CN115078210A - Shale pore structure testing method - Google Patents
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Abstract
A shale pore structure testing method. Providing a sample to be tested and a modeling sample group from different shale cores, measuring a plurality of particle samples in the sample group based on a fluid injection method, so as to obtain the curve of the accumulated pore volume along with the change of the pore diameter, and a plurality of plunger samples in a sample group and a sample to be measured are measured based on the nuclear magnetic resonance method, so as to obtain a curve of the accumulated nuclear magnetic signal quantity changing along with the transverse relaxation time, by utilizing the curve of the accumulated pore volume changing along with the pore diameter of a plurality of particle samples, the curve of the accumulated nuclear magnetic signal quantity changing along with the transverse relaxation time of a plurality of plunger samples and the curve of the accumulated nuclear magnetic signal quantity changing along with the transverse relaxation time of a sample to be detected, determining the relation between the aperture of the sample to be tested and the transverse relaxation time by a fractal geometry method, and converting the transverse relaxation time of the sample to be tested into the aperture by sections by utilizing the relation between the aperture of the sample to be tested and the transverse relaxation time of the sample to be tested. The method can be realized accuratelyT 2 R is converted.
Description
Technical Field
The invention belongs to the technical field of oil-gas exploration, and particularly relates to a shale pore structure testing method.
Background
The pore development characteristics are the key points and difficulties in shale reservoir characteristic research, the occurrence mode and the gas content of natural gas are controlled, and the selection of a shale gas development mode is influenced, wherein the pore structure is one of the most important pore development characteristic parameters. With the intensive research, the quantitative test method for the pore structure of the shale is developed dramatically. Among the quantitative test methods, the nuclear magnetic resonance test technology has the advantages of capability of providing multi-scale continuous pore size information, low requirement on the specification of a test sample, no damage to the test sample in the test process and the like, and is widely applied to characterization of the shale pore structure.
Unlike conventional pore structure test methods (such as mercury porosimetry and adsorption), the measurement principle of the nuclear magnetic resonance technology is to determine the H of the effective pore fluid of the rock 1+ Transverse relaxation information (hereinafter referred to as T2 relaxation) is obtained to obtain pore distribution information in the sample. The premise that the technology can carry out pore structure characterization is as follows: (1) the relaxation semaphore can reflect effective pore fluid and has a corresponding relation with the effective pore volume measured by a conventional method; (2) there is a certain correlation between the T2 relaxation time and the aperture r, and the conversion of the T2 relaxation time to the aperture r can be shown based on this correlation.
In a conventional reservoir, nuclear magnetic measurement signals mainly come from various pore fluids (including effective pores and ineffective pores), the effective pores and the ineffective pores (mainly capillary bound water) respectively correspond to large-pore-diameter pores and small-pore-diameter pores, and therefore the nuclear magnetic signals of the effective pores are obtained by selecting cutoff values according to the nuclear magnetic resonance test results of samples in two states of saturated water and centrifugal treatment. Furthermore, in conventional sandstone reservoirs, a linear transformation model is often used to perform the transformation of nuclear magnetic resonance T2 into pore size r, which is of the form r ═ C · T2, where C is the ratio of the transverse pore surface relaxation ρ and the pore shape factor F s Combined to a constant which is fixed for a single sampleValues for p and F for conventional sandstone reservoirs due to the relative simplicity of pore type and pore shape s The variation is small over the entire pore size range of the effective pore, so a fixed value of C can be used for sample T 2 R. While for the shale sample, due to its characteristics of porous type and shape, especially the development of pores with distinct properties of organic and inorganic pores, this characteristic results in the absence of uniform ρ and F throughout the pore size range of the effective pores s The value is obtained. Therefore, the conventional linear transformation model can hardly realize reasonable T2-r transformation of the shale sample.
The method shows that the shale structure characterization of nuclear magnetic resonance by simply utilizing the traditional method still has a great deal of problems, and the application of the shale structure characterization in the shale reservoir pore structure characterization is restricted.
Disclosure of Invention
In view of one or more of the above-mentioned problems of the prior art, it is an object of the present invention to provide a shale pore structure testing method, which can effectively avoid the porous type problem encountered when applying the conventional pore size conversion method in a shale reservoir, and realize accurate T 2 R is converted.
In order to achieve the above object, the present invention provides a method for determining a pore structure of shale, comprising the following steps:
providing a sample to be tested and a modeling sample set derived from different shale cores, wherein the modeling sample set comprises a combination of a plurality of particle samples and a plurality of corresponding plunger samples;
measuring the plurality of particle samples based on a fluid injection method to obtain their respective cumulative pore volume versus pore diameter curves;
measuring the sample to be measured and the plurality of plunger samples based on a nuclear magnetic resonance method to obtain respective curves reflecting the change of accumulated nuclear magnetic signal quantity of the effective pores along with transverse relaxation time;
respectively converting the cumulative pore volume change curve of the particle samples along with the pore diameter and the cumulative nuclear magnetic signal quantity change curves of the sample to be detected and the plunger samples along with the transverse relaxation time into a cumulative pore volume fractal characteristic curve and a cumulative nuclear magnetic signal quantity fractal characteristic curve by using a fractal geometric model;
extracting a fractal interval, a critical aperture, and the cumulative pore volume and fractal dimension of each fractal interval from the cumulative pore volume fractal characteristic curves of the particle samples as fractal characteristic parameters of the particle samples, and extracting the fractal interval, the critical transverse relaxation time, and the cumulative nuclear magnetic signal volume and the fractal dimension of each fractal interval from the cumulative nuclear magnetic signal volume fractal characteristic curves of the sample to be detected and the plunger samples as fractal characteristic parameters of the particle samples;
determining one or more correspondence intervals of the cumulative pore volume fractal characteristic curve of the plurality of particle samples and the cumulative nuclear magnetic semaphore fractal characteristic curve of the plurality of plunger samples;
determining a linear relationship between accumulated pore volume and accumulated nuclear magnetic signal quantity, a linear relationship between critical pore diameter and critical transverse relaxation time and a linear relationship between fractal dimension of the plunger samples by using fractal characteristic parameters of the plunger samples and fractal characteristic parameters of the particle samples in each corresponding interval;
determining the relation between the pore diameter and the transverse relaxation time of the sample to be detected in each corresponding interval by utilizing the linear relation between the accumulated pore volumes and the accumulated nuclear magnetic signal quantities of the plurality of particle samples and the plurality of plunger samples, the linear relation between the critical pore diameter and the critical transverse relaxation time and the linear relation between the fractal dimensions thereof, and the fractal dimension and the critical transverse relaxation time of the sample to be detected; and
and in each corresponding interval, utilizing the relation between the aperture and the transverse relaxation time of the sample to be detected to convert the transverse relaxation time of the sample to be detected into the aperture in a segmented manner.
