Coal pore detection method, device, system and medium based on nuclear magnetic resonance

Abstract

The application discloses a raw coal pore detection method, device, system and medium based on nuclear magnetic resonance. The method comprises the following steps: obtaining relaxation time distribution data of each coal sample based on nuclear magnetic resonance T2 test; obtaining total integral areas of a plurality of nuclear magnetic resonance T2 spectra corresponding to each coal sample based on the relaxation time distribution data of each coal sample; performing statistical analysis on the relative standard deviation of the total integral areas of each coal sample to obtain the relative standard deviation corresponding to each coal sample; and determining the coal sample parameters of a target coal sample for detecting the pore structure of the raw coal according to the coal sample parameters of the coal sample corresponding to the relative standard deviation satisfying the preset condition. The raw coal pore detection method provided by the application can determine the target coal sample by selecting the coal sample parameters of the coal sample satisfying the preset condition, so that the result of the nuclear magnetic resonance T2 test for detecting the pore structure of the raw coal is more accurate and stable.

Description

Technical Field

[0001] This invention relates to the field of coal safe mining and utilization technology, specifically to a method, equipment, system, and medium for detecting the porosity of raw coal based on nuclear magnetic resonance. Background Technology

[0002] The pore structure of coal is one of the core intrinsic factors determining its physical, chemical, and engineering mechanical properties, and has a significant impact on the safe mining and clean utilization of coal. In coalbed methane extraction, pore structure directly controls the adsorption capacity and desorption / transport efficiency of gas; in the prevention of spontaneous combustion in coal, pores, as channels for oxygen diffusion and reaction, are directly related to the oxidation kinetics of coal; furthermore, in CO2 geological sequestration and coal liquefaction and gasification, pore structure is also crucial to efficiency and safety. Therefore, achieving high-precision, quantitative characterization of the pore structure of raw coal is essential for scientific research and engineering practice in these fields.

[0003] Currently, scholars both domestically and internationally have conducted extensive research on the pore structure of coal and developed various detection techniques. One existing method for detecting coal pore structure involves applying high pressure to the pores to force mercury intrusion. Based on the relationship between mercury intrusion pressure and volume, mercury intrusion porosimetry data is used to establish criteria for determining the scale of fractures and pores in the coal, thereby calculating the pore structure. Another existing method includes obtaining the pore size distribution of coal samples based on low-temperature liquid nitrogen adsorption experiments, and obtaining the pore size distribution of coal samples based on high-pressure mercury intrusion experiments. Yet another existing method involves using low-field nuclear magnetic resonance (NMR) testing technology to measure parameters such as porosity at different depths of the coal seam.

[0004] Current methods for detecting coal pore structure have inherent limitations when applied to complex, heterogeneous raw coal samples, leading to errors between the measured results and the actual pore structure. For example, mercury intrusion porosimetry, as a destructive method, requires high pressure that irreversibly crushes the fragile pore structure in the coal, altering the original state of the sample and introducing errors. Furthermore, this method relies on an ideal cylindrical pore model, which does not match the actual pore morphology of coal. Gas adsorption methods are highly dependent on the selected theoretical model (such as BET and BJH), with different models yielding vastly different results, exhibiting strong model dependence and limiting the pore size detection range. Nuclear magnetic resonance (NMR) methods are prone to introducing systematic errors, causing systematic deviations in the pore size distribution measurements and making it difficult to achieve precise quantitative results. Summary of the Invention

[0005] In view of this, the present invention provides a method, equipment, system and medium for detecting the porosity of raw coal based on nuclear magnetic resonance, which provides accurate and stable detection results.

[0006] In a first aspect, embodiments of the present invention provide a method for detecting the porosity of raw coal based on nuclear magnetic resonance, comprising:

[0007] Multiple relaxation time distribution data of each coal sample from multiple raw coal samples were obtained based on multiple nuclear magnetic resonance T2 tests; wherein, different coal samples have different coal sample parameters, including coal sample particle size and / or water saturation parameters;

[0008] The total integral area of ​​multiple nuclear magnetic resonance T2 spectra corresponding to each coal sample is determined based on the multiple relaxation time distribution data corresponding to each coal sample.

[0009] Statistical analysis of the relative standard deviation is performed on the multiple total integral areas corresponding to each coal sample to obtain the relative standard deviation of each coal sample.

[0010] Based on the coal sample parameters corresponding to the coal sample whose relative standard deviation meets the preset conditions, a target coal sample is determined, and the porosity detection result of the raw coal is determined based on the nuclear magnetic resonance T2 test result of the target coal sample.

[0011] In some embodiments, determining the target coal sample based on the coal sample parameters corresponding to the coal sample whose relative standard deviation meets preset conditions includes:

[0012] Determine whether the relative standard deviation meets the preset conditions;

[0013] If so, the target coal sample is determined based on the coal sample parameters corresponding to the coal sample whose relative standard deviation meets the preset conditions;

[0014] If not, update the coal sample parameters for each of the described coal samples.

[0015] In some embodiments, the plurality of coal samples includes a plurality of first coal samples and a plurality of second coal samples, wherein the coal sample parameter of each first coal sample is a water saturation parameter, and the coal sample parameter corresponding to each second coal sample is a coal sample particle size, and determining the target coal sample based on the coal sample parameter corresponding to the coal sample that satisfies the preset condition of relative standard deviation includes:

[0016] Based on the saturation parameters of the coal samples whose relative standard deviations meet preset conditions among the multiple first coal samples, the target saturation parameter of the target coal sample is determined.

[0017] The target particle size of the target coal sample is determined based on the coal sample particle size corresponding to the coal sample whose relative standard deviation meets the preset condition among multiple second coal samples.

[0018] Wherein, the saturation parameter corresponding to each of the second coal samples is the target saturation parameter, and the particle size of each of the second coal samples is greater than the particle size of the first coal sample.

