A method for phase division of liquid carbon dioxide phase transition induced fracturing and evaluation of coal seam permeability improvement effect

Abstract

The application discloses a liquid carbon dioxide phase change fracturing stage division and coal seam permeability improvement effect evaluation method, comprising the following steps: collecting mechanical response signals in the fracturing process, extracting time domain peak value, energy characteristic, strain characteristic parameter and constructing stage boundary identification function F; according to the characteristic parameter variation law, the fracturing process is divided into a first action stage dominated by stress waves and a second action stage dominated by high-pressure gas splitting; the action stage is determined by comparing the function F with the preset interval; the gas extraction concentration before and after fracturing, the extraction pure flow, the flow attenuation coefficient and the cumulative extraction amount are obtained; and the comprehensive evaluation conclusion is generated by combining the stage identification result and the engineering response data. The application realizes the closed loop of fracturing process stage quantitative identification and permeability improvement effect evaluation, and provides a reliable basis for on-site process optimization and effect review.

Description

Technical Field

[0001] This invention relates to the field of coal mine gas control and coal seam permeability enhancement technology, specifically to a method for classifying the stages of liquid carbon dioxide phase change-induced fracturing and evaluating the effect of coal seam permeability enhancement. Background Technology

[0002] Coal seam permeability enhancement technology is a key means to solve the problem of gas extraction in high-gas, low-permeability coal seams. Currently, commonly used coal seam permeability enhancement methods in engineering include hydraulic fracturing, water-based fracturing, liquid nitrogen fracturing, high-energy gas fracturing, and deep-hole shaped charge blasting. However, these methods generally have some limitations in field applications. For example, hydraulic fracturing carries the risk of water-locking and reservoir contamination; liquid nitrogen fracturing requires extremely high low-temperature performance from equipment; and high-energy gas fracturing and shaped charge blasting have poor controllability of their effective range and are accompanied by certain safety risks. These factors, to some extent, restrict the applicability and economic efficiency of existing permeability enhancement technologies.

[0003] Liquid carbon dioxide phase change fracturing technology, as an emerging permeability enhancement method, has gradually attracted industry attention due to its advantages such as being waterless, pollution-free, having strong displacement ability, and facilitating the formation of complex fracture networks. In the liquid carbon dioxide phase change fracturing process, it is generally believed that there is an initial stress wave action stage and a subsequent high-pressure carbon dioxide gas fracturing stage, and these two stages have significantly different contributions to the fracturing mechanism and permeability enhancement of the coal body.

[0004] However, existing technologies still have the following shortcomings: First, there is a lack of clear and operable quantitative criteria for accurately defining the boundaries of different stages in the fracturing process, resulting in a lack of basis for adjusting on-site process parameters; second, the evaluation of permeability enhancement effects relies heavily on a single extraction index, failing to effectively correlate the stage identification results of the fracturing process with the final extraction engineering effect, thus leaving process diagnosis and effect verification in a disconnected state. Therefore, there is an urgent need to establish a comprehensive method that integrates process identification and effect evaluation to serve on-site process optimization and effect verification. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for dividing the phases of liquid carbon dioxide phase change fracturing and evaluating the permeability enhancement effect of coal seams.

[0006] To achieve the above objectives, the present invention provides the following technical solution: This application provides a method for dividing the stages of liquid carbon dioxide phase change-induced fracturing and evaluating the permeability enhancement effect of coal seams, including the following steps: S1. During the phase transformation cracking process of liquid carbon dioxide, mechanical response signals of the target area are collected, and the mechanical response signals include at least vibration response parameters and deformation response parameters; S2. The mechanical response signal is preprocessed and features are extracted to obtain multiple feature parameters characterizing the dynamic characteristics of the cracking process. The feature parameters include time-domain peak parameters, energy feature parameters, strain feature parameters, and a stage boundary identification function F constructed based on the above parameters. S3. Based on the variation law of the characteristic parameters, the liquid carbon dioxide phase change fracturing process is divided into a first action stage dominated by stress waves and a second action stage dominated by high-pressure gas splitting. S4. Compare the stage boundary identification function F with the preset interval determination conditions to determine the current action stage and output the stage identification result; S5. Obtain engineering response data for coal seam gas drainage before and after fracturing, wherein the engineering response data includes gas drainage concentration, drainage pure flow rate, flow rate attenuation coefficient and cumulative drainage volume; S6. Combining the stage identification results with the changing trends of the engineering response data, generate a comprehensive evaluation conclusion on the coal seam permeability enhancement effect, and output the stage duration, key characteristic parameters, and permeability enhancement effect level.

[0007] Furthermore, the stage boundary identification function F is a joint criterion function constructed based on a weighted combination of the vibration response parameters and the deformation response parameters, and its expression is: F=w1·(a / a0)+w2·(v / v0)+w3·(E / E0)+w4·(ε / ε0) In the formula, a is the measured acceleration amplitude, a0 is the acceleration reference value; v is the measured velocity amplitude, v0 is the velocity reference value; E is the measured energy characteristic value, E0 is the energy reference value; ε is the measured strain characteristic value, ε0 is the strain reference value; w1, w2, w3, and w4 are weighting coefficients, and satisfy w1+w2+w3+w4=1.

