CN109655394B - Nuclear magnetic resonance T2 spectrum permeability calculation method under constraint of throat structure parameters - Google Patents
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Abstract
The invention discloses a nuclear magnetic resonance T2 spectrum permeability calculation method under the constraint of pore and throat structural parameters, which comprises the following steps: s1: converting the nuclear magnetic resonance T2 spectrum curve into a pseudo capillary pressure curve; s2: calculating pore throat structure parameters according to the pseudo capillary pressure curve; s3: converting the acquired core analysis permeability data and a nuclear magnetic resonance T2 spectrum into a pseudo capillary pressure curve, calculating to obtain pore throat structure parameters and nuclear magnetic resonance logging data, and establishing a pore throat structure parameter and nuclear magnetic resonance T2 spectrum-permeability corresponding relation data volume; s4: selecting a nonlinear mapping algorithm to establish a permeability prediction model; s5: debugging and optimizing the permeability prediction model by adopting a cross validation method, establishing a nonlinear mapping relation model, and forming a final network model; s6: performing point-by-point calculation according to the final network model to obtain nuclear magnetic resonance T2 spectrum permeability under the constraint of pore throat structure parameters; the method can improve the calculation accuracy of the permeability of the tight sandstone by at least one order of magnitude.
Description
Technical Field
The invention relates to the technical field of petroleum geological exploration and logging evaluation, in particular to a nuclear magnetic resonance T2 spectrum permeability calculation method under the constraint of throat structural parameters.
Background
In the process of oil and gas exploration and development, methods for acquiring absolute permeability (permeability for short) generally include methods such as core testing and calculation by using logging information, wherein the permeability of the core testing is the most accurate, and is a direct permeability acquisition method which is commonly used for calibrating the permeability calculated by the logging information. However, the permeability of the core test is limited by the sampling point, and the obtained permeability value is discontinuous. Therefore, it is necessary to establish a logging data permeability calculation model by studying the relationship between permeability and logging response parameters and calculate permeability by using logging data.
The idea of obtaining permeability by using conventional logging data is two, one is to establish a statistical model between permeability and various reservoir parameters and logging response parameters, such as a statistical model of permeability and porosity and a natural gamma relative value, a permeability model established by a shunt unit, a model for calculating permeability by a neural network and the like; another method is to establish the relationship between the permeability and the reservoir characteristic parameters through a rock physical model, such as a Wylie-Rose permeability logging interpretation model, a Timur permeability logging interpretation model and the like. The two methods are complementary, but in the low-permeability reservoir permeability logging evaluation, the calculated permeability is compared with the permeability of core test analysis by using a Wyllie-Rose equation and a Timur equation, and the error is large. Based on the equivalent rock component theory and according to the similarity principle of charge migration and fluid molecule migration, a permeability logging interpretation model between effective flowing porosity and permeability is established on the basis of effective conductive porosity, and the model precision is superior to the Wylie-Rose equation and the Timur equation.
With the development of domestic oil and gas exploration, low-pore and low-permeability reservoirs are receiving more attention. The pore throat structure of the low-pore low-permeability reservoir is complex, the influence on permeability is large, and the methods for calculating the permeability cannot meet the production requirement. Nuclear magnetic resonance logging can measure various formation information such as porosity and pore throat structure, and is increasingly applied to evaluation of permeability of low-porosity and low-permeability reservoirs. A classical nuclear magnetic resonance logging permeability model mainly depends on the fact that nuclear magnetic resonance has relaxation characteristics and diffusion characteristics, and a statistical relational expression is established. By analyzing the correlation between the parameters and the permeability, a final permeability model is obtained and used for calculating a continuous permeability curve. Classical models for permeability calculation using nmr logging are the coats model and the SDR model. The Coates model utilizes mobile fluid, bound fluid, nuclear magnetic porosity to build a model; the SDR model is established by utilizing nuclear magnetism porosity and T2 geometric mean.