[0019] In some embodiments, determining the target saturation parameter of the target coal sample based on the saturation parameter corresponding to a coal sample whose relative standard deviation among a plurality of first coal samples meets a preset condition includes:

[0020] Determine whether the smallest of the relative standard deviations corresponding to the multiple first coal samples is less than or equal to a first preset value;

[0021] If so, the saturation parameter corresponding to the first coal sample with the smallest relative standard deviation is determined as the target saturation parameter;

[0022] If not, update the saturation parameters of each of the first coal samples.

[0023] In some embodiments, determining the target particle size of the target coal sample based on the particle size of a coal sample whose relative standard deviation among a plurality of second coal samples meets a preset condition includes:

[0024] Determine whether the smallest of the relative standard deviations corresponding to the multiple second coal samples is less than or equal to a second preset value;

[0025] If so, the particle size of the second coal sample with the smallest relative standard deviation is determined as the target particle size;

[0026] If not, update the particle size of each second coal sample.

[0027] In some embodiments, the saturation parameters include the saturation pressure and / or saturation time set for the coal sample to obtain the corresponding degree of saturation in the saturation device.

[0028] Secondly, embodiments of the present invention provide a raw coal porosity detection device based on nuclear magnetic resonance, comprising:

[0029] The data acquisition module is used to acquire multiple relaxation time distribution data of each coal sample from multiple coal samples of raw coal based on multiple nuclear magnetic resonance T2 tests; wherein, different coal samples have different coal sample parameters, including coal sample particle size and / or water saturation parameters;

[0030] The integral area determination module is used to determine the total integral area of ​​multiple nuclear magnetic resonance T2 spectra corresponding to each coal sample based on the multiple relaxation time distribution data corresponding to each coal sample.

[0031] The deviation determination module performs relative standard deviation statistical analysis on the multiple total integral areas corresponding to each coal sample to obtain the relative standard deviation of each coal sample.

[0032] The target coal sample determination module determines the target coal sample based on the coal sample parameters corresponding to the coal sample whose relative standard deviation meets the preset conditions, so as to determine the porosity detection result of the raw coal based on the nuclear magnetic resonance T2 test result of the target coal sample.

[0033] Thirdly, embodiments of the present invention provide a raw coal porosity detection device based on nuclear magnetic resonance, comprising: a processor and a memory, wherein the memory is used to store programs, instructions or code, and the processor is used to execute the programs, instructions or code in the memory to complete the raw coal porosity detection method as described in any of the foregoing.

[0034] Fourthly, embodiments of the present invention provide a raw coal porosity detection system based on nuclear magnetic resonance, including a water-saturated device, a nuclear magnetic resonance T2 testing device, and the raw coal porosity detection device as described in the third aspect above;

[0035] The saturation device is used to obtain multiple coal samples with different degrees of saturation, and the nuclear magnetic resonance T2 testing equipment is used to perform nuclear magnetic resonance T2 testing on each coal sample to obtain the relaxation time distribution data of each coal sample.

[0036] Fifthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which is loaded by a processor to execute the raw coal porosity detection method as described in any of the preceding claims.

[0037] In several embodiments provided by this invention, relaxation time distribution data obtained from multiple NMR T2 tests of coal samples with different parameters are acquired respectively. Based on the multiple relaxation time distribution data of each coal sample, the total integrated area of ​​multiple NMR T2 spectra corresponding to each coal sample is obtained. Then, statistical analysis of the relative standard deviation of the multiple total integrated areas of each coal sample is performed to obtain the relative standard deviation corresponding to each coal sample. Finally, based on the coal sample parameters corresponding to the relative standard deviation that meets preset conditions, the coal sample parameters for detecting the pore structure of raw coal are determined. Since the target coal sample is determined based on the coal sample parameters of a coal sample whose relative standard deviation meets preset conditions, and the magnitude of the relative standard deviation characterizes the consistency and stability of multiple NMR T2 test results for the corresponding coal sample parameters, the smaller the relative standard deviation, the higher the consistency of the NMR T2 test measurement results, the smaller the random error, and the more reliable the data. Therefore, the raw coal porosity detection method provided in this application selects coal sample parameters of a coal sample whose relative standard deviation meets preset conditions to determine the target coal sample. By setting the preset conditions, the consistency and stability of multiple NMR T2 test results of the target coal sample can meet the requirements. This results in smaller random errors, more reliable and accurate data, and higher consistency among multiple test results, based on the NMR T2 test results of the target coal sample. Using the target coal sample parameters determined by this invention as the test sample for raw coal pore structure detection, the target coal sample can reproduce the original coal pore structure and fluid occurrence state in terms of NMR response characteristics, avoiding systematic deviations introduced by improper sample preparation parameters. Based on the target coal sample determined by this invention, the raw coal pore structure is detected using NMR, which has experimental repeatability and provides a guarantee for the accurate analysis of coal pore structure. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of the process flow corresponding to the method for detecting the porosity of raw coal based on nuclear magnetic resonance according to some embodiments of the present invention.

[0040] Figure 2 This is a schematic diagram of the method flow for determining the target coal sample in the raw coal porosity detection method provided according to some embodiments of the present invention.

[0041] Figure 3This is a schematic diagram of the method flow for determining the target coal sample in the raw coal porosity detection method provided according to other embodiments of the present invention.

[0042] Figure 4 This is an RSD result diagram corresponding to the raw coal porosity detection method provided based on some embodiments of the present invention.

[0043] Figure 5 This is another RSD result diagram corresponding to the raw coal porosity detection method provided based on some embodiments of the present invention.

[0044] Figure 6 A schematic diagram of the structure of a raw coal porosity detection device based on nuclear magnetic resonance provided according to some embodiments of the present invention.

[0045] Figure 7 A schematic diagram of the structure of a raw coal porosity detection device based on nuclear magnetic resonance provided according to other embodiments of the present invention.

