Reservoir microscopic connectivity characterization method based on pore throat boundary intelligent identification

Abstract

The invention relates to the technical field of geological reservoirs, in particular to a reservoir microcosmic connectivity characterization method based on pore throat boundary intelligent identification, which comprises the following steps of: performing threshold segmentation processing on a rock casting body slice by using image analysis software, and depicting partition images of pore throats and skeleton particles; based on the partition image, combining image analysis software to measure and extract the diameter, perimeter and area of the pore throat, and calculating quantitative parameters of the pore throat structure; constructing a one-dimensional Gaussian distribution model of the nuclear magnetic logging of the reservoir; based on a one-dimensional Gaussian distribution model, establishing a Gaussian collaborative factor for pore structure evaluation; based on the quantitative parameters of the pore throat structure and the established Gaussian collaborative factors, establishing a multi-factor evaluation mathematical method to establish a multi-element evaluation model of the pore structure; and performing multivariate nonlinear fitting on the porous structure multivariate evaluation model, the nuclear magnetic movable fluid saturation and the reservoir pressure measurement fluidity, and constructing a quantitative characterization model for characterizing the reservoir microscopic connectivity. According to the method, the microscopic connectivity of the reservoir can be accurately, rapidly and quantitatively evaluated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological reservoirs, and more particularly to a reservoir micro-connection characterization method based on intelligent identification of pore throat boundaries. BACKGROUND

[0002] The complexity of pore throat structure directly determines the percolation capacity of offshore strong heterogeneous reservoirs, which has an important influence on oil and gas field development and production. The more diverse the pore throat morphology, the more uneven the distribution size, the more complex the structure, and the stronger the heterogeneity, thereby leading to poorer reservoir properties. At present, the evaluation of reservoir connectivity is usually studied by using a well-seismic combined sand body description method. The well-seismic combined sand body description method mainly has good application effect in places where wells are many and seismic resolution is high, and it is difficult to characterize the internal micro-pore throat distribution of offshore strong heterogeneous reservoirs. Although the evaluation method based on flow units is a combination of macro and micro, it mainly evaluates from the aspect of physical properties and cannot reflect the difference in micro-pore throat distribution inside the reservoir. The above two methods are difficult to accurately and quickly quantitatively evaluate the micro-spatial connectivity of the reservoir. SUMMARY

[0003] The present application aims to overcome the problem in the prior art that the evaluation method is difficult to accurately and quickly quantitatively evaluate the micro-spatial connectivity of the reservoir, and provides a reservoir micro-connection characterization method based on intelligent identification of pore throat boundaries.

[0004] To solve the above technical problems, the technical scheme adopted by the present application is as follows: A reservoir micro-connection characterization method based on intelligent identification of pore throat boundaries, comprising the following steps: using an image analysis software to perform threshold segmentation processing on a rock casting thin section to draw a partition image of pore throats and skeleton particles; based on the partition image, combining the image analysis software to measure and extract pore throat diameter, circumference and area, and calculating pore throat structure quantitative parameters based on the pore throat diameter, circumference and area; constructing a one-dimensional Gaussian distribution model of reservoir nuclear magnetic logging; based on the constructed one-dimensional Gaussian distribution model, establishing a Gaussian synergy factor for pore structure evaluation; based on the obtained pore throat structure quantitative parameters and the established Gaussian synergy factor, using a multi-factor evaluation mathematical method to establish a pore structure multi-element evaluation model based on reservoir micro-parameters; performing multi-element nonlinear fitting on the pore structure multi-element evaluation model, nuclear magnetic movable fluid saturation and reservoir pressure measurement flow rate to construct a quantitative characterization model for characterizing reservoir micro-connection.

[0005] Preferably, the expression of the one-dimensional Gaussian distribution model is as follows:

[0006] Wherein, p is the T2 spectrum distribution of reservoir nuclear magnetic logging; W is the height of the nuclear magnetic curve peak; logT is the logarithmic expression of relaxation time; logμ is the logarithmic expression of the median of the nuclear magnetic curve pore peak; logσ is the standard deviation of the nuclear magnetic curve corresponding to the pore radius.

[0007] Preferably, in the step S4, the expression of the Gaussian synergy factor is as follows: γ=Φ*logμ Wherein, Φ is the effective porosity measured by nuclear magnetic logging, and the unit is %.

