Coal-methane desorption amount prediction method and system based on solid-liquid phase adsorption theory

Abstract

The application discloses a method and system for predicting desorption amount of tectonic coal methane based on solid-liquid phase adsorption theory, and relates to the technical field of coal mine gas extraction and coal bed gas development. The method comprises the following steps: obtaining a first pore structure parameter vector of tectonic coal before reservoir reconstruction and a second pore structure parameter vector of tectonic coal after reservoir reconstruction; inputting the first and second pore structure parameter vectors into a pre-constructed solid-liquid phase two-component adsorption model respectively to obtain first and second total adsorption amounts, wherein the model is used for mapping any input pore structure parameter into a corresponding total methane adsorption amount, and the total methane adsorption amount is decomposed into the sum of a surface adsorption component and a micropore filling component; taking the difference between the first total adsorption amount and the second total adsorption amount as a cumulative desorption amount, and outputting the first and second total adsorption amounts and the cumulative desorption amount. Thus, the whole semi-quantitative evaluation from the reservoir reconstruction scheme to the microstructure change and then to the macroscopic desorption amount prediction is realized.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of coal mine gas extraction and coalbed methane development technology, and in particular to a method and system for predicting the amount of methane desorption in structural coal based on solid-liquid phase adsorption theory. Background Technology

[0002] Tectonic coal is prevalent in southwestern my country, characterized by low permeability, high adsorption, and difficulty in desorption, which severely restricts the efficiency of surface coalbed methane extraction and underground gas drainage. Coal reservoir stimulation (such as hydraulic fracturing, CO2 or N2 fracturing, and acidizing) is the fundamental way to improve the gas production of tectonic coal. However, the main bottleneck currently faced by the technology lies in the dynamic evolution of the internal pore and fracture structure of tectonic coal reservoirs after stimulation, and the mechanism by which this evolution process controls the conversion of methane from the adsorbed state to the free state remains unclear.

[0003] While some existing technologies propose dual-model methods (such as the superposition of the Langmuir equation and the Dubinin-Astakhov equation) to decompose methane adsorption into surface adsorption and micropore filling, these models are mostly static. Their parameters are measured only for a specific coal sample and fail to incorporate the dynamic evolution of pore structure (such as specific surface area, micropore volume, and interconnected porosity) during reservoir stimulation as independent variables. This makes it impossible to predict desorption changes in the same reservoir under different stimulation schemes, limiting the models' guidance for field production enhancement. Furthermore, traditional single-phase Langmuir models cannot describe the release process of methane bound in high-density adsorbed states within micro- and nano-pores after pore structure changes. Therefore, a new method with predictive capabilities is needed that couples the dynamic evolution characteristics of pore structure with two-phase adsorption-desorption theory. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for predicting the amount of methane desorption in structural coal based on the solid-liquid phase adsorption theory, which realizes a semi-quantitative evaluation of the entire process from reservoir stimulation schemes to microstructural changes and macroscopic desorption prediction.

[0005] In a first aspect, embodiments of the present invention provide a method for predicting the amount of methane desorption in structural coal based on solid-liquid phase adsorption theory, comprising:

[0006] Obtain the first pore structure parameter vector of the coal before reservoir stimulation and the second pore structure parameter vector after reservoir stimulation; both the first and second pore structure parameter vectors include at least specific surface area, micropore volume and interconnected porosity.

[0007] The first pore structure parameter vector and the second pore structure parameter vector after reservoir modification are respectively input into a pre-constructed solid-liquid phase two-component adsorption model to obtain the first total adsorption amount and the second total adsorption amount. The model is used to map any input pore structure parameter to a corresponding total methane adsorption amount. The total methane adsorption amount is decomposed into the sum of surface adsorption component and micropore filling component. The surface adsorption component is controlled by specific surface area, and the micropore filling component is controlled by micropore volume and interconnected porosity.

[0008] The difference between the first total adsorption amount and the second total adsorption amount is taken as the cumulative desorption amount, and the first total adsorption amount, the second total adsorption amount, and the cumulative desorption amount are output.

[0009] In a preferred embodiment, the specific surface area, micropore volume, and interconnected porosity are obtained by characterizing the tectonic coal sample using a combination of nuclear magnetic resonance, CT scanning, mercury intrusion porosimetry, and low-temperature nitrogen adsorption tests; wherein, the interconnected porosity is calculated by distinguishing between mobile fluids and bound fluids using the nuclear magnetic resonance T2 cutoff value method.

[0010] As a preferred embodiment, the solid-liquid phase two-component adsorption model is constructed through the following steps:

[0011] Using isothermal adsorption experimental data of constructed coal samples before and after modification, the parameters in the model were calibrated by the least squares method.

[0012] The parameters include the limiting solid-phase adsorption coefficient, adsorption equilibrium constant, characteristic free energy coefficient, pore heterogeneity index, and high-density adsorbed phase density.

[0013] As a preferred implementation, the calibrated model is combined with a giant canonical Monte Carlo molecular simulation to calculate the adsorption and desorption characteristics within an extended range of pore structure parameters and pressures. An empirical correction coefficient is determined by comparing the molecular simulation results with isothermal adsorption experimental data to correct the total adsorption amount output by the model.