However, the application effect of the classical nuclear magnetic resonance permeability model in the permeability calculation of the low-porosity and low-permeability reservoir is not ideal, and the main reason is that the heterogeneity of the low-porosity and low-permeability reservoir is serious, and the related parameters of the model cannot sufficiently reflect the characteristics of the saturation of the bound water, the pore throat structure and the like; the permeability calculation method utilizing the nuclear magnetic resonance logging T2 spectrum curve form weakens the important function of pore throat structure parameters on permeability calculation.
Disclosure of Invention
The invention aims to solve the technical problem of providing a nuclear magnetic resonance T2 spectrum permeability calculation method under the constraint of pore and throat structural parameters.
The technical scheme adopted by the invention for solving the technical problems is as follows: a nuclear magnetic resonance T2 spectrum permeability calculation method under the constraint of pore and throat structural parameters comprises the following steps:
s1: converting the nuclear magnetic resonance T2 spectrum curve into a pseudo capillary pressure curve;
s2: calculating pore throat structure parameters according to the pseudo capillary pressure curve, wherein the pore throat structure parameters comprise a displacement pressure, a maximum pore throat radius, a pore throat radius median value, a pore throat mean value, a main flow pore throat radius average value and a sorting coefficient;
s3: converting the acquired rock core analysis permeability data and a nuclear magnetic resonance T2 spectrum into a pseudo capillary pressure curve, and then calculating to obtain a pore throat structure parameter and nuclear magnetic resonance logging data, and establishing a pore throat structure parameter and a nuclear magnetic resonance T2 spectrum-permeability corresponding relation data body (Him, T2ij, Pij, ki) (m is 1, l, i is 1, n), wherein H refers to the pore throat structure parameter value, T2 refers to the nuclear magnetic resonance T2 spectrum time component discrete value, P refers to the discrete value of the T2 spectrum amplitude component, k refers to the permeability value, i refers to the acquired rock core analysis permeability sample point sequence number, m refers to the pore throat structure parameter sequence number, j refers to the component sequence number, l refers to the number of the pore throat structure parameter at each depth point, and n refers to the acquired rock core test permeability sample point number;
s4: selecting a nonlinear mapping algorithm to establish a nuclear magnetic resonance T2 spectrum permeability prediction model under the constraint of pore throat structure parameters;
s5: debugging and optimizing a nuclear magnetic resonance T2 spectrum permeability prediction model under the constraint of pore throat structure parameters by adopting a cross validation method, establishing a nonlinear mapping relation model, and forming a final network model;
s6: and performing point-by-point calculation according to the final network model to obtain the nuclear magnetic resonance T2 spectrum permeability under the constraint of pore throat structure parameters.
Preferably, in step S1, the nuclear magnetic resonance T2 spectrum curve is converted into a pseudo capillary pressure curve, comprising the steps of:
step S11: calculating a scale conversion coefficient C between a nuclear magnetic resonance T2 spectrum and a capillary pressure curve according to a similarity principle, and converting a linear relation of a formula PC (C/T2) to obtain a pseudo capillary pressure curve; or
Step S12: and calculating a transverse scale coefficient between the nuclear magnetic resonance T2 spectrum and the capillary pressure curve and longitudinal scale coefficients of large and small apertures according to a two-dimensional segmentation equal-area method, and calculating to obtain a pseudo capillary pressure curve.
Preferably, in step S2,
the displacement pressure refers to the pressure of a non-wet phase entering the maximum throat of the rock sample, the calculation method is that the mercury inlet saturation of adjacent pressure points is compared from the lowest pressure point, and the starting point of the two mercury inlet saturation difference of more than 1% is the displacement pressure;
the maximum pore throat radius refers to the pore throat radius corresponding to the displacement pressure;
the pore throat radius median value is the pore throat radius value corresponding to 50% mercury saturation;
the pore throat mean value is an average position for describing the value of experimental data, namely an average position for representing the distribution of the whole pore throat;
the average value of the radius of the primary flow pore throat refers to the average value of the radius of the pore throat when the permeability contribution value is accumulated to 95 percent;
the sorting coefficient is a parameter reflecting the degree of uniformity of pore throat size.