[0008] Preferably, the pore throat structure quantitative parameter includes reservoir pore throat sorting coefficient, reservoir pore throat homogeneity coefficient, reservoir pore throat fractal dimension, reservoir pore shape factor, reservoir throat tortuosity and reservoir pore throat coordination number.

[0009] Preferably, the expression of the pore structure multi-evaluation model is as follows: F1=-0.528*SC+0.426*U+0.534*SD-0.563*SF-0.717*G+0.405*CN-0.212*γ F2=0.353*SC-0.241*U-0.795*SD+0.827*SF+0.557*G-0.218*CN+0.453*γ K=(0.514 / 0.959)*F1+(0.445 / 0.959)*F2 Wherein, F1 is the main factor of the multi-evaluation model, F2 is the secondary factor of the multi-evaluation model; K is the pore structure multi-evaluation parameter; SC is the reservoir pore throat sorting coefficient; U is the reservoir pore throat homogeneity coefficient; SD is the reservoir pore throat fractal dimension; SF is the reservoir pore shape factor; G is the reservoir throat tortuosity; CN is the reservoir pore throat coordination number.

[0010] Preferably, the expression of the quantitative characterization model is as follows: λ=0.48977-5.28401*K+0.0598*S movable +10.64324*K 2 -0.0011*S movable 2 Wherein, λ is the reservoir pressure flow rate, and the unit is mD / cP; S movable is the reservoir movable fluid saturation, and the unit is %.

[0011] Preferably, the image analysis software is Image J image analysis software or PCAS image analysis software.

[0012] Preferably, the rock casting thin section is subjected to threshold segmentation processing by using Image J image analysis software, a Canny operator is nested in the Image J image analysis software, and the Canny operator is used to identify the gray value of the rock skeleton particle and the pore throat boundary, so as to delineate the partition image of the pore throat and the skeleton particle.

[0013] Preferably, in the MATLAB software, the pore structure multi-evaluation model, the nuclear magnetic movable fluid saturation and the reservoir pressure measuring flow rate are subjected to multi-nonlinear fitting, and a quantitative characterization model for characterizing the reservoir micro connectivity is constructed.

[0014] Preferably, the quantitative characterization model adopts a mathematical method of non-linear surface fitting of Parabola2D to perform non-linear surface fitting on the pore structure multi-evaluation model and the nuclear magnetic movable fluid saturation.

[0015] Compared with the prior art, the beneficial effects of the present application are: the present application analyzes the pore throat structure quantitative parameters obtained based on the image analysis software, and obtains a pore structure multi-evaluation model of the reservoir micro parameter through the mathematical method of multi-factor evaluation after evaluation, the value calculated by the pore structure multi-evaluation model has a good positive correlation with the reservoir pressure measuring flow rate and the movable fluid saturation, and the greater the value calculated by the pore structure multi-evaluation model, the better the reservoir connectivity. Compared with the prior art, the present application can accurately and quickly quantitatively evaluate the reservoir micro connectivity. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A flow chart of the reservoir micro connectivity characterization method based on intelligent identification of pore throat boundaries of the present application; Figure 2 A rock casting thin section under a microscope in the reservoir micro connectivity characterization method based on intelligent identification of pore throat boundaries of the present application; Figure 3 A partition image in the reservoir micro connectivity characterization method based on intelligent identification of pore throat boundaries of the present application; Figure 4 A multi-dimensional connectivity quantitative evaluation model chart in the reservoir micro connectivity characterization method based on intelligent identification of pore throat boundaries of the present application; Figure 5 A relationship graph between the predicted pressure measuring flow rate and the actually measured pressure measuring flow rate in Example 2 of the reservoir micro connectivity characterization method based on intelligent identification of pore throat boundaries of the present application; Figure 6 A relationship graph between the predicted pressure measuring flow rate and the pore structure multi-evaluation parameter K in the B gas field of the Qiongdongnan Basin in Example 3 of the reservoir micro connectivity characterization method based on intelligent identification of pore throat boundaries of the present application. DETAILED DESCRIPTION

[0017] The present invention will be further described below with reference to specific embodiments.

