CN115078438A - Method for establishing pore network model based on nuclear magnetic resonance test digital core - Google Patents
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Abstract
The invention discloses a method for establishing a pore network model based on a nuclear magnetic resonance test digital core, which comprises the steps of dividing clusters based on T1 and T2 relaxation time images, and quantifying four cluster pores of organic matters, free water, methane and hydroxyl; correcting the digital core matrix data by combining the digital core gray value and the nuclear magnetic resonance data; the method comprises the steps of establishing an equivalent pore throat model for digital core data higher than CT test resolution on the basis of Avizo software, and performing super-resolution analysis on data such as organic matter pores lower than the CT test digital core resolution by combining micro-nano pore distribution obtained by nuclear magnetic resonance with the cluster division above, so as to obtain a pore throat model capable of representing a micro-nano pore structure. According to the invention, the digital core is supplemented by nuclear magnetic resonance test data, so that the complex micro-nano pore structure is represented. After the shale digital core is supplemented, the model can introduce the adsorption and desorption processes of shale gas in matrix pores under the real condition.
Description
Technical Field
The invention relates to the technical field of gas reservoir development, in particular to the field of unconventional oil and gas development, and specifically relates to a method for establishing a pore network model based on digital core test
Background
As an important unconventional oil and gas resource, the development of shale gas and dense gas is a major growing point of natural gas production worldwide. In recent years, with the development and investment of natural gas industry in China, shale gas and shale gas development technologies are developed to different degrees, but the comprehensive utilization capacity is poor, mainly because the output and the input of unconventional natural gas are lower under the current natural gas price. In order to overcome the defect, the fine description of the reservoir structures of the shale gas and the tight gas is the basis for researching the multiphase flow mechanism in the production process and improving the recovery ratio, and has great significance for the development of tight gas and shale gas wells.
Scholars at home and abroad often obtain a pore network model based on an algorithm by performing CT (computed tomography) test on a field rock sample to obtain a digital core comprehensively representing a microscopic pore structure. However, the resolution of the obtained digital core is higher than that of the nano-pores which mainly contribute to the yield in the unconventional gas reservoir exploitation due to the limitation of the precision of the CT equipment, so that the pore network model manufactured and obtained by the current method can only represent the micro-pores with larger sizes, the influence of the nano-pores is ignored, and the adsorption and desorption of gas in the nano-pores cannot be accurately quantified.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a technique for establishing a high-precision pore network model, which is easier to implement and can make up for the disadvantage of low resolution of the digital core.
The technical scheme of the invention is as follows:
step 1: the method comprises the steps of performing digital core test without processing a sample, performing first nuclear magnetic test after obtaining sample structure data, namely performing dry sample test, and obtaining T1 and T2 maps; after the test, the sample is soaked in distilled water, and is placed in a vacuum tank to be saturated for 6 days, the surface moisture is quickly wiped dry, and a second nuclear magnetic test, namely a wet sample test, is carried out, so that a T2 spectrum is obtained.
Step 2: analyzing T1 and T2 spectrums in a dry sample nuclear magnetic test in Step1, making corresponding cloud charts of relaxation time T1 and T2, analyzing, dividing the cloud chart image into two halves according to the upper left corner region and the lower right corner region, analyzing cloud chart clustering, dividing organic matter clustering and free water clustering, and dividing organic matter and inorganic matter pores which are lower than the resolution ratio of a digital core based on the dry sample T1/2 spectrum, wet sample pore distribution and organic matter clustering.
Preferably, the specific contents of organic matter and inorganic matter pores divided based on the T2 spectrum and the organic matter agglomeration in Step2 and lower than the resolution of the digital core are as follows:
s201, establishing a matrix: and taking the T2 map as the abscissa of the matrix, the T1 map as the ordinate of the matrix, and the accumulated value of the corresponding semaphore under T1 and T2 as the numerical value of the corresponding coordinate in the matrix, so as to draw the cloud picture by the matrix.