Preferably, in step S3, the core test permeability data, the pore throat structure parameter, and the nmr logging data are obtained by performing core homing on permeability measured by a core laboratory, and then calculating a correspondence relationship between the permeability data, the nmr permeability parameter, and the nmr T2 spectrum and the nmr T2 spectrum, which are converted into pseudo capillary pressure curves.
Preferably, in step S4, the nonlinear mapping algorithm employs a BP neural network method.
Preferably, the BP neural network method adopts a single hidden layer network, the number of input layer units is the discrete value n of the nuclear magnetic resonance T2 spectral component, the number of output layer units is 1, and the number of single hidden layer neurons is obtained by calculating according to the following formula: n is1＝(n+1)1/2+ a, wherein a is a constant between 1 and 10.
Preferably, in step S5, the cross-validation method is that, in a given modeling sample, most samples are used for building the nonlinear mapping relationship model, a small part of samples are left for model verification, and the prediction error square sum of the small part of samples is calculated.
Preferably, the majority of samples are 70% of the samples and the minority of samples are 30% of the samples.
Preferably, in the process of establishing the nonlinear mapping relation model, an iterative algorithm is used for continuously calculating the 70% of samples; after each iteration is completed, evaluating 15% of the sample cross-validated data; when the calculation accuracy of the 70% sample is difficult to improve, the network model corresponding to the minimum error obtained by calculating the data of the 15% sample subjected to cross validation is used as the final network model, and the final network model is tested by using the data of the last 15% test.
Preferably, in step S6, performing point-by-point calculation on the whole wellbore section by using the final network model to obtain the nuclear magnetic resonance T2 spectrum permeability under the constraint of pore-throat structure parameters.
The implementation of the invention has the following beneficial effects: the nuclear magnetic resonance T2 spectrum permeability calculation method under the constraint of the pore throat structural parameters solves the problem that the permeability accuracy for evaluating the low-porosity and low-permeability reservoir is low due to weakening of the pore throat structural parameters by using the permeability calculation method of the nuclear magnetic resonance T2 spectrum curve form, not only considers all pore structure information contained in the nuclear magnetic resonance T2 spectrum, but also strengthens the function of the pore throat structural parameters in permeability calculation. And a large amount of nuclear magnetic resonance experiments of the rock core are not needed, the precision is high, the implementation is easy, and the production requirement can be met.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a method for calculating the permeability of a nuclear magnetic resonance T2 spectrum under the constraint of pore-throat structural parameters according to the invention;
FIG. 2 is a schematic diagram of the accuracy of a permeability model constructed by the nuclear magnetic resonance T2 spectrum and nuclear magnetic resonance T2 spectrum of 30 sampling points, the calculated median radius value after the nuclear magnetic resonance T2 spectrum is converted into a capillary pressure curve, and the permeability of core analysis;
FIG. 3 is a schematic diagram of the processing result of the logging data of the nuclear magnetic resonance T2 spectrum permeability calculation method under the constraint of pore throat structure parameters.
Detailed Description
As shown in fig. 1, the invention relates to a permeability calculation method of a nuclear magnetic resonance T2 spectrum under the constraint of pore throat structural parameters, which is used for solving the problem that the permeability accuracy for evaluating a low-pore hypotonic reservoir is low due to weakening of the pore throat structural parameters by using the permeability calculation method of a nuclear magnetic resonance T2 spectrum curve form, and comprises the following steps:
s1: converting the nuclear magnetic resonance T2 spectrum curve into a pseudo capillary pressure curve; in this step, the nmr T2 spectrum curve is converted to a pseudo capillary pressure curve, including the following methods:
step S11: calculating a scale conversion coefficient C between a nuclear magnetic resonance T2 spectrum and a capillary pressure curve according to a similarity principle, and converting a linear relation of a formula PC (C/T2) to obtain a pseudo capillary pressure curve; or
Step S12: and calculating a transverse scale coefficient between the nuclear magnetic resonance T2 spectrum and the capillary pressure curve and longitudinal scale coefficients of large and small apertures according to a two-dimensional segmentation equal-area method, and calculating to obtain a pseudo capillary pressure curve.
