A shale reservoir full-scale micro-pore throat structure characterization method

Abstract

The present application belongs to the technical field of oil and gas field development, and discloses a shale reservoir full-scale micro-pore throat structure characterization method. The method comprises the following steps: sample preparation scanning is performed to obtain millimeter-level sub-sample CT pore throat images and micron-level sub-sample FIB-SEM pore throat images; image eigenvalues are calculated, cluster analysis is performed to obtain the distribution, mean value and variance of image gray scale, a pore diameter value D used for splicing the pore diameter distributions of the two images is determined, an image intensity gray scale judgment threshold is determined, CT porosity and FIB-SEM porosity are calculated, and micron and nanometer scale three-dimensional ball stick models are constructed; the pore diameter distributions of the micron scale three-dimensional ball stick model in which the pore diameter is greater than D and the nanometer scale three-dimensional ball stick model in which the pore diameter is less than or equal to D are spliced to construct a full-scale pore diameter distribution physical model; and a mathematical characterization model is constructed. The present application can obtain full-scale pore diameter distribution characteristics of a shale sample, clearly determine the corresponding unit sample quality pore volume of different scale pores and the proportion of different pore diameters, and is simple in operation and calculation and efficient in method.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of oil and gas field development, and particularly relates to a shale reservoir full-scale micro-pore throat structure characterization method. BACKGROUND

[0002] The pore size distribution span of shale reservoir core is very large, including micro-pores (pore diameter < 2 nm), mesopores (2 nm ≤ pore diameter ≤ 50 nm) and macro-pores (pore diameter > 50 nm), and accurate acquisition of shale full-scale pore size distribution characteristics is the basis of micro-pore reservoir evaluation, which has important guiding significance for studying shale oil and gas occurrence regularity.

[0003] At present, the test means for studying shale sample pore size distribution mainly include low-temperature carbon dioxide adsorption, low-temperature nitrogen adsorption, high-pressure mercury injection and small-angle neutron scattering technology. Among them, the pore size range of low-temperature carbon dioxide adsorption test is micro-pore, the pore size range of low-temperature nitrogen adsorption test is mesopore and part of macro-pore, the pore size range of high-pressure mercury injection test is mesopore and macro-pore, and the small-angle neutron scattering technology can measure the core with a pore diameter less than 100 nm.

[0004] In order to characterize the full-scale micro-pore throat structure of shale reservoir, the prior art mainly takes low-temperature carbon dioxide adsorption test, low-temperature nitrogen adsorption test and high-pressure mercury injection test as the experimental basis, the first pore size distribution data of the sample are measured by low-temperature gas adsorption method on a sample, the second pore size distribution data of the sample are obtained by performing corresponding high-pressure mercury injection method test on the parallel sample, the overlapping pore diameter is determined, and the first pore size distribution and the second pore size distribution are spliced, and the proportion of micro-pore, mesopore and macro-pore in the shale sample is obtained by using the obtained data, and the full-scale pore size distribution data of the measured shale sample are obtained. However, the technical scheme has the following defects:

[0005] (1) When combining multiple test methods, the test principles of different test methods are different, the data difference is large when splicing, and the measurement result is inaccurate;

[0006] (2) Since the core has heterogeneity, gas adsorption and mercury injection are not non-destructive experiments, therefore, the existing test method needs to set parallel samples (adjacent samples of the same depth drilled from the same full-diameter core) for gas test and mercury injection test, and the data accuracy is poor. SUMMARY

[0007] The present application aims to solve at least one of the technical problems existing in the prior art or related art, and provides a shale reservoir full-scale micro-pore throat structure characterization method, which can acquire the full-scale pore size distribution characteristics of shale sample, clearly determine the pore volume per unit sample mass corresponding to different scale pores, and then calculate the proportion of different pore diameters, which is simple in operation and calculation, and efficient in method.

