Method for generating large-scale heterogeneous pore throat network under joint control of macro and micro parameters

Abstract

The invention discloses a macro-micro parameter co-controlled large-scale heterogeneous pore throat network generation method, which comprises the following steps of: partitioning a set simulation region, randomly generating a plurality of mineral particle cores in each partition, simulating the growth of the mineral particle cores by utilizing a four-parameter random growth method, and generating a large-scale heterogeneous pore throat network. Taking the boundary of the core mineral of each mineral particle as potential pores and throats, and carrying out inversion to obtain the position distribution of each pore and throat; extracting position distribution of optimized pores and throats by using a genetic algorithm, matching coordination numbers for each pore and throat, and allocating target growth radius values for the pores and throats of each matching coordination number by setting microscopic pore-throat structure parameters of each partition; and utilizing a four-parameter random growth method to simulate that fluid in the pores and the throats in each partition corrodes the core of the mineral particles until the radiuses of the pores and the throats in each partition reach the target growth radius value one by one, and generating a pore-throat network image of the set simulation area.

Description

Technical Field

[0001] The invention relates to the technical field of oil development, and in particular to a method for generating a large-scale heterogeneous pore throat network by jointly controlling macroscopic and microscopic parameters. Background Art

[0002] With the updating of instruments and equipment and the development of observation methods, microfluidics technology has gradually become an important experimental means in the field of oil and gas geology to deepen the understanding of seepage laws and expand the explanation of mechanisms due to its outstanding advantages of microscopic visualization and convenience.

[0003] At present, physical simulation methods at the micro-nano scale are mainly explored from two perspectives: micro-local and macro-overall. The former focuses on phase changes and fluid storage types in pore throat structures, while the latter focuses on the macroscopic distribution characteristics of fluids in complex porous media structures. The comprehensive application of the two can more systematically clarify the seepage mechanism of reservoir fluids, more effectively evaluate the remaining oil utilization capacity and recovery potential, and provide theoretical support and experimental basis for the adjustment of measures in the later stage of oilfield development.

[0004] Among these, designing and generating models that can truly reflect the microscopic pore structure characteristics of rocks is crucial for microscopic physical simulation experiments and cross-scale numerical simulations, and is the basis for conducting related research.

[0005] Previously, the generation of microscopic pore-throat network model patterns was often based on cast thin sections or CT scan images, and on the basis of extracting the pore-throat structure, it was completed through image stitching and denoising techniques. However, due to the limitation of image accuracy, the size of the images obtained by cast thin sections and CT scans are small, and the range of pore-throat structural features that can be displayed is limited. The microscopic model formed by the stitching design can obtain larger-scale images, but it is still far from the research scale of the core and the macroscopic level of the mine, and its repetitive structure lacks theoretical support, and the reliability of the upscaling application of the results is questionable.

[0006] At present, there are two generation methods for large-scale pore-throat network modeling: equivalent modeling and random modeling. For equivalent modeling, even if the pore and throat size distributions are similar and the porosity is similar, if the spatial topological structure is quite different, the physical parameters such as permeability of the pore-throat network model will still show large differences. The random modeling method can only consider two constraints, namely porosity and correlation function, resulting in poor connectivity of the generated model. The traditional four-parameter random growth QSGS method is further developed on the basis of random modeling, simulating the growth process of rock minerals and can effectively control the anisotropy of the model. However, the constraint condition only considers the single factor of porosity, and the simulation degree of the pore throat morphology and connectivity of the model is relatively poor. Summary of the invention

[0007] The purpose of the present invention is to provide a large-scale heterogeneous pore throat network generation method controlled by macro and micro parameters, so as to solve the problem in the prior art that the existing modeling methods cannot meet the requirements of fast speed, high simulation degree and large-scale pore throat network image generation.