S2: calculating pore throat structure parameters according to the pseudo capillary pressure curve, wherein the pore throat structure parameters comprise a displacement pressure, a maximum pore throat radius, a pore throat radius median value, a pore throat mean value, a main flow pore throat radius average value and a sorting coefficient; in the step S2, in step S2,
the displacement pressure refers to the pressure of a non-wet phase entering the maximum throat of the rock sample, the calculation method is that the mercury inlet saturation of adjacent pressure points is compared from the lowest pressure point, and the starting point of the two mercury inlet saturation difference of more than 1% is the displacement pressure;
the maximum pore throat radius refers to the pore throat radius corresponding to the displacement pressure;
the pore throat radius median value is the pore throat radius value corresponding to 50% mercury saturation;
the pore throat mean value is an average position for describing the value of experimental data, namely an average position for representing the distribution of the whole pore throat;
the average value of the radius of the primary flow pore throat refers to the average value of the radius of the pore throat when the permeability contribution value is accumulated to 95 percent;
the sorting coefficient is a parameter reflecting the degree of uniformity of pore throat size.
In this example, the average value of pore throat, the average value of radius of main flow pore throat and the sorting coefficient were calculated by the following equations (1), (2) and (3)
In the formula rMRz and Sp are respectively the average value of pore throats, the average value of the radius of the main flowing pore throat and the sorting coefficient; Δ S, i is the mercury saturation measured in the ith interval; r isiIs the throat radius corresponding to Δ Si.
S3: the method comprises the steps of converting a pseudo capillary pressure curve by utilizing collected rock core analysis permeability data and a nuclear magnetic T2 spectrum, then calculating hole throat structure parameters and nuclear magnetic resonance logging data, and establishing a hole throat structure parameter and nuclear magnetic resonance T2 spectrum-permeability corresponding relation data body (Him, T2ij, Pij, ki) (m is 1, l, i is 1, n), wherein the data body consists of known rock core analysis permeability data, nuclear magnetic resonance T2 spectrum data of the same depth point after a rock core is reset and hole throat structure parameters calculated after the nuclear magnetic resonance T2 spectrum of the depth point is converted into the capillary pressure curve. Wherein, H refers to the value of the pore throat structure parameter, T2 refers to the discrete value of the nuclear magnetic resonance T2 spectrum time component, P refers to the discrete value of the T2 spectrum amplitude component, k refers to the permeability value, i refers to the serial number of the collected rock core test permeability sample point, m refers to the serial number of the pore throat structure parameter, j refers to the component serial number, l is the number of the pore throat structure parameter of each depth point, and n is the number of the collected rock core test permeability sample point.
It should be understood that, in step S3, the core analysis permeability data, the pore throat structure parameters, and the nmr logging data are obtained by performing core homing according to the permeability measured in the core laboratory, and then calculating the correspondence between the nmr permeability data, the nmr permeability parameters, and the nmr T2 spectrum and the nmr T2 spectrum after transforming the pseudo capillary pressure curve.
S4: selecting a nonlinear mapping algorithm to establish a nuclear magnetic resonance T2 spectrum permeability prediction model under the constraint of pore throat structure parameters; in the present embodiment, in step S4The nonlinear mapping algorithm adopts a BP neural network method, further, the BP neural network method adopts a single hidden layer network, the number of input layer units is the discrete value n of the nuclear magnetic resonance T2 spectral components, the number of output layer units is 1, and the number of single hidden layer neurons is obtained by calculation according to the following formula: n is1＝(n+1)1/2+ a, wherein a is a constant between 1 and 10.