[0008] To achieve the above technical purposes, the present application adopts the following technical solutions:

[0009] A shale reservoir full-scale micro-pore throat structure characterization method, the method specifically comprises the following steps:

[0010] Step S1: sample preparation scanning;

[0011] Select a shale sample to drill a millimeter-level sub-sample, and obtain a CT pore throat image by CT scanning;

[0012] Mill out a micron-level sub-sample from the millimeter-level sub-sample, and obtain a FIB-SEM pore throat image by FIB-SEM scanning;

[0013] Step S2: digital three-dimensional model reconstruction;

[0014] Calculate the eigenvalue of each image, and obtain the distribution, mean and variance of the gray scale of each class of image by cluster analysis;

[0015] According to the image gray scale distribution frequency, establish the distribution relationship between the CT pore throat image and the FIB-SEM pore throat image, and determine the pore size value D for splicing the CT pore throat image and the FIB-SEM pore throat image;

[0016] According to the image gray scale variance, determine the threshold value for image intensity judgment, and according to the threshold value, distinguish the pore throat structure of the image and calculate the CT porosity and the FIB-SEM porosity;

[0017] Import the image into three-dimensional visualization software, and construct micron-scale three-dimensional ball-stick models and nanometer-scale three-dimensional ball-stick models according to the CT porosity and the FIB-SEM porosity, respectively;

[0018] Step S3: splice and construct a full-scale pore size distribution physical model;

[0019] Physically splice the pore size distribution of the micron-scale three-dimensional ball-stick model and the pore size distribution of the nanometer-scale three-dimensional ball-stick model, and construct a full-scale pore size distribution physical model;

[0020] Step S4: based on the characteristic parameters in the full-scale pore size distribution physical model, construct a full-scale pore size distribution mathematical characterization model.

[0021] Further, in step S1, the millimeter-level sub-sample is a cylindrical sub-sample.

[0022] Further, the step S2 specifically comprises:

[0023] Step S201: normalize the image data of the CT pore throat image and the FIB-SEM pore throat image;

[0024] Step S202: sequentially calculate the feature value of each image based on the machine learning algorithm, use the K-means algorithm to perform cluster analysis on the image, and calculate the distribution, mean and variance of the gray scale of each class of image;

[0025] Step S203: according to the image gray scale distribution frequency, establish the distribution relationship between the CT pore throat image and the FIB-SEM pore throat image, and determine the pore size value D for pore size distribution splicing;

[0026] According to the image gray scale variance calculation result, determine the threshold value;

[0027] Step S204: determine the pore throat structure according to the threshold value:

[0028] If the image intensity is less than or equal to the threshold value, the pore throat structure of the corresponding image is a pore;

[0029] If the image intensity is greater than the threshold value, the pore throat structure of the corresponding image is a matrix;

[0030] Step S205: calculate the CT porosity and FIB-SEM porosity according to the pores extracted from the image;

[0031] Step S206: compare the calculated CT porosity, FIB-SEM porosity with the experimentally measured helium porosity, and adjust the size of the threshold value according to the comparison result;

[0032] According to the updated threshold value, repeat steps S204-S205, recalculate and update the CT porosity and FIB-SEM porosity until the comparison result of the finally calculated and updated CT porosity, FIB-SEM porosity and the experimentally measured helium porosity is within the set error range;

[0033] Step S207: import the CT pore throat image and the FIB-SEM pore throat image into the three-dimensional visualization software;

[0034] According to the CT porosity, construct a micron-scale three-dimensional ball-stick model which can quantitatively represent the pore throat parameters;

[0035] According to the FIB-SEM porosity, construct a nanometer-scale three-dimensional ball-stick model which can quantitatively represent the pore throat parameters.

[0036] Further, in step S203, the threshold value is determined according to the image gray scale variance calculation result, specifically:

[0037] The image intensity corresponding to the minimum image gray scale variance is the threshold value.

[0038] Further, in the step S206, the calculated CT porosity, FIB-SEM porosity and the experimentally measured helium porosity are compared, and the size of the threshold value is adjusted according to the comparison result, specifically including:

[0039] According to the experimentally measured helium porosity of the shale sample to be measured;

[0040] The calculated CT porosity, FIB-SEM porosity and the experimentally measured helium porosity are compared to determine whether the selected threshold value is appropriate:

[0041] If the comparison result does not exceed the set error range, the selected threshold value is appropriate;

[0042] If the comparison result exceeds the set error range, the threshold value is adjusted and updated, and the specific adjustment method is:

[0043] If the porosity calculated according to the threshold value is greater than the helium porosity, the threshold value is reduced, and vice versa.