[0008] In order to solve the above technical problems, the present invention specifically provides the following technical solutions:

[0009] The large-scale heterogeneous pore throat network generation method controlled by macro and micro parameters includes:

[0010] Step 100, partitioning the set simulation area, randomly generating multiple mineral grain cores in each partition, determining the number of grain cores according to the target porosity and the ideal pore throat model, simulating the growth of the mineral grain cores using a four-parameter random growth method, and considering the boundaries of the minerals in each mineral grain core as potential pores and throats, and inverting to obtain the position distribution of each pore and throat;

[0011] Step 200, using a genetic algorithm to extract and optimize the position distribution of pores and throats, matching the coordination number for each pore and throat, and assigning a target growth radius value to each pore and throat matching the coordination number by setting the microscopic pore-throat structure parameters of each partition;

[0012] Step 300, using the four-parameter random growth method to simulate the dissolution of fluid in the pores and throats in each partition toward the core of the mineral particles, until the radius of the pores and throats in each partition reaches the target growth radius value one by one, and generate a pore-throat network image of the set simulation area.

[0013] As a preferred solution of the present invention, control parameters are obtained from the experiment of the target core sample, and the control parameters include phase permeability partition, porosity, pore radius distribution, throat radius distribution, particle size radius distribution and coordination number distribution;

[0014] The constraint parameter system of the large-scale pore throat network generation method based on the four-parameter random growth method is distinguished by phase permeability zoning, and different microscopic pore constraint parameter systems in each zone are established.

[0015] Setting porosity parameters controls the final volume distribution of constrained mineral grains and pore throats;

[0016] Set the pore radius distribution, throat radius distribution, particle size radius distribution and coordination number distribution to control the microscopic morphology and structure of constrained mineral particles, pores and throats;

[0017] As a preferred solution of the present invention, the number of randomly generated mineral particle cores in each partition is determined using an ideal model of a regular hexagonal pore throat network, specifically including:

[0018] Assuming that the pores are spherical and the throats are stick-shaped, when the entire regional space is filled with this basic structure, the number of pores and the number of mineral particle cores in the simulation area under ideal conditions are calculated;

[0019] Based on the calculated values ​​of the ideal model, the final determined number of pores and particles will be adjusted and optimized according to the ratio of the number of particles and pores in the real core.

[0020] As a preferred solution of the present invention, the randomly generated mineral particle core is regarded as a mineral each time, and the corresponding porosity of each mineral is set, so that more precise control can be achieved;

[0021] In order to speed up the calculation, the interaction between the mineral particle cores can be ignored in the process of simulating the growth of the mineral particle cores.

[0022] As a preferred solution of the present invention, based on the position distribution of each pore and throat, a connected domain algorithm is used to identify and extract the boundary lines between the mineral particle cores and the intersection points of the boundary lines in the position distribution results, and the boundary lines are defined as throats, and the intersection points of the boundary lines are defined as potential pores.

[0023] As a preferred solution of the present invention, based on the experimentally obtained coordination number of the target core sample, the fitness of the pores and throats of each partition and the target coordination number is calculated by a genetic algorithm, and the pores or throats in the partition are deleted according to the fitness, so as to complete the optimization of the pores and throats of each partition;

[0024] Among them, the fitness calculation function of the genetic algorithm is:

[0025]

[0026] Where n is the number of partitions in the model area, m is the total number of extracted pores in each partition, and c i,j is the coordination number of the jth pore in the ith partition calculated based on the individual chromosomes, c t i,j For each partition, the target coordination number distribution of all pores, sort(c i,j ) and sort(c t i,j ) is a sorting function; sum is a summation function, and the summation function is set as a penalty item to prevent the genetic algorithm from setting the pores as dead holes.

[0027] As a preferred solution of the present invention, by setting the microscopic pore throat structure parameters of each partition, a distribution rule is set to allocate a target growth radius value to each pore and throat matching the coordination number, and the distribution rule specifically includes:

[0028] For the pores and throats in each partition, random sampling is performed according to the given pore throat radius;

[0029] The higher the extracted pore area and the more merged pores, the larger the pore radius is assigned;

[0030] The radius of the throat cannot exceed the smallest pore radius of the pores at both ends of the partition.