It can be understood that the permeability prediction model is established based on a BP neural network nonlinear mapping algorithm. The BP neural network method is a multi-layer feedforward network trained according to an error reverse propagation algorithm, and the weight and the threshold value of the network are continuously adjusted through reverse propagation by using a steepest descent method, so that the sum of squares of errors of the network is minimum. Because the neural network is too sensitive, a model with strong generalization capability can be obtained only by modeling a large sample, the method requires the number of samples to be as large as possible, namely n value is as large as possible, and because the BP neural network uses a gradient descent algorithm to carry out global optimization, the problem that the gradient descent method is easy to fall into local optimization is considered, when the model is established, input (pore throat structure parameters and sampling point nuclear magnetic resonance T2 spectrum) and output (permeability) are normalized, so that the generalization capability of the model is improved. For example, 3 throat parameters, 30 sample point nmr T2 spectra can be modeled using a 33 x 10 x 1 network structure.
S5: debugging and optimizing a nuclear magnetic resonance T2 spectrum permeability prediction model under the constraint of pore throat structure parameters by adopting a cross validation method, establishing a nonlinear mapping relation model, and forming a final network model; it will be appreciated that model debugging is performed using a cross-validation approach to achieve the best results.
In step S5, the cross-validation method is that, in a given modeling sample, most samples are used for building a nonlinear mapping relation model, a small part of samples are left for model verification, and the prediction error square sum of the small part of samples is calculated. For a preferred embodiment, the majority of samples are 70% samples and the minority of samples are 30% samples.
Continuously calculating 70% of samples by using an iterative algorithm in the process of establishing the nonlinear mapping relation model; after each iteration is completed, evaluating 15% of the sample cross-validated data; when the calculation accuracy of the 70% sample is difficult to improve, the network model corresponding to the minimum error obtained by calculating the data of the 15% sample subjected to cross validation is used as the final network model, and the final network model is tested by using the data of the last 15% test.
S6: and performing point-by-point calculation according to the final network model to obtain the nuclear magnetic resonance T2 spectrum permeability under the constraint of pore throat structure parameters.
Specifically, in step S6, the final network model obtained in step S5 is used to calculate the whole wellbore section point by point, and the permeability of the nmr T2 spectrum under the constraint of the pore-throat structure parameters is obtained.
Furthermore, the models involved in step S5 and step S6 of the present invention are both based on the permeability prediction model in step S4, and are debugged and optimized by a cross validation method, etc. to form a final network model, and the final network model is used to perform point-by-point calculation on the whole well section, so as to obtain the nuclear magnetic resonance T2 spectrum permeability under the constraint of pore throat structure parameters.
According to the invention, a pore throat structure parameter, a nuclear magnetic resonance T2 spectrum and permeability corresponding relation data volume is established by utilizing the acquired core analysis permeability data, nuclear magnetic resonance T2 spectrum data of the same depth point after the core is reset and pore throat structure data calculated after the nuclear magnetic resonance T2 spectrum of the depth point is converted into a capillary pressure curve. And establishing an optimal pore throat structure, T2 morphological distribution and permeability nonlinear mapping by adopting a nonlinear approximation BP neural network algorithm and through parameter normalization processing, cross validation and algorithm parameter optimization to obtain a nuclear magnetic resonance T2 spectral curve morphological permeability calculation model under the constraint of the pore throat structure, thereby realizing continuous reservoir permeability calculation. Compared with the traditional permeability calculation model based on nuclear magnetic resonance data, the permeability calculation method based on the nuclear magnetic resonance data can improve the calculation precision of the tight sandstone permeability by at least one order of magnitude.
FIG. 2 is a schematic diagram of the accuracy of a permeability model constructed by using nuclear magnetic resonance T2 spectra and nuclear magnetic resonance T2 spectra of 30 sampling points, calculated radius median values after the nuclear magnetic resonance T2 spectra are converted into a capillary pressure curve, and core analysis permeability. The data volume is 151 data, the upper left graph in the graph is the accuracy of the return judgment after 70% of samples are modeled, the upper right graph is 15% of cross validation data, the lower left graph is 15% of test data, and the lower right graph is the prediction result of all the last sample points. From the results, it can be seen that the modeling correlation coefficient reaches R ═ 0.95, the cross validation accuracy reaches R ═ 0.69, and the test accuracy reaches R ═ 0.78. Therefore, the model has high precision and the calculated permeability value is reliable.