[0014] In a preferred embodiment, the method further includes establishing a throat size evaluation index:

[0015] The effective desorption critical pore throat diameter is defined based on the dynamic diameter of methane molecules, wherein the critical pore throat diameter is equal to the product of the dynamic diameter of methane molecules and a preset comprehensive coefficient, which is determined by comparing and calibrating isothermal adsorption-desorption experimental data with pore size distribution.

[0016] When the actual pore throat diameter is greater than the critical pore throat diameter, methane is determined to have undergone effective desorption; otherwise, methane is determined to exist in a micropore-filled high-density adsorption state.

[0017] As a preferred embodiment, the method further includes: calculating the fractal dimension of the pore surface using the Frenkel-Halsey-Hill equation based on low-temperature nitrogen adsorption data, so as to quantify the heterogeneity and roughness of the pore structure and assist in calibrating the pore non-uniformity index in the model.

[0018] The fractal dimension is calculated based on adsorption isotherm data with relative pressure in the range of 0.05 to 0.35.

[0019] In a preferred embodiment, the cumulative desorption amount is used for quantitative comparison of the gas production effect of different reservoir stimulation schemes; the reservoir stimulation schemes include at least one of the following: hydraulic fracturing, carbon dioxide fracturing, nitrogen fracturing, acidizing, microwave radiation, electrical pulse fracturing, and liquid nitrogen freeze-thaw treatment.

[0020] As a preferred embodiment, during carbon dioxide fracturing, supercritical carbon dioxide forms a weakly acidic fluid under aqueous conditions, which dissolves carbonate minerals in the tectonic coal, thereby forming micro-nano-scale dissolution pores near the fracture wall, achieving a synergistic enhancement of increased micropore volume and improved connectivity efficiency.

[0021] As a preferred embodiment, the formula for the solid-liquid phase two-component adsorption model is as follows:

[0022] ;

[0023] in, This represents the total adsorption amount. This is the surface adsorption component. For micropore filling components; This refers to gas pressure; For temperature; This is a dynamically evolving vector of pore structure parameters.

[0024] Secondly, embodiments of the present invention also provide a predictive system for coal methane desorption based on solid-liquid phase adsorption theory, used to perform the method described in any of the above embodiments, including:

[0025] The pore structure parameter acquisition module is used to acquire the first pore structure parameter vector of the tectonic coal before reservoir stimulation and the second pore structure parameter vector after reservoir stimulation; both the first pore structure parameter vector and the second pore structure parameter vector include at least specific surface area, micropore volume and interconnected porosity.

[0026] The adsorption capacity calculation module is used to input the first pore structure parameter vector and the second pore structure parameter vector after reservoir modification into a pre-constructed solid-liquid phase dual-component adsorption model to obtain the first total adsorption capacity and the second total adsorption capacity. The model is used to map any input pore structure parameter to a corresponding total methane adsorption capacity. The total methane adsorption capacity is decomposed into the sum of surface adsorption component and micropore filling component. The surface adsorption component is controlled by specific surface area, and the micropore filling component is controlled by micropore volume and interconnected porosity.

[0027] The desorption amount calculation and output module is used to take the difference between the first total adsorption amount and the second total adsorption amount as the cumulative desorption amount, and output the first total adsorption amount, the second total adsorption amount and the cumulative desorption amount.

[0028] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising:

[0029] One or more processors;

[0030] Storage device for storing one or more programs;

[0031] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for predicting the amount of coal methane desorption based on solid-liquid phase adsorption theory, as described in any embodiment of the present invention.

[0032] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for predicting the amount of methane desorption from coal based on solid-liquid phase adsorption theory as described in any embodiment of the present invention.

[0033] Compared to existing technologies, the following beneficial effects have been achieved:

[0034] (1) For the first time, this invention takes the dynamic evolution parameters of pore and fracture structure during reservoir stimulation as independent variables and introduces a solid-liquid phase adsorption model with dual components of surface adsorption and micropore filling. This breaks through the limitation of traditional static models that can only describe a single coal sample and provides a new theoretical framework for understanding the microscopic mechanism of reservoir stimulation promoting methane desorption.

[0035] (2) This invention quantitatively characterizes the pore structure parameters before and after the modification by combining multiple experimental methods and uses them as direct input to the model. Combined with giant canonical Monte Carlo molecular simulation correction, it realizes the semi-quantitative evaluation of the entire process from reservoir modification scheme to microstructure change and macroscopic desorption prediction, which solves the problem that traditional methods cannot predict the modification effect.

[0036] (3) This method clarifies the minimum set of experimental input parameters required, and the physical meaning of the model parameters is clear. After the engineers calibrate the parameters based on the limited core experiments of the target reservoir, they can simulate and compare the effects of various potential modification schemes, providing a direct decision-making basis for formulating scientific, reasonable and targeted structural coal reservoir modification technology schemes. Attached Figure Description

[0037] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. The drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0038] Figure 1 This is a flowchart of a method for predicting the amount of coal methane desorption based on solid-liquid phase adsorption theory, provided by an embodiment of the present invention.

