and the preset extraction efficiency threshold, generate a real-time extraction effect level signal.

2. The method for real-time evaluation of borehole gas extraction effectiveness as described in claim 1, characterized in that, The synchronous acquisition of gas flow rate data, rock stress data, and geological structure data of the target borehole area includes: Real-time gas flow pulse signals are obtained by using a multi-parameter sensor array inside the borehole as the gas flow data for the target borehole. Stress sensors are used to measure the stress fluctuation waveform of the surrounding rock in the borehole as rock mass stress data; Fault azimuth parameters and fracture density distribution maps of the borehole location area were retrieved from the geological database as geological structural data.

3. The method for real-time evaluation of borehole gas extraction effectiveness as described in claim 1, characterized in that, The calculation of the migration path weight values ​​of each grid point in the borehole radial direction, combining the spatial angle and the intensity gradient field, includes: A polar coordinate system is established with the fault strike as the reference axis, and the intensity gradient field is projected onto the polar coordinate system. The azimuth alignment between the grid points and the fault is quantified using the cosine function of the included angle, wherein the azimuth alignment is: ； in, It is the aforementioned orientation alignment. It is the fault azimuth angle. It is the azimuth angle of the grid point; An exponential decay function is used to correlate the azimuth alignment with the fracture development density to obtain a migration path weight value characterizing the preferred gas migration path. The formula for calculating the migration path weight value is as follows: ； in, This is the weight value of the transport path. It is the aforementioned orientation alignment. It is the density of the fracture development. It is the distance attenuation coefficient. It is the radial distance extending outward from the center of the borehole. It is the azimuth angle of the grid point.

4. The method for real-time evaluation of borehole gas extraction effectiveness as described in claim 1, characterized in that, The penetration rate evolution model is as follows: ； in, Radial distance and time Permeability at that location As the benchmark penetration rate, These are the geological structural influencing factors. For reference stress, It is the radial distance extending outward from the center of the borehole. For time variables, The rock mass stress data, It is the stress sensitivity coefficient.

5. The method for real-time evaluation of borehole gas extraction effectiveness as described in claim 1, characterized in that, The time-varying integral equation for solving the effective extraction radius includes: A radial flow integral equation with a permeability evolution model as the variable is established based on Darcy's law; Based on the radial flow integral equation, the gas flow data is used as an integral constraint to solve for the effective extraction radius; Calculate the ratio of the effective extraction radius to the designed extraction radius, and output the effective extraction coefficient.

6. The method for real-time evaluation of borehole gas extraction effectiveness as described in claim 5, characterized in that, The radial flow integral equation is: ； in, This refers to the gas flow data. It is the height of the coal seam. It is the pressure difference between reservoir pressure and borehole pressure. It's the viscosity of the gas. It is the borehole wall radius. It is the effective extraction radius. It is the radial distance of the borehole. and time Permeability at that location It is the radial distance extending outward from the center of the borehole. It is a time variable.

7. The method for real-time evaluation of borehole gas extraction effectiveness as described in claim 1, characterized in that, The step of generating a real-time sampling effect level signal based on the comparison between the effective sampling coefficient and the preset sampling efficiency threshold includes: Preset multi-level extraction efficiency threshold range; The effective sampling coefficient is matched to the corresponding multi-level sampling efficiency threshold range, and the level identifier is activated according to the matching result, wherein the level identifier is encoded as a digital signal output.

8. A real-time evaluation system for borehole gas extraction effectiveness, characterized in that, The system includes: The data acquisition module is used to simultaneously acquire gas flow data, rock stress data, and geological structure data of the target borehole area. The weight distribution generation module is used to generate a spatial weight distribution matrix controlling the gas migration path based on the fault azimuth parameters and fracture density distribution map in the geological structure data, including: The spatial angle between the fault strike and the borehole axis is calculated based on the fault azimuth parameters in the geological structure data, wherein the spatial angle is generated based on the fault azimuth and the grid point azimuth. An intensity gradient field for fracture development is generated based on the fracture density distribution map in the geological structure data. The migration path weights of each grid point in the borehole radial direction are calculated by combining the spatial angle and the intensity gradient field. The weight values ​​of the transport path are mapped to three-dimensional matrix elements to generate a spatial weight distribution matrix; A permeability model construction module is used to construct a permeability evolution model along the borehole radial direction based on the spatial weight distribution matrix and the rock mass stress data, including: The migration path weight values ​​in the spatial weight distribution matrix are normalized to obtain the geological structure influence factor. Calculate the stress sensitivity coefficient based on the rock mass stress data; Based on the Coulomb fracture criterion, a stress conversion operator between permeability and the rock mass stress data is derived. By integrating the geological structure influencing factors and the stress transformation operator, a permeability evolution model is generated, and the permeability evolution model outputs a permeability evolution field that varies with radial distance and time; The effective extraction coefficient generation module is used to input the gas flow data into the permeability evolution model, and output the effective extraction coefficient of the borehole by solving the time-varying integral equation of the effective extraction radius. The extraction effect determination module is used to generate a real-time extraction effect level signal based on the comparison between the effective extraction coefficient and the preset extraction efficiency threshold.

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