[0046] Figure 8 A schematic diagram of the structure of a raw coal porosity detection system based on nuclear magnetic resonance provided according to some embodiments of the present invention. Detailed Implementation

[0047] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0048] Existing nuclear magnetic resonance (NMR) methods often rely on empirical values ​​or rough calibrations when selecting key parameters such as saturation pressure, saturation time, and coal particle size, lacking systematic experimental research as a basis. This leads to systematic errors and causes systematic deviations in the pore size distribution measurement results, making it difficult to achieve precise quantitative goals. This application provides a method, equipment, system, and medium for detecting the pore size of raw coal based on NMR that can preserve the original structure of the coal sample to the greatest extent, reduce model dependence and human intervention, and effectively correct core parameters to eliminate systematic errors. This achieves near-zero error and high-precision characterization of the pore structure of raw coal, providing reliable data support for research on coalbed methane development and coal spontaneous combustion prevention.

[0049] Please see Figure 1 The diagram shown is a flowchart illustrating the raw coal porosity detection method based on nuclear magnetic resonance according to some embodiments of the present invention. The raw coal pore structure method provided in this embodiment includes steps S02, S04, S06, and S08, each described below.

[0050] S02: Obtain multiple relaxation time distribution data of each coal sample from multiple raw coal samples based on multiple nuclear magnetic resonance T2 tests; wherein, different coal samples have different coal sample parameters, including coal sample particle size and / or water saturation parameters.

[0051] Raw coal refers to the coal to be tested for pore structure. Pore structure testing of coal mainly involves detecting the size and number of pores, and may also include pore connectivity parameters and / or morphological characteristic parameters. All coal samples are of the same type, meaning they are coal of the same material type as the raw coal to be tested.

[0052] In this embodiment, a coal sample refers to a coal sample that has undergone appropriate processing to acquire specific coal sample parameters. These parameters include coal sample particle size, and may also include water saturation parameters, or both. Coal sample particle size is a parameter characterizing the size of coal sample particles, while water saturation parameters characterize the degree of water saturation in the coal sample. Water saturation parameters may include, but are not limited to, water saturation time and water saturation pressure. Water saturation time and water saturation pressure refer to the water saturation time and pressure required to be set in the water saturation device to obtain a coal sample with a corresponding degree of water saturation. In some embodiments, the coal sample parameters refer to key direct quantities for precisely controlling the nuclear magnetic resonance T2 test, including water saturation pressure, water saturation time, and coal sample particle size.

[0053] Nuclear magnetic resonance (NMR) T2 testing of coal samples is a method that uses low-field NMR technology to analyze the occurrence state and pore structure of moisture or other fluids in coal. Its core principle is to measure the transverse relaxation time (T2) of hydrogen nuclei in the coal sample to reflect the relationship between the degree of fluid confinement and the pore size of the coal. The relaxation time distribution data in NMR T2 refers to the data set characterizing the microstructure and dynamic properties of coal pores by measuring and analyzing the distribution of relaxation times (longitudinal relaxation time T1, transverse relaxation time T2, etc.) of hydrogen nuclei within the pores of the coal sample.

[0054] Methods for obtaining multiple coal samples may include, but are not limited to: collecting multiple coal samples of the same type, with different coal sample parameters. Each coal sample is sieved and polished with fine sand until the surface is free of obvious cracks and has a specific particle size. Each coal sample is placed in a saturation device, and by setting different saturation pressures and controlling the saturation time, a specific degree of saturation is achieved.

[0055] In some embodiments, coal samples with different specific saturation levels can be prepared by controlling the saturation pressure and saturation time, and each coal sample in each specific saturation level can have a different particle size, thereby constructing multiple coal samples.

[0056] The number of coal samples corresponding to each coal sample parameter can be one. Multiple NMR T2 tests are then performed on this single coal sample to obtain multiple test results. Alternatively, the number of coal samples corresponding to each coal sample parameter can be two or more. Multiple NMR T2 tests are then performed on multiple coal samples with the same coal sample parameter, yielding multiple corresponding test results. The multiple relaxation time distribution data corresponding to each coal sample can be based on the results of multiple NMR T2 tests on a single coal sample, or on the results of separate NMR T2 tests on multiple coal samples with the same coal sample parameter. In this embodiment, each coal sample refers to the coal sample corresponding to each coal sample parameter, and the number of each coal sample can be one or more.

[0057] S04: Determine the total integral area of ​​multiple nuclear magnetic resonance T2 spectra corresponding to each coal sample based on the multiple relaxation time distribution data.

[0058] The total integrated area of ​​the nuclear magnetic resonance T2 spectrum (i.e., the peak area of ​​the T2 spectrum) is usually calculated by summing the signal amplitude values ​​at each point on the T2 distribution curve, rather than directly using the signal amplitude values ​​or the simple curve integral area.

[0059] Nuclear magnetic resonance T2 testing of each coal sample can include, but is not limited to, using the interaction mechanism between hydrogen-containing fluid and coal rock, and combining the relaxation signal of hydrogen-containing fluid in the pores inside the coal rock to characterize the microstructure and fluid transport characteristics of the coal sample.

[0060] The analysis of fluids in coal and rock pores and fractures often uses the transverse relaxation time T2, which characterizes relaxation mechanisms including surface relaxation, diffusion relaxation, and free relaxation. The relationship between these three can be expressed as:

[0061] ;

[0062] Where T2D represents diffusion relaxation time, and its unit is ms; T2B represents free relaxation time, and its unit is ms; T2S represents surface relaxation time, and its unit is ms.

[0063] Since the contributions of both the free relaxation time T2B and the diffusion relaxation time T2D are much smaller than those of surface relaxation, the above formula for the transverse relaxation time T2 can be approximately expressed as:

[0064] ;

[0065] In the formula, ρ2 is the surface relaxation rate, and ρ2 is a fixed value for the same type of coal sample, with the unit being um / ms; V is the volume of hydrogen-containing fluid in the pores inside the coal sample; and S is the pore surface area.

