CN108875140A - A kind of heavy crude reservoir asphaltene deposits absorption damage analogy method based on digital cores model - Google Patents

Abstract

The invention discloses a method for simulating adsorption damage of asphaltene deposition in heavy oil reservoirs based on a digital core model, using the original digital core model containing various rock mineral components as a reference, combining asphaltene deposition conditions and The results of laboratory experiments on asphaltenes of different rock minerals are closely combined with digital cores through methods such as model-based discrete point stability discrimination, asphaltene deposition, and adsorption simulation. Finally, by analyzing the microstructure and pore-permeability changes of the digital core model before and after the reservoir damage, the degree of reservoir damage caused by asphaltene deposition and adsorption in heavy oil reservoirs under different simulation conditions is obtained. The proposal further expands the application of digital core technology in the field of oil and gas field development, and also provides a new method for the simulation and research of the damage process of asphaltene deposition and adsorption in heavy oil reservoirs.

Description

A simulation of asphaltene deposition adsorption damage in heavy oil reservoirs based on digital core model method

technical field

The invention belongs to the technical field of oil and gas field development, and relates to the analysis of damage caused by asphaltenes in heavy oil reservoirs, in particular to a simulation method for asphaltene deposition adsorption damage in heavy oil reservoirs based on a digital core model, which is a method for different simulations Heavy oil reservoir bitumen based on a digital core model containing multiple rock mineral components due to the reservoir damage process caused by the deposition of asphaltenes in the heavy oil reservoir and the adsorption on the surface of different rock minerals Mass deposition damage simulation technology.

Background technique

During steam injection development of heavy oil reservoirs, due to changes in temperature and pressure in the reservoir, it is very easy to cause changes in the properties of reservoir rocks and fluids, resulting in different types of reservoir damage. Supercritical steam injection boilers can inject steam with a high dryness above 300°C, and the pressure can generally reach 6-8MPa. During the thermal recovery process, the heavy components such as colloids and asphaltene in heavy oil will change with temperature and pressure. Due to changes in the field and the properties of crude oil components, the macromolecular polar compounds in crude oil continue to aggregate to form complex asphaltene aggregates, and the polar compounds that make up asphaltene contain different types of macromolecular nitrogen-containing, sulfur-containing and Oxygen compounds, where oxygen-containing compounds include phenols and various organic acids, sulfur-containing compounds include thiophene, etc., nitrogen-containing compounds include pyridine, carbazole, etc., and different types of heteroatom compounds play a role in the adsorption process of asphaltenes and rock minerals. play an important role, especially nitrogen-containing and oxygen-containing compounds. Asphaltene aggregates containing surface active groups have positive charges and strong polarity, and clay minerals are typical aluminosilicates, with tetrahedral Si-OH and octahedral Al-OH groups on the surface, And the surface of the wafer is -OH, and these polar groups provide more adsorption sites for the adsorption process of asphaltenes on the surface of clay minerals. Adsorption occurs on the surface of rock minerals. Coagulated asphaltenes contain more polar components, which are easily adsorbed in different clay minerals and aggravate reservoir damage.

With the continuous development of computer technology and instrumental analysis methods, the development of digital core theory and related technologies has been promoted, and digital core reconstruction technology has attracted more and more attention as a rapidly developing micro-scale reservoir simulation analysis method. , and its application has also been extended from the initial research on the basic physical properties of rocks to many aspects such as seepage, enrichment, and electrical properties of rocks in pores. Clay minerals are an important part of reservoir rock minerals. In addition to their strong plasticity when exposed to water, most of them also have strong adsorption and ion exchange properties; they are the main control factors that cause reservoir damage, while asphalt Asphaltene is easily deposited and adsorbed on the surface of different types of rock minerals under changing conditions such as reservoir temperature and pressure. Therefore, the present invention proposes a dense The simulation method of asphaltene deposition damage in oil reservoirs simulates the reservoir damage caused by asphaltene deposition in heavy oil reservoirs under different conditions through digital core models constructed based on numerical methods and combined with laboratory experimental research results.

