Coal rock pore structure nitrogen adsorption and mercury injection combined characterization method and device

Abstract

The application discloses a coal rock pore structure nitrogen adsorption and mercury injection combined characterization method and device, obtains the mercury injection experimental data of the coal rock to be measured, and identifies the fractal dimension, so that the stable range of pore filling is changed into the mercury injection demarcation point of mutation; the sample compressibility coefficient is calculated based on the compression dominant interval data after the mercury injection demarcation point, and the mercury injection curve is corrected to obtain the real pore filling curve; the nitrogen adsorption experimental data of the coal rock to be measured are obtained, the single-layer maximum adsorption capacity is calculated, the FHH fractal model is constructed based on the single-layer adsorption capacity, and the nitrogen adsorption demarcation point of the change of the fractal dimension slope is identified; the nitrogen adsorption demarcation point is input into the BJH model to calculate the upper limit pore diameter of the nitrogen adsorption method, the pore diameter distribution of the mercury injection method is calculated based on the real pore filling curve, and the lower limit pore diameter is determined; the upper limit pore diameter and the lower limit pore diameter are spliced to obtain the full pore diameter distribution characterization result of the coal rock to be measured, and the unified and continuous characterization in the full pore diameter range is realized.

Description

Technical Field

[0001] This application relates to the field of coal and rock reservoir physical properties and pore structure characterization technology, specifically to a method and apparatus for combined nitrogen adsorption and mercury intrusion porosimetry characterization of coal and rock pore structure. Background Technology

[0002] Quantitative characterization of coal and rock pore structure is a crucial foundation for evaluating the occurrence and seepage characteristics of coalbed methane. Currently, nitrogen adsorption and mercury porosimetry are the two most widely used pore size analysis methods.

[0003] Nitrogen adsorption is based on the principle of physical adsorption of gases, and its theoretical measurement range is approximately 2–100 nm in pore size, which can accurately characterize the microporous structure in coal and rock. However, at higher relative pressures, the accuracy of characterizing large pores (>50 nm) decreases significantly due to factors such as capillary condensation, adsorption-desorption hysteresis, and isotherm model extrapolation.

[0004] Mercury intrusion porosimetry relies on applying external pressure to non-wetting mercury to induce it into pores. The theoretical measurement range is approximately 3 nm–50 μm, and it is relatively sensitive for characterizing mesopores and macropores. However, in high-pressure zones, the rock sample framework undergoes compressive deformation, leading to systematic errors in the pore size distribution of the high-pressure section (corresponding to the micropore portion).

[0005] Therefore, it is evident that nitrogen adsorption has biases in the macropore range, while mercury intrusion porosimetry is inaccurate in measuring micropores in high-pressure regions. Neither method alone can obtain a true and continuous pore size distribution. To achieve full-scale characterization from micropores to macropores, it is usually necessary to combine the results of the two tests. Traditional methods often use empirical values ​​(e.g., 50 nm) as the junction point between nitrogen adsorption and mercury intrusion porosimetry. However, this empirical value ignores the differences in surface energy state, pore morphology, and mercury intrusion compression effect among different coal samples, which can easily lead to abrupt changes in pore volume at the junction point, affecting the continuity and authenticity of the full pore size distribution, and thus impacting the accuracy and physical consistency of coal and rock pore structure characterization. Summary of the Invention

[0006] Therefore, this application provides a method and apparatus for joint characterization of coal and rock pore structure by nitrogen adsorption and mercury intrusion porosimetry, in order to solve the problem of discontinuous and unrealistic pore size distribution caused by the use of fixed pore size splicing in the joint characterization of nitrogen adsorption and mercury intrusion porosimetry in the prior art.

[0007] To achieve the above objectives, this application provides the following technical solution:

[0008] Firstly, a method for characterizing the pore structure of coal and rock using a combination of nitrogen adsorption and mercury porosimetry includes:

[0009] Step 1: Obtain mercury intrusion porosimetry data of the coal and rock to be tested, perform fractal analysis on the mercury intrusion porosimetry data, and identify the mercury intrusion porosimetry boundary point where the fractal dimension changes from the stable range characterizing pore filling to the point where abrupt changes.

[0010] Step 2: Based on the data of the compression-dominant interval after the mercury intrusion porosimetry (MIP) boundary point, calculate the sample compressibility coefficient of the coal and rock sample to be tested, and correct the mercury intrusion porosimetry curve according to the sample compressibility coefficient to eliminate the volume error caused by matrix compression and obtain the true pore filling curve.

[0011] Step 3: Obtain nitrogen adsorption experimental data of the coal and rock to be tested, calculate the maximum adsorption capacity of a single layer, construct an FHH fractal model based on the single-layer adsorption capacity, and identify the nitrogen adsorption boundary point where the fractal dimension slope changes by performing fractal analysis on the FHH fractal model.

[0012] Step 4: Input the nitrogen adsorption boundary point into the BJH model to calculate the upper limit pore size of the nitrogen adsorption method, calculate the pore size distribution of the mercury intrusion porosimetry method based on the real pore filling curve, and determine the lower limit pore size corresponding to the upper limit pore size.

[0013] Step 5: Combine the upper limit pore size with the lower limit pore size to obtain the full pore size distribution characterization result of the coal and rock to be tested.

[0014] Optionally, in step 1, the identification of the fractal dimension changes from a stable range characterizing pore filling to a mercury intrusion porosimetry boundary point where abrupt changes occur, specifically including:

[0015] The mercury intrusion porosimetry experimental data were standardized by units, and the mercury volume change rate under unit pressure change was calculated by numerical differentiation. The mercury volume change rate was linearly fitted in a double logarithmic coordinate system to calculate the fractal dimension of different pressure ranges. The fractal dimension was transformed from being stable in the range of 2-3 to the pressure point corresponding to the sudden change and exceeding 3, which was determined as the mercury intrusion porosimetry boundary point.

