Quantitative characterization method for pore structure of low-permeability reservoir based on multi-peak fitting of nuclear magnetic resonance T2 spectrum

Abstract

The invention discloses a quantitative characterization method for a low-permeability reservoir pore structure based on nuclear magnetic resonance T2 spectrum multi-peak fitting, and relates to the technical field of reservoir pore structure characterization, the nuclear magnetic resonance T2 spectrum is preprocessed, and Gaussian multi-peak fitting decomposition is performed on the preprocessed data; according to the peak area, the peak center and the peak width of each sub-peak, carrying out quantitative characterization on the porosity contribution and the aperture range of the corresponding micropores, mesopores and macropores; the sub-peak parameters obtained through multi-peak fitting are introduced into the permeability prediction model, the model for predicting the permeability based on the NMR T2 relaxation data is corrected based on the multi-peak fitting result, the permeability model is constructed, the permeability is predicted more accurately, and the method has great significance in promoting reservoir prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir pore structure characterization, and in particular to a method for quantitatively characterizing the pore structure of a low-permeability reservoir based on multi-peak fitting of a nuclear magnetic resonance T2 spectrum. Background Art

[0002] In oil and gas exploration and development, accurate pore structure characterization is crucial for assessing reservoir quality, predicting permeability, and optimizing production strategies. Currently, a variety of experimental methods have been developed for pore structure characterization in low-permeability sandstone reservoirs, including nano-CT scanning, scanning electron microscopy (SEM), high-pressure mercury injection (HPMI), nuclear magnetic resonance (NMR) measurements, and low-temperature nitrogen / carbon dioxide adsorption. These techniques can, to a certain extent, help researchers understand key rock indicators such as porosity, pore throat distribution, and pore connectivity.

[0003] Nuclear magnetic resonance (NMR) technology is a commonly used method for pore structure evaluation in oilfields and research laboratories due to its non-destructive nature, ability to perform tests under near-in-situ conditions, and the ability to accurately reflect the fluid properties within pores. NMR T2 spectra exhibit a consistent relationship with pore size, effectively reflecting porosity and pore size distribution.

[0004] In practical applications, traditional NMR analysis methods mainly rely on parameterized techniques (such as FFI, BVI, T 2cutoff 、T 2gm Pore ​​types are classified or summarized based on qualitative peak type judgments (e.g., single peak, double peak, triple peak). However, with the increasing complexity of reservoir types, these methods still have shortcomings in distinguishing multiple pore types (micropores, mesopores, and macropores) and accurately extracting the physical meaning of each sub-peak.

[0005] Defects and shortcomings of existing technology:

[0006] (1) Qualitative classification relies on experience and lacks stability

[0007] Many previous studies have qualitatively identified different pore structures by comparing NMR T2 spectra of saturated brine and centrifuged samples, or by relying on researchers' experience to distinguish between single and double peaks. These methods, which rely on manual experience, are time-consuming and often lack stability, making them unsuitable for large-scale, diverse sample analysis.

[0008] (2) The scope of application of existing parameter methods is limited

[0009] Common NMR parameters (such as T 2cutoff, FFI, BVI, etc.) can only classify pores into two categories: "bound fluid" and "free fluid". It is difficult to further subdivide pores into more types of pores such as micropores, mesopores, and macropores. In addition, the parameters (T 2gm 、T 2max 、T 2mid etc.) may be highly correlated with each other, making it difficult to highlight key pore structure differences.

[0010] (3) Lack of clear explanation of the physical meaning of parameters after multi-peak fitting

[0011] Although previous studies have shown that multi-peak fitting can effectively improve the resolution of complex T2 spectra, there is a lack of systematic explanation of the physical meaning of the peak area, peak center, and peak width of each sub-peak, which has limited the application of this method in actual engineering.

[0012] (4) Insufficient connection with subsequent reservoir permeability prediction

[0013] Some studies only stay at the stage of peak decomposition of NMR T2 spectra, and have not established a close connection with subsequent permeability prediction, making it difficult to form a complete analysis and application process based on multi-peak fitting. Summary of the Invention

[0014] In order to solve the above technical problems, the present invention proposes a quantitative characterization method for the pore structure of low-permeability reservoirs based on multi-peak fitting of nuclear magnetic resonance T2 spectrum, which pre-processes the nuclear magnetic resonance T2 spectrum and performs Gaussian multi-peak fitting decomposition on the pre-processed data;

[0015] Combining the peak area, peak center and peak width of each sub-peak, the porosity and pore size distribution of micropores, mesopores and macropores are quantitatively characterized.