[0008] Furthermore, the interval determination condition is based on the inflection point of the numerical change of the stage boundary identification function F in the fracturing process, and when the value of F is in the first numerical interval, it is determined to be the first action stage, and when the value of F is in the second numerical interval, it is determined to be the second action stage.

[0009] Furthermore, the duration of the first action phase is from the crack initiation time to the first moment, during which the peak amplitude of the vibration response parameter is higher than the first threshold and the amplitude of the deformation response parameter is lower than the second threshold; the duration of the second action phase is from the first moment to the second moment, during which the peak amplitude of the vibration response parameter is lower than the third threshold and the amplitude of the deformation response parameter is higher than the fourth threshold.

[0010] Furthermore, in step S5, the flow rate attenuation coefficient α is obtained by fitting the relationship between the extracted pure flow rate and time to an exponential attenuation model, wherein the exponential attenuation model is: q t =q0·e (-αt) In the formula, q t Let q0 be the pure gas extraction flow rate at time t, t be the extraction time, and α be the flow rate attenuation coefficient.

[0011] Furthermore, the cumulative extraction volume Q J The calculation formula is obtained based on the initial pure gas extraction flow rate q0 and the flow rate attenuation coefficient α, and is as follows: Q J =1440·q0 / α In the formula, Q J This represents the cumulative sampling volume over any given time period.

[0012] Furthermore, the difficulty of coal seam extraction is classified according to the numerical range of the flow attenuation coefficient α: When α < 0.003, it is judged as easy to extract; When 0.003≤α≤0.05, it is determined to be at the extractable level; When α > 0.05, it is judged as difficult to extract.

[0013] Furthermore, the generation rules for the anti-reflective effect level in S6 include: Compare the direction and magnitude of the change in the flow attenuation coefficient α before and after fracturing; The increase ratio of the gas extraction concentration and the pure extraction flow rate is combined; The duration of the second action phase in the stage identification result is used as an auxiliary criterion. When the duration of the second action phase is greater than the preset effective action time and the flow attenuation coefficient α decreases from greater than 0.05 to less than 0.05, the anti-penetration effect is determined to be significant.

[0014] A coal seam permeability enhancement effect evaluation system implementing the above method includes: The data acquisition unit is used to acquire the mechanical response signal during the liquid carbon dioxide phase change fracturing process and the engineering response data of coal seam gas extraction before and after fracturing. The feature extraction unit, connected to the data acquisition unit, is used to extract time-domain peak parameters, energy characteristic parameters, and strain characteristic parameters from the mechanical response signal, and to construct a stage boundary identification function F; A stage determination unit, connected to the feature extraction unit, is used to output the current active stage based on the comparison result between the stage boundary recognition function F and the preset interval determination condition. An effect evaluation unit, connected to the stage determination unit, is used to generate comprehensive evaluation information of the anti-reflection effect based on the engineering response data and the stage determination results; The output unit is used to output the stage recognition results, feature parameters, and evaluation conclusions of the anti-reflection effect.

[0015] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, performs the steps of the method described above.

[0016] Compared with the prior art, this application has the following beneficial effects: The method provided by this invention collects mechanical response signals during the liquid carbon dioxide phase change fracturing process, extracts multi-dimensional feature parameters including time-domain peak values, energy characteristics, strain characteristics, and stage boundary identification function F, and clearly divides the fracturing process into a first action stage dominated by stress waves and a second action stage dominated by high-pressure gas splitting. The method then uses the stage boundary identification function F to compare with preset interval judgment conditions to achieve quantitative identification of stage boundaries, solving the problem of inaccurate differentiation of action stage boundaries in existing technologies. Based on this, the method further acquires engineering response data of coal seam gas extraction before and after fracturing, including extraction concentration, pure flow rate, flow rate attenuation coefficient, and cumulative extraction volume. The method correlates the stage identification results with the changing trends of the engineering response data to generate a comprehensive evaluation conclusion including stage duration, key feature parameters, and permeability enhancement effect level. This achieves a closed-loop evaluation from fracturing process identification to permeability enhancement effect verification, providing direct and quantitative decision-making basis for on-site process parameter optimization and construction effect verification, and significantly improving the engineering operability and evaluation accuracy of liquid carbon dioxide phase change fracturing permeability enhancement technology. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention.

[0018] Figure 2 This is a flowchart of the construction of the mechanical response signal feature extraction and stage boundary identification function F in this invention.

[0019] Figure 3 This is a structural block diagram of the coal seam permeability enhancement effect evaluation system of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and 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.

[0021] Furthermore, in this invention, an element referred to as fixed to or disposed on another element may be directly disposed on the other element, or there may be an intermediate element. When an element is considered to be connected to another element, it may be directly connected to the other element, or there may be an intermediate element present simultaneously. The terms vertical, horizontal, left, right, and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0022] Example 1 See Figure 1 , Figure 2 This application provides a method for dividing the stages of phase change fracturing caused by liquid carbon dioxide and evaluating the permeability enhancement effect of coal seams, characterized by the following steps: S1 collects mechanical response signals in the target area during the phase transformation cracking process of liquid carbon dioxide. The mechanical response signals include at least vibration response parameters and deformation response parameters. During liquid carbon dioxide phase change fracturing tests or on-site construction, monitoring devices are deployed in the target coal seam area. Vibration response parameters are acquired using vibration monitoring devices (such as accelerometers and velocity sensors) to obtain dynamic response data of particle motion during fracturing. Deformation response parameters are acquired using strain monitoring devices (such as strain gauges and strain meters) to obtain deformation data of the coal and rock mass during fracturing. During acquisition, the raw mechanical response signals of the entire fracturing process are continuously recorded. These mechanical response signals include at least parameters reflecting vibration characteristics and parameters reflecting deformation characteristics. This acquisition method can completely capture all dynamic process information from the start to the end of fracturing, providing a data foundation for subsequent feature extraction and stage division.