[0031] As a preferred solution of the present invention, a four-parameter random growth method is used to simulate the dissolution of fluids in the pores and throats of each partition toward the core of the mineral particles. When introducing the fluid dissolution process:

[0032] The total area of ​​pores is set as the primary pores between mineral grains and the epigenetic dissolution areas between grains;

[0033] The percentage of the area of ​​the epigenetic dissolution zone between adjacent mineral grain cores to the total pore area is set as the dissolution threshold. When any pore reaches the dissolution threshold, dissolution stops.

[0034] As a preferred solution of the present invention, after the pore and throat radius allocation of each partition is completed, an initial pore-throat network image is formed, and the throat is thickened by using an image expansion algorithm. The specific method of thickening the throat by using the image expansion algorithm includes:

[0035] Get the initial image size and create a completely black image of the same size;

[0036] Traverse each pixel and determine whether the two images intersect with each other with non-zero common elements;

[0037] After the traversal is completed, the target image is output.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention combines the advantages of both equivalent modeling and random modeling algorithms, inverts the geological formation of pores and throats, simulates mineral growth and pore dissolution processes, and uses the distribution of four parameters, namely porosity, coordination number, pore radius and throat radius, to control the generation process at each stage, thereby achieving highly simulated, large-scale, controllable and heterogeneous pore-throat network modeling. Among them, an improved genetic algorithm is used for fitting the coordination number distribution, which has the advantages of shorter convergence time and faster calculation speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0041] Figure 1 This is the core casting thin section image and pore throat network image of the X reservoir in this embodiment;

[0042] Figure 2 This is the pore throat inversion process of this implementation method;

[0043] Figure 3 This is a flow chart of rock and mineral growth in this embodiment;

[0044] Figure 4 Simulate the mineral growth and boundary extraction results for this implementation;

[0045] Figure 5 This is a comparison diagram of the implementation of the expansion algorithm in this embodiment;

[0046] Figure 6 The genetic algorithm principle for this implementation method;

[0047] Figure 7 This is a flow chart of the implementation of the expansion algorithm in this embodiment;

[0048] Figure 8 This is a comparison diagram of pore growth and dissolution before and after growth in this embodiment;

[0049] Fig. 9 This is a schematic diagram of distance transformation in this implementation mode;

[0050] Fig.10 This is a flow chart of the algorithm for pore growth and dissolution in this embodiment;

[0051] Fig.11 The pore throat network generation result under the porosity gradient of this implementation method;

[0052] Fig.12 The pore throat network generation result diagram under the porosity gradient of this embodiment;

[0053] Fig.13 The results of pore throat network generation for various image sizes under custom partitioning in this implementation mode are as follows;

[0054] Fig.14 are the microscopic pore throat structure parameters of the X reservoir core obtained in this implementation mode;

[0055] Fig.15Schematic diagram of pore range under different coordination numbers in this embodiment. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] like Figures 1 to 13 As shown, this embodiment provides a method for generating a large-scale heterogeneous pore throat network controlled by macro and micro parameters, which includes two growth processes:

[0058] The first growth process: The formation of pores and throats is inverted through mineral growth to determine the locations of pores and throats. In this process, phase permeability zoning and porosity are introduced to control the number and location of pores and throats.

[0059] After determining the distribution positions of pores and throats, an improved genetic algorithm is used to optimize the distribution of throats to further match the distribution of target coordination numbers, and according to the microscopic pore-throat structural parameters of each partition, a radius value is assigned to each pore and throat according to the target microscopic pore-throat structural parameter distribution.

[0060] The second growth process: inverts the pore formation process, that is, while the minerals grow between particles, they are constantly washed, detached and dissolved by the fluid. By finely controlling the formation process of each pore, the pores have specified sizes and complex and diverse shapes.