[0044] Further, in the step S4, the characteristic parameters in the full-scale pore size distribution physical model include pore size and distribution frequency.

[0045] Further, in the step S4, the full-scale pore size distribution mathematical representation model includes:

[0046] The shale pore structure type proportion calculation formula and the shale pore structure type gray feature calculation formula.

[0047] Further, the shale pore structure type proportion calculation formula is:

[0048]

[0049] In the above formula, φ i represents the proportion of i-type pore structure; N i represents the number of i-type pore structure; represents the sum of the number of all types of pore structure.

[0050] Further, the gray features of different typical pore structure types in shale include the gray mean of different types of pore structure and the gray standard deviation of different types of pore structure.

[0051] Further, the gray mean calculation formula of different types of pore structure is:

[0052]

[0053] The gray standard deviation calculation formula of different types of pore structure is:

[0054]

[0055] In the formula, GA i represents the gray mean value of the i-type pore structure; represents the gray mean value of the kth i-type pore structure; N i represents the number of the i-type pore structure; GS i represents the gray standard deviation of the i-type pore structure.

[0056] Further, in the step S2, the pore throat image of the CT and the pore throat image of the FIB-SEM are spliced by using the pore diameter value D = 500-700nm.

[0057] Compared with the prior art, the present application has the following beneficial effects:

[0058] (1) The shale reservoir full-scale micro-pore throat structure characterization method provided by the present application can obtain the full-scale pore size distribution characteristics of the shale sample, and can determine the corresponding pore volume per unit sample mass of different scales, and then calculate the proportion of different pore sizes, which is simple in operation and calculation, and efficient in method.

[0059] (2) The shale reservoir full-scale micro-pore throat structure characterization method provided by the present application can calculate the characteristic value of each image in turn based on the machine learning algorithm, and can perform clustering analysis on the image by using the K-means algorithm, and can determine the pore diameter value D used for splicing the pore size distribution of the CT and FIB-SEM images by calculating and calculating the distribution, mean and variance of the gray scale of each class according to the image gray scale distribution frequency, and can determine the threshold value for image intensity judgment according to the image gray variance, and can determine the pore throat structure of the image and calculate the CT porosity and FIB-SEM porosity according to the threshold value, and finally, the threshold value can be verified and adjusted by means of helium porosity to obtain a more reasonable threshold value (since the selection range of the threshold value in the prior art is large, and the rationality thereof cannot be determined), and the pore throat structure and porosity more accurate and closer to the actual situation can be calculated according to the adjusted threshold value, so as to ensure the accuracy of the full-scale pore size distribution physical model obtained by finally splicing, and improve the accuracy of the prediction result.

[0060] (3) The shale reservoir full-scale micro-pore throat structure characterization method provided by the present application can meet the demand for full-scale characterization of the pore size of the same shale sample, and the present application can be measured at room temperature without using parallel samples, and the measurement conditions and sample form are not limited. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 A flow chart of a shale reservoir full-scale micro-pore throat structure characterization method in the embodiment of the present application;

[0062] Figure 2 A micron-scale three-dimensional ball-stick model structure schematic diagram in the embodiment of the present application;

[0063] Figure 3 A nanometer-scale three-dimensional ball-stick model structure schematic diagram in the embodiment of the present application;

[0064] Figure 4 A full-scale pore diameter distribution curve diagram of a measured shale sample in the embodiment of the present application;

[0065] Figure 5 A full-scale throat diameter distribution curve diagram of a measured shale sample in the embodiment of the present application. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0067] In combination with Figure 1 The embodiment of the present application provides a shale reservoir full-scale micro-pore throat structure characterization method, mainly by constructing a micron-scale three-dimensional ball-stick model and a nanometer-scale three-dimensional ball-stick model, respectively obtaining pore size distribution above 600 nm and below 600 nm, splicing to establish a full-scale pore size distribution physical model, and constructing a full-scale pore size distribution mathematical characterization model according to the characteristic parameters (pore size, distribution frequency) of the full-scale pore size distribution physical model, so as to obtain full-scale pore size distribution characteristics of a shale sample, and to determine the pore volume per unit sample mass corresponding to different scale pores, and then to calculate the proportion occupied by different pore sizes, which is simple in operation and calculation, and efficient in method.