[0023] S2 preprocesses and extracts features from the mechanical response signal to obtain multiple feature parameters characterizing the dynamic characteristics of the cracking process. The feature parameters include time-domain peak parameters, energy feature parameters, strain feature parameters, and a stage boundary identification function F constructed based on the above parameters. First, the acquired raw mechanical response signal is preprocessed, including signal denoising, baseline correction, and effective event extraction, to eliminate the effects of environmental interference and instrument drift. Then, key feature parameters are extracted from the preprocessed signal, specifically including: Peak parameters in the time domain: extracting peak acceleration and peak velocity from the vibration response signal; Energy characteristic parameters: Peak energy reflecting the intensity of fracturing energy release, calculated based on vibration response signals; Strain characteristic parameters: Extract the first principal strain from the deformation response signal.

[0024] The extraction of the above parameters can be achieved using conventional signal processing methods. For example, peak acceleration and peak velocity can be obtained through time-domain waveform extremum search; peak energy can be obtained by integrating the square of the velocity signal within the effective time window; and the first principal strain can be calculated from strain monitoring data using elasticity formulas.

[0025] Based on this, a feature function F is constructed to identify stage boundaries. Function F is a joint criterion based on the above parameters. It is constructed by normalizing each parameter and then weighting and combining them to achieve the fusion of multi-dimensional information and improve the accuracy and stability of stage boundary identification.

[0026] S3 divides the liquid carbon dioxide phase change fracturing process into a first action stage dominated by stress waves and a second action stage dominated by high-pressure gas splitting, based on the variation law of the characteristic parameters. Based on the variation of the characteristic parameters extracted in step S2 throughout the entire fracturing time history, the liquid carbon dioxide phase change fracturing process can be divided into two stages with significantly different mechanistic characteristics. In the initial stage of fracturing, the peak amplitude of the vibration response parameters is extremely high, with concentrated and instantaneous energy release, while the amplitude of the deformation response parameters is relatively small. This stage corresponds to the stress wave action process excited by the instantaneous phase change of liquid carbon dioxide, and is called the first action stage (stress wave-dominated stage). After the stress wave action, the amplitude of the vibration response parameters decreases significantly, while the deformation response parameters continue to increase, indicating that high-pressure carbon dioxide gas begins to invade along the fracture and drive further fracture expansion. This stage is called the second action stage (high-pressure gas splitting-dominated stage).

[0027] S4 compares the stage boundary identification function F with the preset interval determination conditions to determine the current action stage and outputs the stage identification result. To achieve quantitative determination of the action stage, the stage boundary identification function F value calculated in real time or offline is compared with pre-calibrated interval determination conditions. When the F value is within the first numerical interval, the current moment is determined to be in the first action stage; when the F value is within the second numerical interval, the current moment is determined to be in the second action stage. Through this step, the start and end times and duration of each action stage in the fracturing process can be automatically and clearly identified, and the corresponding stage identification results can be output.

[0028] S5 acquires engineering response data for coal seam gas drainage before and after fracturing. The engineering response data includes gas drainage concentration, pure drainage flow rate, flow rate attenuation coefficient, and cumulative drainage volume. After determining the fracturing stage, further response data reflecting the effectiveness of the permeability enhancement project are obtained. During coal seam gas drainage before and after fracturing, the following data are continuously monitored and recorded: gas drainage concentration (gas volume percentage in the drainage pipeline), pure drainage flow rate (pure gas volume extracted per unit time), flow rate decay coefficient (parameter characterizing the rate of decay of drainage flow rate over time), and cumulative drainage volume (total pure gas volume extracted within a certain time period).

[0029] S6 combines the stage identification results with the changing trends of the engineering response data to generate a comprehensive evaluation conclusion on the coal seam permeability enhancement effect, and outputs the stage duration, key characteristic parameters, and permeability enhancement effect level.

[0030] The stage identification results output in step S4 (including stage division results, stage duration, key characteristic parameters of each stage, etc.) are correlated with the engineering response data obtained in step S5. By comparing the changing trends of extraction concentration, pure flow rate, flow rate attenuation coefficient, and cumulative extraction volume before and after fracturing, and combining this with the degree of fracturing effect reflected in the stage identification results, a comprehensive evaluation conclusion on the permeability enhancement effect is generated. The output comprehensive conclusion includes at least: stage division results, key parameters of each stage, stage duration, changes in extraction difficulty, and permeability enhancement effect level.

[0031] In one specific embodiment, the stage boundary identification function F is a joint criterion function constructed based on a weighted combination of the vibration response parameters and the deformation response parameters, and its expression is: F=w1·(a / a0)+w2·(v / v0)+w3·(E / E0)+w4·(ε / ε0) In the formula, a is the measured acceleration amplitude, a0 is the acceleration reference value; v is the measured velocity amplitude, v0 is the velocity reference value; E is the measured energy characteristic value, E0 is the energy reference value; ε is the measured strain characteristic value, ε0 is the strain reference value; w1, w2, w3, and w4 are weighting coefficients, and satisfy w1+w2+w3+w4=1.