[0061] The specific implementation steps include:

[0062] Step 100, partition the set simulation area, and randomly generate multiple mineral grain cores in each partition, determine the number of grain cores in each zone according to the porosity and pore throat ideal model, use the four-parameter random growth method to simulate the growth of mineral grain cores, and regard the boundaries of each mineral grain core mineral as potential pores and throats, and invert to obtain the position distribution of each pore and throat;

[0063] Step 200, using a genetic algorithm to extract and optimize the position distribution of pores and throats, matching the coordination number for each pore and throat, and assigning a target growth radius value to each pore and throat matching the coordination number by setting the microscopic pore-throat structure parameters of each partition;

[0064] Step 300, using the four-parameter random growth method to simulate the dissolution of fluid in the pores and throats in each partition toward the core of the mineral particles, until the radius of the pores and throats in each partition reaches the target growth radius value one by one, and generate a pore-throat network image of the set simulation area.

[0065] The microscopic pore throat structure parameters and phase permeability zoning are obtained from the experiments on the target core samples, wherein the microscopic pore throat structure parameters include porosity, porosity radius distribution, roar radius distribution, particle size radius distribution and coordination number distribution;

[0066] The zones in the set simulation area are distinguished by phase permeability zoning, and the microscopic pore throat structure parameters in each zone are established according to the differentiation results.

[0067] wherein the coordination number is matched to the pores and throats of each partition through the coordination number distribution;

[0068] The target growth radius of pores and roaring channels in the partition is controlled by the porosity radius distribution and roaring channel radius distribution; the particle size radius of the core of the mineral particles in the partition is controlled by the particle size radius distribution.

[0069] The above data are used to obtain the representative microscopic pore throat characteristics of the target reservoir rock:

[0070] like Figure 1 As shown in the figure, the core samples in this paper were taken from the X reservoir, which are gray-brown fine-grained sandstone and gray-brown medium-fine-grained sandstone. Figure 1 (a) and Figure 1 (c) is a thin section image of the casting shown.

[0071] Figure 1 (a) 34.45% of the pores have diameters less than 100 μm, and the remaining pores have diameters greater than 100 μm. Most of the pores have diameters in the range of 75 μm to 100 μm and 100 μm to 125 μm. The radius of the main flow throat is mostly between 1 and 10 μm, which belongs to a large-pore, fine-throat homogeneous reservoir.

[0072] Figure 1 (c) 61.30% of the pores have a diameter less than 100 μm, and most of the pores are in the size range of 50 μm to 75 μm and 75 μm to 100 μm. The remaining pores are larger than 100 μm in diameter, and about 89.03% of the flow throat radii are between 1 and 10 μm, which belongs to a medium-pore fine-throat homogeneous reservoir.

[0073] right Figure 1 (a) and Figure 1 (c) Binarization is performed using the color threshold segmentation method to extract the blue pores and extract the throats with light blue along the edges of the mineral particles. Figure 1(b) and Figure 1 (d) Image of the pore throat network shown.

[0074] Based on the pore-throat network image of the casting thin section or the CT digital core, the microscopic pore-throat structure parameter characteristics including porosity, coordination number distribution, pore radius distribution and throat radius distribution can be statistically analyzed. The specific data rules are shown in Fig.13 .

[0075] The number of pores and mineral particles in each partition are the main parameters that need to be obtained through calculation.

[0076] The density (number) of the growth of the mineral particle core simulated by the four-parameter random growth method is constrained by phase permeability partitioning, and the mineral particle cores with different diameter distributions in each partition are obtained;

[0077] The porosity parameter is the percentage of the final pore throat volume (area) of the model to the total volume (area) of the region.

[0078] The ideal model of the pore-throat network of regular hexagons is used to calculate the number of randomly generated mineral grain cores in each partition of the simulation area, including:

[0079] The pores are assumed to be circular (spherical) and the throats are rectangular (stick-shaped), and the number of pores and the number of mineral particle cores in the simulation area under ideal conditions are calculated;

[0080] Furthermore, based on the calculated values ​​of the ideal model, the final determined numbers of pores and particles will be adjusted and optimized according to the ratio of the number of particles and pores in the real core.