[0016] By introducing the sub-peak parameters obtained by multi-peak fitting into the permeability prediction model, the permeability prediction model based on NMR T2 relaxation data was modified based on the multi-peak fitting results to construct a permeability model.

[0017] In a preferred embodiment, the NMR T2 spectrum is preprocessed to obtain the NMR porosity

[0018]

[0019] in, is the i-th component of the transverse relaxation time T2 2i The porosity of n is the total number of components;

[0020] Actual cumulative distribution function F of NMR porosity * for:

[0021]

[0022] Actual porosity probability density function f * (logT2) is:

[0023]

[0024] In a preferred embodiment, it is assumed that the distribution of the preprocessed data is formed by the linear superposition of multiple Gaussian distributions, and the porosity probability density function f(logT2) of the total peak is:

[0025]

[0026] Where y0 is the baseline, f j (logT2) is the porosity probability density function of the sub-peak, A j is the area of ​​the jth sub-peak, G j (logT2) is the Gaussian distribution of the jth sub-peak, and m is the number of sub-peaks;

[0027] The area A of the jth sub-peak j and NMR porosity The following relationship is satisfied:

[0028]

[0029] Among them, logT 2min and logT 2max are the minimum and maximum values ​​of the transverse relaxation time T2 under logarithm; transform the Gaussian distribution function:

[0030]

[0031] Among them, w j is the full width at half maximum (FWHM) of the jth sub-peak; c j is the center of the j-th sub-peak.

[0032] In a preferred embodiment, the R-squared value reflecting the degree of fit is calculated as follows:

[0033]

[0034] Where N is the total number of data points after Gaussian multi-peak fitting of a nuclear magnetic resonance T2 spectrum, f k * (logT2) is the kth data value of the porosity probability density function of the actual total peak, f k (logT2) is the kth data value of the porosity probability density function of the total peak after fitting, is the average value of the porosity probability density function of the total peak after Gaussian multi-peak fitting.

[0035] In a preferred embodiment, in the permeability prediction model, T 2gm :

[0036]

[0037] Taking the logarithm of both sides simplifies to:

[0038]

[0039] log(T 2gm ) is expressed by the porosity probability density function of the total number of peaks obtained by Gaussian multi-peak fitting:

[0040]

[0041] In a preferred embodiment, logT' 2gm Redefined as the logarithmic geometric mean of the peak parameters based on a Gaussian multi-peak fit:

[0042]

[0043] In a preferred embodiment, log(T 2gm ) log(T' 2gm ) to approximate, log(T' 2gm ) is applied to the permeability prediction model, and the permeability K equation becomes:

[0044]

[0045] The permeability prediction model was modified to exclude the peak parameters related to micropores and the calculation was as follows:

[0046]

[0047] Where a, b, and c are constants.

[0048] Compared with the prior art, the present invention has the following beneficial technical effects:

[0049] 1. Quantitatively decompose complex T2 spectra to overcome the limitations of empirical qualitative division

[0050] The present invention decomposes the NMR T2 spectrum into several independent components through Gaussian multi-peak fitting. Each component corresponds to a different pore size range and physical property characteristics, which greatly improves the ability to distinguish micropores, mesopores and macropores in complex reservoirs and reduces the reliance on the experience and judgment of scientific researchers.

[0051] The pore components in the T2 spectrum are usually distinguished by fixed values, but in this method, there is no fixed boundary between different types of pores. They are all concentrated near the center of the peak. The farther away from the peak center, the fewer pores of that type. Figure 3 Figure B illustrates the crossover between different pore types. For example, subpeaks 1 and 2 intersect at a log(T2) value of 0.8, and the content of peak 3 is zero at this point. This observation indicates that at this pore size, the sample contains equal amounts of micropores and mesopores, with no macropores present. Compared to previous studies, this finding more accurately reflects the actual pore size distribution and provides a deeper understanding of the pore distribution pattern.