S202, nuclear magnetic data selection: for the semaphore under the joint analysis of T1 and T2 relaxation time, because the time of the hydrogen nucleus under the action of a nuclear magnetic field and the surrounding crystal lattice is longer than the action time between the nucleus and the atomic nucleus, only T1 and T2 data are selected, namely, the cloud picture matrix coordinate is divided averagely according to the curve of y-x, and then the upper left part is selected for analysis.
S203: grouping: due to the existence of solid organic matter in shale, kerogen rich in hydroxyl structures and methane, the structure of shale is considered from the complexity level: solid organic matter > kerogen > methane, and therefore the T1 relaxation times follow a complex order under the influence of its lattice. Therefore, it is considered that the signal of the upper left part of the cloud under the low T2 relaxation time and the high T1 relaxation time, i.e., the signal response of the solid organic matter cluster in the nuclear magnetic experiment, is smaller than the solid organic matter cluster in the T1 relaxation time, i.e., the cluster below the solid organic matter in the cloud is a hydroxyl cluster, and the cluster with the minimum T1 relaxation time, i.e., the cluster at the lower left corner of the cloud, is a methane cluster.
S204: quantification of clustered data: converting the signal of the solid organic matter into an accurate volume through a nuclear magnetic middle calibration test; for T2 relaxation time data in organic matter clusters, based on a distilled water nuclear magnetic test rule of the same principle, the distribution rule of solid organic matter is considered to be the same as a signal measurement rule, so that a single cluster is analyzed by combining a T1 matrix cloud picture and a T2 matrix cloud picture obtained in the step S201 and several clusters under low T2 relaxation time in the step S203, and the T2 relaxation time of the separated cluster matrix data is analyzed through an image method or a numerical value to represent the distribution ratio of different clusters in a sample.
Preferably, the specific method for characterizing the cluster distribution in step S204 is as follows:
s2041 Using python to read the Matrix, a Matrix [ X ] is established for the abscissa distribution of the corresponding clusters in the Matrix][Y min :Y max ]And ordinate [ Y min ,Y max ]
S2042 sets Matrix name as Matrix, and uses Python to traverse Matrix X in Matrix array X corresponding to value Matrix X][Y min :Y max ]And calculating the corresponding signal quantity ratio, wherein the corresponding formula is as follows:
wherein: matrix [ X ]][Y min :Y max ]For the accumulated value in the blob under Matrix column X in the traversal process, SUM (Matrix [ X)]) The accumulated value is the value under the matrix column X in the traversal process.
Through the steps, the occupation ratios of different clusters under the corresponding transverse relaxation time T2 are obtained, namely (T2, part (T2))
Step3, analyzing T2 maps in a dry sample and a wet sample of the sample, converting T2 relaxation time into pore size and corresponding pore proportion through shape factors of equivalent capillary, processing two groups of data, and obtaining the pore size and the corresponding pore proportion which can be swept by distilled water; and combining the four clusters obtained by the dry sample test in Step2, and removing the influence of originally mobile water and organic matters in the sample.
The method for converting the T2 relaxation time to pore size and corresponding pore fraction as the equivalent capillary form factor described in Step3 is preferably:
s301: based on the signal quantities of the different clusters obtained in S204 under the same transverse relaxation time T2, the pore distribution in the sample is accurately corrected: the testing range of the transverse relaxation time T2 in the nuclear magnetic test is the same, programming software is used for traversing the signal quantity of the transverse relaxation time T2 in the dry sample and the wet sample, and the corresponding signal quantity of organic matters, methane and hydroxyl clusters in the dry sample is subtracted from the signal quantity in the wet sample, so that the corresponding relaxation time and the signal quantity which can be used for representing the swept pores are obtained.
S302: converting the transverse relaxation time in the nuclear magnetic curve obtained in the step S301 into a pore size, if the sizes in three directions in the digital core resolution grid are the same, setting the shape factor to 2, and if the sizes in the digital core grid are different, wherein the length in the direction of the maximum ruler/the length in the direction of the minimum ruler is greater than 2, setting the shape factor to 3, and specifically converting the equation into:
r c ＝βF s T 2
wherein T is 2 Is the relaxation time; f s Is a pore shape factor; beta is transverse surface relaxation rate of rock
The method can obtain equivalent spherical pore distribution.