[0081] The pore throat area of ​​the ideal model satisfies the formula:

[0082] πNR p 2 +2cNR t (L-2R p ) = φS;

[0083] N is the number of pores, Rp is the average pore radius, Rt is the average throat radius, L is the average pore spacing, c is the average coordination number, φ is the porosity, and S is the area of ​​the partition.

[0084] Consider N pores randomly distributed in a region of area S. When the throat area is ignored, we have:

[0085] NL 2 ≈S≈πNR p 2 / φ;

[0086] However, in the algorithm, pores that are close to each other are merged into one, so the number of particles is often less than 2 / 3 of the number of pores.

[0087] Based on a large number of models, this embodiment sets this parameter to 0.78, that is, the number of pores is 1.3 times the number of mineral particle cores. At this time, the mineral particle cores and pores are loose and orderly, which is more natural and realistic.

[0088] This embodiment only assumes that the rock pore structure can be approximated as an ideal hexagonal pore throat network, but it can also be replaced by other ideal unit forms.

[0089] use Fig.13 The microscopic pore throat structure data of the medium-fine grained sandstone core sample, the mineral distribution map obtained is as follows Figure 4 (a) and Figure 4 (b) as shown.

[0090] The randomly generated mineral particle cores in each partition are regarded as a mineral, and the interaction between the mineral particle cores is ignored in the process of simulating the growth of the mineral particle cores using a four-parameter random growth method.

[0091] According to the position distribution of each pore and throat, the connected domain algorithm is used to identify and extract the boundary lines between the mineral particle cores and the intersection points of the boundary lines in the position distribution results, and the boundary lines are defined as throats, and the intersection points of the boundary lines are defined as potential pores.

[0092] When the length of the throat is less than the value l, it is converted into a pore. Let the pore equivalent radius be Rp and the pore area be Sp, where:

[0093] S p =πR p 2 ;

[0094] When the coordination number is 3, such as Fig.15 As shown in (a), we have: l / Rp=2.

[0095] When the coordination number is 4, such as Fig.15 As shown in (b), assuming that the distance between pore 1 and pore 2 is greater than l, and a pair of pores have been merged in pore 1 and pore 2, the final pore shape is a square and two semicircles, which satisfies: l / Rp=1.33 (equivalent radius).

[0096] Therefore, it can be considered that the more merged pores there are, the smaller the value of l / Rp is, and at the same time, in the pore radius distribution, the pore tends to be assigned a larger value.

[0097] In real cores, although some mineral particles have obvious boundaries, they do not form channels for fluid to pass through, so these mineral particles and the surrounding mineral particles can be regarded as one particle.

[0098] In other words, some pores or throats can be deleted. Selecting appropriate pores or throats for deletion can better fit the distribution of coordination number and particle size.

[0099] This paper makes an adaptive improvement to the genetic algorithm, changing the values ​​originally input into the genetic algorithm in decimal to binary, where 0 means deletion and 1 means retention, and the length of the binary is the number of throats in the model.

[0100] The flowchart of the genetic algorithm is as follows Figure 6 As shown, the natural selection adopts the roulette method, and the gene recombination part adopts the single-point crossover operator.

[0101] In the improved genetic algorithm, each individual has only one chromosome, which consists of 0 and 1. The value of each position represents whether the corresponding numbered throat is deleted.

[0102] Based on the experimentally obtained coordination number of the target core sample, the fitness of the pores and throats of each partition and the target coordination number is calculated by a genetic algorithm, and the pores or throats in the partition are deleted according to the fitness to complete the optimization of the pores and throats of each partition;

[0103] Among them, the fitness calculation function of the genetic algorithm is:

[0104]

[0105] Where n is the number of partitions in the model area, m is the total number of extracted pores in each partition, and c i,j is the coordination number of the jth pore in the ith partition calculated based on the individual chromosomes, c t i,j For each partition, the target coordination number distribution of all pores, sort(c i,j ) and sort(c t i,j ) is a sorting function; sum is a summation function, and the summation function is set as a penalty item to prevent the genetic algorithm from setting the pores as dead holes.