Contents of the invention

In order to overcome the above-mentioned shortcoming of prior art, the object of the present invention is to provide a kind of heavy oil reservoir asphaltene deposition adsorption damage simulation method based on digital rock core model, combine indoor experiment result with field data through digital rock core technology, for research It provides a means for the reservoir damage process caused by asphaltene deposition and adsorption in heavy oil reservoirs.

In order to achieve the above object, the technical scheme adopted in the present invention is:

A method for simulating adsorption damage of asphaltene deposition in heavy oil reservoirs based on a digital core model, characterized in that it includes the following steps:

Step 1. Based on the two-dimensional information of the real reservoir, a digital core model containing various rock minerals is constructed based on the improved hybrid algorithm and clustering algorithm;

Step 2. Under different simulation conditions (different temperatures, different pressures, different types of crude oil, different types of water, and different wetting environments), the deposition amount of asphaltene and the adsorption of asphaltene on the surface of different types of rock minerals are obtained through laboratory experiments;

Step 3. Based on the asphaltene deposition ratio of different crude oils under different simulation conditions obtained through laboratory experiments, on the basis of the original digital core model containing various rock minerals, the deposited asphaltene is placed in the pore space in proportion, and the deposition is output Digital core model after asphaltenes;

Step 4: Based on the adsorption characteristics of asphaltene on the surface of different types of rock minerals under different simulation conditions, the asphaltene deposited in the pore space is placed on the surface of different types of rock minerals according to the adsorption characteristics obtained from laboratory experiments, and the asphaltene after adsorption is output. Digital core model.

In the step 1, the two-dimensional information of the real reservoir includes cast thin sections, rock particle size distribution, clay mineral distribution, and clay mineral occurrence characteristics. The specific model construction steps include:

In the first step, when using the process method to construct the basic digital core model, the total content of clay minerals is considered. During the deposition process, according to the particle size distribution of the real reservoir, the radius of the sedimentary particles is randomly selected. The size of the sedimentary particles is not only changed by the original The particle size distribution of sedimentary particles is determined, and the ratio between clay minerals and sandstone particles in the reservoir is additionally considered. The deposition process is simulated on the basis of satisfying the high-energy environment and the principle of falling simulation with the largest gravity potential energy gradient. Combined with the real core porosity, the selected pressure The real factor controls the porosity of the digital core model;

In the second step, the spatial occupancy of a unit pixel point, that is, the three types of point, line, and surface occupancy, is assigned a weight according to its contribution to the instability of the neighborhood, where the surface is 5, and the edge is 3. The point is 2; when selecting the exchange unit voxel point, calculate the instability contribution S of the voxel point and the adjacent occupancy point, line and surface, and based on the process of energy value decline in the simulated annealing algorithm, introduce The parameter S _d of the contribution degree of the exchange unit voxel point to the instability of its neighborhood is used to judge the exchangeability of the exchange point and improve the effectiveness of the exchange unit voxel point, where S _d is related to the system energy in the simulation process dimensionless value of :

S _d ＝N×β(E ₀ -E _i /△E _max ) (1)

In the formula, N is the number of neighborhood contact points influenced by a unit voxel point, dimensionless; β is the instability coefficient of a unit voxel point to the neighborhood, dimensionless; E ₀ is the initial energy of the system, dimensionless; E _i is the energy of the system after the i-th cooling, dimensionless; ΔE _max is the energy difference between the initial model and the reference model system based on the two-dimensional information of reservoir rocks, dimensionless, the initial model refers to the basic number constructed by the process method core model;

The third step is to use the improved hybrid algorithm to construct the initial digital core model as follows:

① Establish a reference model based on two-dimensional information of reservoir rocks, use the basic digital core model constructed by the process method as the initial model of the improved hybrid algorithm, set the initial temperature, and calculate the relevant parameters of the initial system, including autocorrelation function and linear path functions, fractal characteristic functions and energy values;