[0039] Figure 2 These are scanning electron microscope (SEM) images of the structured coal samples provided in this embodiment of the invention.

[0040] Figure 3 This is an example diagram showing the methane isothermal adsorption / desorption data of a coal sample provided in an embodiment of the present invention;

[0041] Figure 4 This is an overall technical roadmap of the method for predicting the amount of methane desorption in coal based on solid-liquid phase adsorption theory provided in the embodiments of the present invention;

[0042] Figure 5 This is a schematic diagram of a coal methane desorption prediction system based on solid-liquid phase adsorption theory provided in an embodiment of the present invention.

[0043] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0044] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0045] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as being processed sequentially, many of these operations (or steps) may be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations may be rearranged. The process may be terminated when its operation is completed, but may also have additional steps not included in the figures. The process may correspond to a method, function, procedure, subroutine, subroutine, etc.

[0046] Example 1

[0047] like Figure 1 As shown, Embodiment 1 of the present invention provides a method 100 for predicting the amount of methane desorption in structural coal based on solid-liquid phase adsorption theory. The method 100 specifically includes the following steps:

[0048] Step S110: Obtain the first pore structure parameter vector of the tectonic coal before reservoir stimulation and the second pore structure parameter vector after reservoir stimulation; both the first and second pore structure parameter vectors include at least specific surface area, micropore volume and interconnected porosity.

[0049] As a preferred embodiment, the above parameters are obtained in the following manner:

[0050] (1) Sample collection: Select representative coal seams from the working face of the tectonic coal development mining area, collect different types of fresh blocky tectonic coal samples, such as fractured coal, granular coal, and mylonite, seal them with plastic film on site, pack them into boxes and transport them to the laboratory.

[0051] (2) Sample preparation: In the laboratory, the coal sample is divided into three parts: one part is stored at room temperature for later use; another part is frozen in a freezer to preserve the original structure; the remaining part is crushed and sieved into 60-80 mesh particles according to the requirements of subsequent experiments, or drilled into a cylindrical coal core of a specific size while maintaining the integrity of the structure.

[0052] (3) Composition and structure analysis: The coal sample was subjected to industrial analysis and vitrinite reflectance measurement to determine the coal rank. The mineral composition and acid-soluble components of the coal, such as calcite and dolomite, were analyzed using X-ray diffraction (XRD); the microstructure and fracture development characteristics of the original coal sample were observed using scanning electron microscopy (SEM).

[0053] like Figure 2 The image shown is a SEM image of a typical structural coal sample at 2000x magnification. Microfractures and mineral particles are visible on the surface of the coal matrix, providing an original structural reference for subsequent reservoir stimulation.

[0054] As a preferred embodiment, the testing and characterization of the dynamic evolution characteristics of the pore and fracture structure before and after reservoir stimulation are as follows:

[0055] (1) Characterization of the original pore and fracture structure: The initial pore size distribution, pore volume, specific surface area and connectivity information are obtained for joint characterization. Specifically, the original coal sample is taken and the following equipment is used for acquisition:

[0056] Non-destructive testing was performed using the MacroMR12-150H-I coal body structure and coalbed methane analysis and imaging system (NMR) to obtain... Spectrum, through The cutoff value method distinguishes between movable and bound fluids. The cutoff value was determined through centrifugation experiments or empirical calibration methods. The original interconnected porosity was calculated. and original total porosity ;

[0057] Using X-ray computed tomography (CT) imaging system to perform three-dimensional scanning of coal samples, the internal macroscopic fracture network model can be reconstructed to intuitively identify the fracture distribution;

[0058] The pore size distribution of mesopores and macropores was determined using an AutoPore IV9510 fully automated mercury porosimeter.

[0059] The original specific surface area of ​​micropores was determined using a 3H-2000PS2 fully automatic specific surface area and pore size measuring instrument. Original average micropore diameter and original micropore volume .

[0060] (2) Reservoir stimulation experiment: Using a triaxial fracturing experimental device, under high confining pressure (simulating high ground stress, such as 10MPa), H2O, CO2 or N2 fracturing stimulation experiments were carried out on cylindrical coal cores, as well as stimulation experiments with different acidizing agents and proppants, to simulate the real reservoir stimulation process.

[0061] (3) Re-characterization of pore and fracture structure after modification: For the coal samples that have completed the modification experiment, repeat the above NMR, CT, mercury porosimetry, and specific surface area tests to obtain the modified interconnected porosity. , Improve total porosity Modify specific surface area Modify the average micropore diameter and modification of micropore volume

[0062] (4) Establish a microscopic model and evaluation criteria for pores and cracks, specifically including:

[0063] Pore ​​throat size evaluation index: Establish an evaluation index for effective desorption pore structure. Methane molecular dynamics diameter. Given a diameter of 0.38 nm and considering a double-sided adsorption layer, the critical pore throat diameter for effective desorption is defined. for:

[0064] ;

[0065] in, The dynamic diameter of the methane molecule is 0.38 nm, and the coefficient is... The physical meaning of includes the combined contribution of the adsorption layer and the geometric constraint of the pore throat, and its value range is . This coefficient was obtained through isothermal adsorption-desorption experiments and pore size distribution comparisons of coal rank samples, and is not a fixed constant. In this embodiment, it was fitted for a specific coal sample. Take 5, which is the critical pore throat diameter. It is approximately equal to 1.9 nm.