[0032] The baseline values ​​a0, v0, E0, and ε0 can be determined by averaging multiple measurements under experimental conditions or by theoretically calculated reference values. The weighting coefficients w1 to w4 can be set according to the sensitivity of each parameter to stage changes. For example, the weights of acceleration and velocity terms can be appropriately increased during the stress wave stage, and the weight of strain terms can be appropriately increased during the gas splitting stage. Through the above weighted combination, the function F integrates multiple parameters with different dimensions and large amplitude differences into a dimensionless comprehensive index, effectively reducing the uncertainty caused by the fluctuation of a single parameter in stage determination.

[0033] In one specific implementation, the interval determination condition is based on the inflection point of the numerical change of the stage boundary identification function F in the fracturing process, and when the value of F is in the first numerical interval, it is determined to be the first action stage, and when the value of F is in the second numerical interval, it is determined to be the second action stage.

[0034] The interval determination criteria are based on the pre-calibrated inflection points of the numerical changes of the stage boundary identification function F during the fracturing process. In practical applications, the curve of the function F over time can be plotted by analyzing typical fracturing test data, and the obvious numerical change characteristics (such as inflection points or steps) appearing in the transition region between the first and second action stages can be observed. Based on these change characteristics, the boundary values ​​of the first and second numerical intervals are determined. When the value of function F is within the first numerical interval, the current moment is determined to be the first action stage; when the value of function F is within the second numerical interval, the current moment is determined to be the second action stage.

[0035] In one specific embodiment, the duration of the first action phase is from the crack initiation time to the first moment, during which the peak amplitude of the vibration response parameter is higher than a first threshold and the amplitude of the deformation response parameter is lower than a second threshold; the duration of the second action phase is from the first moment to the second moment, during which the peak amplitude of the vibration response parameter is lower than a third threshold and the amplitude of the deformation response parameter is higher than a fourth threshold.

[0036] The duration of the first action stage is from the fracturing initiation moment to the first moment, which corresponds to the moment when the stage boundary identification function F crosses from the first numerical interval to the second numerical interval. During this stage, the peak amplitude of vibration response parameters (such as acceleration and velocity) is higher than the first threshold, indicating that the stress wave action is strong; the amplitude of deformation response parameters (such as the first principal strain) is lower than the second threshold, indicating that the coal body has not yet undergone significant quasi-static deformation.

[0037] The duration of the second action phase is from the first moment to the second moment, which corresponds to the moment when the fracturing process is basically over and the signal decays to the background level. During this phase, the peak amplitude of the vibration response parameter is lower than the third threshold, while the amplitude of the deformation response parameter is higher than the fourth threshold, reflecting the continuous driving of the fracture by the high-pressure carbon dioxide gas and the significant deformation of the coal body.

[0038] In one specific implementation, in step S5, the flow attenuation coefficient α is obtained by fitting the relationship between the extracted pure flow rate and time to an exponential attenuation model, wherein the exponential attenuation model is: q t =q0·e (-αt) In the formula, q tLet q0 be the pure gas extraction flow rate at time t, t be the extraction time, and α be the flow rate attenuation coefficient.

[0039] In practice, the pure extraction flow rate q corresponding to different times t recorded during the gas extraction process after fracturing of the borehole will be used. t The data was fitted nonlinearly using the exponential decay model described above to obtain the model parameters q0 and α. The value of α directly reflects the decay rate of the gas extraction flow rate; the smaller the α, the slower the flow rate decays and the better the sustainability of the extraction.

[0040] In one specific implementation, the cumulative extraction volume Q J The calculation formula is obtained based on the initial pure gas extraction flow rate q0 and the flow rate attenuation coefficient α, and is as follows: Q J =1440·q0 / α In the formula, Q J q0 represents the cumulative extraction volume over any given time period; q0 represents the initial pure gas extraction flow rate. α is the flow rate attenuation coefficient.

[0041] When the unit of q0 is m³ / min and the unit of α is d - ¹When the coefficient is 1440, it is used to convert minute flow rate to daily cumulative flow rate; if other unit systems are used, the coefficient should be adjusted accordingly. This formula can be used to quickly estimate the total gas extraction volume within a certain extraction cycle, providing a quantitative basis for performance evaluation.

[0042] In one specific implementation, the difficulty of coal seam extraction is classified according to the numerical range of the flow attenuation coefficient α: When α < 0.003, it is judged as easy to extract; When 0.003≤α≤0.05, it is determined to be at the extractable level; When α > 0.05, it is judged as difficult to extract.

[0043] The above grading standards are empirical thresholds derived from a large amount of field extraction data. By comparing the α values ​​calculated before and after fracturing with the above intervals, the effectiveness of permeability enhancement measures in improving the ease of coal seam extraction can be intuitively determined.