② On the basis of ensuring the randomness of the simulated annealing cooling process, calculate the contribution S of the 26 spatial occupancy points of the exchange unit voxel to the instability of the neighborhood; when S>S _d , it is considered that the point is less unstable High, it can be used as an exchange point for system update; when S<S _d , repeat step ②;

③Calculate the relevant parameters of the system after exchanging unit voxels, including single-point probability function, autocorrelation function, linear path function, fractal function and energy value, and calculate the energy difference ΔE with the system before the exchange; when ΔE<0 , to update the system; when ΔE>0, judge whether the system is updated according to the Metropolis criterion, that is, accept the system update under a certain probability condition; if the system update condition is not satisfied after the judgment, return to step ②;

④ Judging the termination condition of the internal circulation, that is, judging whether the energy difference of the system is less than the set minimum energy difference under the same temperature condition; Set the failure rate f _f of the system update to avoid this phenomenon, where:

In the formula, N _f is the number of update failures that lead to system energy recovery; N is the total number of system updates;

When f _f is greater than a certain value, the cooling process is carried out, and the cooling process adopts an equal-ratio cooling scheme, and returns to step ②;

⑤ When the simulation process temperature drops to the final set temperature or the system energy difference ΔE from the previous cooling is less than the set value, the entire simulation process is terminated;

As constraints, the statistical functions used in the simulated annealing algorithm include: single-point probability function P(r), autocorrelation function, linear path function and fractal function, using the autocorrelation function and linear path function to perform annealing simulation on the initial system, when After the model has certain fractal characteristics, the fractal function is introduced to further constrain the reconstruction model;

The fourth step is to compare the spherical rock particles in the initial digital core model reconstructed by the hybrid algorithm with the original spherical rock particles in the basic digital core model constructed in the process method and take the complement of the two, and make the initial digital core model Divided into three categories: rock framework phase, pore phase and clay mineral phase;

The fifth step is to use the Hoshen-Kopelman algorithm to count and divide the clay mineral groups in the initial digital core model, in which the probability of being occupied by M phase is c, and the probability of being occupied by T phase is 1-c. For each occupancy i of , when it is occupied by the M phase, a group label is assigned to the occupancy Where α is the characteristic symbol of group labeling, t is the number of group labeling, and the labeling of a certain discrete point is represented by a series of natural numbers:

There is only one natural number in this series of natural numbers that is an accurate label for the group α, which is And this value is the minimum value of all natural numbers in the collection (3), and the relation between other each group mark is given by the following integer set:

Among them, only is a positive integer element, and this value is the number of M phases in the group. When marking the tth time, if the number of M phases in the group is less than the number of M phases in the group α in the previous marking process, then the The difference is expressed as the number of T phases of the group α corresponding to t times, and the other elements in (4) are all negative integers, reflecting label with other groups Relationship, and The relation of is expressed by formula (5):

Check whether the judged discrete point has an adjacent discrete point that has been scanned. If the adjacent discrete point is T-phase, assign the current judged discrete point a new group mark; if there is an adjacent discrete point that has been assigned a group mark, assign the current grid and adjacent discrete points the same mark; if more than one adjacent discrete point has been assigned a group mark, and the group marks are different, assign the same mark to all the discrete points in the group Finally, count and divide the number and size of clay mineral phase groups in the model;

In the sixth step, the larger connected group is the clay mineral group whose group size in the clay phase is larger than that of the adjacent matrix particle, and the clay mineral group with the larger size of the clay mineral phase group in the initial digital core model is analyzed by the K-means algorithm. Mineral groups are divided, the specific steps are as follows:

① Read the collection of data samples;

②Set the number k of sample clusters, and randomly select k data samples as the initial data sample cluster centers;

③ Calculate the Euclidean distance, calculate the Euclidean geometric distance from each data in the data sample to each cluster center, and then divide the data into the clusters corresponding to the corresponding different cluster centers according to the minimum error square sum criterion function ;

④ Update the cluster center, use the mean value of all data in each cluster as the new center of each cluster, and recalculate the value of the new cluster center with the minimum error square sum criterion;