[0018] Example 1 like Figure 1 As shown, a method for characterizing reservoir micro-connectivity based on intelligent pore-throat boundary identification includes the following steps: S1: Use image analysis software to perform threshold segmentation on thin sections of rock castings to characterize the partitioned images of pore throats and skeletal particles; S2: Based on the partitioned image, the diameter, perimeter and area of ​​the pore throat are measured and extracted using image analysis software, and the quantitative parameters of the pore throat structure are calculated based on the diameter, perimeter and area of ​​the pore throat. S3: Construct a one-dimensional Gaussian distribution model for reservoir nuclear magnetic resonance logging; S4: Establish the Gaussian synergistic factor for pore structure evaluation based on the one-dimensional Gaussian distribution model constructed in step S3; S5: Based on the quantitative parameters of pore throat structure obtained in step S2 and the Gaussian synergistic factor established in step S4, a multivariate evaluation model of pore structure based on reservoir micro parameters is established using a multi-factor evaluation mathematical method. S6: By performing multivariate nonlinear fitting of the pore structure multivariate evaluation model, nuclear magnetic resonance mobile fluid saturation and reservoir pressure measurement mobility, a quantitative characterization model for reservoir micro-connectivity is constructed. This quantitative characterization model can quickly and accurately predict the connectivity of complex oil and gas reservoirs and evaluate the fluid flow capacity in the reservoir.

[0019] It should be noted that in step S1, the rock casting thin section is obtained by cutting and grinding a rock core, then filling and casting it with blue resin. The pore throats are all blue, while the rock grains and minerals are not stained and retain their natural color. The rock casting thin section is a photograph taken after observation under a polarizing microscope, such as... Figure 2 As shown.

[0020] It should also be noted that in step S2, those skilled in the art know that "quantitative parameters of the pore throat structure are calculated based on the pore throat diameter, circumference and area, and then through software plug-ins and empirical formulas", which will not be elaborated in detail here.

[0021] In step S3, the one-dimensional Gaussian distribution model is a commonly used continuous probability distribution function in mathematics and statistics. It has the effect of smoothing the multi-peaked and noisy T2 relaxation time spectrum obtained by nuclear magnetic resonance logging and avoiding local fluctuation interference.

[0022] In step S5, multifactor evaluation is a mathematical method based on the idea of ​​dimensionality reduction. It explores the correlation coefficient matrix between variables to aggregate a complex set of variables into a few independent common factors, so as to reflect the inherent relationship between the original variables, while minimizing the loss of original data information.

[0023] In step S6, reservoir pressure mobility is a parameter obtained during well testing via drill pipe testing (DST) to measure pressure transients. It characterizes the fluid's flow capacity within the formation and reflects reservoir connectivity; higher pressure mobility indicates better connectivity. DST is a dynamic parameter of the formation fluids obtained during drilling by using temporary completion tools to perform short-term pressure recovery tests on the target formation. Reservoir mobile fluid saturation, obtained from nuclear magnetic resonance logging, is an important parameter for reservoir evaluation; higher mobile fluid saturation indicates greater reservoir development potential.

[0024] In step S1, the image analysis software is either ImageJ or PCAS. In step S1, ImageJ is used to perform threshold segmentation on the thin rock casting. The Canny operator is nested within ImageJ to identify the grayscale values ​​of the rock skeleton particles and pore throat boundaries, thus characterizing the partitioned image of the pore throat and skeleton particles. It should be noted that ImageJ is an open-source image analysis software that can be used for porosity system identification and quantitative analysis. It can generate partitioned images based on the grayscale values ​​of rock particles and pore throat boundaries, such as... Figure 3 As shown, the diameter and area of ​​the pore throat were measured and statistically analyzed. The Canny operator can identify subtle differences in pixel grayscale values ​​in a specific region and is a differential operator for detecting image edges. The image showing the formation of pore throat and skeletal particle partitioning is characterized by rock particles being depicted as black, while the pore throat appears white and is automatically segmented, as shown... Figure 3 As shown.

[0025] In step S3, the expression for the one-dimensional Gaussian distribution model is as follows:

[0026] Where p is the T2 spectrum distribution of reservoir NMR logging; W is the height of the NMR curve peak; logT is the logarithmic representation of relaxation time; logμ is the logarithmic representation of the median of the pore peak of the NMR curve; and logσ is the standard deviation of the pore radius corresponding to the NMR curve.