[0044] In one specific implementation, the rule for generating the anti-reflective effect level in S6 includes: Compare the direction and magnitude of the change in the flow attenuation coefficient α before and after fracturing; The increase ratio of the gas extraction concentration and the pure extraction flow rate is combined; The duration of the second action phase in the stage identification result is used as an auxiliary criterion. When the duration of the second action phase is greater than the preset effective action time and the flow attenuation coefficient α decreases from greater than 0.05 to less than 0.05, the anti-penetration effect is determined to be significant.

[0045] The specific rules for generating the anti-reflective effect level include: (1) Compare the direction and magnitude of the change in the flow rate attenuation coefficient α before and after fracturing; if the value of α decreases significantly, it indicates that the attenuation rate of gas extraction flow rate slows down after fracturing and the permeability enhancement effect is obvious.

[0046] (2) The evaluation is based on the increase in gas extraction concentration and pure flow rate; the increase in concentration and pure flow rate directly reflects the improvement in coal seam permeability and the enhancement of gas desorption capacity.

[0047] (3) The duration of the second action stage in the stage identification results is used as an auxiliary criterion; the duration of the second action stage reflects the sufficiency of the high-pressure carbon dioxide gas fracturing effect; when the duration of the second action stage is greater than the preset effective action time, and the flow rate attenuation coefficient α decreases from greater than 0.05 before fracturing (difficult extraction range) to less than or equal to 0.05 after fracturing (extraction or easy extraction range), the permeability enhancement effect is judged to be significant.

[0048] By comprehensively evaluating the above multi-dimensional and multi-indicator approaches, the characteristics of the cracking process are directly linked to the engineering results, making the evaluation conclusions more comprehensive and reliable.

[0049] Example 2 Please see Figure 3 This embodiment provides a coal seam permeability enhancement effect evaluation system for implementing the above method, including: The data acquisition unit is used to acquire the mechanical response signal during the liquid carbon dioxide phase change fracturing process and the engineering response data of coal seam gas extraction before and after fracturing. The feature extraction unit, connected to the data acquisition unit, is used to extract time-domain peak parameters, energy characteristic parameters, and strain characteristic parameters from the mechanical response signal, and to construct a stage boundary identification function F; A stage determination unit, connected to the feature extraction unit, is used to output the current active stage based on the comparison result between the stage boundary recognition function F and the preset interval determination condition. An effect evaluation unit, connected to the stage determination unit, is used to generate comprehensive evaluation information of the anti-reflection effect based on the engineering response data and the stage determination results; The output unit is used to output the stage recognition results, feature parameters, and evaluation conclusions of the anti-reflection effect.

[0050] The system includes a data acquisition unit, a feature extraction unit, a stage determination unit, an effect evaluation unit, and an output unit.

[0051] The data acquisition unit is used to acquire the mechanical response signals during the liquid carbon dioxide phase change fracturing process and the engineering response data of coal seam gas drainage before and after fracturing. This unit can consist of vibration sensors, strain sensors, gas drainage parameter monitoring instruments, and corresponding data acquisition modules deployed in the field or test environment.

[0052] The feature extraction unit is connected to the data acquisition unit and is used to preprocess the acquired mechanical response signal and extract the time-domain peak parameter, energy characteristic parameter, and strain characteristic parameter from it. At the same time, it constructs the stage boundary identification function F according to the preset benchmark value and weight coefficient.

[0053] The stage determination unit is connected to the feature extraction unit. It is used to compare the calculated stage boundary recognition function F value with the pre-stored interval determination conditions, determine the current action stage, and output the stage recognition result.

[0054] The effect evaluation unit is connected to the stage determination unit. It is used to receive engineering response data, fit the flow attenuation coefficient α according to the exponential attenuation model, and generate comprehensive evaluation information of the anti-transparency effect by combining the stage identification results.

[0055] The output unit is connected to the effect evaluation unit and is used to output the stage recognition results, feature parameters and anti-reflection effect evaluation conclusions in the form of a visual interface or data report.

[0056] The data flow and collaborative workflow between the units of this system correspond to steps S1 to S6 of the method described in Example 1, enabling automated and systematic evaluation of the permeability enhancement effect of liquid carbon dioxide phase change cracking.

[0057] Example 3 This embodiment provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0058] When executed by a processor, the computer program can implement all or part of the steps of the method described in Embodiment 1. Specifically, the program may include instructions for performing the following operations: reading mechanical response signal data and extracting feature parameters; calculating the stage boundary identification function F; comparing the F value with a preset interval to determine the action stage; reading engineering response data and fitting a flow attenuation coefficient α; generating and outputting a comprehensive evaluation conclusion based on the stage identification result and the engineering response data.

[0059] The computer-readable storage medium can be any medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk. Through this storage medium, the method of the present invention can be easily deployed in the industrial control computer, server, or portable analysis terminal of a coal mine safety monitoring system, realizing the software application of the method.

[0060] Example 4 The following detailed description of the implementation process of the method of the present invention in a coal mine is provided in conjunction with a specific application scenario. This embodiment takes a gas extraction project of a high-gas, low-permeability coal seam as the background, implements liquid carbon dioxide phase change fracturing and permeability enhancement operation, and uses the method of the present invention to divide the action stages and evaluate the permeability enhancement effect.