⑤ Iterative discrimination, comparing the value calculated in step ④ with the value obtained in the previous calculation, if the difference between the two is less than or equal to the preset critical value, then stop the iteration, otherwise repeat step ③ to iterate;

⑥ Output data samples and clustering results, including the cluster center and size of each cluster;

In the seventh step, when the discrete point on the boundary of the clay mineral group is a single rock particle, the clay mineral group is divided into metasomatous forms, which are mainly distributed in the rock particles in the form of a single discrete point; when the clay mineral group When the adjacent discrete points on the boundary of the mineral group are single rock skeleton particles and pores, the clay mineral phase group is divided into the filling form of the particle surface;

When the adjacent discrete points on the boundary of a clay mineral group are multiple rock skeleton particles and pores, the clay mineral group is classified as an intergranular filling form;

The clay mineral groups in the form of metasomatism, particle surface filling and intergranular filling are marked as A, B, and C, respectively; finally, the distribution of clay mineral groups with different structures and different types of clay mineral groups are obtained;

In the eighth step, based on the Hoshen-Kopelman algorithm and the K-means algorithm, the size and quantity distribution of clay mineral groups in the initial digital core model, and the type and quantity distribution of clay mineral groups obtained according to the structure are obtained, combined with the real reservoir clay content According to the size and structure characteristics of the clay mineral phase group, the clay minerals in the model are given corresponding clay properties, and a digital core model containing multi-component rock mineral distribution is obtained.

In said step 2, the indoor experiment includes simulating the original reservoir and under different production conditions, when the reservoir temperature, pressure, intralayer fluid and injection fluid properties change, the change of asphaltene deposition in crude oil; asphaltene in The adsorption law and adsorption characteristics of rock mineral surfaces, including the adsorption amount, adsorption constant and maximum adsorption capacity of asphaltene on different types of rock mineral surfaces.

Obtaining the digital core model after exporting deposited asphaltenes in said step 3 refers to simulating the deposition process of asphaltenes, and the steps are as follows:

In the first step, the pore volume of the digital core is obtained from the original digital core model containing various rock minerals;

The second step is based on the deposition ratio of asphaltenes in crude oil obtained in step 2 under different simulation conditions (different temperatures, different pressures, different crude oil types, different water types, and different wetting conditions) and the asphaltene content obtained in step 1. The pore volume of the digital core model of rock mineral composition, calculate the amount of asphaltene deposition in the pores of the original digital core model containing multiple rock minerals;

The third step is to use the smallest unit voxel point in the digital core model containing multiple rock mineral components obtained in step 1 as the basic deposition simulation unit, and randomly place the asphaltenes to be deposited with the basic deposition simulation unit as the largest simulation unit On pore space occupation until all asphaltenes are deposited.

Obtaining the digital core model after outputting the adsorbed asphaltenes in the step 4 refers to simulating the adsorption process of the asphaltenes, and the steps are as follows:

The first step is to read the digital core model containing various rock minerals after asphaltene deposition;

In the second step, based on the laboratory experimental results obtained in step 2, input the adsorption equilibrium constant and maximum adsorption capacity parameters of different types of rock minerals under different conditions;

The third step is to determine the group quantity and size of different types of rock minerals in the original digital core model containing multiple rock mineral components according to the Hoshen-Kopelman group division and statistical algorithm. Adsorption characteristic relationship to determine the adsorption amount of asphaltene on the surface of different types of rock minerals;

The fourth step is to combine the size of various rock mineral groups in the original digital core model containing multiple rock mineral components and the maximum adsorption capacity of asphaltene on the surface of different types of rock minerals obtained in experiments under different simulation conditions to calculate the rock mineral content in the model. The total adsorption capacity of the group;

In the fifth step, when the maximum adsorption capacity of the rock mineral group is greater than the asphaltene deposition mass, the asphaltene adsorption ratio on the rock mineral surface is determined according to the adsorption characteristic constant under the simulated conditions. The total adsorption amount of asphaltene on the surface of various rock minerals It is controlled by the amount of asphaltene deposition; when the maximum adsorption capacity of the rock mineral group is less than or equal to the deposition mass of asphaltene, the asphaltene adsorption ratio on the rock mineral surface is determined according to the maximum adsorption capacity of various rock minerals under the simulated conditions, The total adsorption amount of asphaltene on the surface of various rock minerals is controlled by the maximum adsorption capacity;