[0016] Optionally, in step 2, calculating the compressibility coefficient of the coal and rock sample to be tested specifically includes:

[0017] In the compression-dominant region after the mercury intrusion boundary point, the mercury intrusion volume and pressure data are linearly fitted to obtain the volume change rate; based on the solid skeleton volume and non-mercury-intrusive micropore volume of the coal and rock sample to be tested, the effective volume of the compression-dominant region is determined; the compressibility coefficient of the sample is calculated according to the volume change rate and the effective volume.

[0018] Optionally, in step 3, the calculation of the maximum adsorption capacity of a single layer specifically includes: processing the nitrogen adsorption isotherm data using the BET model, performing linear fitting within a selected linear interval, and calculating the maximum adsorption capacity of a single layer based on the slope and intercept obtained from the fitting.

[0019] Optionally, in step 3, the construction of the FHH fractal model based on the monolayer adsorption amount, and the identification of the nitrogen adsorption boundary point where the fractal dimension slope changes through fractal analysis of the FHH fractal model, specifically includes:

[0020] An FHH fractal curve is constructed based on the maximum adsorption capacity of the monolayer; the slope change points on the FHH fractal curve are identified, and the slope change points correspond to two linear intervals, representing the surface adsorption-dominated stage and the capillary condensation-dominated stage, respectively; the relative pressure corresponding to the slope change points is determined as the nitrogen adsorption boundary point.

[0021] Optionally, in step 4, the step of inputting the nitrogen adsorption boundary point into the BJH model to calculate the upper limit pore size of the nitrogen adsorption method specifically includes:

[0022] Substitute the nitrogen adsorption boundary point into the Kelvin equation to calculate the capillary condensation radius; calculate the adsorption layer thickness corresponding to the nitrogen adsorption boundary point according to the Halsey empirical film thickness formula; calculate the actual pore radius based on the capillary condensation radius and the adsorption layer thickness, and use the actual pore radius as the upper limit pore diameter of the nitrogen adsorption method.

[0023] Optionally, in step 4, calculating the pore size distribution of the mercury intrusion porosimetry based on the actual pore filling curve and determining the lower limit pore size corresponding to the upper limit pore size specifically includes:

[0024] The actual pore filling curve is converted into the pore size distribution of mercury intrusion porosimetry using the Washburn equation; the pressure corresponding to the mercury intrusion porosimetry boundary point is used to calculate the lower limit pore size of mercury intrusion porosimetry using the Washburn equation, and the lower limit pore size corresponds to the upper limit pore size of the nitrogen adsorption method.

[0025] Secondly, a combined nitrogen adsorption and mercury injection characterization device for coal and rock pore structures includes:

[0026] The mercury intrusion porosimetry (MIP) boundary point determination module is used to acquire MIP experimental data of the coal and rock to be tested, perform fractal analysis on the MIP experimental data, and identify the MIP boundary point where the fractal dimension changes from the stable range characterizing pore filling to the point where abrupt changes.

[0027] The mercury intrusion porosimetry curve correction module is used to calculate the sample compressibility coefficient of the coal and rock sample to be tested based on the data of the compression-dominant interval after the mercury intrusion porosimetry boundary point, and to correct the mercury intrusion porosimetry curve according to the sample compressibility coefficient, so as to eliminate the volume error caused by matrix compression and obtain the true pore filling curve.

[0028] The nitrogen adsorption boundary point determination module is used to acquire nitrogen adsorption experimental data of the coal and rock to be tested, calculate the maximum adsorption capacity of a single layer, construct an FHH fractal model based on the single-layer adsorption capacity, and identify the nitrogen adsorption boundary point where the fractal dimension slope changes by performing fractal analysis on the FHH fractal model.

[0029] The pore size upper and lower limit determination module is used to input the nitrogen adsorption boundary point into the BJH model to calculate the upper limit pore size of the nitrogen adsorption method, calculate the pore size distribution of the mercury intrusion porosimetry method based on the real pore filling curve, and determine the lower limit pore size corresponding to the upper limit pore size.

[0030] The aperture splicing module is used to splice the upper limit aperture and the lower limit aperture to obtain the full aperture distribution characterization result of the coal and rock to be tested.

[0031] Thirdly, a computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of a combined nitrogen adsorption and mercury intrusion porosimetry characterization method for coal and rock pore structures.

[0032] Fourthly, a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of a combined characterization of nitrogen adsorption and mercury intrusion porosimetry of a coal and rock pore structure.

[0033] Compared with the prior art, this application has at least the following beneficial effects:

[0034] This application provides a combined nitrogen adsorption and mercury intrusion porosimetry (MIP) characterization method for the pore structure of coal and rock, comprising: acquiring MIP experimental data of the coal and rock to be tested and identifying the MIP boundary point where the fractal dimension changes from the stable range characterizing pore filling to an abrupt change; calculating the compressibility coefficient of the sample based on the data of the compressibility-dominant interval after the MIP boundary point, and correcting the MIP curve to obtain the true pore filling curve; acquiring nitrogen adsorption experimental data of the coal and rock to be tested and calculating the maximum adsorption capacity of a single layer, constructing an FHH fractal model based on the single layer adsorption capacity, and identifying the nitrogen adsorption boundary point where the fractal dimension slope changes; inputting the nitrogen adsorption boundary point into the BJH model to calculate the upper limit pore size of the nitrogen adsorption method, calculating the pore size distribution of the mercury intrusion porosimetry method based on the true pore filling curve, and determining the lower limit pore size; and splicing the upper limit pore size and the lower limit pore size to obtain the full pore size distribution characterization result of the coal and rock to be tested. This method combines the differences in surface energy state, pore morphology and mercury intrusion compression effect of different coal samples to achieve unified and continuous characterization across the entire pore size range, thereby improving the accuracy and physical consistency of coal and rock pore structure characterization. Attached Figure Description

[0035] To more intuitively illustrate the prior art and this application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary drawings, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).