[0066] When the actual throat diameter Larger than the critical throat diameter When the actual pore throat diameter is [not specified], methane molecules can undergo effective desorption and diffusion; Less than or equal to the critical orifice throat diameter Methane is primarily controlled by a strong adsorption potential, forming a high-density adsorption state filled with micropores, which is referred to as a quasi-liquid state in engineering and is difficult to desorb directly.

[0067] Fractal characterization of pore structure: Based on low-temperature nitrogen adsorption data, the fractal dimension of the pore surface was calculated using the Frenkel-Halsey-Hill (FHH) equation. The formula used to quantify the heterogeneity and roughness of pore structures is:

[0068] ;

[0069] in, In relative pressure The adsorption volume below, This is the monolayer adsorption volume. fractal dimension (usually) C is a constant related to the coal sample, determined by the linear regression intercept. This fractal dimension calculation is based on a specific relative pressure range of the low-temperature nitrogen adsorption isotherm (usually taken as...). To ensure the applicability of the FHH model, Adsorption equilibrium pressure : Saturated vapor pressure of the adsorbate.

[0070] Step S120: Input the first pore structure parameter vector and the second pore structure parameter vector after reservoir modification into the pre-constructed solid-liquid phase dual-component adsorption model to obtain the first total adsorption amount and the second total adsorption amount. The model is used to map any input pore structure parameter to a corresponding total methane adsorption amount. The total methane adsorption amount is decomposed into the sum of the surface adsorption component and the micropore filling component. The surface adsorption component is controlled by the specific surface area, and the micropore filling component is controlled by the micropore volume and the interconnected porosity.

[0071] As a preferred embodiment, the solid-liquid phase two-component adsorption model is constructed and calibrated in the following manner:

[0072] (1) Isothermal adsorption / desorption experiment: High-pressure gas isothermal adsorption was used to test the adsorption and desorption data of methane on coal samples before and after reservoir modification using a high-pressure gas isothermal adsorption instrument, and isothermal adsorption and desorption curves were plotted. For example... Figure 3 The figure shows an example of methane isothermal adsorption / desorption experimental data of the original coal sample at 30℃, demonstrating the relationship between relative pressure and adsorption and desorption amounts.

[0073] (2) Solid-liquid phase adsorption theoretical physical model: This theory divides the occurrence state of methane in the micro-nano pores of coal into two parts: the surface adsorption component (solid phase) controlled by surface energy and the micropore filling component (engineering termed 'quasi-liquid state', i.e., liquid phase adsorption component) controlled by the micropore filling mechanism, with the total adsorption amount... The sum of the two:

[0074]

[0075] In the formula, This is the surface adsorption component. For filling components of micropores. For dynamically evolving pore structure parameter vectors, . Total adsorption capacity, which is the sum of surface adsorption capacity and high-density adsorption capacity; Gas pressure; Temperature; SSA: Micropore specific surface area; : Micropore volume; Average micropore diameter; : Connectivity porosity; Total porosity.

[0076] The key difference of this invention is that the changes in pore structure parameters caused by reservoir stimulation are introduced as independent variables into the adsorption model, rather than being used only for the interpretation of results.

[0077] (3) Constructing a mathematical model for surface adsorption components: Surface adsorption components The Langmuir equation, which is affected by specific surface area, is used to describe this:

[0078] ;

[0079] in, This represents the limiting solid-phase adsorption capacity. This represents the adsorption equilibrium constant. Reservoir stimulation affects the specific surface area (SSA) of the micropores. Within the experimental conditions, the two show an approximately positive correlation, which can be expressed as follows: This relationship is an approximate expression within the range of data in this experiment and does not constitute a universal linear relationship. The proportionality coefficient related to coal rank was determined experimentally. The limiting solid-phase adsorption capacity, within the experimental conditions, shows an approximately positive correlation with the specific surface area of ​​the micropores, i.e. (This relationship applies only to the data range of this experiment and does not constitute a universal linear relationship.) Adsorption equilibrium constant; The proportionality coefficient related to coal rank was determined experimentally.