The sixth step is to calculate and sort the "adsorption distance" between asphaltene and clay, where the "adsorption distance" is related to the adsorption ratio of each clay;

The seventh step is to calculate the stability of the adjacent pore occupancy of the rock mineral group boundary according to the above-mentioned space occupancy stability discrimination method, and place the asphaltenes on the pore occupancy with higher priority according to the "adsorption distance" , if the clay reaches the maximum adsorption capacity and the total adsorption capacity is satisfied, the simulation process ends, otherwise continue to simulate according to the above process.

By comparing the changes of the adsorption volume of asphaltene on the surface of rock minerals in the digital core model before and after the asphaltene deposition damage in heavy oil reservoirs under different simulation conditions, and the changes of porosity and permeability in the digital core model, further research on asphalt in heavy oil reservoirs The effect on reservoir microstructure before and after qualitative damage.

Compared with the prior art, a simulation method of asphaltene deposition adsorption damage in heavy oil reservoirs based on a digital core model of the present invention proposes a method based on a digital core model by combining limited mine data with laboratory test results It is a new method for simulation research of reservoir damage caused by asphaltene deposition and adsorption. This method first uses limited mine data to construct a digital core model containing a variety of rock mineral types and occurrences; secondly, under different simulation conditions ( temperature, pressure, fluid properties, etc.) to study the asphaltene deposition change law in crude oil and the adsorption law of asphaltene on the surface of rock minerals. Simulation and other methods closely combine the laboratory research results with digital cores, and realize the simulation of the adsorption damage process of asphaltene deposition in heavy oil reservoirs based on digital core technology. According to the analysis, the degree of damage to the reservoir caused by asphaltene deposition and adsorption in heavy oil reservoirs under different simulation conditions is obtained. The study of sediment adsorption damage provides a new method.

Description of drawings

Figure 1 is a flow chart of digital core model reconstruction with multiple rock minerals.

Fig. 2 is a flow chart of asphaltene adsorption operation.

Figure 3 is a digital core model containing various rock minerals.

Figure 4 shows the distribution of clay groups in the digital core model containing various rock minerals.

Figure 5 is a model of different types of clay minerals and their distribution.

Fig. 6 is a two-dimensional model after asphaltene deposition in the simulated condensate model at 80 °C.

Fig. 7 is the asphaltene deposition model under different simulation conditions.

Figure 8 is a two-dimensional model of asphaltene adsorption in the simulated condensate model under 180°C water-humidity conditions.

Fig. 9 is the asphaltene adsorption model under different simulation conditions.

Detailed ways

The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments, but the present invention is not limited to the following embodiments.

The present invention specifically relates to a method for simulating adsorption damage of asphaltene deposition in heavy oil reservoirs based on a digital core model. The simulation of asphaltene deposition and adsorption processes in heavy oil reservoirs in this embodiment is achieved through the following steps:

What is used in the embodiment is the digital core reconstruction technology containing multiple rock minerals based on the two-dimensional information of the reservoir rock. The two-dimensional information of the reservoir rock contained in it mainly includes the particle size distribution of the reservoir, casting data, porosity, Rock mineral content and occurrence distribution, etc.

(1) Construction of the original digital core model containing various rock minerals