[0036] Figure 1 A flowchart of a method for joint characterization of coal and rock pore structure by nitrogen adsorption and mercury porosimetry provided in Embodiment 1 of this application;

[0037] Figure 2 This is a schematic diagram showing the results of fractal dimension calculation and mutation point identification of the original mercury intrusion data provided in Embodiment 1 of this application;

[0038] Figure 3 This is a linear fitting diagram of the volume and pressure at the high-pressure end of the mercury intrusion porosimetry system provided in Embodiment 1 of this application.

[0039] Figure 4 This is a comparison chart of mercury intrusion data before and after compressibility correction provided in Embodiment 1 of this application;

[0040] Figure 5 This is a schematic diagram of the fractal fitting results of the low-pressure mercury intrusion data after compressibility correction provided in Embodiment 1 of this application;

[0041] Figure 6 This is a schematic diagram of the bilinear characteristics of the nitrogen adsorption fractal curve provided in Embodiment 1 of this application;

[0042] Figure 7 This is a graph showing the relationship between the thickness t of the adsorption layer and relative pressure, provided in Embodiment 1 of this application.

[0043] Figure 8 This is a schematic diagram of the original empirical splicing aperture distribution provided in Embodiment 1 of this application;

[0044] Figure 9 This is a schematic diagram of the fractal splicing aperture distribution provided in Embodiment 1 of this application. Detailed Implementation

[0045] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] In the description of this application: unless otherwise stated, "a plurality of" means two or more. The terms "first," "second," "third," etc., in this application are intended to distinguish the objects referred to and do not have any special meaning in terms of technical connotation (e.g., they should not be construed as an emphasis on importance or order). Expressions such as "including," "comprising," and "having" also mean "not limited to" (certain units, components, materials, steps, etc.).

[0047] The terms used in this application, such as "upper," "lower," "left," "right," and "middle," are generally used to indicate the general relative positional relationship for the purpose of intuitive understanding by referring to the accompanying drawings, and are not absolute limitations on the positional relationship in the actual product.

[0048] Invention principle:

[0049] Based on fractal theory, this application proposes that the geometric complexity of the pore surface and pore volume of porous materials under different pressures or relative pressures can be characterized by fractal dimension. The change in fractal dimension not only reflects the multi-scale characteristics of the pore structure, but also reveals the shift in the experimental process from "real pore filling" to "dominance of other physical mechanisms".

[0050] In mercury intrusion porosimetry (MIP) experiments, at lower pressures, mercury sequentially penetrates pores from largest to smallest. The volume change primarily reflects the pore-filling process, and the fractal dimension at this stage is generally between 2 and 3, characterizing the true pore structure. When the pressure further increases to the high-pressure zone, the sample undergoes significant volume compression. The mercury intrusion volume shows a near-linear relationship with the pressure, and the mercury volume change is no longer controlled by pore intrusion but mainly by matrix compression. At this point, the fractal dimension undergoes a sudden change and may exceed 3, marking the transition from the "pore-filling stage" to the "compressibility-dominated stage."

[0051] In nitrogen adsorption experiments, under low relative pressure, adsorbed molecules mainly form a monolayer, and the change in adsorption amount is controlled by surface fractal characteristics, resulting in a low fractal dimension. As the relative pressure increases, multilayer adsorption and capillary condensation gradually occur, with more fine pores being covered by the adsorption layer, increasing the system complexity and raising the fractal dimension. When the relative pressure further increases to approximately 0.9 or higher, the adsorption layer thickness becomes comparable to the pore size, the cylindrical pore assumption of the BJH model fails, capillary condensation becomes dominant, the system tends to become smoother, and the fractal dimension reaches its peak and then begins to decline. This peak value or the inflection point of its rate of change characterizes the effective measurement upper limit of the nitrogen adsorption method.

[0052] By analyzing the variation patterns of fractal dimension in the two sets of experiments above, the "compressibility abrupt change point" in the mercury intrusion porosimetry experiment and the "capillary condensation abrupt change point" in the nitrogen adsorption experiment can be identified respectively. These two points correspond to the boundaries of their respective effective measurement intervals. Using the pore sizes corresponding to these two abrupt change points as splicing boundaries, adaptive splicing of the pore size distributions of nitrogen adsorption and mercury intrusion porosimetry can be achieved without using fixed empirical pore sizes.

[0053] Example 1

[0054] Based on the above-mentioned inventive principles, this embodiment provides a method for joint characterization of coal and rock pore structures using nitrogen adsorption and mercury intrusion porosimetry. This method automatically determines the effective measurement range of each experiment through fractal dimension abrupt change points and performs adaptive splicing of pore size distribution, thereby achieving a natural transition and unified interpretation of different experimental results. Figure 1 As shown, the method includes:

[0055] S1: Obtain mercury intrusion porosimetry data of the coal and rock to be tested, perform fractal analysis on the mercury intrusion porosimetry data, and identify the mercury intrusion porosimetry boundary point where the fractal dimension changes from the stable range characterizing pore filling to the point where abrupt changes.

[0056] Specifically, coal and rock samples to be tested are selected and tested using an automatic mercury intrusion porosimeter under normal temperature conditions to obtain mercury intrusion test data. Based on the mercury intrusion test data, a curve showing the relationship between applied pressure and mercury volume is obtained. During the experiment, the experimental pressure should cover the complete pore filling and high-pressure compression process, with the maximum pressure typically exceeding 200 MPa.

[0057] This step, in identifying the transition point where the fractal dimension changes from a stable range characterizing pore filling to the abrupt mercury intrusion porosimetry boundary, includes:

[0058] S101: Perform unit standardization on mercury intrusion test data and calculate the rate of change of mercury volume under unit pressure change by numerical differentiation;

[0059] More specifically, this step standardizes the units of the mercury porosimetry experimental data to obtain continuous data. – Data sequence, and the rate of change of mercury volume under unit pressure change is calculated by numerical differentiation:

[0060]

[0061] in:

[0062] and These represent two adjacent pressure points. and The cumulative mercury volume below (unit: cm³ / g);

[0063] and This indicates the corresponding applied pressure value (unit: MPa);

[0064] This represents the rate of change in mercury volume under a unit pressure change within this range (unit: ).