[0080] (4) Constructing a mathematical model for the micropore filling component: micropore filling component The model employs the Dubinin-Astakhov (DA) micropore filling theory. Given that the coal reservoir temperature is higher than the methane critical temperature, this model uses the virtual saturated vapor pressure widely used in the literature. (Obtained through extrapolation of the Dubinin empirical formula) to ensure the physical consistency of the DA equation under supercritical conditions, and the fill factor. and micropore filling components They are respectively:

[0081] ;

[0082] in: The saturated vapor pressure of the adsorbate. This is the universal gas constant. For temperature; Characteristic adsorption free energy, relative to average micropore diameter They exhibit an inverse correlation, which is approximated in this embodiment using an empirical fitting form. ; The characteristic adsorption free energy coefficient is related to the coal rank and the chemical composition of the micropore wall, and was determined through experimental fitting. In this embodiment, an index reflecting the non-uniformity of pores is used. ( (in nm) : Linear fitting coefficients for the porosity non-uniformity index The dimensions are ; For effective micropore volume This is an approximate characterization parameter for connectivity efficiency; For micropore volume; For interconnected porosity; Total porosity; The density of the high-density adsorbed phase of adsorbed methane is referenced to the density of liquid methane at the standard boiling point (approximately). Initial values ​​are set and determined by fitting isothermal adsorption experimental data, typically falling within... Within the range.

[0083] Step S130: Take the difference between the first total adsorption amount and the second total adsorption amount as the cumulative desorption amount, and output the first total adsorption amount, the second total adsorption amount and the cumulative desorption amount.

[0084] As a preferred embodiment, combined with Figure 4 As shown, this embodiment constructs a dynamic mathematical model of desorption capacity and performs engineering predictions in the following manner:

[0085] (1) Definition of cumulative desorption: The amount of reservoir pressure from the original pressure Reduce to current pressure methane uptake per unit mass of coal sample at time for:

[0086] ;

[0087] in: Total porosity The amount of methane desorbed per unit mass of coal sample, i.e., the cumulative desorption amount; : Original reservoir pressure; Total adsorption amount under the original reservoir pressure conditions; : Total adsorption amount under current pressure conditions.

[0088] (2) Model parameter calibration: Using the isothermal adsorption experimental data of the original coal sample and the modified coal sample obtained by the GAI-100 high-pressure gas isothermal adsorption instrument, the key parameters were fitted using the least squares method: , , , , A quantitative mapping relationship between the parameters and their corresponding measurable pore structure parameters was established. Based on existing adsorption theory and experimental data, the functional forms of each parameter were determined as follows:

[0089] Limiting solid-phase adsorption coefficient With characteristic free energy coefficient It can be approximated as a constant under specific coal rank and temperature, and determined through fitting;

[0090] Adsorption equilibrium constant Within the range of constant temperature and specific coal samples, it can be approximated as a constant, and the influence of its small changes on the model is absorbed through the overall parameter fitting.

[0091] Pore ​​heterogeneity index With average micropore diameter It exhibits a locally approximate linear relationship, in the form of: ;

[0092] High density adsorbed phase density Treat them as weak variables and fit them to their mean or near-constant values.

[0093] None of the above parameters are universally applicable across coal ranks or regions; their values ​​need to be calibrated through specific coal sample experiments.

[0094] (3) Molecular simulation (MC) extrapolation and correction: The preliminary mathematical model calibrated by the experimental data was implanted into a molecular simulation program based on the giant canonical Monte Carlo method (GCMC). Within this program, based on the structural information measured by CT and SEM in step S110, a molecular model of the ink bottle pores with different pore sizes and geometries was constructed. The size parameters of the pore model were set according to the statistical results of the pore size distribution obtained from CT and nitrogen adsorption tests. The Lennard-Jones 12-6 potential was used to describe the interactions between methane molecules and with the coal surface. The coal surface was approximated using an equivalent potential field. Through molecular simulation, adsorption and desorption characteristics under a wider range of pore structure parameters and pressures were calculated. The simulation results were systematically compared with the experimental data to determine the empirical correction coefficients used for overall correction of the model calculation results. That is, the corrected adsorption amount The calculation formula is:

[0095] ;

[0096] in Determined through fitting, typically within the range of 0.8 to 1.2, the aforementioned... The introduction of is to compensate for the scale difference between macroscopic models and molecular-scale simulations. Its physical meaning is an equivalent representation of multi-scale effects, rather than an arbitrary adjustment parameter.

[0097] (4) Project dynamic prediction:

[0098] The minimum set of input parameters required to perform a prediction includes:

[0099] (a) Isothermal adsorption curve data of at least one set of original coal samples;

[0100] (b) Specific surface area of ​​original and modified coal samples Average micropore diameter and micropore volume ;

[0101] (c) Connectivity porosity of original and modified coal samples and total porosity .

[0102] The model will not run if any of the above parameters are missing.

[0103] For any new reservoir stimulation scheme, it is only necessary to obtain the pore structure parameter vector after stimulation through experiments or predictions. By inputting the corrected mathematical model, the adsorption isotherm under this modification scheme can be obtained.

[0104] A fundamental assumption of this model is that during the short-term production phase, the modified pore and fracture structure parameters can be approximated as stable and unchanged, and the subsequent gas production process is entirely controlled by this new pore structure. However, under long-term conditions, structural evolution caused by geostress closure and adsorption expansion needs to be considered. This is combined with reservoir pressure variation trends given by the mass balance equation or experimental data. Cumulative desorption at different times The adsorption amount is calculated from the difference between the initial adsorption amount before modification and the adsorption amount at the current pressure after modification.