[0061] The target coal seam is the main coal seam of a certain coal mine, with an average thickness of 3.2m, an original gas content of 12.5m³ / t, a gas pressure of 1.8MPa, and a permeability coefficient of 0.008m² / (MPa²·d), which is a typical high-gas, low-permeability coal seam. Before the implementation of liquid carbon dioxide phase change fracturing and permeability enhancement measures, the concentration of gas extracted from boreholes in this area has been maintained between 6% and 10% for a long time, with a small and rapid decrease in the pure flow rate, resulting in unsatisfactory extraction effect. Fracturing boreholes were drilled in the target coal seam, with a depth of 80m and a diameter of 94mm. A liquid carbon dioxide phase change fracturing device was used for the fracturing operation, with approximately 30L of liquid carbon dioxide injected into each borehole. Monitoring boreholes were set up at distances of 5m, 10m, and 15m from the fracturing boreholes to install vibration monitoring devices and strain monitoring devices.

[0062] The vibration monitoring device uses a triaxial accelerometer and velocity sensor, with a sampling frequency of 100kHz to ensure complete capture of high-frequency transient signals during the stress wave action phase. The strain monitoring device uses resistance strain gauges attached to the borehole wall and works in conjunction with a dynamic strain gauge for data acquisition, with a sampling frequency of 100kHz. In addition, gas drainage parameter monitoring instruments are installed at the fracturing borehole and adjacent drainage boreholes to continuously record engineering response data such as gas drainage concentration and pure drainage flow rate.

[0063] The data acquisition system was activated, triggering the liquid carbon dioxide phase change fracturing device. From the moment fracturing began, the vibration monitoring device and strain monitoring device synchronously and continuously acquired mechanical response signals, with a recording duration set to 20ms to ensure complete coverage of the entire process during the stress wave action stage and the high-pressure carbon dioxide gas splitting action stage. After the acquisition was completed, the raw data of acceleration time history curves, velocity time history curves, energy time history curves, and strain time history curves at each monitoring point were obtained.

[0064] Taking a monitoring point 5m away from the fracturing borehole as an example, the typical characteristics of the collected data are as follows: the acceleration signal shows a sharp high-amplitude pulse in a very short time after fracturing, and then decays rapidly; the velocity signal shows similar characteristics; the strain signal has a small amplitude in the initial stage, and then gradually increases and lasts for a long time.

[0065] The acquired raw mechanical response signal was preprocessed, using wavelet thresholding to filter out high-frequency noise and baseline correction to eliminate zero drift. Subsequently, the following feature parameters were extracted from the processed signal: Peak acceleration a: Searching for the global maximum value on the acceleration time history curve, we get a = 25.46g; Peak velocity v: Searching for the global maximum value on the velocity time history curve yields v = 249.51 μm / s; Peak energy E: Integrating the square of the velocity signal over the effective time window, we get E = 9.48 × 10⁻⁶. 10 (μm / s)²; The first principal strain ε: calculated from strain monitoring data, ε = 0.0049.

[0066] Based on the experimental conditions, the following baseline values ​​were set: acceleration baseline a0 = 25g, velocity baseline v0 = 250μm / s, and energy baseline E0 = 1.0 × 10⁻⁶. 11 (μm / s)², strain reference value ε0=0.01. The weighting coefficients are set after statistical analysis based on the sensitivity of each parameter to stage changes as follows: w1=0.3, w2=0.3, w3=0.2, w4=0.2.

[0067] Substitute the expression for the stage boundary identification function F into the calculation: F=0.3×(25.46 / 25)+0.3×(249.51 / 250)+0.2×(9.48×10 10 / 1.0×10 11 )+0.2×(0.0049 / 0.01) =0.3×1.0184+0.3×0.9980+0.2×0.948+0.2×0.49 =0.3055+0.2994+0.1896+0.098 =0.8925 Similarly, a sliding window calculation was performed on the mechanical response signals at different times during the entire cracking process to obtain the sequence of F-value changes over time.

[0068] Analyzing the variation patterns of characteristic parameters throughout the entire fracturing time history reveals that: within the time interval of 0–1.551 ms, the amplitudes of acceleration and velocity are at a high level, with peak acceleration reaching 25.46 g, peak velocity reaching 249.51 μm / s, and energy reaching as high as 9.48 × 10⁻⁶. 10 The stress wave intensity was (μm / s)², while the first principal strain was only 0.0049, indicating that stress wave action dominated this stage and the coal body had not yet undergone significant quasi-static deformation. Therefore, this period was divided into the first action stage (stress wave dominant stage).

[0069] Within the time frame of 1.551–12.606 ms, the amplitudes of acceleration and velocity decreased significantly, with the peak acceleration dropping to 6.527 g, the peak velocity to 63.97 μm / s, and the energy to 1.47 × 10⁻⁶. 7 (μm / s)², while the first principal strain increased to 0.0430, indicating that the stress wave had attenuated at this stage, and the high-pressure carbon dioxide gas began to have a continuous splitting and propagation effect on the coal body fractures. Therefore, this period is divided into the second action stage (the high-pressure gas splitting-dominated stage).

[0070] Based on the calibration results of the experimental data, the first numerical range of the stage boundary identification function F is (-0.376, 0.678], and the second numerical range is (0.678, 2.406). The F-value sequence calculated in step S2 is compared with the above ranges: During the period from 0 to 1.551 ms, the F value fluctuated between 0.3 and 0.6, all falling within the first value range, which was determined to be the first action stage; Around 1.551ms, the F value shows a significant jump, rising from below 0.678 to above 0.678; During the period from 1.551 to 12.606 ms, the F value fluctuated between 0.8 and 2.2, all falling within the second value range, and was determined to be the second action stage.