According to the method of the present invention, further, the step of measuring the sample to be measured and the plurality of plunger samples based on the nmr method to obtain their respective curves reflecting the variation of the cumulative nmr signal amount of the effective pore with the transverse relaxation time includes:
measuring the sample to be measured and the plurality of plunger samples in a saturated water treatment state and a drying treatment state by a nuclear magnetic resonance method to obtain respective transverse relaxation time T2 spectrums thereof;
obtaining nuclear magnetic signal intensity curves of the sample to be detected and the plunger samples in a saturated water treatment state and a dry treatment state respectively by utilizing transverse relaxation time T2 spectrums of the plunger samples;
performing difference operation on the nuclear magnetic signal intensity curves of the sample to be detected and the plunger samples in a saturated water treatment state and a drying treatment state to obtain respective nuclear magnetic signal intensity difference curves capable of reflecting effective pores; and
and obtaining the variation curve of the accumulated nuclear magnetic signal quantity along with the transverse relaxation time of the sample to be detected and the plunger samples by calculating the nuclear magnetic signal intensity difference curves of the sample to be detected and the plunger samples.
According to the method of the present invention, further, the linear relationship between the cumulative pore volume and the cumulative nuclear magnetic signal amount is represented by the following formula:
V i ＝a 1 ·Q i +b 1
wherein V i Denotes the cumulative pore volume, Q, in the correspondence interval i i Represents the cumulative nuclear magnetic signal amount within the correspondence interval i, and a 1 And b 1 Is a constant term;
the linear relationship between the critical aperture and the critical transverse relaxation time is represented by the following equation:
r i，i+1 ＝a 2 ·T 2(i，i+1) +b 2
wherein r is i，i+1 Denotes the critical aperture, T, between the correspondence interval i and the correspondence interval i +1 2(i，i+1) Represents the critical transverse relaxation time between the correspondence interval i and the correspondence interval i + l, and a 2 And b 2 Is a constant term;
a linear relationship between fractal dimensions of the plurality of particle samples and the plurality of plunger samples is represented by:
D inv，i ＝c·D nmr，i +d
wherein D inv，i Representing the fractal dimension, D, of each particle sample within the correspondence interval i nmr，i Representing the fractal dimension of each plunger sample within the corresponding interval i, and c and d are constant terms; and the relationship between the pore size and the transverse relaxation time of the sample to be measured is represented by the following formula:
r＝m·T 2 n
wherein r represents the pore diameter, T 2 Representing the transverse relaxation time, m and n are respectively the transformation coefficients defined as:
wherein D nmr Is the fractal dimension of a sample to be detected in the corresponding interval i, and c and d are constant terms defined above; and T 2，max Is the maximum transverse relaxation time, a, of the sample to be measured within the corresponding interval i 2 And b 2 Is a constant term as defined above.
According to the method of the invention, further, the fractal dimension D of each particle sample in the corresponding interval i inv，i Fractal dimension D of each plunger sample in corresponding interval i nmr，i When equal or close, the relationship between the pore diameter and the transverse relaxation time of the sample to be tested is represented by the following formula:
r＝m·T 2
wherein r represents the pore diameter, T 2 Representing the transverse relaxation time, C is the conversion coefficient defined as:
wherein T is 2，min Is the minimum transverse relaxation time of the sample to be tested in the corresponding interval i.
According to the method of the present invention, further, a fractal geometric model for converting the cumulative pore volume fractal characteristic curve is represented by the following formula:
log V＝(3-D inv )·log(r)+(D inv -3)·log(r max )-log(V t )
wherein r represents the pore diameter, V represents the cumulative pore volume of pores having a pore diameter less than r, r max Denotes the maximum pore diameter, V t Denotes a pore diameter smaller than r max Cumulative pore volume of, and D inv A fractal dimension representing a cumulative pore volume fractal characteristic curve; and is
The fractal geometric model for converting the curve of the accumulated nuclear magnetic semaphore along with the transverse relaxation time is represented by the following formula:
log Q＝(3-D nmr )·log(T 2 )+(D nmr -3)·log(T 2max )-log(Q t )
wherein Q represents a relaxation time less than T in the transverse direction 2 The accumulated semaphore, T, in the interval (b) of 2 Representing transverse relaxation time, T 2，max Denotes the maximum transverse relaxation time, Q t Denotes a relaxation time less than T in the transverse direction 2，max And D is the cumulative semaphore of the section (b) of nmr The fractal dimension of the fractal characteristic curve of the nuclear magnetic semaphore is accumulated.
According to the method of the present invention, the step of determining one or more corresponding intervals of the cumulative pore volume fractal characteristic curve of the plurality of particle samples and the cumulative nuclear magnetic signal volume fractal characteristic curve of the plurality of plunger samples further comprises:
determining a linear relationship between the cumulative pore volume of the plurality of particle samples and the cumulative nuclear magnetic signal volume of the plurality of plunger samples by a linear regression method in different independent or combined fractal intervals;
determining an independent or combined fractal interval corresponding to the linear relationship having the largest fitting coefficient from among fitting coefficients of linear relationships between the cumulative pore volumes of the plurality of particle samples and the cumulative nuclear magnetic signal volumes of the plurality of plunger samples in different independent or combined fractal intervals as a corresponding interval; and
the remaining correspondence intervals are determined in the same manner.
The method according to the present invention, further comprising the step of measuring the plurality of particle samples based on a fluid injection method to obtain their respective cumulative pore volume as a function of pore diameter, comprising:
by N 2 Isothermal adsorption method, CO 2 Respectively measuring each particle sample by an isothermal adsorption method and a mercury pressing method to obtain a pore size distribution curve with different pore size distribution ranges of each particle sample;
obtaining a pore size distribution curve with a full pore size distribution range of each particle sample by a full pore size characterization method using the pore size distribution curve with a different pore size distribution range of the particle sample; and
the cumulative pore volume as a function of pore diameter was obtained by calculation from the pore diameter distribution curve having the full pore diameter distribution range for each particle sample.
Advantageous effects
The invention realizes the nuclear magnetic resonance T by adopting a fractal geometry method 2 The segmented conversion of the signal to the pore structure information effectively avoids the problem of porous types when the traditional conversion method is applied to the shale reservoir, and can realize accurate T 2 R is converted. In addition, the invention further abandons the utilization of T 2 Conventional method for obtaining cut-off value to obtain effective pore relaxation time T 2 Spectroscopic method, nuclear magnetic signal intensity curve (i.e. transverse relaxation time T) obtained by drying treatment and saturated water treatment of a sample 2 Spectra), a relaxation time T is obtained after the subtraction process that truly reflects the effective porosity 2 Spectrum, which makes the physical quantity measured by nuclear magnetic resonance and the physical quantity measured by fluid injection method have comparability, thereby realizing more accurate T 2 R is converted.