[0066] Assuming the pores inside the coal sample have a simple structure, the expression for the surface relaxation time T2S can be transformed into:

[0067] ;

[0068] F S This is the pore geometry factor in the coal sample, and its value is closely related to the pore geometry. For example, in some embodiments, the F-factor for spherical pores... S The value is 3, F of columnar pores S The value is 2. r is the pore radius, and its unit is nm. Based on the above formula, the relationship between the characteristic parameters of coal sample pores and the transverse relaxation time T2 can be established, which is the basis for quantitative characterization of pore structure using nuclear magnetic resonance technology.

[0069] For a continuously distributed transverse relaxation time T2 spectrum, its total signal area A is obtained by integrating the spectral function:

[0070] ;

[0071] Where f(T2) is the T2 relaxation time distribution function, which characterizes the relaxation time distribution data of the coal sample and is used to represent the contribution intensity of different relaxation time components to the total signal area. The total signal area A is the total integrated area of ​​the nuclear magnetic resonance T2 spectrum, and its value is proportional to the fluid volume in the pores. It can be directly used to calculate the total pore volume of the coal sample, so it is an ideal parameter for evaluating measurement repeatability.

[0072] S06: Perform relative standard deviation statistical analysis on the multiple total integral areas corresponding to each coal sample to obtain the relative standard deviation corresponding to each coal sample.

[0073] The relative standard deviation (RSD) is an important metric in statistics used to measure the dispersion of values ​​in a dataset. It represents the ratio of the standard deviation to the mean of the dataset, and is usually expressed as a percentage.

[0074] In this embodiment, each coal sample can undergo n repeated NMR T2 tests to obtain multiple relaxation time distribution data for that coal sample. In other embodiments, multiple coal samples with the same parameters can also be subjected to sequential or multiple NMR T2 tests to obtain multiple relaxation time distribution data for each coal sample. The total integrated area of ​​the n NMR T2 spectra corresponding to the n relaxation time distribution data of each coal sample can be represented by A1, A2, ..., An, respectively. In this embodiment, to evaluate the repeatability and reliability of the NMR T2 test results of the coal samples, the relative standard deviation statistical analysis is performed on the total integrated area Ai (1≤i≤n) of the NMR T2 spectra obtained after the experiment of each coal sample to obtain the relative standard deviation RSD corresponding to the multiple NMR T2 tests of each coal sample. The calculation is based on the following formula:

[0075] ;

[0076] Where s is the standard deviation of the total integrated area of ​​the nuclear magnetic resonance T2 spectrum corresponding to each coal sample, and its calculation formula is:

[0077] ;

[0078] in, The sample mean of the total integrated area of ​​the nth nuclear magnetic resonance T2 spectrum for each coal sample is calculated using the following formula:

[0079] ;

[0080] The relative standard deviation (RSD) of each coal sample is expressed as a percentage (%). The smaller the value, the higher the consistency of the detection results of the pore structure of the corresponding coal sample in multiple nuclear magnetic resonance T2 tests, the smaller the random error, and the more reliable the data.

[0081] s is the sample standard deviation, which reflects the degree of dispersion between a single nuclear magnetic resonance T2 measurement and the mean value in a single corresponding coal sample; is the arithmetic mean of the integral area of ​​the T2 spectrum of n parallel NMR T2 tests for each coal sample; Ai is the integral area of ​​the T2 spectrum of the i-th test; and the number of multiple relaxation time distribution data for each coal sample is the number of parallel NMR tests performed on each coal sample.

[0082] S08: Determine the target coal sample based on the coal sample parameters corresponding to the coal sample whose relative standard deviation meets the preset conditions, so as to determine the porosity detection result of the raw coal based on the nuclear magnetic resonance T2 test result of the target coal sample.

[0083] The smaller the relative standard deviation (RSD) of each coal sample, the higher the consistency, the smaller the random error, and the more reliable the data in the multiple NMR T2 tests of the corresponding coal sample's pore structure. Therefore, by identifying and selecting the coal sample parameters corresponding to the sample with the smallest RSD among multiple coal samples, the coal sample parameters that minimize the measurement error of the raw coal pore structure can be determined. Here, the target coal sample refers to the coal sample used to determine the pore structure of the raw coal based on its NMR T2 test results.

[0084] The method for detecting the porosity of raw coal provided in this invention involves acquiring relaxation time distribution data from multiple NMR T2 tests of coal samples with different parameters, obtaining the total integrated area of ​​multiple NMR T2 spectra for each coal sample based on the relaxation time distribution data, and then performing statistical analysis on the relative standard deviation of the multiple total integrated areas for each coal sample to obtain the relative standard deviation for each coal sample. Finally, based on the coal sample parameters corresponding to the relative standard deviation that meets preset conditions, the coal sample parameters for detecting the pore structure of raw coal are determined. Since the target coal sample is determined based on the coal sample parameters of a coal sample whose relative standard deviation meets the preset conditions, the magnitude of the relative standard deviation characterizes the consistency and stability of the results of multiple NMR T2 tests of the corresponding coal sample parameters. Therefore, selecting the coal sample parameters of a coal sample whose relative standard deviation meets the preset conditions to determine the target coal sample makes the results of detecting the pore structure of raw coal by NMR T2 testing of the target coal sample more accurate and stable.

[0085] In some embodiments, a relative standard deviation meeting a preset condition can refer to the coal sample with the smallest relative standard deviation among multiple coal samples. The target coal sample determined based on the coal sample parameters of the coal sample whose relative standard deviation meets the preset condition can obtain test data with high consistency, small random error, and high reliability when subjected to multiple NMR T2 tests, thereby obtaining accurate raw coal porosity detection results and providing reliable data support for coalbed methane development and gas extraction. In other embodiments, a relative deviation meeting a preset condition can also refer to the coal sample with the smallest relative standard deviation among multiple coal samples that is less than a preset value. By setting the preset value, a target coal sample that makes the raw coal porosity detection results more accurate can be obtained. Determining the target coal sample includes determining the coal sample parameters of the target coal sample, such as coal sample particle size and / or saturation parameters, and the saturation parameters may further include saturation pressure and / or saturation time.