[0071] Therefore, the output phase identification results can be clearly defined: the duration of the first action phase is 1.551ms, and the duration of the second action phase is 11.055ms.

[0072] The results indicate that the high-pressure carbon dioxide gas fracturing effect lasted for a sufficient duration and the fractures expanded sufficiently, providing process-level support for achieving the anti-permeability effect.

[0073] After the fracturing operation was completed, gas drainage monitoring was conducted on the target extraction borehole for 30 consecutive days, recording the daily gas drainage concentration and pure flow rate. Simultaneously, drainage data prior to fracturing was obtained from adjacent boreholes that had not undergone fracturing as a control.

[0074] Pre-fracturing extraction data showed that the initial extraction flow rate q0 was 0.85 m³ / min; the extraction concentration fluctuated between 7% and 10%; the data on the change of extraction flow rate over time were analyzed using an exponential decay model q t =q0·e (-αt) By fitting the data, the pre-fracture flow attenuation coefficient αprevious was obtained as 0.011d. - ¹, corresponding to the level of difficulty of extraction as extractable (0.003≤0.011≤0.05).

[0075] Post-fracture extraction data showed that the initial pure extraction flow rate q0 after fracture was 1.52 m³ / min, an increase of approximately 79% compared to before fracture; the extraction concentration increased to 18%–24%, with an average concentration of approximately 22%, more than double that before fracture. Using an exponential decay model, the post-fracture flow rate decay coefficient α after fracture was found to be 0.007d. - ¹, the corresponding extraction difficulty level is still classified as extractable, but the α value has decreased by about 36% compared with the pre-fracture level, indicating that the flow rate attenuation rate has slowed down significantly.

[0076] Cumulative extraction volume Q J According to formula Q J =1440·q0 / α Calculation: Before rupture: Q J Front = 1440 × 0.85 / 0.011 ≈ 111,273 m³ After cracking: Q J The final volume is approximately 1440 × 1.52 / 0.007 ≈ 312,686 m³. It is evident that, within the same extraction cycle, the cumulative gas extraction volume after fracturing is approximately 2.8 times that before fracturing, demonstrating a significant permeability enhancement effect.

[0077] The stage identification results of step S4 are correlated with the engineering response data of step S5: The phase identification results show that the duration of the second action phase is 11.055ms, which is greater than the preset effective action duration threshold (e.g., 8ms), indicating that the high-pressure gas fracturing effect is sufficient and the fracture network is well developed. Engineering response data shows that the flow rate attenuation coefficient α after fracturing decreased from 0.011 to 0.007, a decrease of 36%. Although the extraction difficulty level is still within the extractable level, the significant reduction in the α value indicates a significant improvement in the sustainability of extraction. The extraction concentration increased from 8% to 22%, the pure extraction flow rate increased by 79%, and the cumulative extraction volume increased by approximately 181%.

[0078] Based on the above analysis and in accordance with the rules for generating the permeability enhancement effect level, the duration of the second action stage is longer than the preset effective action time, and the flow attenuation coefficient α shows a significant decreasing trend. At the same time, the extraction concentration, pure flow rate, and cumulative extraction volume are all significantly improved. Therefore, the permeability enhancement effect is determined to be "significant level".

[0079] The final comprehensive conclusions include: Stage division results: stress wave action stage (0~1.551ms), high-pressure carbon dioxide gas splitting action stage (1.551~12.606ms); Key parameters for each stage: Peak acceleration of 25.46g, peak velocity of 249.51μm / s, and peak energy of 9.48×10⁻⁶ during the first action stage. 10 (μm / s)², first principal strain 0.0049; peak acceleration of the second action phase 6.527g, peak velocity 63.97μm / s, peak energy 1.47×10 7 (μm / s)², first principal strain 0.0430.

[0080] Phase duration: First phase 1.551ms, second phase 11.055ms; Changes in extraction difficulty: Before fracturing, α = 0.011 (extraction possible); after fracturing, α = 0.007 (extraction possible), with the attenuation rate decreasing by 36%. Enhancement effect level: Significant.

[0081] The above evaluation conclusions can directly serve the optimization of subsequent fracturing parameters and the adjustment of construction techniques. For example, based on the correlation between the duration of the second action stage and the permeability enhancement effect, the amount of liquid carbon dioxide injected or the release rate can be optimized to extend the high-pressure gas fracturing time and further improve the permeability enhancement effect. At the same time, the quantitative indicators output by this method also provide a unified and comparable evaluation basis for comparing the effects of different fracturing techniques.

[0082] It should be noted that the specific values ​​given in this embodiment (such as acceleration 25.46g, velocity 249.51μm / s, time boundary 1.551ms, attenuation coefficients 0.011 and 0.007, etc.) are all derived from actual experiments and field monitoring data, and are used to specifically illustrate the implementation of the present invention. In actual engineering applications, the above values ​​may vary due to factors such as coal seam occurrence conditions, charge parameters, and monitoring arrangements. Those skilled in the art can, within the framework of the present invention, achieve accurate identification of the fracturing process and reasonable evaluation of the permeability enhancement effect under different working conditions by adaptively calibrating the benchmark values ​​and thresholds. These adaptive adjustments should all be considered to fall within the protection scope of the present invention.