Drawings
FIG. 1 shows experimental test results and data processing results of injection pore structure of sample # 3 in the example of the present invention;
FIG. 2 shows the cumulative pore volume as a function of pore diameter for the full pore diameter range obtained for samples # 1-8 in examples of the present invention;
FIG. 3 shows T in saturated water and in dry state obtained from samples # 1 to # 8 in examples of the present invention 2 Spectra and difference values T obtained by calculation 2 The change curve of the spectrum and the accumulated nuclear magnetic signal quantity thereof along with the transverse relaxation time;
FIG. 4 is a graph showing the fractal analysis of the cumulative pore volume as a function of pore diameter for samples # 1-8 in examples of the present invention;
FIG. 5 is a schematic diagram showing the analysis of the fractal characteristic analysis of the cumulative nuclear magnetic semaphore distribution curves of samples # 1-8 in the examples of the present invention;
FIG. 6 shows a comparison of the linear relationship between the cumulative signal amount and the cumulative pore volume for samples # 1-8 in various fractal intervals according to the present invention;
FIG. 7 shows r of samples # 1-8 in examples of the present invention max -T max And D inv -D nmr A related relationship chart of (1);
FIG. 8 shows the comparison of the NMR explained pore structure of sample No. 9 in the example of the present invention with the results of the pore structure test by injection.
Detailed Description
The method according to the invention comprises the following steps: providing a sample to be tested and a modeling sample set derived from different shale cores, wherein the modeling sample set comprises a combination of a plurality of particle samples and a plurality of corresponding plunger samples; measuring the plurality of particle samples based on a fluid injection method to obtain their respective cumulative pore volume versus pore diameter curves; measuring the sample to be detected and the plurality of plunger samples based on a nuclear magnetic resonance method to obtain respective accumulated nuclear magnetic signal quantity variation curves along with transverse relaxation time; respectively converting the cumulative pore volume change curve of the particle samples along with the pore diameter and the cumulative nuclear magnetic signal quantity change curves of the sample to be detected and the plunger samples along with the transverse relaxation time into a cumulative pore volume fractal characteristic curve and a cumulative nuclear magnetic signal quantity fractal characteristic curve by using a fractal geometric model; extracting a fractal interval, a critical aperture, and the cumulative pore volume and fractal dimension of each fractal interval from the cumulative pore volume fractal characteristic curves of the particle samples as fractal characteristic parameters of the particle samples, and extracting the fractal interval, the critical transverse relaxation time, and the cumulative nuclear magnetic signal volume and the fractal dimension of each fractal interval from the cumulative nuclear magnetic signal volume fractal characteristic curves of the sample to be detected and the plunger samples as fractal characteristic parameters of the particle samples; determining one or more correspondence intervals of the cumulative pore volume fractal characteristic curves of the plurality of particle samples and the cumulative nuclear magnetic semaphore fractal characteristic curves of the plurality of plunger samples; determining a linear relationship between accumulated pore volume and accumulated nuclear magnetic signal quantity, a linear relationship between critical pore diameter and critical transverse relaxation time and a linear relationship between fractal dimension of the plunger samples by using fractal characteristic parameters of the plunger samples and fractal characteristic parameters of the particle samples in each corresponding interval; determining the relation between the pore diameter and the transverse relaxation time of the sample to be detected in each corresponding interval by utilizing the linear relation between the accumulated pore volumes and the accumulated nuclear magnetic signal quantities of the plurality of particle samples and the plurality of plunger samples, the linear relation between the critical pore diameter and the critical transverse relaxation time and the linear relation between the fractal dimensions thereof, and the fractal dimension and the critical transverse relaxation time of the sample to be detected; and in each corresponding interval, utilizing the relation between the aperture and the transverse relaxation time of the sample to be detected to convert the transverse relaxation time of the sample to be detected into the aperture in a segmented manner.
According to the method, the sectional conversion of the nuclear magnetic resonance T2 signal to the pore structure information is realized by adopting the fractal geometry, the problem of the porous type when the traditional conversion method is applied to the shale reservoir is effectively avoided, and the accurate T can be realized 2 R is converted.
In the method of the invention, a shale core sample is selected, and a sample to be tested and a modeling sample are prepared. The samples to be tested and the modeling sample groups are from different shale cores. The sample to be tested is typically a plunger sample, while the modeled sample typically comprises a combination of a plurality of particle samples and corresponding plunger samples. In one or more embodiments, the plunger sample size may be a cylinder with a length controlled between 3 and 5cm, and the pellet sample size may be 20 to 35 mesh (pellet diameter between 0.85mm and 0.5 mm). Methods of preparing samples are known in the art.
In the method of the present invention, the plurality of particle samples may be measured based on a fluid injection method to obtain their respective cumulative pore volume versus pore diameter curves. Preferably, the fluid injection method may include N 2 Isothermal adsorption method, CO 2 A combination of isothermal adsorption and mercury intrusion. Preferably, by N 2 Isothermal adsorption method, CO 2 Respectively measuring each particle sample by an isothermal adsorption method and a mercury pressing method to obtain a pore size distribution curve with different pore size distribution ranges of each particle sample; obtaining a pore size distribution curve with a full pore size distribution range of each particle sample by a full pore size characterization method using the pore size distribution curve with a different pore size distribution range of the particle sample; and a cumulative pore volume as a function of pore diameter is obtained by calculation from the pore diameter distribution curve having the full pore diameter distribution range for each sample of particles. N is a radical of 2 Isothermal adsorption method, CO 2 Both isothermal adsorption and mercury porosimetry are known in the art. The conditions for the drying process may include: the drying temperature is 70 ℃, and the drying time is 48 h. In the passage of N 2 Isothermal adsorption method, CO 2 After the isothermal adsorption method and the mercury pressing method respectively obtain the pore size distribution of each particle sample, the full pore size distribution characteristic of the shale pores can be obtained through a full pore size characterization method of the shale particle samples. The full aperture characterization method can be selected from Yu Y.X., Luo X.R., Wang Z.X., et al.A new correction method for correction injection characterization (MICP) to correction of the pore structure of shape [ J.]Journal of Natural Gas Science and Engineering, 2019, 68: 102896. Tong (Chinese character of 'tong')The cumulative pore volume curve with the pore diameter is obtained by calculation according to the full pore diameter distribution curve of the sample.
In the method of the present invention, the sample to be measured and the plurality of plunger samples are measured based on a nuclear magnetic resonance method to obtain their respective cumulative nuclear magnetic signal amount versus transverse relaxation time curves. Nuclear magnetic resonance methods may be known in the art and may be performed using a nuclear magnetic resonance core analyzer.
As a preferred embodiment, the sample to be measured and the plurality of plunger samples may be measured in a saturated water treatment state and a dry treatment state by a nuclear magnetic resonance method to obtain their respective transverse relaxation time T2 spectra; obtaining nuclear magnetic signal intensity curves of the sample to be detected and the plunger samples in a saturated water treatment state and a dry treatment state respectively by utilizing transverse relaxation time T2 spectrums of the plunger samples; performing difference operation on the nuclear magnetic signal intensity curves of the sample to be detected and the plunger samples in a saturated water treatment state and a drying treatment state to obtain respective nuclear magnetic signal intensity difference curves capable of reflecting effective pores; and obtaining the change curve of the accumulated nuclear magnetic signal quantity reflecting the effective pore along with the transverse relaxation time by calculating the nuclear magnetic signal intensity difference curve of the sample to be detected and the plunger samples.