The construction of the digital core model containing a variety of rock minerals is carried out according to the process shown in Figure 1, in which the process method is used to construct the basic digital core model to simulate the deposition process on the basis of satisfying the high-energy environment and the principle of falling simulation with the largest gravity potential energy gradient , combined with the porosity of the real core, the compaction factor is selected to control the porosity of the digital core. In order to construct a digital core model containing multi-component rock minerals, during the deposition process, when the radius of spherical particles is randomly selected according to the real particle size, the volume occupied by other types of rock minerals is considered, so the size of sedimentary particles is not only determined by the original particle size distribution decision, with additional consideration of the ratios between other types of rock minerals and reservoir sandstone grains. When using the hybrid algorithm to construct the initial digital core model, the spatial occupancy (point, line, and surface) of a unit pixel point is assigned a weight according to its contribution to the instability of the neighborhood, where the surface is 5 and the edge is 3. , the point is 2; when selecting the exchange unit voxel point, calculate the instability contribution S of the voxel point and the neighboring occupancy point, line and surface, and based on the process of energy value decline in the simulated annealing algorithm, Introduce the parameter S _d of the contribution degree of the voxel point of the exchange unit to the instability of its neighborhood, judge the exchangeability of the voxel point of the exchange unit, and improve the effectiveness of the voxel point of the exchange unit, where S _d is the same as the simulation process The dimensionless value of the energy dependence of the system in :

S _d ＝N×β(E ₀ -E _i /△E _max ) (1)

In the formula, N is the number of neighborhood contact points influenced by a unit voxel point, dimensionless; β is the instability coefficient of a unit voxel point to the neighborhood, dimensionless; E ₀ is the initial energy of the system, dimensionless; E _i is the energy of the system after the i-th cooling, dimensionless; ΔE _max is the energy difference between the initial model and the reference model system, dimensionless.

As constraints, statistical functions commonly used in simulated annealing algorithms include: single-point probability functions, autocorrelation functions, linear path functions, and fractal functions, etc., using autocorrelation functions and linear path functions to anneal the initial system. When the model has a certain After the fractal features are introduced, the fractal function is introduced to further constrain the reconstruction model.

The spherical rock particles in the initial digital core model reconstructed by the hybrid algorithm were compared with the original spherical rock particles in the basic model reconstructed by the process method, and the model was preliminarily divided into rock skeleton phase (R), pore phase (P) and There are three types of clay mineral phases (C), in which the C phases are distributed in the form of irregular discrete groups of different sizes in space, and the 3D reconstruction model needs to be divided into two phases before the model operation, the pore phase and the rock skeleton phase. Merge into one phase T, and use the Hoshen-Kopelman algorithm to obtain the number, size and quantity of the clay groups in the model. Since there are some connected clay groups with larger sizes in the statistically divided clay mineral groups, while in the real reservoir Different types of clay minerals also have connectivity and contact on the surface of rock particles; K-means clustering algorithm can be used to cluster the clay minerals around the rock particles (cluster center) according to their belonging relationship. Therefore, the present invention effectively divides the clay mineral groups divided by the Hoshen-Kopelman algorithm by taking the center of the rock particle and all larger-sized clay mineral groups as data samples of the K-means algorithm. For large-sized connected clay mineral groups, they can be divided into multiple effective clay groups attached to the surface of rock particles according to the K-means algorithm.

Common clay minerals in reservoirs include montmorillonite, illite, illite-montmorillonite, chlorite, and kaolinite; the common distribution forms are intergranular pore filling, particle encrusting, metasomatism, and encrusting lining, etc., and The distribution characteristics of different clays are also different. The filling forms of clay minerals in the reconstruction model are mainly particle surface filling (single clay surface filling, multiple clay surface filling and layered clay surface filling), intergranular filling (clay filling between double grains, clay filling between multiple grains) and granular Therefore, when constructing the clay minerals of the digital core model, combined with the actual clay distribution form, the main form of clay mineral group distribution is divided into: intergranular filling according to the adjacent relationship between a single clay group and rock skeleton particles , Particle surface filling and replacement.

Based on the Hoshen-Kopelman algorithm, the size and quantity distribution of clay mineral groups in the model are obtained, and the type and quantity distribution of clay mineral groups in the reconstructed model are obtained according to the structure division, combined with the real reservoir clay content and distribution and the main clay mineral structure characteristics, According to the size and structural characteristics of clay mineral groups, the clay minerals in the model are endowed with corresponding clay properties, and a three-dimensional reconstructed porous media model containing different types of clay mineral distribution is obtained, as shown in Figure 3.