[0027] Furthermore, in step S4, the expression for the Gaussian cooperativity factor is as follows: γ=Φ*logμ wherein, Φ is the effective porosity measured by nuclear magnetic logging, and its unit is %. It should be noted that the Gaussian coordination factor γ is the effective porosity Φ obtained by nuclear magnetic logging, which can describe the reservoir space size and pore throat network development degree of movable fluid in the reservoir, and log μ is the logarithm of the median of the pore peak of the nuclear magnetic curve, which can represent the average radius of the dominant seepage channel, and the product of the two can more comprehensively evaluate the seepage capacity of the reservoir pore, the larger the γ is, the more homogeneous the pore throat structure is, and the better the reservoir seepage performance is.

[0028] In addition, in step S5, the pore throat structure quantitative parameter includes a reservoir pore throat sorting coefficient, a reservoir pore throat homogeneity coefficient, a reservoir pore throat fractal dimension, a reservoir pore shape factor, a reservoir throat tortuosity, and a reservoir pore throat coordination number.

[0029] It should be noted that the reservoir pore throat fractal dimension: the reservoir pore throat fractal dimension can be used to characterize the complexity of the reservoir pore throat structure, the two-dimensional image pore throat fractal dimension distribution range is between 1-2, the closer the fractal dimension is to 2, the more irregular the pore throat structure is, the worse the reservoir property is, and the more complex the reservoir space is. The reservoir pore throat fractal dimension is obtained by plug-in operation on the partition image, and the box counting method in the plug-in is used to output the pore throat fractal dimension.

[0030] Reservoir pore throat sorting coefficient: the reservoir sorting coefficient can reflect the dispersion degree of the reservoir pore throat size, the reservoir pore throat sorting coefficient is distributed between 1-2, the wider the reservoir pore throat diameter distribution range is, the closer the reservoir pore throat sorting coefficient is to 2, the worse the pore throat sorting is, and the stronger the reservoir space heterogeneity is. The reservoir pore throat sorting coefficient is calculated by ascending arrangement and statistics of the pore throat diameter data, and the Trask formula is used to calculate SC= , wherein SC is the sorting coefficient; d75 is the pore throat diameter corresponding to 75% of the proportion; d25 is the pore throat diameter corresponding to 25% of the proportion. Reservoir pore throat homogeneity coefficient: the reservoir pore throat homogeneity coefficient represents the uniformity of the reservoir pore throat size, and the weaker the reservoir pore throat heterogeneity is, the closer the reservoir pore throat homogeneity coefficient is to 1. The reservoir pore throat homogeneity coefficient is obtained by calculating the ratio of the maximum pore throat diameter to the average pore throat diameter in the cast body slice, U= , wherein U is the homogeneity coefficient; d max is the maximum pore throat radius; d ave is the average pore throat diameter.

[0031] Reservoir pore shape factor: the reservoir pore shape factor represents the deviation degree of the reservoir pore cross section from the ideal circular pore throat shape, and the ideal circular reservoir pore shape factor is the maximum value 1. The closer the pore throat cross section is to the ideal circle, the more regular the pore throat shape is, the larger the shape factor is, the more favorable the reservoir pore throat is for fluid to pass through, and the weaker the heterogeneity is. The reservoir pore shape factor is calculated after the length and area of the pore throat cross section are measured by image analysis, SF= where SF is the reservoir pore shape factor; A is the pore throat cross-sectional area; and P is the pore throat cross-sectional perimeter.

[0032] Reservoir throat tortuosity: The reservoir throat tortuosity represents the degree of bending of the reservoir throat, and the ideal straight throat tortuosity is 1. The more curved the throat, the greater the tortuosity, and the stronger the reservoir throat heterogeneity. The tortuosity is calculated based on the cast thin section scanning image by measuring the throat path, and T = where T is the tortuosity; L a is the actual distance of fluid flowing through the throat; and L s is the straight-line distance of fluid flow.

[0033] Reservoir pore throat coordination number: The reservoir pore throat coordination number represents the number of throats connected to the pores in the reservoir. The more throats connected to each pore, the greater the pore throat coordination number, the better the connectivity between the pore throats, and the more conducive to fluid flow. The pore throat coordination number is calculated by locating the pores by the centroid and counting the number of throats connected thereto.