[0086] In some embodiments, coal sample parameters can be coal sample particle size, or saturation pressure and saturation time characterizing the degree of saturation. The target coal sample is determined based on the coal sample corresponding to the relative standard deviation meeting preset conditions, including determining the coal sample particle size, saturation pressure, and saturation time of the target coal sample. The aforementioned multiple different coal samples can be multiple coal samples with different coal sample particle sizes and / or saturation parameters. By performing RSD comparison analysis on the integral area A of multiple NMR T2 spectra of each coal sample under different saturation pressures, saturation times, and coal sample particle sizes, the stability of the test method for testing the pore structure of raw coal using NMR T2 can be effectively evaluated when testing coal samples with certain coal sample parameters. The smaller the RSD value, the higher the consistency of multiple measurements of a coal sample with a certain coal sample parameter in multiple NMR T2 tests, the smaller the random error, and the more reliable the data. Therefore, by identifying and selecting the coal sample with the smallest RSD value among multiple coal samples with different parameters, the target coal sample parameter that minimizes the measurement error of the raw coal pore structure can be determined, i.e., the target coal sample is determined. The target coal sample parameters can be a combination of optimal saturation pressure, saturation time, and coal sample particle size.

[0087] Compared with existing technologies, some embodiments of the present invention provide a method for detecting the pore structure of raw coal based on nuclear magnetic resonance. This method can, but is not limited to, analyze multiple key direct quantities such as saturation time, saturation pressure, and coal sample particle size in nuclear magnetic resonance T2 testing, and establish a precise calibration relationship between these quantities and the pore structure parameters of raw coal. This significantly reduces the uncertainty brought about by empirical parameters and achieves high-precision, near-zero-error quantitative characterization of key indicators such as porosity and pore size distribution of raw coal, providing reliable data support for coalbed methane development and gas extraction.

[0088] Please see Figure 2 The diagram shows a flowchart illustrating the method for determining target coal sample parameters in a raw coal porosity detection method according to some embodiments of the present invention. In some embodiments, S08 includes S082, S084, and S086, and the descriptions of each step are as follows.

[0089] S082: Determine whether there is a relative standard deviation that meets the preset conditions. If the result of this step is yes, proceed to S084; otherwise, proceed to S086.

[0090] S084: Determine the target coal sample based on the coal sample parameters corresponding to the coal sample whose relative standard deviation meets the preset conditions.

[0091] S086: Update the coal sample parameters for each coal sample.

[0092] Determining whether a relative standard deviation meets a preset condition may include, but is not limited to, comparing the relative standard deviations of each coal sample to determine the smallest one among multiple coal samples. Then, determining the relationship between this smallest relative standard deviation and a preset value, and whether the smallest relative standard deviation is less than or equal to the preset value, indicates that the smallest relative standard deviation meets the preset condition. If so, it means that the smallest relative standard deviation meets the preset condition, i.e., step S082 determines that a relative standard deviation meeting the preset condition exists. If the smallest relative standard deviation is greater than the preset value, it is determined that there is no relative standard deviation among the multiple relative standard deviations corresponding to the current multiple coal samples that meets the preset condition. In this case, the coal sample parameters need to be updated, i.e., the coal sample needs to be updated. The process from steps S02 to S06 is then repeated for multiple coal samples with the new parameters until it is determined that one of the currently obtained relative standard deviations meets the preset condition.

[0093] Updating the coal sample parameters of each coal sample can be done by determining the parameters of each coal sample to be updated based on the relationship between the smallest of several currently obtained relative standard deviations and a preset value, and then displaying or directly controlling the water saturation device to set the updated water saturation parameters. By determining the target coal sample based on the coal sample parameters corresponding to the relative standard deviation that meets preset conditions, the target coal sample with the expected detection effect can be obtained, making the accuracy and stability of the porosity detection results of raw coal precisely controllable.

[0094] In other embodiments, the multiple coal samples in S02 include multiple first coal samples and multiple second coal samples. The coal sample parameter of each first coal sample is its saturation parameter, meaning each first coal sample has a different saturation parameter but the same particle size. The coal sample parameter corresponding to each second coal sample is its particle size, meaning each second coal sample has the same saturation parameter but different particle sizes. A flowchart illustrating the method for determining the target coal sample in the raw coal porosity detection method provided in this embodiment is shown below. Figure 3 As shown, it includes the following steps:

[0095] S11: Obtain multiple first coal samples. All first coal samples have the same particle size, which can be set to a relatively small value. The multiple first coal samples are sequentially placed into a saturation device with different combinations of saturation pressure and saturation time to control saturation and obtain different degrees of saturation; that is, the multiple first coal samples are multiple coal samples with different degrees of saturation.

[0096] S12: Perform nuclear magnetic resonance T2 tests on each of the first coal samples to obtain the corresponding relaxation time distribution data and the total integrated area of ​​the nuclear magnetic resonance T2 spectrum.

[0097] S13: Repeat the NMR T2 test multiple times for each first coal sample to obtain multiple relaxation time distribution data and the total integrated area of ​​the NMR T2 spectrum for each first coal sample. In some embodiments, the number of repeated measurements (including the first measurement in S12) may be greater than or equal to 5.

[0098] S14: Calculate the relative standard deviation of the total integrated area of ​​the T2 NMR spectrum for each group corresponding to each first coal sample.

[0099] S15: Determine whether the relative standard deviation of each first coal sample currently calculated meets the preset conditions. The preset conditions here can be whether each relative standard deviation has reached the set minimum value. If yes, proceed to S16; otherwise, proceed to S17.