[0052] 2. Accurately extract multi-peak parameters and explain their physical meaning to enhance the interpretability of results

[0053] Each sub-peak can be quantified using peak area, peak center, and peak width. Peak area reflects the porosity contribution, peak center reflects the center of the pore size distribution, and peak width represents the pore size distribution range. This parameterization has clear petrophysical meaning and is easy to apply in reservoir evaluation, numerical simulation, and field interpretation.

[0054] 3. Improve the permeability prediction model and improve prediction accuracy

[0055] By introducing the sub-peak parameters obtained by multi-peak fitting into the permeability prediction model, the traditional method can effectively overcome the limitation of single T 2cutoff or T 2gm dependency. Figure 5 A shows the results of the original SDR model, with an R-squared value of 0.8202. Figure 5 Figure B shows the results of the modified model, which shows that the R-squared value has increased to 0.8718. This indicates that the modified model has higher accuracy and can more accurately predict permeability, which is of great significance for promoting reservoir prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Middle: Figure A shows the principle of Gaussian multi-peak fitting algorithm for NMR T2 spectrum; Figure B shows the specific parameter A of sub-peak j 、w j and c j ;

[0057] Figure 2 Middle: Figure A represents the original NMR T2 spectrum of the low-permeability sandstone sample in the Daluhu area; Figure B represents the spectrum after Gaussian multi-peak fitting in the Daluhu area;

[0058] Figure 3 Middle: Figure A represents the original NMR T2 spectrum of the low-permeability sandstone sample in the Mahaidong area; Figure B represents the spectrum after Gaussian multi-peak fitting in the Mahaidong area;

[0059] Figure 4 is the log(T' 2gm ) and log(T 2gm ) intersection diagram;

[0060] Figure 5 Middle: Figure A represents the relationship between the logarithmic permeability and the coefficient of the original SDR model in the Mahaidong area; Figure B represents the three-dimensional relationship between the logarithmic permeability and the coefficient of the revised SDR model in the Mahaidong area. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0062] This paper proposes a quantitative pore structure characterization method for low-permeability sandstone reservoirs or other complex reservoirs based on multi-peak fitting of nuclear magnetic resonance (NMR) T2 spectra. First, the NMR T2 spectrum is preprocessed, treating it as a linear superposition of several logarithmic Gaussian distributions. The preprocessed data is then decomposed using Gaussian multi-peak fitting. Subsequently, parameters such as the peak area A, peak center c, and peak width w of each sub-peak are combined to accurately and quantitatively characterize the porosity contribution and pore size range of micropores, mesopores, and macropores. The results are then applied to permeability prediction.

[0063] 1. NMR T2 spectrum data preprocessing and conversion.

[0064] Preliminary analysis indicates that when the transverse relaxation time T2 exceeds 1000 ms, small pores, primarily corresponding to microcracks, are observed in the sandstone samples. These microcracks are negligible in number and therefore excluded from experimental data processing. Furthermore, since the raw data represent porosity increments, they were converted to a porosity probability density function for more precise data analysis. The calculation procedure is as follows.

[0065] The sum of the porosity increments of all sampling points is the porosity of the sample, which can be expressed as follows:

[0066]

[0067] in, is the NMR porosity of the sample, is the i-th component of the transverse relaxation time T2 2i The porosity (also called porosity increment) has a total of n components.

[0068] Therefore, the actual cumulative distribution function of NMR porosity F * It can be calculated using the following formula:

[0069]

[0070] Finally, the actual porosity probability density function f * It can be calculated by logarithmic differentiation of equation (2) as follows:

[0071]

[0072] 2. Gaussian multi-peak fitting

[0073] In the initial stages of the Gaussian peak fitting algorithm, the experimental data are carefully examined to identify potential components or sub-peaks. Subsequently, a series of Gaussian distributions are used as models to accurately characterize these components, with each Gaussian distribution representing an independent component. The algorithm uses a nonlinear least-squares method to iteratively optimize the parameters of the Gaussian model, aiming to minimize the deviation between the composite curve fitted by the model and the experimental data. The R-squared value is used to assess the goodness of fit between the processed distribution and the actual distribution to ensure the accuracy and validity of the model.