(2) Distribution characteristics of rock minerals in digital core model

The porosity of the reference model reservoir is 26.38%, the permeability is 0.614μm ² , and the shale content is 12.36%. The clay content distribution is as follows: montmorillonite 40.8%, kaolinite 19.1%, chlorite 27.4%, illite 6.3% . Among them, the occurrence of montmorillonite is mainly in the form of particle encrustation, and there are some forms of intergranular filling; kaolinite is filled with intergranular pores and distributed in the form of dispersed particle aggregates; chlorite is lined with encrustation and intergranular filling and replacement-like distribution; the distribution of illite includes intergranular filling, replacement and thin-film distribution.

① Distribution of clay groups in the reconstruction model

The distribution of different clay mineral groups in the digital core model based on the Hoshen-Kopelman algorithm is shown in Fig. 4, where the largest clay mineral group has a size of 27953 voxels, and the smallest clay mineral group has a size of 1 voxel ( The number of groups is 9432). Clay groups with a group size of less than 11 voxels accounted for only 1.91% of all clay groups; while the main clay groups were distributed between 10000 voxels and 25000 voxels, accounting for 97.29% of the total clay volume. The overall distribution of clay groups presents the characteristics of "major groups dominated and small groups dispersed", which is similar to the distribution of clay minerals in actual reservoirs.

②Statistics of clay minerals in the model after structural division

According to the structure discrimination of clay groups, all clay mineral groups are divided into three main types according to their occurrence: surface filling, intergranular filling and metasomatism. Among them, there are 4685 groups of clay mineral groups distributed in the form of intergranular filling, accounting for 67.13% of the total volume of clay; there are 4530 groups of clay mineral groups in the form of surface filling, accounting for 32.30% of the total volume of clay; and The metasomatized clay minerals are distributed sporadically in the rock particles, and their content only accounts for 0.28% of the total clay volume; the larger clay mineral groups in the model are mainly in the form of surface filling and intergranular filling.

Through the division and structural discrimination of clay mineral groups in the digital core model, each clay mineral group is gradually marked with different attributes (including group size, serial number, occurrence, etc.). Therefore, combined with the relevant information of the real reservoir (including clay content, clay type, clay occurrence, etc.), the clay minerals in the model are divided into different clay types according to the clay content and occurrence characteristics, and montmorillonite is the The clay mineral with the most content accounts for 40.84% of the total volume of clay minerals, chlorite accounts for 27.43%, kaolinite accounts for 19.11%, and illite accounts for 6.28%. And for clay mineral groups of different sizes, the distribution of various clay mineral groups is relatively uniform.

③Distribution of clay minerals in the digital core model containing multiple rock minerals

Judging from the distribution of clay minerals in each layer, the distribution of clay minerals includes some clay mineral particles with a group size of less than 5, while the distribution of large group clay minerals is filled between particles (double particles and multiple particles), The surface of the particles is mainly filled (altered clay, cladding lining, film type), and there are a small amount of clay minerals distributed in an alternate manner.

It can be seen from Figure 5a and Figure 5e that due to the high content of montmorillonite in the model, it is mainly filled in continuous sheets and attached to the surface of the rock matrix, while the clay group of montmorillonite is mainly filled between particles and on the surface. The forms are distributed in the reconstruction model, and the number of groups is 2117 and 1935 respectively; the content of intergranular filling montmorillonite and surface filling montmorillonite account for 41.41% and 58.39% of the total volume of clay minerals; the largest intergranular The size of the filled smectite-like group is 22716 voxels, and the size of the largest surface-filled smectite-like group is 21273 voxels; it can be seen from Figure 5b and Figure 5f that the chlorite is ring-shaped and partially connected The groups are distributed in the model. There are 900 groups in the intergranular filling chlorite, accounting for 62.53% of the total volume of clay minerals; there are 975 groups in the surface filling chlorite, accounting for 37.14% of the total volume of clay minerals. %, the largest intergranular filling and surface filling chlorite-like groups are 22,767 voxels and 21,193 voxels respectively; it can be seen from Figure 5c and Figure 5g that kaolinite is generally distributed in the form of intergranular filling in Among the reservoir rocks, the distribution of clay minerals in the model shows that intergranular filling is the main distribution form of kaolinite in the model, accounting for 98.58% of the total clay volume, and the largest intergranular filling clay-like group has a size of 27953 voxel; from Fig. 5d and Fig. 5h, it can be seen that the occurrence of illite in the model includes intergranular filling, surface filling and replacement forms, of which surface filling and intergranular filling illite account for 41.32% and 58.12% respectively . Metasomatism is distributed in all four clay minerals, and is mainly distributed among rock particles in the form of sporadic distribution. The number of metasomatous clay groups in montmorillonite, chlorite, kaolinite and illite is 504, respectively. , 619, 61 and 244, the clay-bearing three-dimensional porous media model constructed is in good agreement with the distribution and occurrence of clay minerals in real reservoirs.