As shown in fig. 3, the result of processing the logging data by the nmr T2 spectrum permeability calculation method under the constraint of pore throat structure parameters provided by the present invention is shown, wherein the first trace in the graph is a natural gamma curve, the second trace is depth, the third trace is nmr T2 spectrum form, the fourth scatter point is core analysis permeability, and the fourth trace is the permeability value calculated by the present invention. It can be obviously seen that the calculation of the permeability by using the nuclear magnetic resonance T2 spectrum constrained by the pore throat structural parameters is effective, does not need a large number of nuclear magnetic resonance experiments on the rock core, has higher precision, is easy to realize, and can meet the production requirements.
The implementation of the invention has the following beneficial effects: the nuclear magnetic resonance T2 spectrum permeability calculation method under the constraint of the pore throat structural parameters solves the problem that the permeability accuracy for evaluating a low-pore low-permeability reservoir is low due to weakening of the pore throat structural parameters by using a permeability calculation method of a nuclear magnetic resonance logging T2 spectrum curve form, not only considers all pore structure information contained in a nuclear magnetic resonance T2 spectrum, but also strengthens the function of the pore throat structural parameters in permeability calculation. And a large amount of nuclear magnetic resonance experiments of the rock core are not needed, the precision is high, the implementation is easy, and the production requirement can be met.
It is to be understood that the foregoing examples, while indicating the preferred embodiments of the invention, are given by way of illustration and description, and are not to be construed as limiting the scope of the invention; it should be noted that, for those skilled in the art, the above technical features can be freely combined, and several changes and modifications can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention; therefore, all equivalent changes and modifications made within the scope of the claims of the present invention should be covered by the claims of the present invention.
Claims (6)
1. A nuclear magnetic resonance T2 spectrum permeability calculation method under the constraint of pore and throat structural parameters is characterized by comprising the following steps:
s1: converting the nuclear magnetic resonance T2 spectrum curve into a pseudo capillary pressure curve;
s2: calculating pore throat structure parameters according to the pseudo capillary pressure curve, wherein the pore throat structure parameters comprise a displacement pressure, a maximum pore throat radius, a pore throat radius median value, a pore throat mean value, a main flow pore throat radius average value and a sorting coefficient; in the step S2, in step S2,
the displacement pressure refers to the pressure of a non-wet phase entering the maximum throat of the rock sample, the calculation method is that the mercury inlet saturation of adjacent pressure points is compared from the lowest pressure point, and the starting point of the two mercury inlet saturation difference of more than 1% is the displacement pressure;
the maximum pore throat radius refers to the pore throat radius corresponding to the displacement pressure;
the pore throat radius median value is the pore throat radius value corresponding to 50% mercury saturation;
the pore throat mean value is an average position for describing the value of experimental data, namely an average position for representing the distribution of the whole pore throat;
the average value of the radius of the primary flow pore throat refers to the average value of the radius of the pore throat when the permeability contribution value is accumulated to 95 percent;
the sorting coefficient is a parameter reflecting the uniformity of pore throat size;
calculating the mean value of the pore throat, the mean value of the radius of the main flow pore throat and the sorting coefficient by using the following formulas (1), (2) and (3)
In the formula rMRz and Sp are respectively the average value of pore throats, the average value of the radius of the main flowing pore throat and the sorting coefficient; Δ Si is the mercury saturation measured in the ith interval; r isiIs the throat radius corresponding to Δ Si;
s3: converting a pseudo capillary pressure curve by using collected rock core analysis permeability data and a nuclear magnetic resonance T2 spectrum, and then calculating to obtain a pore throat structure parameter and nuclear magnetic resonance logging data, and establishing a pore throat structure parameter and a nuclear magnetic resonance T2 spectrum-permeability corresponding relation data body (Him, T2ij, Pij, ki) (m is 1, l, i is 1, n), wherein H refers to the pore throat structure parameter value, T2 refers to the nuclear magnetic resonance T2 spectrum time component discrete value, P refers to the discrete value of the T2 spectrum amplitude component, k refers to the permeability value, i refers to the collected rock core analysis permeability sample point serial number, m refers to the pore throat structure parameter serial number, j refers to the component serial number, l refers to the number of the pore throat structure parameter of each depth point, and n refers to the collected rock core test permeability sample point number;