Step 4: and filtering data obtained in the digital core test through Avizo software, correcting and dividing the data by combining the partition and segmentation functions in the software and the pore distribution obtained in the nuclear magnetic resonance test, and dividing the data into organic matters, inorganic minerals and pores on the basis of keeping the test resolution. And converting the segmented digital rock core into an equivalent pore network ball stick model through a PNM module in Avizo software by a maximum ball algorithm.
Preferably, the specific process of modifying and segmenting the digital core through the segmentation and segmentation modules described in Step4 is as follows:
s401: filtering the digital core image according to gray values through a software segmentation module in Avizo, roughly dividing the lithology according to a manually selected gray threshold value, and dividing the core into a plurality of continuous small clusters with the same lithology through a segmentation module. And (3) deriving data of the digital rock core, and establishing a four-dimensional matrix, wherein the three-dimensional coordinates of the matrix represent the coordinates of the corresponding position of the digital rock core, and numerical values in the matrix are the corresponding gray value of the image and the serial cluster number.
S402: the lithology matrix obtained in S401 is traversed using Python programming. And optimizing different lithological thresholds manually selected in the previous step, counting lithological component proportion divided according to the thresholds in the matrix data to correct the volume proportion representing the resolution of the digital core, comparing the lithological component proportion with the nuclear magnetic test data of the pore in the nuclear magnetic test, and iteratively changing the thresholds for multiple times through program circulation to obtain the pore and organic matter mass capable of accurately representing the resolution of the digital core above.
Preferably, the specific method for dividing the lithology according to the threshold in step S402 is as follows:
s4021: and reading matrix data by using Python, traversing the serial cluster numbers in the matrix, extracting the continuous clusters which are segmented and identified as pores in the previous step, counting the upper and lower limits of the three-dimensional coordinates, and determining the coordinates (x, y, z) of the central points of the continuous clusters. And reading and counting Matrix [ x-id: x + id ] [ y-id: y + id ] [ z-id: z + id ] data. Wherein: and i represents the number of iteration steps in the traversal process. For the unit vector matrix corresponding relation in the matrix data, the unit vector matrix corresponding relation is as follows:
easy obtaining:
for the case where r is equal to I,
the reading is different according to the iteration number I
In-process correspondence matrix [ x, y, z ]]And counting the pore space ratio under the corresponding iteration times by using the serial cluster numbers in the vectors, and determining that the pore space ratio is lower than 1/4 to obtain the maximum equivalent sphere pore space. And marking the vector in the last process of iteration, and setting the vector as a boundary in the next traversal process, namely, cutting off the cluster.
S4022: and after the S4021 process, re-reading the middle point of the cluster which is cut off for 2n times in the previous step in the continuous clusters, and repeating the steps until the size of the cut cluster is equal to the pore of the resolution of the digital core.
Step 5: performing super-resolution analysis: based on the pore distribution and the corresponding pore proportion obtained in Step3, the gray level in the digital core matrix data in Step4 is corrected based on Avizo software to obtain the pore distribution condition meeting the nuclear magnetic test, and the digital core data can be subjected to flow simulation through commercial software simulation and self-programming software. The invention has the beneficial effects that: the specific process of super-resolution analysis in the Step5 process is as follows:
s501 because the pore distribution size of each mineral is generally determined to follow normal distribution, and the pore size of the organic matter in the shale is minimum and lower than the pore distribution value, based on the pores lower than the resolution of the digital core in the nuclear magnetic data, the pore distribution in the small-scale direction in the curve is used for fitting the pore size of the organic matter conforming to the normal distribution
S502, the porosity and the gray value are approximately in an EXP relation by using an HU gray conversion algorithm in the digital rock core measuring process, and therefore, the gray value data in the organic matter cluster in the statistical matrix is extracted and processed to describe the porosity; and carrying out iterative statistics on the processed data to obtain organic matter pore distribution meeting the nuclear magnetic data test.