[0083] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0084] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for dividing the stages of phase change-induced fracturing by liquid carbon dioxide and evaluating the permeability enhancement effect of coal seams, characterized in that, Includes the following steps: S1. During the phase transformation cracking process of liquid carbon dioxide, mechanical response signals of the target area are collected, and the mechanical response signals include at least vibration response parameters and deformation response parameters; S2. The mechanical response signal is preprocessed and features are extracted to obtain multiple feature parameters characterizing the dynamic characteristics of the cracking process. The feature parameters include time-domain peak parameters, energy feature parameters, strain feature parameters, and a stage boundary identification function F constructed based on the above parameters. S3. Based on the variation law of the characteristic parameters, the liquid carbon dioxide phase change fracturing process is divided into a first action stage dominated by stress waves and a second action stage dominated by high-pressure gas splitting. S4. Compare the stage boundary identification function F with the preset interval determination conditions to determine the current action stage and output the stage identification result; S5. Obtain engineering response data for coal seam gas drainage before and after fracturing, wherein the engineering response data includes gas drainage concentration, drainage pure flow rate, flow rate attenuation coefficient and cumulative drainage volume; S6. Combining the stage identification results with the changing trends of the engineering response data, generate a comprehensive evaluation conclusion on the coal seam permeability enhancement effect, and output the stage duration, key characteristic parameters, and permeability enhancement effect level.

2. The method according to claim 1, characterized in that, The stage boundary identification function F is a joint criterion function constructed based on a weighted combination of the vibration response parameters and the deformation response parameters, and its expression is: F=w1·(a / a0)+w2·(v / v0)+w3·(E / E0)+w4·(ε / ε0) In the formula, a is the measured acceleration amplitude, a0 is the acceleration reference value; v is the measured velocity amplitude, v0 is the velocity reference value; E is the measured energy characteristic value, E0 is the energy reference value; ε is the measured strain characteristic value, ε0 is the strain reference value; w1, w2, w3, and w4 are weighting coefficients, and w1+w2+w3+w4=1.

3. The method according to claim 2, characterized in that, The interval determination criteria are pre-calibrated based on the inflection point of the numerical change of the stage boundary identification function F during the fracturing process: When the value of F is within the first value range, it is determined to be the first action stage; When the value of F is within the second numerical range, it is determined to be the second action stage.

4. The method according to claim 1, characterized in that, The duration of the first action phase is from the crack initiation time to the first moment. During this phase, the peak amplitude of the vibration response parameter is higher than the first threshold, and the amplitude of the deformation response parameter is lower than the second threshold. The duration of the second action phase is from the first moment to the second moment. During this phase, the peak amplitude of the vibration response parameter is lower than the third threshold, and the amplitude of the deformation response parameter is higher than the fourth threshold.

5. The method according to claim 1, characterized in that, In step S5, the flow attenuation coefficient α is obtained by fitting the relationship between the extracted pure flow rate and time to an exponential attenuation model, wherein the exponential attenuation model is: q t =q0·e (-αt) In the formula, q t Let q0 be the pure gas extraction flow rate at time t, t be the extraction time, and α be the flow rate attenuation coefficient.

6. The method according to claim 5, characterized in that, The cumulative extraction volume Q J The calculation formula is obtained based on the initial pure gas extraction flow rate q0 and the flow rate attenuation coefficient α, and is as follows: Q J =1440·q0 / α In the formula, Q J This represents the cumulative sampling volume over any given time period.

7. The method according to claim 5 or 6, characterized in that, The difficulty of coal seam extraction is classified according to the numerical range of the flow attenuation coefficient α: When α < 0.003, it is judged as easy to extract; When 0.003≤α≤0.05, it is determined to be of a extractable level; When α > 0.05, it is judged as difficult to extract.

8. The method according to claim 1, characterized in that, The generation rules for the anti-reflective effect level in S6 include: Compare the direction and magnitude of the change in the flow attenuation coefficient α before and after fracturing; The increase ratio of the gas extraction concentration and the pure extraction flow rate is combined; The duration of the second action phase in the stage identification result is used as an auxiliary criterion. When the duration of the second action phase is greater than the preset effective action time and the flow attenuation coefficient α decreases from greater than 0.05 to less than 0.05, the anti-penetration effect is determined to be significant.

9. A coal seam permeability enhancement effect evaluation system implementing the method according to any one of claims 1 to 8, characterized in that, include: The data acquisition unit is used to acquire the mechanical response signal during the liquid carbon dioxide phase change fracturing process and the engineering response data of coal seam gas extraction before and after fracturing. The feature extraction unit, connected to the data acquisition unit, is used to extract time-domain peak parameters, energy characteristic parameters, and strain characteristic parameters from the mechanical response signal, and to construct a stage boundary identification function F; A stage determination unit, connected to the feature extraction unit, is used to output the current active stage based on the comparison result between the stage boundary recognition function F and the preset interval determination condition. An effect evaluation unit, connected to the stage determination unit, is used to generate comprehensive evaluation information of the anti-reflection effect based on the engineering response data and the stage determination results; The output unit is used to output the stage recognition results, feature parameters, and evaluation conclusions of the anti-reflection effect.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 8.

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