[0106] By setting the microscopic pore throat structure parameters of each partition, a distribution rule is set to allocate a target growth radius value for each pore and throat matching the coordination number. The distribution rule specifically includes:

[0107] For the pores and throats in each partition, random sampling is performed according to the given pore throat radius;

[0108] The higher the extracted pore area and the more merged pores, the larger the pore radius is assigned;

[0109] The radius of the throat cannot exceed the smallest pore radius of the pores at both ends of the partition.

[0110] The four-parameter random growth method is used to simulate the dissolution of fluids in the pores and throats of each partition toward the core of the mineral particles. When introducing the fluid dissolution process:

[0111] The area of ​​pores is set to include the spaces between mineral particles and the dissolution areas of mineral particles;

[0112] Among them, the percentage of the area between adjacent mineral particles to the void area is set as the dissolution threshold;

[0113] Among them, when the area of ​​a mineral particle core reaches the dissolution threshold, the space left by the adjacent mineral particle cores during the growth process will not be included in the inter-particle range of the current mineral particle core.

[0114] After the pore and throat radius allocation of each partition is completed, an initial pore-throat network image is formed, and the throat is thickened using an image expansion algorithm. The specific method of thickening the throat using the image expansion algorithm includes:

[0115] Get the initial image size and create a completely black image of the same size;

[0116] Traverse each pixel and determine whether the two images intersect with each other with non-zero common elements;

[0117] After the traversal is completed, the target image is output.

[0118] According to the assigned pore and throat radii, the QSGS algorithm is used to simulate the erosion of fluid on rock mineral particles.

[0119] In this process, the distance between each pixel in the image and the pore or throat is determined by distance transformation to determine the range of possible erosion.

[0120] In order to find the location distribution of pores in the image, it is necessary to determine the distance between each pixel in the image and the pore or throat through distance transformation;

[0121] Define the zero point set on the image as G = {(x 1 ,y 1 ),(x 2 ,y 2 ),…,(x n ,y n )}, then the distance transformation function is:

[0122]

[0123] Where i and j are the positions of the points in the image, and x and y are the positions of any point in the zero point, that is, (x, y)∈G.

[0124] The distance transformation results Dp and Dt of the pore and throat are calculated respectively, and the final comprehensive transformation result Dc is:

[0125] D c =0.5D p +0.5D t ;

[0126] The image obtained by the above formula is as follows Fig. 9 (a) and Fig. 9 As shown in (b), the brighter the color in the image, the farther away it is from the pores and throats.

[0127] When the cutoff distance is larger, the area of ​​the pores is larger and the area of ​​the adjacent mineral particles is smaller, which means the erosion is more serious. When the cutoff distance is larger, the area of ​​the pores is larger and the area of ​​the adjacent mineral particles is smaller, which means the erosion is more serious.

[0128] The simulation of pore-throat structure generated by fluid erosion uses the four-parameter random growth method (QSGS) to simulate the relative growth of liquid phase to rock phase. The fluid in the pores begins to dissolve the particles continuously until the radius of each pore and throat reaches the assigned value and the porosity reaches the predetermined value, and the erosion expansion stops.

[0129] In the process of mineral growth inversion, pores with different numbers continuously fill the empty space through the four-parameter random growth method, and the growth probability is 1.0. In the process of fluid dissolution, the fluid in the pores still "grows" into the mineral through the four-parameter random growth method.

[0130] Generate multiple microscopic pore throat network images, count their particle sizes, and select the results with the particle size radius distribution range closest to the target particle size radius distribution.