S5021, processing the gray data to obtain dimensionless parameters capable of expressing relative density, wherein the corresponding formula is as follows:
den i ＝ln(greyshade i )
s5022, traversing the dimensionless density data to obtain the maximum den in the data max And minimum value den min Deem den max Is pure organic matter dimensionless density. The organic matter dimensionless relative porosity can be characterized by the following formula:
wherein phi is ri And den i Representing dimensionless relative porosity and dimensionless relative density values in the corresponding volume elements of the digital rock core; den (r) void Represents the dimensionless relative density values of the pores of the digital core.
S5023: for expected mu and standard deviation sigma in equivalent distribution normal distribution data fitted in the S501 process, the minimum organic matter pore and digital core resolution d in the nuclear magnetic test min ，d res Is divided into 100 segments for the upper and lower bounds on average, with d i (i ═ 1, 2.., 100), then the corresponding dimensionless relative porosity can be expressed as:
s5024: and (3) an iterative process: setting den min Is a den void The initial value of (a) is combined with the tanh function to describe the den under the nuclear magnetic resonance test and the super-resolution analysis void The similarity degree of the lower curve is shown as the following formula:
when in the process of iteration, the user can select the target,
the iterative process is carried out till den void Ending the reaction when the temperature is less than or equal to 0. In an iterative process f (den) void ) Most preferablyDecimal determination den void And calculating the relative porosity of the corresponding position of the digital rock core.
S5024: and reading the relative porosity data of the clusters divided into the organic matters in the matrix, analyzing corresponding digital core microelements in the matrix, and acquiring the pore volume of the corresponding microelements according to the size of the microelements (1-porosity in the microelements). Based on the pore ratio in the nuclear magnetic resonance test data, distributing organic matter pores according to the sequence from large-size pore microelements to small-size pore microelements, counting the sizes and the number of the organic matter pores of the corresponding matrix coordinate microelements, and supplementing the nano-grade organic matter pores in the pore throat model obtained in the previous step.
S503, supplementing the pore network model obtained by Avizo software in the Step4 process based on the data obtained in the above process, and supplementing the coordination channels connected with the spherical pores in the pore network model, wherein the corresponding matrix structure is the coordination channel number and the corresponding pore distribution data.
On the basis of ensuring easy implementation, compared with the prior art, the method is more practical in screening the main control factors influencing the yield of the dense gas, and has a profound meaning on the subsequent prediction and research of the yield of the dense gas.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for optimizing a digital core to establish a pore network model based on a nuclear magnetic resonance test according to the present invention;
FIG. 2 is a digital core cluster segmented by Avizo software during Step2 in example 1;
FIG. 3 is a model of the network of macropore pores in the digital core extracted by Avizo in example 1;
Detailed Description
The invention is further illustrated with reference to the following figures and examples. It should be noted that, in the present application, the embodiments and the technical features of the embodiments may be combined with each other without conflict. It is noted that, unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The use of the terms "comprising" or "including" and the like in the present disclosure is intended to mean that the elements or items listed before the term cover the elements or items listed after the term and their equivalents, but not to exclude other elements or items.
Example 1
As shown in fig. 1, a method for optimizing a digital core to build a pore network model based on a nuclear magnetic resonance test includes the following steps:
step 1: the method comprises the steps of performing digital core test without processing a sample, performing first nuclear magnetic test after obtaining sample structure data, namely performing dry sample test, and obtaining T1 and T2 maps; after the test, the sample is soaked in distilled water, and is placed in a vacuum tank to be saturated for 6 days, the surface moisture is quickly wiped dry, and a second nuclear magnetic test, namely a wet sample test, is carried out, so that a T2 spectrum is obtained.