[0105] ;

[0106] in: The original total adsorption capacity is derived from the surface adsorption capacity. With high density adsorption capacity The sum constitutes; : A vector of pore structure parameters after reservoir modification, including the modified specific surface area, micropore volume, average micropore diameter, connectivity porosity, and total porosity; : Vector of original pore structure parameters before reservoir stimulation; : Original reservoir pressure; The reservoir pressure is a function of time, given by the mass balance equation or measured data. Production time; : Cumulative desorption amount per unit mass of coal sample at different times; : Total adsorption capacity under the original pressure and original pore structure conditions before modification; : Total adsorption capacity under the current pressure and new pore structure conditions after modification.

[0107] By comparing different renovation schemes The curve enables the prediction and optimization of coalbed methane production and gas extraction. The model prediction results can be verified by comparison with field pilot tests or historical production data.

[0108] Based on the above embodiments, the core beneficial effects of the present invention are as follows:

[0109] (1) For the first time, this invention takes the dynamic evolution parameters of pore and fracture structure during reservoir stimulation as independent variables and introduces a solid-liquid phase adsorption model with dual components of surface adsorption and micropore filling. This breaks through the limitation of traditional static models that can only describe a single coal sample and provides a new theoretical framework for understanding the microscopic mechanism of reservoir stimulation promoting methane desorption.

[0110] (2) This invention quantitatively characterizes the pore structure parameters before and after the modification by combining multiple experimental methods and uses them as direct input to the model. Combined with giant canonical Monte Carlo molecular simulation correction, it realizes the semi-quantitative evaluation of the entire process from reservoir modification scheme to microstructure change and macroscopic desorption prediction, which solves the problem that traditional methods cannot predict the modification effect.

[0111] (3) This method clarifies the minimum set of experimental input parameters required, and the physical meaning of the model parameters is clear. After the engineers calibrate the parameters based on the limited core experiments of the target reservoir, they can simulate and compare the effects of various potential modification schemes, providing a direct decision-making basis for formulating scientific, reasonable and targeted structural coal reservoir modification technology schemes.

[0112] Example 2

[0113] This example uses the prediction of methane desorption after CO fracturing in a tectonic coal reservoir in a mining area of ​​Guizhou Province as an example:

[0114] (1) Sample collection and processing: Representative fragmented coal samples were collected from the K15 coal seam of a coal mine in Guizhou Province and transported to the laboratory in a sealed manner. Some samples were crushed to 60-80 mesh for basic analysis and isothermal adsorption; some samples were drilled into Φ50mm×100mm cylindrical cores for triaxial fracturing and CT / NMR testing.

[0115] (2) Initial structural characterization:

[0116] XRD analysis confirmed that the coal sample contained a certain amount of carbonate minerals (such as calcite), providing a material basis for subsequent dissolution and pore-enhancing.

[0117] NMR and mercury porosimetry were used to obtain... .

[0118] The original micropore parameters were obtained by analyzing the surface area using a specific surface area analyzer: .

[0119] Based on the critical coefficient k≈5 calibrated in this embodiment, ≈1.9nm, much smaller This indicates that a large amount of methane in the original coal sample was bound in a high-density adsorbed state (quasi-liquid state) with micropores.

[0120] (2) Reservoir stimulation experiment: A cylindrical core was loaded into a triaxial fracturing experimental device, and a confining pressure of 10 MPa was applied. Supercritical CO2 under water-bearing conditions was injected. CT scan confirmed that a large number of new tensile fractures were generated after stimulation. The increase in micropore volume after fracturing may be due to the following mechanisms: (a) High-pressure CO2 drives the formation of new fractures and connects the original isolated pores; (b) Supercritical CO2 forms a weakly acidic fluid under water-bearing conditions, which dissolves carbonate minerals (such as calcite) and forms new micro-nano-scale dissolution pores near the fracture wall, thereby promoting the simultaneous increase of micropore volume and effective connectivity.

[0121] (4) Re-characterization of the modified structure: The parameters after modification were obtained by testing:

[0122] Connectivity porosity ;

[0123] Total porosity ;

[0124] The micropore parameters become: , , ;

[0125] Calculating the effective micropore volume: Original ≈0.009 cm 3 / g, after modification ≈0.0135 cm 3 / g. Key parameters and All increased significantly.

[0126] (5) Model parameter calibration: Isothermal adsorption experiments of two batches of coal samples at 30℃. Key parameters were calibrated through fitting:

[0127] solid phase adsorption coefficient ;

[0128] Adsorption equilibrium constant ;

[0129] Characteristic free energy coefficient ;

[0130] High density adsorbed phase density The average value is approximately 0.39 g / cm³. 3 (falling between 0.35 and 0.45 g / cm³) 3 (within the range)

[0131] The non-uniformity exponent is approximately linearly related as follows: Corresponding to the original version ≈1.80, after modification ≈2.06, this relationship applies only to this batch of samples.