The characteristics of the shale sample are obviously different from those of a conventional reservoir, and are mainly shown in that (1) effective pores are not only large-aperture pores, but also develop in a plurality of scales such as micron-scale, submicron-scale and nanometer-scale and are not negligible; (2) shale is rich in a large amount of organic matter, which is rich in H 1+ Nuclear magnetic signals may be provided; (3) the clay mineral content is high, and a large amount of clay mineral binds water to provide nuclear magnetic signals. In addition, the characteristic that the pore size of the shale tiny pores is greatly developed also causes that the fluid in the effective pores cannot be removed by centrifugal treatment. Therefore, the traditional method of obtaining nuclear magnetic signal quantity of effective pores by centrifugal processing and setting a cut-off value is not suitable for shale.
According to the preferred embodiment, the method further abandons the utilization of T 2 Conventional method for obtaining cut-off value to obtain effective pore relaxation time T 2 Spectroscopic method, transverse relaxation time T obtained by drying treatment and saturated water treatment of sample 2 Spectra (nuclear magnetic signal intensity curves), obtained after subtraction, of a relaxation time T that truly reflects the effective porosity 2 Spectrum, which makes the physical quantity measured by nuclear magnetic resonance and the physical quantity measured by fluid injection method have comparability, thereby realizing more accurate T 2 R is converted.
In the method, a fractal geometric model is used for converting the pore diameter distribution curves of the particle samples and the difference T2 spectrums of the sample to be detected and the plunger samples into a cumulative pore volume fractal characteristic curve and a cumulative nuclear magnetic signal quantity fractal characteristic curve respectively. Extracting a fractal interval, a critical aperture, and the cumulative pore volume and fractal dimension of each fractal interval from the cumulative pore volume fractal characteristic curves of the particle samples as fractal characteristic parameters of the particle samples, and extracting the fractal interval, the critical transverse relaxation time, and the cumulative nuclear magnetic signal volume and the fractal dimension of each fractal interval from the cumulative nuclear magnetic signal volume fractal characteristic curves of the sample to be detected and the plunger samples as fractal characteristic parameters of the particle samples.
Specifically, a fractal geometric model is adopted for fractal feature analysis, and the formula is expressed as follows:
wherein r is max Represents the maximum pore diameter, nm; r is min Represents the minimum pore size, nm; r represents any pore size in the pore size distribution range, nm; v represents the cumulative pore volume, cm, of pores having a pore diameter of less than r 3 /g；V t Represents the cumulative pore volume in cm at the maximum pore diameter 3 (ii)/g; d denotes the fractal dimension. At r min In the case of r, equation (1) can be simplified as:
for the cumulative pore volume versus pore diameter data obtained for the fluid injection method, equation (2) can be modified as follows:
log V＝(3-D inv )·log(r)+(D inv -3)·log(r max )-log(V t ) Formula (3)
Wherein r represents the pore diameter, V represents the cumulative pore volume of pores having a pore diameter less than r, r max Denotes the maximum pore diameter, V t Denotes a pore diameter smaller than r max Cumulative pore volume of, and D inv Representing the fractal dimension.
For the curve data of the accumulated nuclear magnetic semaphore obtained based on the nuclear magnetic resonance method along with the change of the transverse relaxation time, due to T 2 Time has a correlation with pore diameter, semaphore Q, and pore volume V, so equation (3) can be converted to:
log Q＝(3-D nmr )·log(T 2 )+(D nmr -3)·log(T 2max )-log(Q t ) Formula (4)
Wherein Q represents a relaxation time less than T in the transverse direction 2 The accumulated semaphore, T, in the interval (b) of 2 Representing transverse relaxation time, T 2，max Denotes the maximum transverse relaxation time, Q t Denotes a relaxation time less than T in the transverse direction 2，max The cumulative semaphore possessed in the interval of (D), and nmr is the fractal dimension.
Based on the formula (3) and the formula (4), in the linear coordinate system, the data points with fractal features will present a linear relationship in the log (r) -log (v) and log (T2) -log (q) relationship diagrams, which can be described by the following formula:
log(V)＝α inv ·log(r)+β inv formula (5)
log(Q)＝α nmr ·log(T2)+β nmr Formula (6)
Fractal dimension D inv And D nmr The following can be used to calculate:
D inv ＝3-α inv formula (7)
D nmr ＝3-α nmr Formula (8)
Specifically, the fractal feature analysis and extraction parameters may include:
(1) fractal dimension D inv And D nmr The piecewise characteristic analysis of the method determines the number n of the segments and calculates the fractal dimension D of the data in each segment inv，i And D nmr，i . Wherein i is a positive integer less than or equal to the number n of segments.
(2) Extracting a fractal interval segmentation critical point, wherein the formula is as follows:
r i，i+1 ＝(β inv，i+1 -β inv，i) /(α inv，i -α inv，i+1 ) Formula (9)
T 2(i，i+1) ＝(β nmr，i+1 -β nmr，i )/(α nmr，i -α nmr，i+1 ) Formula (10)
Wherein r is i，i+1 And T 2(i，i+1 ) Respectively representing the critical aperture and the critical transverse relaxation time of the ith section and the (i + 1) th section;
(3) counting the cumulative volume V of the pore in each section i And the cumulative semaphore Q per unit mass of rock i 。
According to the method, one or more correspondence intervals of the cumulative pore volume fractal characteristic curve of the plurality of particle samples and the cumulative nuclear magnetic signal volume fractal characteristic curve of the plurality of plunger samples are determined. Preferably, a linear relationship between the cumulative pore volume of the plurality of particle samples and the cumulative nuclear magnetic signal volume of the plurality of plunger samples is determined by linear regression in different independent or combined fractal intervals;
determining an independent or combined fractal interval corresponding to the linear relationship having the largest fitting coefficient from among fitting coefficients of linear relationships between the cumulative pore volumes of the plurality of particle samples and the cumulative nuclear magnetic signal volumes of the plurality of plunger samples in different independent or combined fractal intervals as a corresponding interval; and the remaining correspondence intervals are determined in the same manner.
And in each corresponding interval, determining the linear relation between the accumulated pore volume and the accumulated nuclear magnetic signal quantity, the linear relation between the critical pore diameter and the critical transverse relaxation time and the linear relation between the fractal dimension of the plunger samples by using the fractal characteristic parameters of the particle samples and the fractal characteristic parameters of the plunger samples. And determining the relation between the pore diameter and the transverse relaxation time of the sample to be detected in each corresponding interval by utilizing the linear relation between the accumulated pore volumes and the accumulated nuclear magnetic signal quantities of the particle samples and the plunger samples, the linear relation between the critical pore diameter and the critical transverse relaxation time and the linear relation between the fractal dimensions thereof, and the fractal dimension and the critical transverse relaxation time of the sample to be detected.