[0100] S16: Determine the saturation pressure and saturation time of the first coal sample with the smallest relative standard deviation as the target saturation pressure and target saturation time of the target coal sample. Then execute S18.

[0101] S17: Adjust the combined parameters of saturation time and saturation pressure for each first coal sample. In this embodiment, each first coal sample refers to a coal sample with the same particle size but different saturation parameters. The direction and magnitude of the adjustment of saturation pressure and saturation time can be determined based on the deviation between the currently obtained relative standard deviations and preset values.

[0102] S18: Using the target saturation pressure and saturation time as the saturation parameters for each second coal sample, obtain multiple second coal samples with different particle sizes but the same saturation parameters. The particle size of each of the multiple second coal samples can be larger than that of the first coal sample.

[0103] S19: Perform nuclear magnetic resonance T2 tests on each of the second coal samples to obtain the corresponding relaxation time distribution data and the total integrated area of ​​the nuclear magnetic resonance T2 spectrum.

[0104] S20: Repeat the NMR T2 test multiple times for each second coal sample to obtain multiple relaxation time distribution data and the total integrated area of ​​the NMR T2 spectrum for each second coal sample. In some embodiments, the number of repeated measurements (including the first measurement in S12) may be greater than or equal to 5.

[0105] S21: Calculate the relative standard deviation of the total integrated area of ​​the nuclear magnetic resonance T2 spectrum for each group corresponding to each second coal sample.

[0106] S22: Determine whether the relative standard deviation of each second coal sample currently calculated meets the preset conditions. The preset conditions here can be whether each relative standard deviation has reached the set minimum value. If yes, proceed to S23; otherwise, proceed to S24.

[0107] S23: Determine the particle size of the second coal sample with the smallest relative standard deviation as the target particle size of the target coal sample, so as to determine the target coal sample.

[0108] S24: Adjust the particle size of each second coal sample. In this embodiment, each second coal sample refers to a coal sample with the same saturation parameter but different particle sizes. The direction and magnitude of the particle size adjustment can be determined based on the deviation between the currently obtained relative standard deviation and the preset value.

[0109] In some embodiments, 10-mesh coal particles (which can be the first coal sample) are first tested under different combinations of saturation time and pressure, with at least five parallel samples (the first coal sample) set up for each group. If the RSD does not reach the set minimum value, the saturation parameters of the sample are adjusted and the test is repeated; if the RSD reaches the set minimum value, the saturation pressure and saturation time of the coal sample corresponding to the RSD reaching the set minimum value are recorded as the optimal saturation pressure and time. The RSD results of 10-mesh coal particles under different saturation times and pressures are as follows. Figure 4 As shown, the RSD value was lowest when the saturation time was 36 hours and the saturation pressure was 1 MPa. Therefore, a saturation time of 36 hours and a saturation pressure of 1 MPa were selected as the optimal saturation parameters. Subsequently, under the conditions of a saturation time of 36 hours and a saturation pressure of 1 MPa, nuclear magnetic resonance (NMR) tests were performed on coal samples of different particle sizes. The RSD value was repeatedly measured and calculated for each particle size group. If the RSD was not minimized, the particle size parameters were adjusted; if the RSD was minimized, the optimal particle size value was determined. The RSD results for coal samples of different particle sizes under the optimal saturation parameters are shown below. Figure 5 As shown, the RSD value is lowest when the coal particle size is 20 mesh. Therefore, a saturation time of 36 hours, a saturation pressure of 1 MPa, and a coal particle size of 20 mesh represent the optimal combination of saturation pressure, saturation time, and coal particle size to minimize the measurement error of the raw coal pore structure. Clearly, the raw coal pore detection method provided by this invention can determine the target coal sample that minimizes the detection error. Using this target coal sample for raw coal detection can yield more accurate and stable pore detection results.

[0110] According to the above embodiment, S08 may include: determining the target saturation parameter of the target coal sample based on the saturation parameter corresponding to the coal sample whose relative standard deviation meets the preset condition among a plurality of first coal samples; and determining the target particle size of the target coal sample based on the particle size corresponding to the coal sample whose relative standard deviation meets the preset condition among a plurality of second coal samples. Wherein, the saturation parameter corresponding to each second coal sample is the target saturation parameter, and the particle size corresponding to each second coal sample is greater than the particle size corresponding to the first coal sample.

[0111] In some embodiments, determining the target saturation parameter of a target coal sample based on the saturation parameter corresponding to a coal sample whose relative standard deviation meets a preset condition among a plurality of first coal samples further includes: determining whether the smallest of the relative standard deviations corresponding to the plurality of first coal samples is less than or equal to a first preset value; if yes, determining the saturation parameter corresponding to the first coal sample with the smallest relative standard deviation as the target saturation parameter; if no, updating the saturation parameter of each first coal sample.

[0112] In some embodiments, determining the target particle size of a target coal sample based on the saturation parameter corresponding to the second coal sample whose relative standard deviation meets a preset condition among a plurality of second coal samples includes: determining whether the smallest of the relative standard deviations corresponding to the plurality of second coal samples is less than or equal to a second preset value; if yes, determining the coal sample particle size corresponding to the second coal sample with the smallest relative standard deviation as the target particle size; if no, updating the coal sample particle size of each second coal sample.

[0113] This invention first sets up multiple first coal samples with fixed particle sizes and varied saturation parameters, performing multiple sets of NMR T2 tests to determine the target saturation pressure and target saturation time for more accurate and stable test results. Then, multiple second coal samples are set up, using the target saturation pressure and target saturation time as their saturation parameters, and each second coal sample has a different particle size. This determines the corresponding particle size for more accurate and stable test results. Determining the target coal sample parameters step-by-step is more conducive to improving the accuracy of pore structure detection.