Step 2: analyzing T1 and T2 spectrums in a dry sample nuclear magnetic test in Step1, making corresponding cloud charts of relaxation time T1 and T2, analyzing, dividing the cloud chart image into two halves according to the upper left corner region and the lower right corner region, analyzing cloud chart clustering, dividing organic matter clustering and free water clustering, and dividing organic matter and inorganic matter pores which are lower than the resolution ratio of a digital core based on the dry sample T1/2 spectrum, wet sample pore distribution and organic matter clustering. FIG. 2 shows the clustering according to the current threshold segmentation, and some of the results are shown in tables 1 and 2:
TABLE 1 relaxation time data of T1 and T2 after testing (parts)
| Relaxation time (ms) | 0.01 | 0.010719 | 0.01149 | 0.012316 | … | 8119.84499 | 8703.591361 | 9329.304026 | 10000 |
| T1 percentage (%) | 1.70E-08 | 3.59E-08 | 7.46E-08 | 1.52E-07 | … | 7.98E+01 | 6.83E-08 | 1.75E-08 | 4.17E-09 |
| T2 percentage (%) | 0.001054 | 0.00141318 | 0.00187055 | 0.00244497 | … | 8.93E+01 | 4.96E-08 | 1.27E-08 | 3.02E-09 |
Note: because there are many sample data, only some sample data are listed in Table 1, where "…" indicates that there is unlisted data
TABLE 2 pore distribution data (parts) corrected for nuclear magnetic data
| Pore size (um) | 0.0012 | 0.00128627 | 0.00137874 | 0.00147786 | … | 974.381399 | 1044.43096 | 1119.51648 | 1200 |
| Volume ratio (%) | 0.000256 | 0.000469 | 0.000816 | 0.00137 | … | 0 | 0 | 0 | 0 |
Step3, analyzing T2 maps in a dry sample and a wet sample of the sample, converting T2 relaxation time into pore size and corresponding pore proportion through shape factors of equivalent capillary, processing two groups of data, and obtaining the pore size and the corresponding pore proportion which can be swept by distilled water; combining four clusters obtained by the dry sample test in Step2, and removing the influence of original movable water and organic matters in the sample; some of the results are shown in Table 3
TABLE 3 three-substance agglomeration data after removal of running water (part)
| Pore size (um) | 0.0012 | 0.00128627 | 0.00137874 | 0.00147786 | … | 974.381399 | 1044.43096 | 1119.51648 | 1200 |
| Solid organic matter (%) | 0.0299475 | 0.031998935 | 0.033762917 | 0.03517822 | … | 0 | 0 | 0 | 0 |
| Hydroxyl radical agglomeration (%) | 0.007900356 | 0.006445003 | 0.005194746 | 0.004136854 | … | 0 | 0 | 0 | 0 |
| Methane (%) | 0 | 0 | 0 | 0 | … | 0.00081628 | 0.000607115 | 0.000446575 | 0.000324871 |
Step 4: and filtering data obtained in the digital core test through Avizo software, correcting and dividing the data into organic matters, inorganic minerals and pores on the basis of keeping the test resolution by combining the partition and segmentation functions in the software and the pore distribution obtained in the nuclear magnetic resonance test. Converting the segmented digital rock core into an equivalent pore network ball stick model through a PNM module in Avizo software by a maximum ball algorithm; the results are shown in Table 4 and FIG. 3
TABLE 4 Macro pore network pore model extracted by Avizo software (section)
TABLE 4 Macro-throat model extracted by Av i zo software (part)
Step 5: performing super-resolution analysis: based on the pore distribution and the corresponding pore proportion obtained in Step3, correcting the gray level in the digital core matrix data in Step4 based on Avizo software to obtain the pore distribution condition meeting the nuclear magnetic test, and performing flow simulation on the digital core data through commercial software simulation and self-programming software; the partial supplemental matrix is shown in table 5.