[0074] Assume that the data distribution after preprocessing is formed by the linear superposition of several Gaussian distributions ( Figure 1 Figure A), its mathematical expression is as follows:

[0075]

[0076] where f(logT2) is the porosity probability density function of the sum of the peaks obtained after applying Gaussian multi-peak fitting, and represents the actual porosity probability density f * The exact fit of y0; y0 is the baseline, in this case: y0 = 0; f j (logT2) is the porosity probability density function of the sub-peak, A j is the area of ​​different sub-peaks, reflecting the weight assigned to each sub-peak; G j (logT2) is the Gaussian distribution of the sub-peak, and m is the number of sub-peaks.

[0077] From equations (1) to (4) and the properties of Gaussian distribution, it can be inferred that the sub-peak area (A j ) and NMR porosity The following relationship is satisfied:

[0078]

[0079] where logT 2min and logT 2maxare the minimum and maximum values ​​of the transverse relaxation time T2 under logarithm.

[0080] In engineering applications, the Gaussian distribution function is transformed:

[0081]

[0082] Among them, w j is the full width at half maximum (FWHM) of the sub-peak; c j It is the center of the sub-peak.

[0083] Therefore, the properties of each sub-peak ( Figure 1 Figure B) can be obtained by specifying the parameter A j 、w j and c j to confirm.

[0084] The R-squared value reflecting the degree of fit can be calculated as follows:

[0085]

[0086] Where N is the total number of data points after Gaussian multi-peak fitting of a nuclear magnetic resonance T2 spectrum, f k * (logT2) is the kth data value of the porosity probability density function of the actual total peak, f k (logT2) is the kth data value of the porosity probability density function of the total peak after fitting, is the average value of the porosity probability density function of the total peak after Gaussian multi-peak fitting.

[0087] 3. Example

[0088] This patent example selects low permeability sandstone samples from the Dalu Lake area of ​​the Dongying Depression in the Bohai Bay Basin and the Mahaidong area of ​​the Qaidam Basin for demonstration and explanation.

[0089] like Figure 2 As shown in the figure, taking the representative sample from Daluhu area as an example, two sub-peaks were finally fitted, and the sum of the peak areas was approximately equal to the NMR porosity (16.7%), indicating that the peak decomposition was consistent with the actual porosity. After data preprocessing, the pore content greater than 100ms was removed ( Figure 2 Figure A in the figure), the original T2 spectrum is converted into a log(T2) spectrum ( Figure 3 Figure B), where the horizontal axis represents the logarithmic value of the T2 relaxation time and the vertical axis represents the probability density function of the porosity.

[0090] contrast Figure 2As shown in Figures A and B, the logarithmic T2 spectrum maintains a similar shape to the original T2 spectrum. This similarity is due to the fact that the intervals between the T2 spectrum data points in the logarithmic coordinate system are approximately equal, resulting in the same relative amplitude of the data points after differentiation.

[0091] like Figure 2 As shown in Figure B, the total peak after fitting has a high degree of agreement with the actual data. The R square value in Table 2 is 0.997, indicating that the fitting curve effectively reflects the characteristics of the original data. The total peak consists of two linearly superimposed sub-peaks, each representing a different pore type. These sub-peaks are compared with Figure 2 Comparison of the centrifugal porosity increment curve in Figure A reveals that peak 1 primarily represents immobile water associated with micropores, while peak 2 primarily represents mobile water associated with mesopores. The log(T2) spectrum not only fully preserves the information of the original NMR T2 spectrum but also effectively decomposes the raw data.

[0092] Peaks 2 and 3 mainly represent the mobile water associated with the mesopores and macropores.

[0093] like Figure 3 As shown in Table 1, taking the representative sample from Mahaidong area as an example, three sub-peaks were finally fitted, and the sum of the peak areas was approximately equal to the NMR porosity (21.11%).

[0094] like Figure 3 As shown in Figure B, similar to the analysis results of representative samples from the Daluhu area, the total peak after fitting has a high degree of agreement with the actual data. The R square value in Table 1 is 0.9975, indicating that the fitting curve effectively reflects the characteristics of the original data. Unlike the samples from the Daluhu area, the total peak of the samples from the Mahaidong area consists of three linearly superimposed sub-peaks. These sub-peaks are compared with the Figure 3 Comparison of the centrifugal porosity increment curves in A reveals that peak 1 mainly represents immobile water associated with micropores, while peaks 2 and 3 mainly represent mobile water associated with mesopores and macropores.

[0095] The above two examples show that this multi-peak fitting quantitative characterization method for pore structure can be effectively applied to analyze samples from different regions.