in step S3, the core analysis permeability data, pore throat structure parameters, and nmr logging data are obtained by performing core homing on permeability measured by a core laboratory, and then calculating a correspondence relationship between the core analysis permeability data, pore throat structure parameters, and nmr logging data, and a nmr T2 spectrum and an nmr T2 spectrum after converting a pseudo capillary pressure curve;
s4: selecting a nonlinear mapping algorithm to establish a nuclear magnetic resonance T2 spectrum permeability prediction model under the constraint of pore throat structure parameters; the nonlinear mapping algorithm adopts a BP neural network method; the BP neural network method adopts a single hidden layer network, the number of input layer units is the discrete value n of the nuclear magnetic resonance T2 spectral component, the number of output layer units is 1, and the number of single hidden layer neurons is obtained by calculation according to the following formula: n is1＝(n+1)1/2+ a, wherein a is a constant between 1 and 10;
s5: debugging and optimizing a nuclear magnetic resonance T2 spectrum permeability prediction model under the constraint of pore throat structure parameters by adopting a cross validation method, establishing a nonlinear mapping relation model, and forming a final network model;
s6: performing point-by-point calculation according to the final network model to obtain nuclear magnetic resonance T2 spectrum permeability under the constraint of pore throat structure parameters; in step S6, the final network model is used to perform point-by-point calculation on the whole well section, and the nuclear magnetic resonance T2 spectrum permeability under the constraint of pore throat structure parameters is obtained.
2. The method for calculating nuclear magnetic resonance T2 spectral permeability under the constraint of pore throat structure parameters of claim 1, wherein in step S1, the nuclear magnetic resonance T2 spectral curve is converted into a pseudo capillary pressure curve, comprising the following steps:
step S11: calculating a scale conversion coefficient C between a nuclear magnetic resonance T2 spectrum and a capillary pressure curve according to a similarity principle, and converting a linear relation of a formula PC (C/T2) to obtain a pseudo capillary pressure curve; or
Step S12: and calculating a transverse scale coefficient between the nuclear magnetic resonance T2 spectrum and the capillary pressure curve and longitudinal scale coefficients of large and small apertures according to a two-dimensional segmentation equal-area method, and calculating to obtain a pseudo capillary pressure curve.
3. The method for calculating nuclear magnetic resonance T2 spectral permeability under the constraint of pore-throat structural parameters of claim 1, wherein the BP neural network method adopts a single hidden layer network, the number of input layer units is the discrete value n of the nuclear magnetic resonance T2 spectral component, the number of output layer units is 1, and the number of single hidden layer neurons is calculated by the following formula: n is1＝(n+1)1/2+ a, wherein a is a constant between 1 and 10.
4. The method for calculating nuclear magnetic resonance T2 spectral permeability under the constraint of pore throat structure parameters in claim 1, wherein in step S5, the cross validation method is that in a given modeling sample, most samples are used for establishing a nonlinear mapping relation model, a small part of samples are left for model verification, and the square sum of prediction errors of the small part of samples is calculated.
5. The method of claim 4, wherein the majority of samples are 70% samples and the minority of samples are 30% samples.
6. The method for calculating nuclear magnetic resonance T2 spectral permeability under the constraint of pore-throat structure parameters according to claim 5, wherein in the process of establishing the nonlinear mapping relation model, an iterative algorithm is used for continuously calculating 70% of samples; after each iteration is completed, evaluating 15% of the sample cross-validated data; when the calculation accuracy of the 70% sample is difficult to improve, the network model corresponding to the minimum error obtained by calculating the data of the 15% sample subjected to cross validation is used as the final network model, and the final network model is tested by using the data of the last 15% test.
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