[0131] Furthermore, the method provided in this embodiment is evaluated:

[0132] In terms of parameter similarity comparison:

[0133] According to the data representation and storage methods, the pore structure characteristic parameters can be divided into two categories: numerical type and vector type.

[0134] Numerical parameters include porosity, and vector parameters include pore radius distribution, throat radius distribution, particle size distribution, and coordination number distribution.

[0135] These parameters can characterize the characteristics of the microscopic pore-throat network and are easy to calculate statistically from images without the aid of any empirical formulas.

[0136] For numerical microscopic pore throat structure parameters, linear normalization is used to calculate the similarity.

[0137] For the vector-type microscopic pore-throat structural parameters, the difference in distribution frequency between the target generation and the result generation in each partition is used as the calculation method for the similarity of each partition.

[0138] The comprehensive similarity of the model is calculated by taking the weighted average of the similarities of each partition, which is used as an evaluation of the authenticity of the pore structure characteristic parameters of the model.

[0139] According to the calculation results, the comprehensive similarity between the generated model and the real model can reach more than 80%, among which the comprehensive similarity of numerical parameters is more than 90%. The throat radius distribution shows a similarity of up to 99.6%.

[0140] Partition control analysis:

[0141] The pore throat network random model realizes the control of microscopic pore throat structure characteristic parameters such as porosity, pore throat radius, and coordination number during the model generation process. Fig.11 This is the generation result of the random model of the pore-throat network, and the result reflects the gradual change of the partition from top to bottom.

[0142] The pores in the upper part of the image are smaller and the rock grains are smaller;

[0143] The pores at the bottom of the image are larger and the rock grains are larger;

[0144] The image shows a relatively uniform gradient from top to bottom, reflecting the macroscopic heterogeneity of the pore-throat network model.

[0145] like Fig.12 As shown in the figure, according to the pore throat network model generation algorithm designed in this paper, the porosity, pore coordination number, pore radius, throat radius, particle size and other parameters of the pore throat network are constrained respectively, and the pore throat network model under the gradual change of these parameters can be obtained. Here, the porosity gradient is taken as an example.

[0146] According to the actual needs of researchers for the model, custom partition maps are provided to generate pore-throat network models of various image sizes, such as Fig.13 shown.

[0147] The model generated by the method provided in this embodiment is highly consistent with the target core in terms of microscopic pore throat structure and macroscopic physical property parameters, and has better simulation performance for the macroscopic-microscopic physical structure of the real core.

[0148] In addition, based on the zoning function, the corresponding pore-throat network model can be generated according to the contour map established by the geological model.

[0149] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. A method for generating a large-scale heterogeneous pore throat network controlled by macro and micro parameters, characterized in that: include: Step 100, partitioning the set simulation area, and randomly generating multiple mineral grain cores in each partition, simulating the growth of the mineral grain core using a four-parameter random growth method, and treating the boundary of each mineral grain core mineral as a potential pore and throat, and inverting to obtain the position distribution of each pore and throat; Step 200, using a genetic algorithm to extract and optimize the position distribution of pores and throats, matching the coordination number for each pore and throat, and assigning a target growth radius value to each pore and throat matching the coordination number by setting the microscopic pore-throat structure parameters of each partition; Step 300, using the four-parameter random growth method to simulate the dissolution of fluid in the pores and throats in each partition toward the core of the mineral particles, until the radius of the pores and throats in each partition reaches the target growth radius value one by one, and generate a pore-throat network image of the set simulation area.

2. The method for generating a large-scale heterogeneous pore throat network controlled by macro and micro parameters according to claim 1, characterized in that: The microscopic pore throat structure parameters and phase permeability zoning are obtained from the experiments on the target core samples, wherein the microscopic pore throat structure parameters include porosity, porosity radius distribution, roar radius distribution, particle size radius distribution and coordination number distribution; The zones in the set simulation area are distinguished by phase permeability zoning, and the microscopic pore throat structure parameters in each zone are established according to the differentiation results. wherein the coordination number is matched to the pores and throats of each partition through the coordination number distribution; The target growth radius of pores and roaring channels in the partition is controlled by the porosity radius distribution and roaring channel radius distribution; The particle size radius distribution of the mineral particle core within the partition is controlled by the particle size radius distribution.