[0096] Table 1 Gaussian multi-peak fitting results of representative samples in Mahaidong area

[0097]

[0098]

[0099] 4. Explanation of the physical meaning of multimodal parameters

[0100] As mentioned above, each sub-peak in the Gaussian distribution can be accurately quantified using the three peak parameters A, w, and c. The parameter calculation results of the samples in the Mahaidong area are shown in Table 2.

[0101] Table 2 Gaussian multi-peak fitting data of NMR T2 spectra of all samples in Mahaidong area

[0102]

[0103]

[0104]

[0105] A is the sub-peak area, reflecting the porosity of each type. The larger the area, the higher the porosity ( Figure 1 Figure B in Figure 1). According to equation (5), the sum of the sub-peak areas is approximately equal to the NMR porosity, and their difference depends on the accuracy of the Gaussian multi-peak fitting. Figure 2 As shown in Figure B, the micropore, mesopore and macropore porosities of the representative sample are 11.826%, 6.796% and 2.671% respectively, and their sum is 21.293%, which is close to the NMR porosity of 21.11%.

[0106] w is the full width at half maximum (FWHM) of the different sub-peaks, which represents the width of the Gaussian distribution and the pore size range within each pore type ( Figure 1 Narrower sub-peaks indicate a more concentrated pore size distribution; wider sub-peaks indicate a more dispersed distribution.

[0107] c is the center of different sub-peaks, representing the center value of each pore type ( Figure 1 (B in Figure 1). The smaller the peak center, the smaller the pore type, and the larger the peak center, the larger the pore type.

[0108] 5. Permeability prediction and application

[0109] There are several models for predicting permeability based on NMR T2 relaxation data, among which the most commonly used are the Coates and SDR models.

[0110] The permeability in the Coates model depends on T 2cutoff value that separates the free fluid index FFI from the bound water volume BVI:

[0111]

[0112] Where K is the permeability (mD), is the NMR effective porosity (%), C is a formation-specific constant reflecting the correlation between rock pore throats and pore diameters, FFI is the fraction of the formation volume occupied by free-flowing fluid, and BVI is the fraction of the formation volume occupied by immobile capillary-bound fluid.

[0113] In the SDR model, the penetration rate depends on T 2gm value:

[0114]

[0115] Where T 2gm is the geometric mean of the T2 distribution and a is a coefficient that depends on the formation type.

[0116] The key to Coates' model is to use T 2cutoff The values ​​of FFI and BVI are obtained. Usually, T 2cutoff was determined by comparing the NMR T2 spectra of centrifuged and saturated samples ( Figure 1 Figure A). In the absence of centrifugation data, T 2cutoff Uses default values ​​based on lithology. This approach has limitations when centrifugation data are lacking or when formation lithologies vary significantly.

[0117] In the SDR model, the key link is T 2gm The calculation is as follows:

[0118]

[0119] Taking the logarithm of both sides of equation (10) can be simplified to:

[0120]

[0121] According to formula (5), log(T 2gm ) can be expressed by the porosity probability density function of the total number of peaks obtained by Gaussian multi-peak fitting:

[0122]

[0123] Formula (12) shows that log(T 2gm ) is approximately equal to the expectation of the Gaussian function f(logT2) divided by the sum of the peak areas.

[0124] From the properties of the Gaussian function, we know that the expectation and variance of a linear combination of random variables are functions of the mean and variance of each variable. This means that the expectation of the Gaussian multi-peak fitting function is the weighted average of the expectations of each sub-peak, where the weight is the peak area A. Therefore, the following equation is satisfied:

[0125]

[0126] In this study, logT' 2gm It is redefined as the logarithmic geometric mean of the peak parameters based on Gaussian multi-peak fitting to distinguish it from the actual logarithmic geometric mean:

[0127]

[0128] Table 2 lists all the samples log (T 2gm ) and logT' 2gm There is a strong linear correlation between the two ( Figure 4 ), R 2 The value is 0.9999. This difference is mainly attributed to the error of Gaussian multi-peak fitting and calculation factors such as data point spacing.