[0065] S102: The fractal dimension of different pressure ranges was calculated by linearly fitting the mercury volume change rate in a double logarithmic coordinate system.

[0066] More specifically, this step will measure the rate of change of mercury volume under a unit pressure change. Convert to logarithmic coordinate form – And perform linear fitting within adjacent pressure ranges:

[0067] After linear fitting, the fitting slope A is obtained. Based on the fitting slope... Obtain the fractal dimension Fitting slope With fractal dimension The relationship is:

[0068] .

[0069] S103: The pressure point at which the fractal dimension changes from being stable in the range of 2-3 to undergoing a sudden change and exceeding 3 is determined as the mercury intrusion saturation point.

[0070] Specifically, fractal dimension Characterizing the complexity of pore structure and volume response features. In the low- to medium-pressure stage, mercury gradually enters pores of decreasing size, and the fractal dimension generally stabilizes between 2 and 3, indicating that the volume change mainly reflects the actual pore filling; when the pressure rises to the high-pressure region, the fractal dimension... The sudden increase to nearly 4 indicates that the change in mercury volume at this stage is no longer caused by pore intrusion, but mainly by the compressive deformation of the sample. Therefore, the pressure corresponding to the abrupt change in fractal dimension... As the dividing point for mercury intrusion: Mercury intrusion into the dominant region (real pore filling); : Compressibility-dominant region (matrix deformed under pressure).

[0071] It is worth noting that some coal and rock samples may exhibit outliers with fractal dimensions greater than 3 in low-pressure zones. This is because matrix compression occurs throughout the mercury intrusion porosimetry process. Although mercury begins to enter the pores during the low-pressure stage, volume changes are still simultaneously affected by pore filling and matrix compression. Therefore, this embodiment corrects the volume data throughout the process by calculating the compressibility coefficient (see step S2) to separate the compressibility contribution. After correction, the fractal dimension in the low-pressure zone is restored to a reasonable range (2–3), thereby ensuring the accuracy of abrupt change point identification.

[0072] S2: Based on the data of the compression-dominant interval after the mercury intrusion osmosis boundary point, calculate the sample compressibility coefficient of the coal and rock sample to be tested, and correct the mercury intrusion osmosis curve according to the sample compressibility coefficient to eliminate the volume error caused by matrix compression and obtain the true pore filling curve.

[0073] Specifically, this step, in calculating the compressibility coefficient of the coal and rock sample to be tested, includes:

[0074] S201: In the compression-dominant region after the mercury intrusion boundary, linear fitting is performed on the mercury intrusion volume and pressure data to obtain the volume change rate.

[0075] More specifically, this step is at the mutation point. In the subsequent compression-dominant region, mercury volume-pressure data were extracted and linearly fitted to obtain the volume change rate k:

[0076] .

[0077] S202: Based on the solid skeleton volume and mercury-inaccessible micropore volume of the coal and rock sample to be tested, determine the effective volume of the compression-dominant region; calculate the sample compressibility coefficient based on the volume change rate and the effective volume.

[0078] More specifically, according to the definition of compressibility, the volumetric compressibility coefficient of a sample in the high-pressure region. (i.e., the compressibility coefficient of the sample) represents the relative deformation rate per unit volume as a function of applied pressure, and can be expressed as:

[0079]

[0080] in, The effective volume representing the dominant compression region comprises two parts: the sample solid framework volume and the volume of the non-mercury-permeable micropores. The solid framework volume is calculated from the sample mass and true density; the non-mercury-permeable micropore volume is determined by combining the minimum pore size of the mercury intrusion porosimetry experiment with the nitrogen adsorption results.

[0081] This step utilizes the obtained sample compressibility coefficient. The mercury intrusion porosimetry curve was corrected point-by-point for volume to eliminate the artificial volume increase caused by sample deformation under pressure. The initial pressure was used as the basis for this correction. Starting from any pressure point The corrected volume calculation formula is:

[0082]

[0083] The revised mercury intrusion porosimetry curves can more accurately reflect the actual pore filling process, providing a reliable basis for subsequent splicing analysis.

[0084] S3: Obtain nitrogen adsorption experimental data of the coal and rock to be tested, calculate the maximum adsorption capacity of a single layer, construct an FHH fractal model based on the single-layer adsorption capacity, and identify the nitrogen adsorption boundary point where the fractal dimension slope changes by performing fractal analysis on the FHH fractal model.

[0085] Specifically, nitrogen adsorption experiments were conducted on the coal and rock samples to be tested at liquid nitrogen temperature (77 K), and the relationship between relative pressure and adsorption amount was recorded to obtain isotherm data points. ,in, This is the saturated vapor pressure of nitrogen. The adsorption volume at the j-th pressure point is expressed in cm³ / g STP.

[0086] This step of calculating the maximum adsorption capacity of a single layer includes: processing the nitrogen adsorption isotherm data using the BET model, performing linear fitting within a selected linear interval, and calculating the maximum adsorption capacity of a single layer based on the slope and intercept obtained from the fitting.

[0087] More specifically, the BET model was used to calculate the maximum adsorption capacity of a single layer. The calculation equation for the BET model is:

[0088] in, Represents the BET constant. This represents the amount of monolayer adsorption to be determined.

[0089] set up Then, within the selected BET linear interval (usually...) By drawing a graph within the square, an approximate linear relationship can be obtained:

[0090] The slope was obtained through linear fitting. and intercept Based on the coefficient relationships in the BET model calculation formula, we have:

[0091]

[0092] in, This represents the maximum adsorption capacity of a single layer, which serves as the benchmark for volume normalization in subsequent FHH fractal analysis.