[0132] (6) Simulation predictions and results:

[0133] The calibrated parameter mapping relationship was incorporated into the prediction program. A simplified pressure decay path (approximately linear or piecewise function) was used in the simulation to characterize the process of reservoir pressure decreasing from 6.0 MPa to the abandoned pressure of 0.1 MPa. The program output showed that, under the improved pore structure parameters described above, the model predicted that the methane recovery rate after CO2 fracturing could be increased from the original estimated range (approximately 20%–30%) to approximately 35%–45%, with a significant improvement in the initial desorption rate. It should be noted that these results are calculated predictions based on the experimentally calibrated model, used for quantitative comparative analysis between different fracturing schemes, and are not directly equivalent to actual engineering production. The specific effects are affected by reservoir heterogeneity and on-site construction conditions, and therefore have a certain range of fluctuation.

[0134] The above results indicate that CO2 fracturing significantly weakens the confinement of high-density adsorbed states through multiple mechanisms, including dissolution and porosimetry, expansion of effective micropore size, and improved connectivity efficiency, making it the main controlling factor for increased desorption. The predicted results can be verified and calibrated through subsequent field pilot test data.

[0135] Example 3

[0136] This example uses a simplified method to predict methane desorption after acidizing a tectonic coal reservoir in a mining area of ​​Guizhou.

[0137] (1) Samples and modification: Another set of crushed coal cores from the same mining area as in Example 2 were selected. XRD confirmed that they were rich in acid-reacting minerals such as dolomite. In a triaxial fracturing experimental device, hydrochloric acid-based acidizing solution was injected under a confining pressure of 10 MPa for acid modification.

[0138] (2) Comparison of structural evolution: The initial parameters before modification were the same as in Example 2. After acid treatment, a large number of new micron-nano scale interconnected channels were generated due to mineral dissolution, and the measured... ≈3.9%, Increased to approximately 0.019 cm 3 / g, effective micropore volume It is about 55% higher than the original, while the specific surface area (SSA) and average pore size are also improved. The change is less than 10%.

[0139] (3) Comparison of Predicted Results and Scheme: By applying the modified pore structure parameters to the same calibration model, the program predicts that under this acidizing modification scheme, the methane recovery rate can be increased from the original approximately 25% to approximately 38%~43%. A comparison of the two embodiments shows that CO2 fracturing is superior in terms of enlarging pore size and increasing desorption rate, while acidizing modification is significantly more effective in improving connectivity efficiency. The method of this invention can quantitatively compare the advantages of different schemes, providing a basis for on-site technology selection.

[0140] The above two examples of different modification types verify the universality and predictive ability of the method of the present invention for various reservoir modification technologies.

[0141] Example 4

[0142] Figure 5 This is a schematic diagram of a coal methane desorption prediction system based on solid-liquid phase adsorption theory provided in Embodiment 4 of the present invention. Figure 5 As shown, the system includes:

[0143] The pore structure parameter acquisition module 510 is used to acquire the first pore structure parameter vector of the tectonic coal before reservoir stimulation and the second pore structure parameter vector after reservoir stimulation; both the first pore structure parameter vector and the second pore structure parameter vector include at least specific surface area, micropore volume and interconnected porosity.

[0144] The adsorption capacity calculation module 520 is used to input the first pore structure parameter vector and the second pore structure parameter vector after reservoir modification into a pre-constructed solid-liquid phase dual-component adsorption model to obtain the first total adsorption capacity and the second total adsorption capacity. The model is used to map any input pore structure parameter to a corresponding total methane adsorption capacity. The total methane adsorption capacity is decomposed into the sum of surface adsorption component and micropore filling component. The surface adsorption component is controlled by specific surface area, and the micropore filling component is controlled by micropore volume and interconnected porosity.

[0145] The desorption amount calculation and output module 530 is used to take the difference between the first total adsorption amount and the second total adsorption amount as the cumulative desorption amount, and output the first total adsorption amount, the second total adsorption amount and the cumulative desorption amount.

[0146] The structural coal methane desorption prediction system based on solid-liquid phase adsorption theory provided in this embodiment of the invention can execute the structural coal methane desorption prediction method based on solid-liquid phase adsorption theory provided in any of the above embodiments of the invention. It has the corresponding functions and beneficial effects of executing the structural coal methane desorption prediction method based on solid-liquid phase adsorption theory. For detailed process, please refer to the relevant operation of the structural coal methane desorption prediction method based on solid-liquid phase adsorption theory in the foregoing embodiments.

[0147] Example 5

[0148] Figure 6 This is a schematic diagram of the structure of an electronic device provided in Embodiment 5 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, and may also represent various forms of mobile devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the invention described and / or claimed herein.

[0149] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0150] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0151] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 executes a method for predicting the amount of methane desorption from coal based on solid-liquid phase adsorption theory, as described above.