TABLE 5 pore throat model supplement matrix #1 (partial)
TABLE 6 pore throat model supplement matrix #2 (part)
Obtained based on the method
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A method for building a pore network model based on a nuclear magnetic resonance test digital core is characterized by comprising the following steps: acquiring the organic matter distribution of the corresponding sample through the spin-lattice relaxation time (T1) and the spin-spin relaxation time (T2) acquired through the nuclear magnetic resonance test, and accurately dividing the lithology of the sample by using Avizo software based on the corresponding data; the method comprises the following steps of performing super-resolution analysis on parts which are lower than resolution and cannot be characterized in a digital core of a sample by the model quantity of nuclear magnetic resonance data corresponding to T2 data, converting the parts into a pore network model which is higher than the resolution of the digital core and can accurately represent the structure of the sample, wherein the model quantity comprises the following steps:
step 1: the method comprises the steps of performing digital core test without processing a sample, performing first nuclear magnetic test after obtaining sample structure data, namely performing dry sample test, and obtaining T1 and T2 maps; after testing, soaking the sample in distilled water, placing the sample in a vacuum tank for saturation for 6 days, quickly wiping off surface moisture, and performing a second nuclear magnetic test, namely a wet sample test, to obtain a T2 map;
step 2: analyzing T1 and T2 spectrums in a dry sample nuclear magnetic test in Step1, making corresponding cloud charts of relaxation time T1 and T2, analyzing, dividing the cloud chart image into two halves according to the upper left corner region and the lower right corner region, taking cloud chart clusters for analysis, dividing organic matter clusters and free water clusters, and dividing organic matter and inorganic matter pores which are lower than the resolution ratio of a digital core based on the dry sample T1/2 spectrum, wet sample pore distribution and organic matter cluster division;
step3: analyzing T2 maps in a dry sample and a wet sample of the sample, converting T2 relaxation time into pore size and corresponding pore proportion through shape factors of equivalent capillary, processing two groups of data, and obtaining the pore size and the corresponding pore proportion which can be swept by distilled water; combining four clusters obtained by the dry sample test in Step2, and removing the influence of original movable water and organic matters in the sample;
step 4: filtering data obtained in a digital core test through Avizo software, correcting and dividing the data by combining the partition and segmentation functions in the software and pore distribution obtained in a nuclear magnetic resonance test, dividing the data into organic matters, inorganic minerals and pores on the basis of keeping test resolution, and converting the divided digital core into an equivalent pore network ball rod model through a maximum sphere algorithm through a PNM (portable network management) module in the Avizo software;
step 5: performing super-resolution analysis: based on the pore distribution and the corresponding pore proportion obtained in Step3, the gray level in the digital core matrix data in Step4 is corrected based on Avizo software to obtain the pore distribution condition meeting the nuclear magnetic test, and the digital core data can be subjected to flow simulation through commercial software simulation and self-programming software.
2. A method for establishing a pore network model based on a nuclear magnetic resonance test digital core is characterized in that specific contents of organic matter and inorganic matter pores which are lower than the resolution of the digital core and are divided based on a T2 spectrum and organic matter agglomeration are as follows, according to Step2 in claim 1:
s201, establishing a matrix: taking a T2 map as a matrix abscissa, a T1 map as a matrix ordinate, and taking an accumulated value of the corresponding semaphore under T1 and T2 as a corresponding coordinate value in the matrix, and making a cloud picture by using the matrix;
s202, nuclear magnetic data selection: for the semaphore under the joint analysis of T1 and T2 relaxation time, because the time of the hydrogen nucleus under the action of a nuclear magnetic field and the surrounding crystal lattice is longer than the action time between the nucleus and the atomic nucleus, only data with T1 being more than T2 are selected, namely, the cloud picture matrix coordinate is divided averagely according to the curve of y being x, and then the upper left part is selected for analysis;
s203: grouping: due to the existence of solid organic matter in shale, kerogen rich in hydroxyl structures and methane, the structure of shale is considered from the complexity level: the solid organic matter > kerogen > methane is adopted, so that T1 relaxation time is arranged according to a complex degree sequence under the action of lattices, therefore, the signal of the upper left part in a cloud picture is regarded as the signal response quantity of the solid organic matter agglomeration in a nuclear magnetic experiment under the low T2 relaxation time and the high T1 relaxation time, compared with the solid organic matter agglomeration, the T1 relaxation time is shorter, namely the agglomeration positioned below the solid organic matter in the cloud picture is a hydroxyl agglomeration, and the T1 relaxation time is the minimum, namely the agglomeration at the lower left corner in the cloud picture is the methane agglomeration;
s204: quantification of clustered data: converting the signal of the solid organic matter into an accurate volume through a nuclear magnetic middle calibration test; for T2 relaxation time data in organic matter clusters, based on a distilled water nuclear magnetic test rule of the same principle, the distribution rule of solid organic matter is considered to be the same as a signal measurement rule, so that a single cluster is analyzed by combining a T1 matrix cloud picture and a T2 matrix cloud picture obtained in the step S201 and several clusters under low T2 relaxation time in the step S203, and the T2 relaxation time of the separated cluster matrix data is analyzed through an image method or a numerical value to represent the distribution ratio of different clusters in a sample.