Specifically, selecting the cumulative pore volume V of different fractal intervals i And the accumulated semaphore Q i To perform interval correspondence analysis. The correspondence intervals should have the following relationship:
in other words, in the interval with the correspondence, the cumulative hole volume and the cumulative nuclear magnetic signal quantity have a better linear correlation relationship, and the piecewise critical point has a better linear relationship;
in the interval with the correspondence, D is obtained through statistics nmr，j -D inv，j (j is an integer from 1 to i), the following relationship is determined:
D inv，j ＝c·D nmr，j + d formula (12).
In the 1 to i sections having the correspondence, the following expressions (2), (4), (11) and (12) are combined:
r＝m·T 2 n formula (13)
Where m, n are coefficients in the conversion process, which are defined as:
as shown above, the key to establishing the T2-r relationship by using the fractal mathematical characteristics lies in the pairs of c, d, a 2 And b 2 And (4) determining parameters.
At D nmr And D inv In the special case of equality or closeness, equation (13) can be rewritten as:
r＝m·T 2 formula (16)
Wherein the value of m is calculated as:
where m is independent of fractal dimension and only T 2，min It is related. As can be seen from the formula (17), in this case, r and T have a correspondence relationship within the interval 2 The ratio of (a) to (b) is a fixed value, i.e. m remains constant over the entire interval and does not follow T 2 The value changes. Therefore, the coefficient m can be represented by any one of r and T with corresponding relation known in the interval 2 Obtained by a ratio of (e.g. r in the interval) min And T 2，min 。
In the method, the transverse relaxation time of the sample to be detected is converted into the aperture in a segmented manner by utilizing the relation between the aperture and the transverse relaxation time of the sample to be detected in each corresponding interval, so that T can be realized 2 Conversion of time to pore size using nuclear magnetic resonance T 2 The spectra explain the pore structure of gas-containing shale.
The following detailed description will be given, with reference to the accompanying drawings, to the following description of the embodiments of the present invention, by way of example of gas shale contained in shanxi group of the urdos basin, but the scope of the present invention is not limited by the embodiments.
Examples
(1) Sample selection and preparation
The method comprises the steps of selecting shale with the thickness not less than 3cm to prepare a test sample, firstly drilling a core pillar with the diameter of 2.54cm along the side surface, flattening two ends of the drilled core pillar to obtain a plunger sample with the length of 3-5 cm, and grinding the rest sample with the two ends flattened to obtain a 20-35-mesh particle sample. In the examples, 1-9# samples were prepared, wherein 9# sample was a validation sample (plunger sample) and the remaining 1-8# samples (pellet sample and plunger sample thereof) were used for the determination of the conversion parameter.
(2) Injection method pore structure test of particle sample and pore size distribution interpretation result of sample
Selecting 20g of 1-8# sample particles, drying at 70 ℃, and then carrying out N treatment 2 Adsorption, CO 2 Adsorption and mercury intrusion tests, the basis of the three tests and the data processing method are listed in table 1. The method adopts Yu Y.X., Luo X.R., Wang Z.X., et al.A. new correction method for correction injection of capacitor pressure (MICP) to characteristics of the pore structure of shell [ J]Journal of Natural Gas Science and Engineering, 2019, 68: 102896, the results of the three experiments are spliced to obtain the pore size distribution curve of the shale sample in the full pore size range.
Table 1 experimental conditions and pore structure interpretation model for pore structure test by injection method
Fig. 1 shows the process of obtaining the full pore size distribution pore structure of sample # 3, and the data of the full pore size pore structure of other samples have the same process. FIGS. 1(a), (b) and (c) are each CO 2 Isothermal adsorption test, N 2 Experimental results of isothermal adsorption test and high-pressure mercury injection test; FIGS. 1(d), (e) and (f) are each CO 2 Isothermal adsorption test, N 2 Pore size distribution interpretation results corresponding to the isothermal adsorption test and the high-pressure mercury intrusion test, wherein the hollow circle is a pore size distribution curve, and the hollow square blocks are accumulatedPore volume versus pore diameter curve; FIGS. 1(g) and (h) are the pore size distribution curves and cumulative pore volume as a function of pore size for the full pore size range obtained for sample # 3. FIG. 2 shows the cumulative pore volume over the full pore size range obtained for samples # 1-8 as a function of pore size.
(3) Saturated water treatment of plunger samples
Respectively putting the plunger samples into a core vacuumizing saturator, vacuumizing for 24h under the condition of 1MPa, and then performing saturated water treatment under the condition of 30MPa, wherein the saturated water is a KCl aqueous solution with the concentration of 30%.
(4) Nuclear magnetic resonance T2 spectrum test of plunger sample in saturated water state
And taking out the saturated core from the vacuum saturator, placing the saturated core in a nuclear magnetic resonance core analyzer, measuring a T2 attenuation curve, and performing inversion to obtain a T2 spectrum of the sample in a saturated water state. The nuclear magnetic resonance core analyzer is selected to be SPEC-PMR-20M (20 MHZ); selecting CPMG pulses for collection, wherein the collection parameters comprise echo interval 0.2ms, waiting time 3000ms, number of echoes 4096 and scanning times 32; the inversion method is SIRT algorithm. The rectangular data points in FIGS. 3(a), (c), (e), (g), (i), (k), (m) and (o) show T in saturated water obtained from the measurements of sample # 1-8, respectively 2 Spectra.
(5) Plunger sample drying process
And putting the core into a drying oven with the temperature set to 70 ℃, continuously drying, weighing every 30 minutes, stopping drying when the mass is not reduced any more or fluctuates in a certain mass interval, taking out the sample from the drying oven, and recording the weight of the dried plunger sample when the sample is taken out.
(6) Nuclear magnetic resonance T2 spectrum testing of plunger samples after drying
Placing the dried rock core in a nuclear magnetic resonance rock core analyzer, and measuring T 2 Attenuation curve and inversion to obtain T of sample in dry state 2 Spectra. The instrumentation and parameters of the NMR are the same as in step 4, with particular emphasis on the T obtained by inversion 2 The spectral data should be correlated with T of saturated water state 2 The spectral data have the same T 2 The relaxation time. FIGS. 3(a), (c),(e) The circular data points in (g), (i), (k), (m) and (o) show the T in the dried state obtained by the measurement of sample # 1-8, respectively 2 Spectra.
(7) Calculating the spectrum of the difference T2 corresponding to the effective pore
Using T in saturated water and dried state 2 Spectrum, performing correspondence T 2 Subtracting the relaxation time signal quantity (as the shaded part in fig. 3) to obtain the T corresponding to the effective aperture 2 And calculating the change curve of the accumulated nuclear magnetic semaphore along with transverse relaxation time according to the semaphore curve. The difference T2 spectra obtained for the samples # 1-8 and their cumulative NMR spectra as a function of transverse relaxation time are shown in FIGS. 3(b), (d), (f), (h), (j), (1), (n) and (p), respectively.