[0093] This step involves constructing an FHH fractal model based on monolayer adsorption capacity. Fractal analysis of the FHH fractal model is then performed to identify the nitrogen adsorption boundary points where the fractal dimension slope changes. This includes:

[0094] S301: Constructing FHH fractal curves based on the maximum adsorption capacity of a single layer;

[0095] More specifically, the nitrogen adsorption experimental data were processed to calculate the variables required for fractal analysis. Based on the FHH (Frenkel–Halsey–Hill) model, the adsorption volume... With relative pressure The relationship is:

[0096]

[0097] in, Indicates the fractal correlation slope ( ), Represents a constant. This represents the maximum adsorption capacity of a single layer calculated using the BET method.

[0098] S302: Identify the slope change points on the FHH fractal curve. The slope change points correspond to two linear intervals, which represent the surface adsorption-dominated stage and the capillary condensation-dominated stage, respectively.

[0099] More specifically, plotting the FHH fractal curves reveals that the nitrogen adsorption process does not follow a single pattern across the entire relative pressure range, but rather exhibits two distinct linear intervals (i.e., the low-pressure segment and the high-pressure segment). The low-pressure segment mainly corresponds to the stage where multilayer adsorption extends along the pore wall surface (i.e., the surface adsorption-dominated stage), at which point the adsorption behavior is controlled by surface roughness and pore wall morphology. In contrast, the high-pressure segment reflects the process of gradually increasing capillary condensation, thickening of the adsorption layer, and formation of liquid phase filling (i.e., the capillary condensation-dominated stage), and its fractal characteristics are significantly different from those of the low-pressure segment.

[0100] Linear fitting was performed on the low-pressure and high-pressure sections respectively to obtain the fractal dimensions of the two sections. Generally, the fractal dimension of the low-pressure section is smaller than that of the high-pressure section, indicating that the system gradually transitions from surface adsorption to volume adsorption.

[0101] S303: The relative pressure corresponding to the point where the slope changes is determined as the nitrogen adsorption boundary point.

[0102] More specifically, the relative pressure corresponding to the intersection point of the two straight lines (i.e., the point where the slope changes) is ( This represents the critical stage where the adsorption mechanism transitions from surface adsorption to capillary condensation. Since liquid nitrogen condensation in the pores significantly intensifies thereafter, the BJH model's assumptions regarding independent cylindrical pores and localized evaporation no longer hold, leading to deviations in the pore size inversion results. Therefore, this embodiment will ( The upper limit of effective measurement for the BJH model is defined as the effective measurement limit, used to determine the reliable analytical range of nitrogen adsorption data.

[0103] S4: Input the nitrogen adsorption boundary point into the BJH model to calculate the upper limit pore size of the nitrogen adsorption method, calculate the pore size distribution of the mercury intrusion porosimetry method based on the real pore filling curve, and determine the lower limit pore size corresponding to the upper limit pore size.

[0104] Specifically, after obtaining the relative pressure corresponding to the fractal abrupt change point, it is substituted into the BJH model used in the same nitrogen adsorption data processing as the sample for pore size conversion.

[0105] Within the BJH algorithm framework, the capillary condensation radius corresponding to the relative pressure is first obtained using the Kelvin equation. Then, the thickness of the adsorption layer is calculated using Halsey's empirical film thickness formula. .

[0106] Under nitrogen at 77 K, capillary condensation radius Calculated according to the Kelvin equation:

[0107]

[0108] in:

[0109] Surface tension of liquid nitrogen, typically around [value missing]. ;

[0110] The molar volume of liquid nitrogen, typically taking the value of approximately m3 / mol;

[0111] Contact angle, which is generally considered to be completely wetted for nitrogen-solid systems. ;

[0112] Gas constant, 8.314 J / ( );

[0113] Experimental temperature, in this example, is the liquid nitrogen temperature. ;

[0114] The relative pressure corresponding to the fractal mutation point.

[0115] Adsorption layer thickness Halsey's empirical film thickness formula is used. In this embodiment, the film thickness is expressed as a t-curve (in angstroms Å) under nitrogen conditions at 77 K:

[0116]

[0117] Wherein, coefficient 3.54 and constant 5 are empirical parameters obtained by fitting nitrogen gas under 77 K conditions.

[0118] After obtaining the capillary condensation radius and the adsorption layer thickness, the actual pore radius is:

[0119]

[0120] in, This refers to the BJH aperture corresponding to the fractal abrupt change point. In this embodiment, the aperture is... The upper limit pore size is defined as the pore size of the nitrogen adsorption-BJH model, meaning that the accuracy of the BJH model significantly decreases above this pore size due to capillary condensation dominating. Subsequently, it is combined with the lower limit pore size obtained from mercury intrusion porosimetry through fractal catastrophe analysis to achieve a continuous transition in pore size distribution between the two testing methods.

[0121] This step, which calculates the pore size distribution of mercury intrusion porosimetry based on the actual pore filling curve and determines the lower limit pore size corresponding to the upper limit pore size, includes: converting the actual pore filling curve into the pore size distribution of mercury intrusion porosimetry using the Washburn equation; calculating the lower limit pore size of mercury intrusion porosimetry using the pressure corresponding to the mercury intrusion porosimetry boundary point using the Washburn equation, and the lower limit pore size corresponding to the upper limit pore size of nitrogen adsorption method.

[0122] S5: The upper limit pore size and the lower limit pore size are spliced ​​together to obtain the full pore size distribution characterization result of the coal and rock to be tested.

[0123] Specifically, in this step, when splicing the upper limit aperture and the lower limit aperture, the aperture range between the two is used as the splicing boundary. If the difference between the two is small, the average value is taken as the final splicing point.

[0124] To make the technical means and objectives of this embodiment easier to understand, this embodiment will be further explained below with reference to specific examples.