[0129] From the above analysis, log(T 2gm ) can be calculated from the peak parameters after Gaussian multi-peak fitting logT' 2gm To approximate. LogT' 2gm Applied to the SDR model, the permeability equation becomes:

[0130]

[0131] Equation (15) shows that permeability is closely related to the peak parameters A and c. Studies have shown that the micropores represented by bound water contribute the least to permeability, and permeability is mainly affected by the mesopores and macropores represented by movable water.

[0132] On this basis, the SDR model was modified to exclude the peak parameters related to micropores, and the calculation was as follows:

[0133]

[0134] Where a, b, and c are constants.

[0135] The calculation results of this model are as follows Figure 5 shown.

[0136] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. The embodiments should therefore be considered illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be encompassed therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A method for quantitative characterization of pore structure of low permeability reservoirs based on multi-peak fitting of nuclear magnetic resonance T2 spectra, characterized in that: Preprocess the nuclear magnetic resonance T2 spectrum and perform Gaussian multi-peak fitting decomposition on the preprocessed data; Combining the peak area, peak center and peak width of each sub-peak, the porosity and pore size distribution of micropores, mesopores and macropores are quantitatively characterized. By introducing the sub-peak parameters obtained by multi-peak fitting into the permeability prediction model, the permeability prediction model based on NMRT2 relaxation data is modified based on the multi-peak fitting results to construct a permeability model.

2. The method for quantitative characterization of pore structure of low permeability reservoirs based on multi-peak fitting of nuclear magnetic resonance T2 spectrum according to claim 1, characterized in that: Preprocess the NMR T2 spectrum to obtain the NMR porosity in, is the i-th component of the transverse relaxation time T2 2i The porosity of n is the total number of components; Actual cumulative distribution function F of NMR porosity * for: Actual porosity probability density function f * (log T2) is:

3. The method for quantitative characterization of pore structure of low permeability reservoirs based on multi-peak fitting of nuclear magnetic resonance T2 spectrum according to claim 2, characterized in that: Assuming that the data distribution after preprocessing is formed by the linear superposition of multiple Gaussian distributions, the porosity probability density function f(log T2) of the total peak is: Where y0 is the baseline, f j (log T2) is the porosity probability density function of the sub-peak, A j is the area of ​​the jth sub-peak, G j (log T2) is the Gaussian distribution of the jth subpeak, and m is the number of subpeaks; The area A of the jth sub-peak j and NMR porosity The following relationship is satisfied: Where log T 2min and log T 2max are the minimum and maximum values ​​of the transverse relaxation time T2 under logarithm; transform the Gaussian distribution function: Among them, w j is the full width at half maximum (FWHM) of the jth sub-peak; c j is the center of the j-th sub-peak.

4. The method for quantitative characterization of pore structure of low permeability reservoirs based on multi-peak fitting of nuclear magnetic resonance T2 spectra according to claim 3, characterized in that: The R-squared value reflecting the degree of fit is calculated as follows: Where N is the total number of data points after Gaussian multi-peak fitting of a nuclear magnetic resonance T2 spectrum, f k * (logT2) is the kth data value of the porosity probability density function of the actual total peak, f k (log T2) is the kth data value of the porosity probability density function of the total peak after fitting, is the average value of the porosity probability density function of the total peak after Gaussian multi-peak fitting.

5. The method for quantitative characterization of pore structure of low permeability reservoirs based on multi-peak fitting of nuclear magnetic resonance T2 spectrum according to claim 4, characterized in that: In the permeability prediction model, T 2gm : Taking the logarithm of both sides simplifies to: log(T 2gm ) is expressed by the porosity probability density function of the total number of peaks obtained by Gaussian multi-peak fitting:

6. The method for quantitative characterization of pore structure of low permeability reservoirs based on multi-peak fitting of nuclear magnetic resonance T2 spectra according to claim 5, characterized in that: Log T' 2gm Redefined as the logarithmic geometric mean of the peak parameters based on a Gaussian multi-peak fit:

7. The method for quantitative characterization of pore structure of low permeability reservoirs based on multi-peak fitting of nuclear magnetic resonance T2 spectra according to claim 6, characterized in that: log(T 2gm ) log(T′) calculated from the peak parameters after Gaussian multi-peak fitting 2gm ) to approximate log(T′ 2gm ) is applied to the permeability prediction model, and the permeability K equation becomes: The permeability prediction model was modified to exclude the peak parameters related to micropores and the calculation was as follows: Where a, b, and c are constants.

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