[0034] where, in step S5, the expression of the pore structure multi-evaluation model is as follows: F1=-0.528*SC+0.426*U+0.534*SD-0.563*SF-0.717*G+0.405*CN-0.212*γ F2=0.353*SC-0.241*U-0.795*SD+0.827*SF+0.557*G-0.218*CN+0.453*γ K=(0.514 / 0.959)*F1+(0.445 / 0.959)*F2 where F1 is the primary factor of the multi-evaluation model, F2 is the secondary factor of the multi-evaluation model; K is the pore structure multi-evaluation parameter; SC is the reservoir pore throat sorting coefficient; U is the reservoir pore throat homogeneity coefficient; SD is the reservoir pore throat fractal dimension; SF is the reservoir pore shape factor; G is the reservoir throat tortuosity; and CN is the reservoir pore throat coordination number.

[0035] In addition, in step S6, the expression of the quantitative characterization model is as follows: λ=0.48977-5.28401*K+0.0598*S movable +10.64324*K 2 -0.0011*S movable 2 where λ is the reservoir pressure-matching mobility, in units of mD / cP; S movable is the reservoir mobile fluid saturation, in units of %.

[0036] In step S6, the pore structure multi-evaluation model, the NMR movable fluid saturation and the reservoir pressure flow rate are nonlinearly fitted in MATLAB software to construct a quantitative characterization model for representing the reservoir micro-connection.

[0037] In addition, in step S6, the quantitative characterization model uses the mathematical method of nonlinear surface fitting of Parabola2D to nonlinearly fit the pore structure multi-evaluation model and the NMR movable fluid saturation, so as to accurately predict the pressure flow rate, as shown in FIG. 4. Figure 4

[0038] Example 2 The pore throat structure of the Huangliu Formation reservoir in the Dongfang X1 gas field is complex. The rock cast thin section image is grayed by using Image J, and the rock particle and pore throat boundary value are identified by using Canny operator. In combination with the pore throat zoning image, the pore throat diameter, circumference, area and other parameters measured by the software are obtained, and six pore throat structure parameter values are obtained by using the calculation formula, as shown in Table 1. Among them, the fractal dimension is distributed in 1.20-1.33, the sorting coefficient is distributed in 1.10-1.86, the homogeneity coefficient is distributed in 1.50-1.95, the shape factor is 0.3866-0.5153, the tortuosity is 1.64-2.21, the pore throat coordination number is 3.32-3.91, and the Gaussian synergy factor is 2.68-12.05. The lower the fractal dimension, the lower the sorting coefficient, the higher the homogeneity coefficient, the larger the shape factor, the smaller the tortuosity, the higher the pore throat coordination number, the larger the Gaussian synergy factor, the higher the pore structure multi-evaluation parameter value, the more homogeneous the reservoir pore throat structure, and the better the connection degree. In combination with the movable fluid saturation obtained by NMR logging, the pressure flow rate is calculated by using the nonlinear surface fitting method, and compared with the actual pressure flow rate, it is found that the coincidence degree is high, as shown in FIG. 4, and it is considered that the multi-dimensional connectivity quantitative evaluation model can accurately represent the reservoir storage space connectivity. Figure 5

[0039] The data is shown in Table 1.

[0040] Example 3 ​​The low-permeability reservoirs in B gas field in Qiongdongnan Basin were verified by examples, and the results are shown in Table 2. The lithology of B gas field is mixed, and the pore throat heterogeneity is also strong. The pore throat structure parameters obtained by image analysis are as follows: fractal dimension 1.21-1.34, sorting coefficient 1.13-1.82, homogeneity coefficient 1.51-1.79, shape factor 0.3296-0.4619, tortuosity 1.75-2.67, pore throat coordination number 3.22-3.52, and Gaussian synergy factor 1.88-9.33 (as shown in the following table). The seven parameters are substituted into the pore structure multi-evaluation model, and the measured flow rate is calculated by using the nonlinear surface fitting method combined with the movable fluid saturation. The calculation results show that the predicted flow rate value has good correlation with the measured data, as shown in Fig. 1, and the model can accurately represent the reservoir connectivity. Figure 6

[0041] The data are shown in Table 2.

[0042] In the specific contents of the above specific embodiments, each technical feature can be combined arbitrarily without contradiction. In order to make the description simple, all possible combinations of the above technical features are not described, but as long as the combination of these technical features does not exist contradiction, it should be considered as the scope of the present application.