(8) Fractal analysis of cumulative pore volume as a function of pore diameter
A plot of the log (r) -log (V) correlation (shown in FIG. 4) was generated in a linear coordinate system using the cumulative pore volume distribution curves of samples # 1 to # 8 (FIG. 2). In a log (r) -log (V) correlation diagram, samples 1-8# can be obviously divided into 3 fractal intervals, and data points have obvious linear relation in each interval. Fitting by utilizing the linear relation to obtain a fitting straight line formula of each interval, and calculating according to a formula 7 to obtain a fractal dimension D inv . The fractal dimension of each fractal region of sample # 1-8 is shown in table 2.
(9) Extraction of key fractal chemical parameters of cumulative pore volume change curve along with pore diameter
On the basis of injection method pore structure data fractal analysis, other key parameters except fractal dimension in pore structure data obtained by injection method test are calculated, and the parameters comprise the pore diameters (calculated according to formula 9) corresponding to the segmented key points in different intervals and the accumulated pore volume in each interval. The data are shown in Table 2.
Table 2 injection testing of key parameters for obtaining pore structure data
(10) Fractal analysis of cumulative nuclear magnetic semaphore curve with transverse relaxation time
Log (T) was generated in a linear coordinate system using the cumulative semaphore curves for samples # 1 to # 8 2 ) Log (q) correlation plot (shown in fig. 5). At log (T) 2 ) Log (q) correlation plot, the sample can be clearly divided into 3 intervals, and the data points have a clear linear relationship in each interval. And fitting by using the linear relation to obtain a fitting straight line formula of each interval, and subtracting the slope of the straight line by using the numerical value 3 to obtain the fractal dimension of the corresponding interval. The fractal dimension of each interval of sample # 1-8 is shown in Table 3.
(11) Extraction of key fractal chemical parameters of curve of accumulated nuclear magnetic semaphore along with transverse relaxation time
On the basis of fractal analysis of the curve of the accumulated nuclear magnetic semaphore changing along with the transverse relaxation time, other key parameters except fractal dimension are calculated, and the parameters comprise the corresponding pore diameters (calculated according to a formula 10) of key segmented points in different intervals and the accumulated nuclear magnetic semaphore in each interval. The above data are shown in Table 3.
TABLE 3 Key parameters of Nuclear magnetic data
(12) Determination of interval corresponding relation between pore structure data of injection method and nuclear magnetic resonance T2 spectrum
Injection pore structure data and NMR T through comparison analysis of NMR signal quantity and pore volume 2 The spectrum interval corresponding relation, the required parameters include accumulated signal quantity and accumulated pore volume, and the key points of the partition of each fractal interval. Wherein the cumulative signal volume and cumulative pore volume are defined as the maximum T in the interval 2 Cumulative signal volume and cumulative pore volume at relaxation time and at maximum pore diameter.
FIG. 6 is a graph of comparative analysis of nuclear magnetic signal and pore volume for samples # 1-8. As can be seen from the graph, the correlation between the cumulative signal amount and the cumulative pore volume in the two different intervals is counted, and under linear fitting, the fitting coefficient of the two is 0.460 in the interval 1, 0.907 in the intervals 1-2, and 0.707 in the intervals 1-3.
As can be seen from tables 2 and 3, D inv，1 And D nmr，1 Are all negative values, since r in formula (2) min The precondition of r is not satisfied, so that the fractal dimension loses the physical significance. Meanwhile, as can be seen from fig. 6, Q1 and V1 have poor correlation, and the ratio of the cumulative signal quantity to the cumulative pore volume is larger than that of Q2vsV2 and Q3vsV3, which indicates that there is no correspondence in the respective fractal intervals 1, and the partial data in the nuclear magnetic fractal interval 1 corresponds to the pore volume fractal interval 2. Thus, according to the above features, the nuclear magnetic semaphore and pore volume fractal region are divided into two corresponding regions (as shown in fig. 7), where the corresponding region one includes a region 1 and a region 2 of the respective fractal regions of the two types of data, and the corresponding region two corresponds to a region 3 of the respective fractal regions of the two types of data. As can be seen from fig. 6 and 7(a), both of the two corresponding sections satisfy the requirement of the formula (11), and have a correspondence.
Meanwhile, selecting a correlation relation between the accumulated semaphore and the accumulated pore volume in the interval with the highest fitting coefficient corresponding to the maximum fitting coefficient to establish conversion between the nuclear magnetic semaphore and the pore volume, and determining a1 and b1 in the formula (11), namely:
V＝0.029·Q+0.107
wherein Q is the nuclear magnetic semaphore, 1/g; v is the pore volume, cm 3 /100g。
(13) Determination of T 2 R conversion relation
On the basis of the corresponding relation, determining T in a segmentation mode in the corresponding area 2 -r conversion relation:
(1) in the corresponding interval one, r is set max ＝r 2，3 ，T max ＝T2 2，3 . From FIG. 7, it can be determined that r is within the corresponding interval max ～T max And D inv ～D nmr The values of c, d, a2 and b2 (shown in Table 5) in formula (14) and formula (15) were obtained, and then the known sample T was used max And D nmr The m and n values of the corresponding section are calculated.
In the above conversion, it is to be noted that when r obtained by a certain T2 conversion is 0.59nm or close to 0.59nm, the conversion of the numerical points T2 to r smaller than the T2 is no longer performed.
TABLE 5 key parameters for T2-r transition in the fractal interval 2 for nuclear magnetic cumulative semaphore
| Parameter name | c | d | a2 | b2 |
| Numerical value | 1.447 | -0.489 | 57.44 | 3.21 |
By using the steps, a T in the corresponding interval of the known band prediction sample is obtained 2，3 And D2, 3, completing the calculation of the conversion relation m and n, and realizing the relation conversion of T2-r. Taking sample 9# to be predicted as an example, the fractal dimension is 2.128, corresponding to T of interval one 2，max Is 1.22ms, based on the above method, m and n in the corresponding interval one are calculated to be 47.96 and 2.13 respectively by using table 5, formula (14) and formula (15), and the conversion formula is:
r＝47.96×T 2 2.13
(2) in the corresponding interval two, because in this interval, the nuclear magnetism is commonThe vibration fractal dimension and the pore volume fractal dimension are similar and are both around 2.90. Therefore, in this paragraph, corresponding to the special case where n is 1 in equation (13), the conversion from T2 to r is simplified to the conversion equation shown in equation (16), i.e., the conversion can be performed by any ratio of r and T2 known to have a corresponding relationship in this interval. R in this interval min (i.e.,) and T 2min Is a known T within the interval 2 R, the m value can be directly converted by using the two values, and in this embodiment, r corresponds to the second interval min ＝r 2，3 ，T 2，min ＝T 2，2，3 The value of m is obtained by calculation from equation (16):
wherein a2 and b2 are listed in Table 5.