[0125] Table 1 Raw data from mercury porosimetry tests on samples from the examples

[0126]

[0127] Table 2 Raw data of nitrogen adsorption test of samples in the examples

[0128]

[0129] Based on the mercury intrusion porosimetry data of selected coal samples, fractal characteristic analysis was performed on the mercury intrusion volume-pressure relationship to verify the applicability and accuracy of the method in this embodiment. Figure 2 As shown, by performing a double logarithmic transformation on the original experimental data and linear fitting in different pressure ranges, the law of fractal dimension variation with pressure was obtained. The results show that the sample exhibits significantly different fractal characteristics in the low-pressure and high-pressure ranges, with the abrupt change in fractal dimension occurring around 27.5 MPa. In the low-pressure stage, the fractal dimension is 3.10. This value is slightly higher than the typical pore fractal range (2–3), indicating that in addition to the volume change caused by mercury intrusion into the pores, there is also a certain degree of matrix compression effect in this range, which is a "pore filling and compression coupling zone". This anomaly belongs to the "special case requiring correction" described in this embodiment, and can be corrected by subsequently calculating the compressibility coefficient. As the pressure further increases, the fractal dimension rises rapidly to 3.92, significantly exceeding 3, indicating that the volume change no longer reflects the mercury infiltration behavior, but is mainly caused by the compression and contraction of the coal skeleton, and the sample enters the compression-dominated stage.

[0130] To verify this assessment, a linear analysis was performed on the mercury volume-pressure curve. The results are as follows: Figure 3 As shown, within the range of 27.5–200 MPa, there is a highly linear correlation between the mercury injection volume and the pressure (R0). 2 =0.99), confirming that mercury hardly enters new pores at this stage, only reflecting the volume compression process. To ensure the accuracy of the compressibility coefficient calculation and avoid interference from the residual pore effect in the low-pressure section, this embodiment selects the high-pressure range of 100–200 MPa for linear fitting to calculate the compressibility coefficient of the sample.

[0131] The true density of the sample was 1.488 g / cm³. Based on nitrogen adsorption results, pores with a diameter of 4–7 nm were not penetrated by mercury, corresponding to a volume of 0.000831 cm³ / g; this portion is defined as the volume of mercury-inaccessible micropores. According to the definition of compressibility in this embodiment, the effective volume of the compressibility-dominant region... It consists of two parts: the volume of the solid framework and the volume of the mercury-inaccessible micropores. The calculation is as follows:

[0132] .

[0133] Based on this volume, the average compressibility coefficient of the sample is calculated by combining it with the mercury volume change rate in the high-pressure section:

[0134] .

[0135] Please see Figure 4 , Figure 4 This demonstrates the volumetric change characteristics of the example sample before and after compressibility correction, as well as the fractal analysis results of the low-pressure section after correction. Figure 4 It can be seen that the volume data before correction (black squares) continued to increase with increasing pressure, while the data after correction (gray dots) shifted significantly downward in the low-pressure area, and the curve shape was more gentle, indicating that the artificial volume caused by matrix compression has been effectively eliminated.

[0136] Please see Figure 5 , Figure 5 The results show the fractal analysis of the low-pressure region after compressibility correction. The calculated fractal dimension D is 2.96, which falls within the typical pore fractal range (2–3). This indicates that the volume change in the corrected low-pressure region mainly reflects the actual intrusion behavior of mercury in the porous system, rather than the compressive shrinkage effect of the sample matrix. This result further verifies the effectiveness of the compressibility correction method proposed in this embodiment. By calculating the compressibility coefficient and correcting the volume of the high-pressure data, the fractal dimension of the low-pressure region can be restored to a reasonable range, thereby ensuring that the mercury intrusion porosimetry data truly reflects the pore structure characteristics and providing a reliable basis for determining the effective measurement range of mercury intrusion porosimetry.

[0137] Further calculations using the pore size corresponding to the fractal abrupt change point of 27.5 MPa revealed that the effective measurement range of mercury porosimetry for this sample is 27 nm–105.027 μm. Within this range, the volume change is entirely caused by mercury intrusion into the true pores, making the characterization results reliable. However, pressures above this range are primarily dominated by sample matrix compression and are unsuitable for pore structure analysis. This example validates the feasibility of the proposed mercury porosimetry data identification and compressibility correction method based on fractal abrupt change points. This method accurately distinguishes between the mercury intrusion-dominated and compression-dominated stages, quantitatively determines the effective measurement range of mercury porosimetry experiments, and provides a precise boundary for reliably stitching together nitrogen adsorption and mercury porosimetry pore size distribution.

[0138] A nitrogen adsorption experiment was conducted on the same coal sample at 77 K to obtain isotherm data for the adsorption branch. Isotherm data were selected within a relative pressure range of approximately 0.05–0.30, and variables were constructed according to the BET equation:

[0139] .

[0140] like Figure 5 A straight line is fitted within the linear interval of BET to obtain the slope. and intercept The maximum adsorption capacity of a single layer was calculated based on the coefficient relationships of the BET equation. :

[0141] cm3 / g.

[0142] In obtaining Then, a double logarithmic transformation was performed on all adsorption branch data according to the FHH model to calculate the fractal analysis variables:

[0143] ,

[0144] And in – Plotting the graph in coordinates yields the following results: Figure 6 As shown, it is clear that the data points are divided into two approximately linear intervals across the entire pressure range: the low-pressure zone points are distributed along one straight line, and the high-pressure zone points are distributed along another straight line. Linear fitting is performed on both segments of data to obtain the slope of the low-pressure zone. Slope of high-pressure zone .

[0145] According to the FHH fractal relationship, the fractal dimension of the nitrogen adsorption process... With slope The relationship is:

[0146] .

[0147] Therefore, the low-pressure morphological dimension of this sample can be obtained. High pressure distinguishes the shape dimension The higher fractal dimension in the low-pressure region indicates that this stage mainly reflects the expansion process of multilayer adsorption along the rough pore walls of the surface; the fractal dimension in the high-pressure region decreases slightly, indicating that the adsorption mechanism gradually shifts to a stage dominated by capillary condensation and pore filling.

[0148] Using the results of two linear fitting segments, the intersection points of the straight lines in the low-pressure and high-pressure segments are extrapolated to obtain the FHH independent variable corresponding to the intersection points. Then by relational formula The relative pressure is calculated in reverse. The calculation results for this sample indicate that the relative pressure corresponding to the fractal abrupt change point is approximately:

[0149] That is, in Nearby, the nitrogen adsorption process shifts from being dominated by surface adsorption to a stage where capillary condensation plays a significant role.