3. A method for establishing a pore network model based on a nuclear magnetic resonance test digital core is characterized in that the specific method for representing the agglomerate distribution in the step S204 in claim 2 is as follows:
(1) using the python read Matrix, the abscissa distribution Matrix [ X ] of the corresponding clusters in the Matrix is established][Y min ：Y max ]And ordinate [ Y min ，Y max ]
(2) Setting Matrix name as Matrix, traversing Matrix X [ X ] with Python][Y min ：Y max ]And calculating the corresponding signal quantity ratio, wherein the corresponding formula is as follows:
wherein: matrix [ X ]][Y min ：Y max ]For the accumulated value in the blob under Matrix column X in the traversal process, SUM (Matrix [ X)]) Adding up values of the matrix array X in the traversal process;
through the above steps, the occupation ratios of different clusters at the corresponding transverse relaxation time T2, i.e., (T2, part (T2)) are obtained.
4. A method for establishing a pore network model based on a nuclear magnetic resonance test digital core is characterized in that the method for converting T2 relaxation time into pore size and corresponding pore proportion by using an equivalent capillary shape factor as defined in Step3 in claim 1 comprises the following steps:
s301: based on the signal quantities of the different clusters obtained in S204 under the same transverse relaxation time T2, the pore distribution in the sample is accurately corrected: the testing range of the transverse relaxation time T2 in the nuclear magnetic test is the same, programming software is used for traversing the signal quantity of the transverse relaxation time T2 in the dry sample and the wet sample, and the corresponding signal quantity of organic matters, methane and hydroxyl clusters in the dry sample is subtracted from the signal quantity in the wet sample to obtain the corresponding relaxation time and signal quantity which can be used for representing the swept pore;
s302: converting the transverse relaxation time in the nuclear magnetic curve obtained in the step S301 into a pore size, if the sizes in three directions in the digital core resolution grid are the same, setting the shape factor to 2, and if the sizes in the digital core grid are different, wherein the length in the direction of the maximum ruler/the length in the direction of the minimum ruler is greater than 2, setting the shape factor to 3, and specifically converting the equation into:
r c ＝βF s T 2
wherein: t is 2 Is the relaxation time; f s Is a pore shape factor; beta is the transverse surface relaxation rate of the rock; the method can obtain equivalent spherical pore distribution.
5. A method for building a pore network model based on a nuclear magnetic resonance test digital core is characterized by comprising the following steps: the specific process of modifying segmented digital core by segmentation and segmentation modules as described in Step4 of claim 1 is:
s401: filtering the digital core image according to a gray value through a software segmentation module in Avizo, roughly dividing lithology according to a gray threshold value selected manually, dividing the core into a plurality of lithologic continuous small clusters through a segmentation module, exporting data of the digital core, and establishing a four-dimensional matrix, wherein three-dimensional coordinates of the matrix represent coordinates of corresponding positions of the digital core, and numerical values in the matrix are the gray value corresponding to the image and the serial cluster number;
s402: and traversing the lithological matrix obtained in the S401 by using Python programming, optimizing different lithological thresholds manually selected in the previous step, counting lithological component proportion divided according to the thresholds in matrix data to correct volume proportion under the resolution of the digital core, comparing the lithological component proportion with nuclear magnetic test data of the pore in the nuclear magnetic test, and circularly changing the thresholds for many times by using the program to obtain the pore and organic matter group which can accurately represent the resolution of the digital core.