Using the above steps, T in the corresponding interval II of the known band prediction sample 2，3 In the case of (3), the conversion relation m is obtained, and the relation conversion of T2-r is realized. Taking sample # 9 as an example, T thereof 2，2，3 1.22ms, calculating m to be 59.63nm/ms, and converting into the formula:
r＝59.63×T 2
(14) pore structure characterization (conversion) of NMR T2 spectra
Nmr T2 spectra using sample # 9 and r ═ rn × T for intervals one and two 2 n Equation (see step 13), the pore size of the test sample is obtained. FIG. 8 shows the effect of the transformation of the relationship T2-r performed according to the procedure described above for sample # 9. As can be seen from FIG. 8, the accurate conversion of T2-r can be realized by using the method, so that the nuclear magnetic resonance test result has excellent matching with the conventional test result.
Claims (7)
1. A shale pore structure determination method, the method comprising the steps of:
providing a sample to be tested and a modeling sample set derived from different shale cores, wherein the modeling sample set comprises a combination of a plurality of particle samples and a plurality of corresponding plunger samples;
measuring the plurality of particle samples based on a fluid injection method to obtain their respective cumulative pore volume versus pore diameter curves;
measuring the sample to be measured and the plurality of plunger samples based on a nuclear magnetic resonance method to obtain respective curves capable of reflecting the change of accumulated nuclear magnetic signal quantity of the effective pores along with transverse relaxation time;
respectively converting the cumulative pore volume change curve of the particle samples along with the pore diameter and the cumulative nuclear magnetic signal quantity change curves of the sample to be detected and the plunger samples along with the transverse relaxation time into a cumulative pore volume fractal characteristic curve and a cumulative nuclear magnetic signal quantity fractal characteristic curve by using a fractal geometric model;
extracting a fractal interval, a critical aperture, and the cumulative pore volume and fractal dimension of each fractal interval from the cumulative pore volume fractal characteristic curves of the particle samples as fractal characteristic parameters of the particle samples, and extracting the fractal interval, the critical transverse relaxation time, and the cumulative nuclear magnetic signal volume and the fractal dimension of each fractal interval from the cumulative nuclear magnetic signal volume fractal characteristic curves of the sample to be detected and the plunger samples as fractal characteristic parameters of the particle samples;
determining one or more correspondence intervals of the cumulative pore volume fractal characteristic curves of the plurality of particle samples and the cumulative nuclear magnetic semaphore fractal characteristic curves of the plurality of plunger samples;
determining a linear relationship between accumulated pore volume and accumulated nuclear magnetic signal quantity, a linear relationship between critical pore diameter and critical transverse relaxation time and a linear relationship between fractal dimension of the plunger samples by using fractal characteristic parameters of the plunger samples and fractal characteristic parameters of the particle samples in each corresponding interval;
determining the relation between the pore diameter and the transverse relaxation time of the sample to be detected in each corresponding interval by utilizing the linear relation between the accumulated pore volumes and the accumulated nuclear magnetic signal quantities of the plurality of particle samples and the plurality of plunger samples, the linear relation between the critical pore diameter and the critical transverse relaxation time and the linear relation between the fractal dimensions thereof, and the fractal dimension and the critical transverse relaxation time of the sample to be detected; and
and in each corresponding interval, utilizing the relation between the aperture and the transverse relaxation time of the sample to be detected to convert the transverse relaxation time of the sample to be detected into the aperture in a segmented manner.
2. The method of claim 1, wherein the step of measuring the sample to be tested and the plurality of plunger samples based on nmr to obtain their respective cumulative nmr signal versus transverse relaxation time curves comprises:
measuring the sample to be measured and the plurality of plunger samples in a saturated water treatment state and a drying treatment state by a nuclear magnetic resonance method to obtain respective transverse relaxation time T2 spectrums thereof;
obtaining nuclear magnetic signal intensity curves of the sample to be detected and the plunger samples in a saturated water treatment state and a dry treatment state respectively by utilizing transverse relaxation time T2 spectrums of the plunger samples;
performing difference operation on the nuclear magnetic signal intensity curves of the sample to be detected and the plunger samples in a saturated water treatment state and a drying treatment state to obtain respective nuclear magnetic signal intensity difference curves capable of reflecting effective pores; and
and obtaining the change curves of the accumulated nuclear magnetic signal quantity reflecting the effective pore along with the transverse relaxation time of the sample to be detected and the plunger samples by calculating the nuclear magnetic signal intensity difference curves of the sample to be detected and the plunger samples.
3. The method of claim 1 or 2, wherein the linear relationship between the cumulative pore volume and the cumulative nuclear magnetic semaphore is represented by the formula:
V i ＝a 1 ·Q i +b 1
wherein V i Denotes the cumulative pore volume, Q, in the correspondence interval i i To representThe cumulative nuclear magnetic signal amount in the correspondence interval i, and a 1 And b 1 Is a constant term;
the linear relationship between the critical aperture and the critical transverse relaxation time is represented by the following equation:
r i，i+1 ＝a 2 ·T 2(i，i+1) +b 2
wherein r is i，i+1 Denotes the critical aperture, T, between the correspondence interval i and the correspondence interval i +1 2(i，i+1) Represents a critical transverse relaxation time between the correspondence interval i and the correspondence interval i +1, and a 2 And b 2 Is a constant term;
a linear relationship between fractal dimensions of the plurality of particle samples and the plurality of plunger samples is represented by:
D inv，i ＝c·D nmr，i +d
wherein D inv，i Representing the fractal dimension, D, of each particle sample within the correspondence interval i nmr，i Representing the fractal dimension of each plunger sample within the correspondence interval i, and c and d are constant terms; and is
The relationship between the pore diameter and the transverse relaxation time of the sample to be measured is represented by the following formula:
r＝m·T 2 n
wherein r represents the pore diameter, T 2 Representing the transverse relaxation time, m and n are respectively the transformation coefficients defined as:
wherein D nmr Is the fractal dimension of a sample to be detected in the corresponding interval i, and c and d are constant terms defined above; and T 2，max Is the maximum transverse relaxation time, a, of the sample to be measured within the corresponding interval i 2 And b 2 Is a constant term as defined above.
4. The method of claim 3, wherein the fractal dimension D when each particle sample is within the correspondence interval i inv，i Fractal dimension D of each plunger sample in corresponding interval i nmr，i When equal or close, the relationship between the pore diameter and the transverse relaxation time of the sample to be tested is represented by the following formula:
r＝m·T 2
wherein r represents the pore diameter, T 2 Representing the transverse relaxation time, m is a conversion coefficient defined as:
wherein T is 2，min Is the minimum transverse relaxation time of the sample to be tested in the corresponding interval i.
5. The method of claim 1 or 2, wherein the fractal geometric model for transforming the cumulative pore volume versus pore diameter curve is represented by the following equation:
log V＝(3-D inv )·log(r)+(D inv -3)·log(r max )-log(V t )
wherein r represents the pore diameter, V represents the cumulative pore volume of pores having a pore diameter less than r, r max Denotes the maximum pore diameter, V t Denotes a pore diameter smaller than r max Cumulative pore volume of, and D inv A fractal dimension representing a cumulative pore volume fractal characteristic curve; and is
The fractal geometric model for converting the curve of the accumulated nuclear magnetic semaphore along with the transverse relaxation time is represented by the following formula:
log Q＝(3-D nmr )·log(T 2 )+(D nmr -3)·log(T 2max )-log(Q t )
wherein Q represents a relaxation time less than T in the transverse direction 2 The accumulated semaphore, T, in the interval (b) of 2 Representing transverse relaxation time, T 2，max Denotes the maximum transverse relaxation time, Q t Denotes a relaxation time less than T in the transverse direction 2，max And D is the cumulative semaphore of the section (b) of nmr The fractal dimension of the fractal characteristic curve of the nuclear magnetic semaphore is accumulated.