3. The method for generating a large-scale heterogeneous pore throat network controlled by macro and micro parameters according to claim 1, characterized in that: The ideal model of the pore-throat network of regular hexagons is used to determine the number of randomly generated mineral grain cores in each partition, including: The pores are set to be spherical and the throats to be stick-shaped, and the number of pores and the number of mineral particle cores in the simulation area under ideal conditions are calculated; Among them, according to the ratio of the number of mineral particle cores to the number of pores obtained from the target core sample experiment, the number of pores and the number of mineral particle cores calculated under ideal conditions are optimized.

4. The method for generating a large-scale heterogeneous pore throat network controlled by macro and micro parameters according to claim 1, characterized in that: Treat each randomly generated mineral grain core in the partition as a mineral, and set the final porosity corresponding to each mineral grain core; In the process of simulating the growth of mineral particle cores using the four-parameter random growth method, the interaction between the mineral particle cores is ignored.

5. The method for generating a large-scale heterogeneous pore throat network controlled by macro and micro parameters according to claim 1, characterized in that: According to the position distribution of each pore and throat, the connected domain algorithm is used to identify and extract the boundary lines between the mineral particle cores and the intersection points of the boundary lines in the position distribution results, and the boundary lines are defined as throats, and the intersection points of the boundary lines are defined as potential pores.

6. The method for generating a large-scale heterogeneous pore throat network controlled by macro and micro parameters according to claim 5, characterized in that: Based on the coordination number obtained experimentally from the target core sample, the fitness of the pores and throats of each partition and the coordination number is calculated by a genetic algorithm, and the pores or throats in the partition are deleted according to the fitness, so as to complete the optimization of the pores and throats of each partition; Among them, the fitness calculation function of the genetic algorithm is: Where n is the number of partitions in the model area, m is the total number of extracted pores in each partition, and c i,j is the coordination number of the jth pore in the ith partition calculated based on the individual chromosomes, c t i,j For each partition, the target coordination number distribution of all pores, sort(c i,j ) and sort(c t i,j ) is a sorting function; sum is a summation function, and the summation function is set as a penalty item to prevent the genetic algorithm from setting the pores as dead holes.

7. The method for generating a large-scale heterogeneous pore throat network controlled by macro and micro parameters according to claim 6, characterized in that: By setting the microscopic pore throat structure parameters of each partition, a distribution rule is set to allocate a target growth radius value for each pore and throat matching the coordination number. The distribution rule specifically includes: For the pores and throats in each partition, random sampling is performed according to the given pore throat radius; The higher the extracted pore area and the more merged pores, the larger the pore radius is assigned; The radius of the throat cannot exceed the smallest pore radius of the pores at both ends of the partition.

8. The method for generating a large-scale heterogeneous pore throat network controlled by macro and micro parameters according to claim 2, characterized in that: The four-parameter random growth method is used to simulate the dissolution of fluids in the pores and throats of each partition toward the core of the mineral particles. When introducing the fluid dissolution process: The total area of ​​pores is set as the primary pores between the mineral grain cores and the post-growth dissolution area of ​​the mineral grain cores; Among them, the percentage of the post-growth dissolution area between adjacent mineral particle cores to the total pore area is set as the dissolution threshold.

9. The method for generating a large-scale heterogeneous pore throat network controlled by macro and micro parameters according to claim 8, characterized in that: After the pore and throat radius allocation of each partition is completed, an initial pore-throat network image is formed, and the throat is thickened using an image expansion algorithm. The specific method of thickening the throat using the image expansion algorithm includes: Get the initial image size and create a completely black image of the same size; Traverse each pixel and determine whether the two images intersect with each other with non-zero common elements; After the traversal is completed, the target image is output.

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