[0043] Obviously, the above embodiments of the present application are only examples for clearly illustrating the present application, and are not intended to limit the implementation modes of the present application. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, all the implementation modes are not required or can not be exhausted. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the claims of the present application.​

Claims

1. A reservoir micro-connection characterization method based on pore throat boundary intelligent identification, characterized by, The method comprises the following steps: The rock casting thin section is subjected to threshold segmentation processing by using an image analysis software to delineate partition images of pore throats and skeleton particles; based on the partition images, pore throat diameter, perimeter and area are measured and extracted by using the image analysis software, pore throat structure quantitative parameters are calculated based on the pore throat diameter, perimeter and area, a one-dimensional Gaussian distribution model of reservoir NMR logging is constructed, a Gaussian synergy factor for pore structure evaluation is established based on the constructed one-dimensional Gaussian distribution model, a pore structure multi-element evaluation model based on reservoir micro parameters is established by using a multi-element evaluation mathematical method based on the obtained pore throat structure quantitative parameters and the established Gaussian synergy factor, and a quantitative characterization model for characterizing reservoir micro connectivity is constructed by performing multi-element nonlinear fitting on the pore structure multi-element evaluation model, NMR movable fluid saturation and reservoir pressure buildup flow rate.

2. The method of claim 1, wherein, The expression of the one-dimensional Gaussian distribution model is as follows: wherein p is T2 spectrum distribution of reservoir NMR logging; W is height of a peak of the NMR curve; logT is logarithm expression of relaxation time; logμ is logarithm expression of median value of a pore peak of the NMR curve; and logσ is standard deviation of a pore radius corresponding to the NMR curve.

3. The reservoir micro-continuity characterization method based on intelligent identification of pore throat boundary according to claim 2, characterized in that, The expression of the Gaussian synergy factor is as follows: γ=Φ*logμ wherein Φ is effective porosity measured by NMR logging, and the unit is %.

4. The reservoir micro-continuity characterization method based on intelligent identification of pore throat boundary according to claim 3, characterized in that, The pore throat structure quantitative parameters include reservoir pore throat sorting coefficient, reservoir pore throat homogeneity coefficient, reservoir pore throat fractal dimension, reservoir pore shape factor, reservoir throat tortuosity and reservoir pore throat coordination number.

5. The reservoir micro-continuity characterization method based on intelligent identification of pore throat boundary according to claim 4, characterized in that, The expression of the pore structure multi-element evaluation model is as follows: F1=-0.528*SC+0.426*U+0.534*SD-0.563*SF-0.717*G+0.405*CN-0.212*γ F2=0.353*SC-0.241*U-0.795*SD+0.827*SF+0.557*G-0.218*CN+0.453*γ K=(0.514 / 0.959)*F1+(0.445 / 0.959)*F2 wherein F1 is a main factor of the multi-element evaluation model, F2 is a secondary factor of the multi-element evaluation model, K is a pore structure multi-element evaluation parameter, SC is reservoir pore throat sorting coefficient, U is reservoir pore throat homogeneity coefficient, SD is reservoir pore throat fractal dimension, SF is reservoir pore shape factor, G is reservoir throat tortuosity, and CN is reservoir pore throat coordination number.

6. The reservoir micro-continuity characterization method based on intelligent identification of pore throat boundary according to claim 5, characterized in that, The expression of the quantitative characterization model is as follows: λ = 0.48977 - 5.28401 * K + 0.0598 * S movable + 10.64324 * K 2 - 0.0011 * S movable 2 where λ is the reservoir pressure-flow rate, in mD / cP; S movable is the reservoir mobile fluid saturation, in %.

7. The method of claim 1, wherein, The image analysis software is Image J image analysis software or PCAS image analysis software.

8. The reservoir micro-continuity characterization method based on intelligent identification of pore throat boundary according to claim 7, characterized in that, The rock casting thin section is subjected to threshold segmentation processing by using the Image J image analysis software, a Canny operator is nested in the Image J image analysis software, gray values of boundaries of rock skeleton particles and pore throats are recognized by using the Canny operator, and partition images of pore throats and skeleton particles are delineated.

9. The reservoir micro-continuity characterization method based on intelligent identification of pore throat boundary according to claim 1, characterized in that, In the MATLAB software, the pore structure multi-element evaluation model, NMR movable fluid saturation and reservoir pressure buildup flow rate are subjected to multi-element nonlinear fitting to construct the quantitative characterization model for characterizing reservoir micro connectivity.

10. The method of claim 1, wherein, The quantitative characterization model adopts a mathematical method of non-linear curved surface fitting of Parabola2D to perform non-linear curved surface fitting on the multi-element evaluation model of pore structure and the nuclear magnetic movable fluid saturation.

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