[0152] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0153] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for predicting the amount of methane desorption in structural coal based on solid-liquid phase adsorption theory, characterized in that, include: Obtain the first pore structure parameter vector of the coal before reservoir stimulation and the second pore structure parameter vector after reservoir stimulation; both the first and second pore structure parameter vectors include at least specific surface area, micropore volume and interconnected porosity. The first pore structure parameter vector and the second pore structure parameter vector after reservoir modification are respectively input into a pre-constructed solid-liquid phase two-component adsorption model to obtain the first total adsorption amount and the second total adsorption amount. The model is used to map any input pore structure parameter to a corresponding total methane adsorption amount. The total methane adsorption amount is decomposed into the sum of surface adsorption component and micropore filling component. The surface adsorption component is controlled by specific surface area, and the micropore filling component is controlled by micropore volume and interconnected porosity. The solid-liquid phase two-component adsorption model is constructed through the following steps: using isothermal adsorption experimental data of the constructed coal sample before and after modification, the parameters in the model are calibrated by the least squares method; wherein, the parameters include the limiting solid phase adsorption coefficient, adsorption equilibrium constant, characteristic free energy coefficient, pore heterogeneity index and high-density adsorption phase density. The calibrated model was combined with a giant canonical Monte Carlo molecular simulation to calculate the adsorption and desorption characteristics within an extended range of pore structure parameters and pressures. An empirical correction coefficient was determined by comparing the molecular simulation results with isothermal adsorption experimental data to correct the total adsorption amount output by the model. The difference between the first total adsorption amount and the second total adsorption amount is taken as the cumulative desorption amount, and the first total adsorption amount, the second total adsorption amount, and the cumulative desorption amount are output.

2. The method according to claim 1, characterized in that, The specific surface area, micropore volume, and interconnected porosity were obtained by characterizing the tectonic coal sample using a combination of nuclear magnetic resonance, CT scanning, mercury intrusion porosimetry, and low-temperature nitrogen adsorption tests. The interconnected porosity was calculated by distinguishing between mobile and bound fluids using the nuclear magnetic resonance T2 cutoff value method.

3. The method according to claim 1, characterized in that, The method also includes establishing a criterion for evaluating the throat size: The effective desorption critical pore throat diameter is defined based on the dynamic diameter of methane molecules, wherein the critical pore throat diameter is equal to the product of the dynamic diameter of methane molecules and a preset comprehensive coefficient, which is determined by comparing and calibrating isothermal adsorption-desorption experimental data with pore size distribution. When the actual pore throat diameter is greater than the critical pore throat diameter, methane is determined to have undergone effective desorption; otherwise, methane is determined to exist in a micropore-filled high-density adsorption state.

4. The method according to claim 1, characterized in that, The method further includes: calculating the fractal dimension of the pore surface based on low-temperature nitrogen adsorption data using the Frenkel-Halsey-Hill equation to quantify the heterogeneity and roughness of the pore structure and to assist in calibrating the pore non-uniformity index in the model. The fractal dimension is calculated based on adsorption isotherm data with relative pressure in the range of 0.05 to 0.

35.

5. The method according to claim 1, characterized in that, The cumulative desorption amount is used for quantitative comparison of the gas production effect of different reservoir stimulation schemes; the reservoir stimulation schemes include at least one of the following: hydraulic fracturing, carbon dioxide fracturing, nitrogen fracturing, acidizing, microwave radiation, electrical pulse fracturing, and liquid nitrogen freeze-thaw treatment.

6. The method according to claim 5, characterized in that, During carbon dioxide fracturing, supercritical carbon dioxide forms a weakly acidic fluid under aqueous conditions, which dissolves carbonate minerals in tectonic coal, thereby forming micro- and nano-scale dissolution pores near the fracture wall, achieving a synergistic enhancement of increased micropore volume and improved connectivity efficiency.

7. The method according to claim 1, characterized in that, The formula for the solid-liquid phase two-component adsorption model is as follows: ； in, This represents the total adsorption amount. This is the surface adsorption component. For micropore filling components; This refers to gas pressure; For temperature; This is a dynamically evolving vector of pore structure parameters.

8. A system for predicting the amount of coal methane desorption based on solid-liquid phase adsorption theory, characterized in that, The system for performing the method according to any one of claims 1 to 7 comprises: The pore structure parameter acquisition module is used to acquire the first pore structure parameter vector of the tectonic coal before reservoir stimulation and the second pore structure parameter vector after reservoir stimulation; both the first pore structure parameter vector and the second pore structure parameter vector include at least specific surface area, micropore volume and interconnected porosity. The adsorption capacity calculation module is used to input the first pore structure parameter vector and the second pore structure parameter vector after reservoir modification into a pre-constructed solid-liquid phase dual-component adsorption model to obtain the first total adsorption capacity and the second total adsorption capacity. The model is used to map any input pore structure parameter to a corresponding total methane adsorption capacity. The total methane adsorption capacity is decomposed into the sum of surface adsorption component and micropore filling component. The surface adsorption component is controlled by specific surface area, and the micropore filling component is controlled by micropore volume and interconnected porosity. The desorption amount calculation and output module is used to take the difference between the first total adsorption amount and the second total adsorption amount as the cumulative desorption amount, and output the first total adsorption amount, the second total adsorption amount and the cumulative desorption amount.

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