[0068] The method specifically includes the following steps:

[0069] Step S1: sample preparation scanning;

[0070] A shale sample is selected, a cylindrical millimeter-level sub-sample with a diameter and length of 3 mm is first drilled, and a CT pore throat image is obtained by CT scanning;

[0071] A micron-level sub-sample is milled from the millimeter-level sub-sample, and a FIB-SEM pore throat image is obtained by FIB-SEM scanning;

[0072] Step S2: digital three-dimensional model reconstruction;

[0073] Step S201: normalize the image data of the CT pore throat image and the FIB-SEM pore throat image, so that the gray value is between 0-255, to facilitate subsequent calculation;

[0074] Step S202: based on the machine learning algorithm, the characteristic value of each image is calculated in turn, the K-means algorithm is used for cluster analysis of the image, and the distribution, mean and variance of the gray value of each class of image are calculated;

[0075] Step S203: according to the image gray scale distribution frequency, the distribution relationship of the CT pore throat image and the FIB-SEM pore throat image is established, and the pore size value D used for pore size distribution splicing is determined, which is used for subsequent physical model;

[0076] Determine the threshold value according to the image gray scale variance calculation result;

[0077] Step S204: determine the pore throat structure according to the threshold value:

[0078] If the image intensity is less than or equal to the threshold value, the pore throat structure of the corresponding image is a pore;

[0079] If the image intensity is greater than the threshold value, the pore throat structure of the corresponding image is a matrix;

[0080] Step S205: calculate the CT porosity and FIB-SEM porosity according to the pores extracted from the image;

[0081] Step S206: compare the calculated CT porosity, FIB-SEM porosity with the experimentally measured helium porosity, and adjust the size of the threshold value according to the comparison result;

[0082] According to the updated threshold value, repeat steps S204-S205, recalculate and update the CT porosity and FIB-SEM porosity, until the comparison result of the finally calculated and updated CT porosity, FIB-SEM porosity and the experimentally measured helium porosity is within the set error range;

[0083] Wherein, the size of the threshold value is adjusted according to the comparison result, which specifically includes:

[0084] According to the experimentally measured helium porosity of the shale sample to be measured;

[0085] Compare the calculated CT porosity, FIB-SEM porosity with the experimentally measured helium porosity, and judge whether the selected threshold value is appropriate:

[0086] If the comparison result is within the set error range, the selected threshold value is appropriate;

[0087] If the comparison result exceeds the set error range, the update threshold is adjusted, and the specific adjustment method is:

[0088] If the porosity calculated according to the threshold is greater than the helium porosity, the threshold is reduced, and vice versa.

[0089] Step S207: Import the CT pore throat image and the FIB-SEM pore throat image into three-dimensional visualization software;

[0090] According to the CT porosity, a micron-scale three-dimensional ball-stick model capable of quantitatively representing pore throat parameters is constructed, as shown in Figure 2 ;

[0091] According to the FIB-SEM porosity, a nanometer-scale three-dimensional ball-stick model capable of quantitatively representing pore throat parameters is constructed, as shown in Figure 3 ;

[0092] Step S3: Splice to build a full-scale pore size distribution physical model;

[0093] In the micron-scale three-dimensional ball-stick model, the pore size distribution of >600nm in the millimeter-level sub-sample is obtained by screening;

[0094] In the nanometer-scale three-dimensional ball-stick model, the pore size distribution of ≤600nm in the micron-level sub-sample is obtained by screening;

[0095] The pore size distribution of >600nm and the pore size distribution of ≤600nm are physically spliced to obtain a full-scale pore size distribution physical model;

[0096] As shown in Figure 4 , the full-scale pore diameter distribution curve of the tested shale sample is shown, which includes three types of pore structure, i.e., siltstone, litharenite and mudstone; Figure 5 The full-scale throat diameter distribution curve of the tested shale sample is shown;

[0097] Step S4: Construct a mathematical representation model of the full-scale pore size distribution;

[0098] Based on the pore size and distribution frequency in the full-scale pore size distribution physical model, a mathematical representation model of the full-scale pore size distribution is constructed, which includes a shale different pore structure type proportion calculation formula and a shale different pore structure type gray scale feature calculation formula.