[0114] Please see Figure 6 As shown in some embodiments, the present invention provides a schematic diagram of a raw coal porosity detection device based on nuclear magnetic resonance (NMR). In this embodiment, the raw coal porosity detection device is represented by a first raw coal porosity detection device 600, which includes a data acquisition module 601, an integral area determination module 602, a deviation determination module 603, and a target coal sample determination module 604. The data acquisition module 601 is used to acquire multiple relaxation time distribution data of each coal sample from multiple raw coal samples based on multiple NMR T2 tests. Different coal samples have different coal sample parameters, including coal sample particle size and / or water saturation parameters. The integral area determination module 602 is used to determine the total integral area of ​​the corresponding multiple NMR T2 spectra based on the multiple relaxation time distribution data corresponding to each coal sample. The deviation determination module 603 is used to perform relative standard deviation statistical analysis on the multiple total integral areas corresponding to each coal sample to obtain the relative standard deviation corresponding to each coal sample. The target coal sample determination module 604 determines the target coal sample based on the coal sample parameters corresponding to the coal sample whose relative standard deviation meets the preset conditions, so as to determine the porosity detection result of the raw coal based on the nuclear magnetic resonance T2 test result of the target coal sample.

[0115] In some embodiments, the target coal sample determination module 604 is specifically used to determine whether there is a relative standard deviation that meets preset conditions; if so, the target coal sample is determined according to the coal sample parameters corresponding to the coal sample whose relative standard deviation meets the preset conditions; if not, the coal sample parameters of each coal sample are updated.

[0116] In some embodiments, the target coal sample determination module 604 is specifically used to determine the target saturation parameter of the target coal sample based on the saturation parameter corresponding to a coal sample whose relative standard deviation meets a preset condition among a plurality of first coal samples, and to determine the target particle size of the target coal sample based on the particle size of a coal sample corresponding to a coal sample whose relative standard deviation meets a preset condition among a plurality of second coal samples.

[0117] In some embodiments, the target coal sample determination module 604 is specifically used to determine whether the smallest of the relative standard deviations corresponding to the plurality of first coal samples is less than or equal to a first preset value; if so, the saturation parameter corresponding to the first coal sample with the smallest relative standard deviation is determined as the target saturation parameter; if not, the saturation parameter of each first coal sample is updated.

[0118] In some embodiments, the target coal sample determination module 604 is specifically used to determine whether the smallest of the relative standard deviations corresponding to the plurality of second coal samples is less than or equal to a second preset value; if yes, the coal sample particle size corresponding to the second coal sample with the smallest relative standard deviation is determined as the target particle size; if no, the coal sample particle size of each second coal sample is updated.

[0119] The first raw coal porosity detection device 600 provided in the various embodiments of the present invention can achieve the same technical effect as the raw coal porosity detection method provided in the corresponding embodiments of the present invention, and will not be described again here.

[0120] Please see Figure 7 As shown, in some other embodiments, the raw coal porosity detection device based on nuclear magnetic resonance provided by the present invention can be represented by a second raw coal porosity detection device 700, which includes a processor 701 and a memory 702. The memory 702 is used to store programs, instructions or code, and the processor 701 is used to execute the programs, instructions or code in the memory to complete the raw coal porosity detection method provided in any of the above embodiments.

[0121] The memory 702 can be random access memory (RAM), flash memory, read-only memory (ROM), EPROM, non-volatile read-only memory (Electronic Programmable ROM), register, hard disk, removable disk, etc.

[0122] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape) or a semiconductor medium (e.g., solid-state disk (SSD)).

[0123] The second raw coal porosity detection device 700 provided in the various embodiments of the present invention can achieve the same technical effect as the raw coal porosity detection method provided in the corresponding embodiments of the present invention, and will not be described again here.

[0124] Please see Figure 8 As shown, in some embodiments, the present invention provides a raw coal porosity detection system 800 based on nuclear magnetic resonance (NMR), which includes a water-saturated device 400, an NMR T2 testing device 500, and a second raw coal porosity detection device 700 according to any embodiment of the present invention. The water-saturated device 400 is used to obtain multiple coal samples with different degrees of water saturation, and the NMR T2 testing device 500 is used to perform NMR T2 testing on each coal sample to obtain relaxation time distribution data of each coal sample.

[0125] The raw coal porosity detection system 800 provided in the various embodiments of the present invention can achieve the same technical effect as the raw coal porosity detection method provided in the corresponding embodiments of the present invention, and will not be described again here.

[0126] This invention also provides a readable storage medium storing a computer program, which is loaded by a processor to execute the raw coal porosity detection method provided in any of the above embodiments. Examples of suitable storage media include random access memory (RAM), flash memory, read-only memory (ROM), EPROM, non-volatile read-only memory (EPROM), registers, hard disks, removable disks, or any other form of storage medium in the art.

[0127] It is understood that the term "connection" in the embodiments of the present invention can be interpreted as "electrical connection," "communication connection," etc., if the connected circuits, units, etc. can transmit electrical signals or data to each other.

[0128] It is understood that the specific examples in this document are only intended to help those skilled in the art better understand the embodiments of the present invention, and are not intended to limit the scope of the present invention.

[0129] It is understood that in the various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0130] It is understood that the various embodiments described in this invention can be implemented individually or in combination, and the embodiments of this invention are not limited in this respect.