[0150] To further verify the physical significance of this abrupt change point, the adsorption layer thickness t at each pressure point was calculated using Halsey's empirical film thickness formula:

[0151]

[0152] Then what was obtained The results of changes in relative pressure are as follows Figure 7 As shown in the figure. It can be seen that when the relative pressure is below 0.84, the film thickness... As pressure increases slowly; however, once it exceeds 0.84, The rapid increase in the value indicates a significant enhancement of the capillary condensation effect within the pores, with the pores beginning to connect via liquid. The assumption of "independent cylindrical pores + local evaporation" on which the BJH model relies no longer holds.

[0153] After determining the relative pressure limit, Substitute into the Kelvin equation to calculate the capillary condensation radius :

[0154]

[0155] Based on this formula, the capillary condensation radius of this sample at the abrupt change point is approximately: .

[0156] At the same time, Halsey's formula in The calculated thickness of the adsorption layer is approximately:

[0157]

[0158] Therefore, the actual hole radius is:

[0159] aperture This is the cutoff pore size of the nitrogen adsorption-BJH model for this sample. It indicates that the BJH model can accurately invert the pore size distribution in the range of pore size less than approximately 10 nm. However, in the high relative pressure range where the pore size is greater than this value, the BJH model is no longer applicable due to capillary condensation and pore connectivity effects, and should be further characterized by mercury intrusion porosimetry data.

[0160] This embodiment demonstrates that by using FHH fractal analysis combined with Halsey film thickness and the Kelvin equation, quantitative results can be obtained for specific samples. The fractal abrupt change point, relative pressure limit, and BJH cutoff pore size provide a precise and reproducible parameter basis for splicing nitrogen adsorption and mercury intrusion porosimetry pore size distributions, such as... Figure 8 and Figure 9 As shown.

[0161] This embodiment provides a method for jointly characterizing the pore structure of coal and rock using nitrogen adsorption and mercury intrusion porosimetry. Based on the fractal evolution characteristics of the sample, it automatically identifies the effective measurement range of both nitrogen adsorption and mercury intrusion porosimetry experiments and determines the spliced ​​pore size, achieving unified and continuous characterization across the entire pore size range. This critical pore size not only reflects the transition of the adsorption mechanism from multilayer adsorption to capillary condensation, but also represents the upper limit of the applicable range of the BJH model and the lower limit of the effective range of the mercury intrusion porosimetry model, achieving a physically continuous connection.

[0162] The nitrogen adsorption and mercury porosimetry combined characterization method for coal and rock pore structures provided in this embodiment can improve the accuracy and physical consistency of coal and rock pore structure characterization, and is applicable to the full-pore-size fine quantitative analysis of complex porous media such as coal, shale, and sandstone.

[0163] Example 2

[0164] This embodiment provides a combined nitrogen adsorption and mercury injection characterization device for the pore structure of coal and rock, comprising:

[0165] The mercury intrusion porosimetry (MIP) boundary point determination module is used to acquire MIP experimental data of the coal and rock to be tested, perform fractal analysis on the MIP experimental data, and identify the MIP boundary point where the fractal dimension changes from the stable range characterizing pore filling to the point where abrupt changes.

[0166] The mercury intrusion porosimetry curve correction module is used to calculate the sample compressibility coefficient of the coal and rock sample to be tested based on the data of the compression-dominant interval after the mercury intrusion porosimetry boundary point, and to correct the mercury intrusion porosimetry curve according to the sample compressibility coefficient, so as to eliminate the volume error caused by matrix compression and obtain the true pore filling curve.

[0167] The nitrogen adsorption boundary point determination module is used to acquire nitrogen adsorption experimental data of the coal and rock to be tested, calculate the maximum adsorption capacity of a single layer, construct an FHH fractal model based on the single-layer adsorption capacity, and identify the nitrogen adsorption boundary point where the fractal dimension slope changes by performing fractal analysis on the FHH fractal model.

[0168] The pore size upper and lower limit determination module is used to input the nitrogen adsorption boundary point into the BJH model to calculate the upper limit pore size of the nitrogen adsorption method, calculate the pore size distribution of the mercury intrusion porosimetry method based on the real pore filling curve, and determine the lower limit pore size corresponding to the upper limit pore size.

[0169] The aperture splicing module is used to splice the upper limit aperture and the lower limit aperture to obtain the full aperture distribution characterization result of the coal and rock to be tested.

[0170] For details on the implementation of each module in a combined nitrogen adsorption and mercury intrusion porosimetry characterization device for coal and rock pore structures, please refer to the above description of the limitations of a combined nitrogen adsorption and mercury intrusion porosimetry characterization method for coal and rock pore structures, which will not be repeated here.

[0171] Example 3

[0172] This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of a combined characterization method of nitrogen adsorption and mercury intrusion porosimetry for coal and rock pore structures.

[0173] Example 4

[0174] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs a step of combined characterization of nitrogen adsorption and mercury intrusion porosimetry of a coal and rock pore structure.

[0175] The technical features of the above embodiments can be combined in any way (as long as there is no contradiction in the combination of these technical features). For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; these embodiments not explicitly written should also be considered to be within the scope of this specification.

Claims

1. A method for combined characterization of coal and rock pore structure by nitrogen adsorption and mercury porosimetry, characterized in that, include: Step 1: Obtain mercury intrusion porosimetry data of the coal and rock to be tested, perform fractal analysis on the mercury intrusion porosimetry data, and identify the mercury intrusion porosimetry boundary point where the fractal dimension changes from the stable range characterizing pore filling to the point where abrupt changes. Step 2: Based on the data of the compression-dominant interval after the mercury intrusion porosimetry (MIP) boundary point, calculate the sample compressibility coefficient of the coal and rock sample to be tested, and correct the mercury intrusion porosimetry curve according to the sample compressibility coefficient to eliminate the volume error caused by matrix compression and obtain the true pore filling curve. Step 3: Obtain nitrogen adsorption experimental data of the coal and rock to be tested, calculate the maximum adsorption capacity of a single layer, construct an FHH fractal model based on the single-layer adsorption capacity, and identify the nitrogen adsorption boundary point where the fractal dimension slope changes by performing fractal analysis on the FHH fractal model. Step 4: Input the nitrogen adsorption boundary point into the BJH model to calculate the upper limit pore size of the nitrogen adsorption method, calculate the pore size distribution of the mercury intrusion porosimetry method based on the real pore filling curve, and determine the lower limit pore size corresponding to the upper limit pore size. Step 5: Combine the upper limit pore size with the lower limit pore size to obtain the full pore size distribution characterization result of the coal and rock to be tested.