6. The method of claim 1 or 2, wherein the step of determining one or more correspondence intervals of the cumulative pore volume fractal characteristic curve of the plurality of particle samples and the cumulative nuclear magnetic semaphore fractal characteristic curve of the plurality of plunger samples comprises:
determining a linear relationship between the cumulative pore volume of the plurality of particle samples and the cumulative nuclear magnetic signal volume of the plurality of plunger samples by a linear regression method in different independent or combined fractal intervals;
determining an independent or combined fractal interval corresponding to the linear relationship having the largest fitting coefficient from among fitting coefficients of linear relationships between the cumulative pore volumes of the plurality of particle samples and the cumulative nuclear magnetic signal volumes of the plurality of plunger samples in different independent or combined fractal intervals as a corresponding interval; and
the remaining correspondence intervals are determined in the same manner.
7. The method of claim 1 or 2, wherein the step of measuring the plurality of particle samples based on a fluid injection method to obtain their respective cumulative pore volume versus pore diameter curves comprises:
by N 2 Isothermal adsorption method, CO 2 Respectively measuring each particle sample by an isothermal adsorption method and a mercury pressing method to obtain a pore size distribution curve with different pore size distribution ranges of each particle sample;
obtaining a pore size distribution curve with a full pore size distribution range of each particle sample by a full pore size characterization method using the pore size distribution curve with a different pore size distribution range of the particle sample; and
the cumulative pore volume as a function of pore diameter was obtained by calculation from the pore diameter distribution curve having the full pore diameter distribution range for each particle sample.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116296946A (en) * | 2023-03-06 | 2023-06-23 | 中国矿业大学(北京) | Shale stratum development degree characterization method and device based on fractal-fluctuation theory |
| CN116465815A (en) * | 2023-06-20 | 2023-07-21 | 浙江大学海南研究院 | Soil mass macro-micro pore structure fractal method and system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103884633A (en) * | 2014-03-05 | 2014-06-25 | 中国石油天然气股份有限公司 | Method and device for determining rock permeability |
| CN106290103A (en) * | 2015-06-12 | 2017-01-04 | 中国石油化工股份有限公司 | The assay method of clay micropore degree in a kind of shale gas reservoir |
| CN108169099A (en) * | 2018-01-17 | 2018-06-15 | 西南石油大学 | A kind of shale gas RESERVOIR PORE STRUCTURE quantitative calculation method based on nuclear magnetic resonance |
| CN108458960A (en) * | 2018-03-27 | 2018-08-28 | 中国石油大学(华东) | A kind of hydrogeneous component of richness organic matter mud shale, porosity and the evaluation method in aperture |
| WO2018195646A1 (en) * | 2017-04-26 | 2018-11-01 | Green Imaging Technologies, Inc. | Methods of nuclear magnetic resonance measurement of crushed porous media |
| CN111624146A (en) * | 2019-02-28 | 2020-09-04 | 中国石油化工股份有限公司 | Method for quantitatively characterizing full-scale pore size distribution characteristics of compact reservoir |
| CN112284999A (en) * | 2020-10-26 | 2021-01-29 | 中国石油大学(华东) | Sandstone pore size distribution determination method and application thereof |
| CN112378943A (en) * | 2020-11-30 | 2021-02-19 | 中国石油大学(华东) | Shale oil saturation evaluation model, evaluation method and application |
-
2022
- 2022-05-05 CN CN202210483341.3A patent/CN115078210B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103884633A (en) * | 2014-03-05 | 2014-06-25 | 中国石油天然气股份有限公司 | Method and device for determining rock permeability |
| CN106290103A (en) * | 2015-06-12 | 2017-01-04 | 中国石油化工股份有限公司 | The assay method of clay micropore degree in a kind of shale gas reservoir |
| WO2018195646A1 (en) * | 2017-04-26 | 2018-11-01 | Green Imaging Technologies, Inc. | Methods of nuclear magnetic resonance measurement of crushed porous media |
| CN108169099A (en) * | 2018-01-17 | 2018-06-15 | 西南石油大学 | A kind of shale gas RESERVOIR PORE STRUCTURE quantitative calculation method based on nuclear magnetic resonance |
| CN108458960A (en) * | 2018-03-27 | 2018-08-28 | 中国石油大学(华东) | A kind of hydrogeneous component of richness organic matter mud shale, porosity and the evaluation method in aperture |
| WO2019184429A1 (en) * | 2018-03-27 | 2019-10-03 | 中国石油大学(华东) | Evaluation method for hydrogen containing ingredient, porosity and aperture of shale rich in organic matters |
| CN111624146A (en) * | 2019-02-28 | 2020-09-04 | 中国石油化工股份有限公司 | Method for quantitatively characterizing full-scale pore size distribution characteristics of compact reservoir |
| CN112284999A (en) * | 2020-10-26 | 2021-01-29 | 中国石油大学(华东) | Sandstone pore size distribution determination method and application thereof |
| CN112378943A (en) * | 2020-11-30 | 2021-02-19 | 中国石油大学(华东) | Shale oil saturation evaluation model, evaluation method and application |
Non-Patent Citations (5)
| Title |
|---|
| 俞雨溪;王宗秀;张凯逊;程明;: "流体注入法定量表征页岩孔隙结构测试方法研究进展", 地质力学学报 * |
| 俞雨溪等: "流体注入法定量表征页岩孔隙结构测试方法研究进展" * |
| 孟昆: "多重分形理论在复杂储层有效性评价中的应用研究" * |
| 张鹏飞: "基于核磁共振技术的页岩油储集、赋存与可流动性研究" * |
| 李志清;孙洋;胡瑞林;赵颖;彭宇;: "基于核磁共振法的页岩纳米孔隙结构特征研究", 工程地质学报 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116296946A (en) * | 2023-03-06 | 2023-06-23 | 中国矿业大学(北京) | Shale stratum development degree characterization method and device based on fractal-fluctuation theory |
| CN116296946B (en) * | 2023-03-06 | 2023-08-11 | 中国矿业大学(北京) | Shale stratum development degree characterization method and device based on fractal-fluctuation theory |
| CN116465815A (en) * | 2023-06-20 | 2023-07-21 | 浙江大学海南研究院 | Soil mass macro-micro pore structure fractal method and system |
| CN116465815B (en) * | 2023-06-20 | 2023-09-01 | 浙江大学海南研究院 | Soil mass macro-micro pore structure fractal method and system |
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