[0099] The shale different pore structure type proportion calculation formula is:

[0100]

[0101] In the above formula (1), φi represents the proportion of i-type pore structure; N i represents the number of i-type pore structure; represents the total number of all types of pore structure;

[0102] The gray scale characteristics of different pore structure types in shale include: the gray scale mean value of different types of pore structure and the gray scale standard deviation of different types of pore structure.

[0103] The gray scale mean value of different types of pore structure is calculated according to the following formula:

[0104]

[0105] The gray scale standard deviation of different types of pore structure is calculated according to the following formula:

[0106]

[0107] In the above formula (2)-(3), GA i represents the gray scale mean value of i-type pore structure; represents the gray scale mean value of the kth i-type pore structure; N i represents the number of i-type pore structure; GS i represents the gray scale standard deviation of i-type pore structure.

[0108] The proportion and gray scale characteristics of different typical pore structure types in shale can be calculated according to the calculation formulas (1)-(3), and the calculation results are shown in Table 1.

[0109] Table 1: Calculation results of the proportion and gray scale characteristics of different typical pore structure types in shale

[0110] Type Microstructure attribute description Proportion (%) Mean gray value Gray variance 1 Dissolution type inorganic matter pore 18.63 183.61 12.27 2 Intergranular type inorganic matter pore 42.05 141.57 17.05 3 Migration organic matter and organic matter pore 29.13 114.79 13.41 4 Intragranular type inorganic matter pore 6.41 132.47 16.23 5 Primary organic matter and organic matter pore 3.78 31.28 11.69

[0111] The single scale and multi-scale analysis of the multi-resolution and multi-dimensional shale micro images can objectively, comprehensively and accurately reveal the pore structure characteristics of shale. In the embodiment of the present application, it is known through analysis that the pore structure characteristics of the measured shale sample are as follows: the pores with a pore radius less than 50 nm are mainly organic matter pores, the pores with a pore radius between 50-500 nm are organic matter pores and inorganic matter pores, and the pores with a pore radius greater than 500 nm are mainly contributed by micro cracks.

[0112] At the same time, based on the mean value and variance of different types of gray scale after classification, the type mode of the image is recognized in space, and combined with different types of ultra-high resolution CT pore throat images and FIB-SEM pore throat images, the fusion of multi-resolution and multi-dimensional shale samples can be realized.

[0113] The above merely illustrates the embodiments of the present application, but should not be used to limit the present application. Any modification, equivalent replacement, and improvement within the scope of the present application should be included in the protection scope of the present application.

Claims

1. A method for full-scale micro-pore throat structure characterization of shale reservoirs, characterized in that, The method specifically comprises the following steps: Step S1: sample preparation scanning; A millimeter-level sub-sample is selected from a shale sample, and a CT pore throat image is obtained by CT scanning; A micron-level sub-sample is milled from the millimeter-level sub-sample, and a FIB-SEM pore throat image is obtained by FIB-SEM scanning; Step S2: digital three-dimensional model reconstruction; The eigenvalue of each image is calculated, and the distribution, mean and variance of the gray scale of each type of image are obtained by cluster analysis; According to the image gray scale distribution frequency, the distribution relationship between the CT pore throat image and the FIB-SEM pore throat image is established, and the pore diameter value D used for pore diameter distribution splicing is determined; According to the image gray scale variance, the threshold value for image intensity judgment is determined, the pore throat structure of the image is distinguished according to the threshold value, and the CT porosity and the FIB-SEM porosity are calculated; The image is imported into a three-dimensional visualization software, and a micron-scale three-dimensional ball-stick model and a nanometer-scale three-dimensional ball-stick model are constructed according to the CT porosity and the FIB-SEM porosity respectively; Step S3: splicing to construct a full-scale pore diameter distribution physical model; The pore diameter distribution of the micron-scale three-dimensional ball-stick model greater than D and the pore diameter distribution of the nanometer-scale three-dimensional ball-stick model less than D are physically spliced to construct a full-scale pore diameter distribution physical model; Step S4: based on the characteristic parameters in the full-scale pore diameter distribution physical model, a full-scale pore diameter distribution mathematical representation model is constructed.