2. The method for combined characterization of coal and rock pore structure by nitrogen adsorption and mercury porosimetry according to claim 1, characterized in that, In step 1, the identification of the fractal dimension changes from a stable range characterizing pore filling to the mercury intrusion porosimetry boundary point where abrupt changes occur, specifically including: The mercury intrusion porosimetry experimental data were standardized by units, and the mercury volume change rate under unit pressure change was calculated by numerical differentiation. The mercury volume change rate was linearly fitted in a double logarithmic coordinate system to calculate the fractal dimension of different pressure ranges. The fractal dimension was transformed from being stable in the range of 2-3 to the pressure point corresponding to the sudden change and exceeding 3, which was determined as the mercury intrusion porosimetry boundary point.

3. The method for combined nitrogen adsorption and mercury injection characterization of coal and rock pore structure according to claim 1, characterized in that, Step 2, calculating the compressibility coefficient of the coal and rock sample to be tested, specifically includes: In the compression-dominant region after the mercury intrusion boundary point, the mercury intrusion volume and pressure data are linearly fitted to obtain the volume change rate; based on the solid skeleton volume and non-mercury-intrusive micropore volume of the coal and rock sample to be tested, the effective volume of the compression-dominant region is determined; the compressibility coefficient of the sample is calculated according to the volume change rate and the effective volume.

4. The method for combined nitrogen adsorption and mercury injection characterization of coal and rock pore structure according to claim 1, characterized in that, In step 3, the calculation of the maximum adsorption capacity of a single layer specifically includes: processing the nitrogen adsorption isotherm data using the BET model, performing linear fitting within a selected linear interval, and calculating the maximum adsorption capacity of a single layer based on the slope and intercept obtained from the fitting.

5. The method for combined nitrogen adsorption and mercury injection characterization of coal and rock pore structure according to claim 1, characterized in that, In step 3, the construction of an FHH fractal model based on the monolayer adsorption amount, and the identification of nitrogen adsorption boundary points where the fractal dimension slope changes through fractal analysis of the FHH fractal model, specifically includes: An FHH fractal curve is constructed based on the maximum adsorption capacity of the monolayer; the slope change points on the FHH fractal curve are identified, and the slope change points correspond to two linear intervals, representing the surface adsorption-dominated stage and the capillary condensation-dominated stage, respectively; the relative pressure corresponding to the slope change points is determined as the nitrogen adsorption boundary point.

6. The method for combined nitrogen adsorption and mercury injection characterization of coal and rock pore structure according to claim 1, characterized in that, In step 4, the step of inputting the nitrogen adsorption boundary point into the BJH model to calculate the upper limit pore size of the nitrogen adsorption method specifically includes: Substitute the nitrogen adsorption boundary point into the Kelvin equation to calculate the capillary condensation radius; calculate the adsorption layer thickness corresponding to the nitrogen adsorption boundary point according to the Halsey empirical film thickness formula; calculate the actual pore radius based on the capillary condensation radius and the adsorption layer thickness, and use the actual pore radius as the upper limit pore diameter of the nitrogen adsorption method.

7. The method for combined nitrogen adsorption and mercury injection characterization of coal and rock pore structure according to claim 1, characterized in that, In step 4, the calculation of the pore size distribution of the mercury intrusion porosimetry based on the actual pore filling curve, and the determination of the lower limit pore size corresponding to the upper limit pore size, specifically includes: The actual pore filling curve is converted into the pore size distribution of mercury intrusion porosimetry using the Washburn equation; the pressure corresponding to the mercury intrusion porosimetry boundary point is used to calculate the lower limit pore size of mercury intrusion porosimetry using the Washburn equation, and the lower limit pore size corresponds to the upper limit pore size of the nitrogen adsorption method.

8. A combined nitrogen adsorption and mercury injection characterization device for the pore structure of coal and rock, characterized in that, include: The mercury intrusion porosimetry (MIP) boundary point determination module is used to acquire MIP experimental data of the coal and rock to be tested, perform fractal analysis on the MIP experimental data, and identify the MIP boundary point where the fractal dimension changes from the stable range characterizing pore filling to the point where abrupt changes. The mercury intrusion porosimetry curve correction module is used to calculate the sample compressibility coefficient of the coal and rock sample to be tested based on the data of the compression-dominant interval after the mercury intrusion porosimetry boundary point, and to correct the mercury intrusion porosimetry curve according to the sample compressibility coefficient, so as to eliminate the volume error caused by matrix compression and obtain the true pore filling curve. The nitrogen adsorption boundary point determination module is used to acquire nitrogen adsorption experimental data of the coal and rock to be tested, calculate the maximum adsorption capacity of a single layer, construct an FHH fractal model based on the single-layer adsorption capacity, and identify the nitrogen adsorption boundary point where the fractal dimension slope changes by performing fractal analysis on the FHH fractal model. The pore size upper and lower limit determination module is used to input the nitrogen adsorption boundary point into the BJH model to calculate the upper limit pore size of the nitrogen adsorption method, calculate the pore size distribution of the mercury intrusion porosimetry method based on the real pore filling curve, and determine the lower limit pore size corresponding to the upper limit pore size. The aperture splicing module is used to splice the upper limit aperture and the lower limit aperture to obtain the full aperture distribution characterization result of the coal and rock to be tested.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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