6. A method for building a pore network model based on a nuclear magnetic resonance test digital core is disclosed in claim 5, wherein the specific method for dividing lithology according to a threshold value in the step S402 is as follows:
s4021: reading Matrix data by using Python, traversing serial cluster numbers in the Matrix, extracting the continuous clusters which are segmented and identified as pores in the previous step, counting the upper and lower limits of three-dimensional coordinates, determining the coordinates (x, y, z) of the central points of the continuous clusters, reading and counting Matrix [ x-id: x + id ] [ y-id: y + id ] [ z-id: z + id ] data; wherein: i represents the number of iteration steps in the traversal process, and for the unit vector matrix in the matrix data, the corresponding relation is as follows:
easy obtaining:
for the case where r is equal to I,
reading the values of the variables r, theta,
in-process correspondence matrix [ x, y, z ]]Counting the number of continuous clusters in the vector, counting the pore ratio under the corresponding iteration times, determining that the pore ratio is lower than 1/4 to be the maximum equivalent sphere pore, marking the vector in the last iteration process, and setting as the boundary in the next traversal process, namely cutting off the clusters;
s4022: and after the S4021 process, re-reading the middle point of the cluster which is cut off for 2n times in the previous step in the continuous clusters, and repeating the steps until the size of the cut cluster is equal to the pore of the resolution of the digital core.
7. A method for building a pore network model based on a nuclear magnetic resonance test digital core is disclosed, wherein the specific process of super-resolution analysis in the Step5 process is as follows:
s501 because the pore distribution size of each mineral is generally determined to follow normal distribution, and the pore size of the organic matter in the shale is minimum and lower than the pore distribution value, based on the pores lower than the resolution of the digital core in the nuclear magnetic data, the pore distribution in the small-scale direction in the curve is used for fitting the pore size of the organic matter conforming to the normal distribution
S502, the porosity and the gray value are approximately in an EXP relation by using an HU gray conversion algorithm in the digital rock core measuring process, and therefore, the gray value data in the organic matter cluster in the statistical matrix is extracted and processed to describe the porosity; carrying out iterative statistics on the processed data to obtain organic matter pore distribution conforming to nuclear magnetic data test;
s503, supplementing the pore network model obtained by Avizo software in the Step4 process based on the data obtained in the above process, and supplementing the coordination channels connected with the spherical pores in the pore network model, wherein the corresponding matrix structure is the coordination channel number and the corresponding pore distribution data.
8. A method for establishing a pore network model based on a nuclear magnetic resonance test digital core is disclosed in claim 6, wherein the specific process of super-resolution analysis in the Step502 process is as follows:
s5021: processing the gray scale data to obtain a dimensionless parameter indicative of relative density, the corresponding formula being:
den i ＝ln(greyshade i )
s5022: traversing dimensionless density data to obtain maximum den in the data max And minimum value den min Deem den max For pure organic matter dimensionless density, the dimensionless relative porosity of the organic matter can be characterized by the following formula:
wherein phi is ri And den i Representing dimensionless phases in corresponding volume elements of digital coresFor porosity and dimensionless relative density values; den (r) void Representing a dimensionless relative density value of the digital core pore;
s5023: for expected mu and standard deviation sigma in equivalent distribution normal distribution data fitted in the S501 process, the minimum organic matter pore and digital core resolution d in the nuclear magnetic test min ，d res Is divided into 100 segments for the upper and lower bounds on average, with d i (i ═ 1, 2.., 100), then the corresponding dimensionless relative porosity can be expressed as:
s5024: and (3) an iterative process: setting den min Is a den void The initial value of (a) is combined with the tanh function to describe the den under the nuclear magnetic resonance test and the super-resolution analysis void The similarity degree of the lower curve is shown as the following formula:
when in the process of iteration, the user can select the target,
the process is iterated until den void Ends at ≦ 0, in an iterative process f (den) void ) Minimum determination den void Calculating the relative porosity of the corresponding position of the digital rock core;
s5024: reading the relative porosity data of the clusters divided into the organic matters in the matrix, analyzing corresponding digital core microelements in the matrix, obtaining the pore volume of the corresponding microelements according to the size of the microelements (1-porosity in the microelements), distributing organic matter pores according to the sequence from the large-size pore microelements to the small-size pore microelements based on the pore occupation ratio in the nuclear magnetic resonance test data, counting the size and the number of the organic matter pores of the corresponding matrix coordinate microelements, and supplementing the nano-grade organic matter pores in the pore throat model obtained in the previous step.
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