[0131] Unless otherwise stated, all technical and scientific terms used in the embodiments of this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of the invention. The term "and / or" as used in this invention includes any and all combinations of one or more of the associated listed items. The singular forms "a," "the," and "the" as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0132] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0133] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for detecting the porosity of raw coal based on nuclear magnetic resonance, characterized in that, include: Multiple relaxation time distribution data of each coal sample from multiple raw coal samples were obtained based on multiple nuclear magnetic resonance T2 tests; wherein, different coal samples have different coal sample parameters, including coal sample particle size and / or water saturation parameters; The total integral area of ​​multiple nuclear magnetic resonance T2 spectra corresponding to each coal sample is determined based on the multiple relaxation time distribution data corresponding to each coal sample. Statistical analysis of the relative standard deviation is performed on the multiple total integral areas corresponding to each coal sample to obtain the relative standard deviation of each coal sample. Based on the coal sample parameters corresponding to the coal sample whose relative standard deviation meets the preset conditions, a target coal sample is determined, and the porosity detection result of the raw coal is determined based on the nuclear magnetic resonance T2 test result of the target coal sample. The step of determining the target coal sample based on the coal sample parameters corresponding to the coal sample that meets the preset conditions according to the relative standard deviation includes: Determine whether the relative standard deviation meets the preset conditions; If so, the target coal sample is determined based on the coal sample parameters corresponding to the coal sample whose relative standard deviation meets the preset conditions; If not, update the coal sample parameters for each of the coal samples described. The step of determining whether there exists a relative standard deviation that satisfies the preset condition includes: comparing the magnitudes of the relative standard deviations corresponding to each coal sample to determine the smallest of the relative standard deviations, and determining whether the smallest relative standard deviation is less than or equal to a preset value. If so, the smallest relative standard deviation is determined to satisfy the preset condition; otherwise, there is no relative standard deviation that satisfies the preset condition.

2. The method for detecting the porosity of raw coal according to claim 1, characterized in that, The plurality of coal samples includes a plurality of first coal samples and a plurality of second coal samples. Each first coal sample has a different water saturation parameter but the same particle size; each second coal sample has a different particle size but the same water saturation parameter. The step of determining the target coal sample based on the coal sample parameters corresponding to the coal samples whose relative standard deviation meets preset conditions includes: Based on the saturation parameters of the coal samples whose relative standard deviations meet preset conditions among the multiple first coal samples, the target saturation parameter of the target coal sample is determined. The target particle size of the target coal sample is determined based on the coal sample particle size corresponding to the coal sample whose relative standard deviation meets the preset condition among multiple second coal samples. Wherein, the saturation parameter corresponding to each of the second coal samples is the target saturation parameter, and the particle size of each of the second coal samples is greater than the particle size of the first coal sample.

3. The method for detecting the porosity of raw coal according to claim 2, characterized in that, The step of determining the target saturation parameter of the target coal sample based on the saturation parameter corresponding to a coal sample whose relative standard deviation among multiple first coal samples meets a preset condition includes: Determine whether the smallest of the relative standard deviations corresponding to the multiple first coal samples is less than or equal to a first preset value; If so, the saturation parameter corresponding to the first coal sample with the smallest relative standard deviation is determined as the target saturation parameter; If not, update the saturation parameters of each of the first coal samples.

4. The method for detecting the porosity of raw coal according to claim 2, characterized in that, The step of determining the target particle size of the target coal sample based on the particle size of a coal sample whose relative standard deviation among multiple second coal samples meets a preset condition includes: Determine whether the smallest of the relative standard deviations corresponding to the multiple second coal samples is less than or equal to a second preset value; If so, the particle size of the second coal sample with the smallest relative standard deviation is determined as the target particle size; If not, update the particle size of each second coal sample.

5. The method for detecting the porosity of raw coal according to any one of claims 1 to 4, characterized in that, The saturation parameters include the saturation pressure and / or saturation time set by the saturation device to achieve the corresponding degree of saturation in the coal sample.

6. A raw coal porosity detection device based on nuclear magnetic resonance, characterized in that, include: The data acquisition module is used to acquire multiple relaxation time distribution data of each coal sample from multiple coal samples of raw coal based on multiple nuclear magnetic resonance T2 tests; wherein, different coal samples have different coal sample parameters, including coal sample particle size and / or water saturation parameters; The integral area determination module is used to determine the total integral area of ​​multiple nuclear magnetic resonance T2 spectra corresponding to each coal sample based on the multiple relaxation time distribution data corresponding to each coal sample. The deviation determination module performs relative standard deviation statistical analysis on the multiple total integral areas corresponding to each coal sample to obtain the relative standard deviation of each coal sample. The target coal sample determination module determines the target coal sample based on the coal sample parameters corresponding to the coal sample whose relative standard deviation meets the preset conditions, so as to determine the porosity detection result of the raw coal based on the nuclear magnetic resonance T2 test result of the target coal sample. The step of determining the target coal sample based on the coal sample parameters corresponding to the coal sample that meets the preset conditions according to the relative standard deviation includes: Determine whether the relative standard deviation meets the preset conditions; If so, the target coal sample is determined based on the coal sample parameters corresponding to the coal sample whose relative standard deviation meets the preset conditions; If not, update the coal sample parameters for each of the coal samples described. The step of determining whether there exists a relative standard deviation that satisfies the preset condition includes: comparing the magnitudes of the relative standard deviations corresponding to each coal sample to determine the smallest of the relative standard deviations, and determining whether the smallest relative standard deviation is less than or equal to a preset value. If so, the smallest relative standard deviation is determined to satisfy the preset condition; otherwise, there is no relative standard deviation that satisfies the preset condition.

7. A raw coal porosity detection device based on nuclear magnetic resonance, characterized in that, include: A processor and a memory, the memory being used to store programs, instructions, or code, and the processor being used to execute the programs, instructions, or code in the memory to perform the raw coal porosity detection method as described in any one of claims 1 to 5.

8. A raw coal porosity detection system based on nuclear magnetic resonance, characterized in that, Includes a water saturation device, a nuclear magnetic resonance T2 spectroscopy testing device, and the raw coal porosity detection device as described in claim 7; The saturation device is used to obtain multiple coal samples with different degrees of saturation, and the nuclear magnetic resonance T2 testing equipment is used to perform nuclear magnetic resonance T2 testing on each coal sample to obtain the relaxation time distribution data of each coal sample.

9. A computer-readable storage medium, characterized in that, The device contains a computer program that is loaded by a processor to execute the raw coal porosity detection method as described in any one of claims 1 to 5.

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