2. The method of claim 1, wherein, In step S1, the millimeter-level sub-sample is a cylindrical sub-sample.

3. The method of claim 1, wherein, The step S2 specifically comprises: Step S201: normalizing the image data of the CT pore throat image and the FIB-SEM pore throat image; Step S202: based on a machine learning algorithm, the eigenvalue of each image is calculated in turn, the K-means algorithm is used for cluster analysis of the image, and the distribution, mean and variance of the gray scale of each type of image are calculated; Step S203: according to the image gray scale distribution frequency, the distribution relationship between the CT pore throat image and the FIB-SEM pore throat image is established, and the pore diameter value D used for pore diameter distribution splicing is determined; The threshold value is determined according to the image gray scale variance calculation result; Step S204: according to the threshold value, the pore throat structure is distinguished: If the image intensity is less than or equal to the threshold value, the pore throat structure of the corresponding image is a pore; If the image intensity is greater than the threshold value, the pore throat structure of the corresponding image is a matrix; Step S205: according to the extracted pores in the image, the CT porosity and the FIB-SEM porosity are calculated; Step S206: the calculated CT porosity and FIB-SEM porosity are compared with the experimentally measured helium porosity, and the size of the threshold value is adjusted according to the comparison result; According to the updated threshold value, steps S204-S205 are repeated to recalculate and update the CT porosity and the FIB-SEM porosity until the comparison result of the finally calculated and updated CT porosity and FIB-SEM porosity with the experimentally measured helium porosity is within a set error range; Step S207: the CT pore throat image and the FIB-SEM pore throat image are imported into a three-dimensional visualization software; According to the CT porosity, a micron-scale three-dimensional ball-stick model capable of quantitatively representing the pore throat parameters is constructed; According to the FIB-SEM porosity construction, a nanoscale three-dimensional ball-stick model capable of quantitatively characterizing pore throat parameters is obtained.

4. The method of claim 3, wherein, In the step S203, the threshold value is determined according to the image gray variance calculation result, specifically as follows: The image intensity corresponding to the minimum value of the image gray variance is the threshold value.

5. The method of claim 4, wherein, In the step S206, the calculated CT porosity, FIB-SEM porosity and the experimentally measured helium porosity are compared, and the size of the threshold value is adjusted according to the comparison result, specifically including: According to the experimentally measured helium porosity of the shale sample to be measured; The calculated CT porosity, FIB-SEM porosity and the experimentally measured helium porosity are compared to determine whether the selected threshold value is appropriate: If the comparison result is not more than the set error range, the selected threshold value is appropriate; If the comparison result exceeds the set error range, the threshold value is adjusted and updated, and the specific adjustment method is as follows: If the porosity calculated according to the threshold value is greater than the helium porosity, the threshold value is reduced, and vice versa.

6. The method of claim 1, wherein, In the step S4, the characteristic parameters in the full-scale pore size distribution physical model include pore size and distribution frequency.

7. The method of claim 6, wherein, In the step S4, the full-scale pore size distribution mathematical representation model includes: The proportion calculation formula of different pore structure types in shale and the gray feature calculation formula of different pore structure types in shale.

8. The method of claim 7, wherein, The proportion calculation formula of different typical pore structure types in shale is: In the above formula, φ i represents the proportion of i-type pore structure; N i represents the number of i-type pore structure; represents the sum of the number of all types of pore structure.

9. The method of claim 7, wherein, The gray features of different typical pore structure types in shale include the gray mean value of different types of pore structure and the gray standard deviation of different types of pore structure.

10. The method of claim 9, wherein, The gray mean value calculation formula of different types of pore structure is: The gray standard deviation calculation formula of different types of pore structure is: In the above formula, GA i represents the gray mean value of the i-type pore structure; represents the gray mean value of the kth i-type pore structure; N i represents the number of the i-type pore structure; GS i represents the gray standard deviation of the i-type pore structure.

11. The method according to any one of claims 1 to 10, characterized in that, In the step S2, the pore size value D used for the pore size distribution splicing of the CT pore throat image and the FIB-SEM pore throat image is 500-700 nm.

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