(3) Construction of damage model after asphaltene deposition in heavy oil reservoirs

Asphaltene in crude oil has different deposition possibilities and amounts under different temperature and pressure conditions; with the continuous deposition of asphaltene in pores, some asphaltene suspended in the crude oil system accumulates Asphaltenes are adsorbed to the surface of rock minerals under the action of hydrogen bonds, metal bonds, acid-base effects, etc.; under different water humidity and temperature conditions, the adsorption process of asphaltenes on the surface of different types of rock minerals satisfies the Langmuir isotherm adsorption equation better According to the Freundlich isothermal adsorption equation, the adsorption performance parameters (adsorption equilibrium constant and maximum adsorption capacity) of different types of rock minerals can be obtained, as shown in Table 1.

Table 1 Changes of clay minerals under high temperature and high pH simulated condensate conditions

The amount of asphaltene deposition is closely related to the temperature, pressure, and changes in the composition of crude oil in the reservoir. From the law of asphaltene deposition in different simulated environments in laboratory experiments, it can be known that the amount of asphaltene deposition in the crude oil samples in the examples varies with The increase of the critical pressure showed a trend of first increasing and then decreasing. At the same time, the maximum deposition amount was 3.27% and 2.61% at 80°C and 180°C respectively. The asphaltene in the original reservoir model was constructed according to the asphaltene deposition simulation Sedimentary model, in which Fig. 6a is a two-dimensional slice of the original digital core model containing various rock minerals, and Fig. 6b is a two-dimensional slice of the digital core model after asphaltene deposition. Asphaltene aggregates are suspended in the reservoir pores, and the original structure of the reservoir model has not changed before and after simulating asphaltene deposition; however, it can be seen from the asphaltene deposition models under different simulation conditions (Figure 7a is 80°C Figure 7b is the digital core model after asphaltene deposition under the condition of 180 °C), and the deposition of asphaltene in the pores of the model is more obvious under low temperature conditions.

(4) Construction of damage model after asphaltene adsorption in heavy oil reservoirs

Asphaltene deposited in crude oil is suspended in the pores of the reservoir in the form of asphaltene aggregates. At the same time, because the rock minerals in the reservoir have strong adsorption properties, there are more adsorption sites on the surface, forming asphaltene aggregates. Adsorption processes on the surface of rock particles provide the necessary material basis. The adsorption process of asphaltene on the surface of rock minerals is not only affected by the properties of different types of rock minerals, but also factors such as temperature and water-humidity conditions have a greater impact on the adsorption process of asphaltene on the surface of rock minerals. The asphaltene adsorption law of different types of rock minerals in the simulated reservoir environment was obtained from laboratory experiments, and the Langmuir adsorption model and the Freundlich adsorption model were used to fit the different adsorption processes, including the Langmuir adsorption equilibrium constant K _L , Freundlich A series of parameters such as adsorption equilibrium constant, maximum adsorption capacity Q _max , nonlinear factor n and equilibrium adsorption capacity Q _e are shown in Table 1. From the content of clay minerals in different types of models and the maximum adsorption capacity of different clay minerals, the maximum adsorption capacity of asphaltene on the surface of rock minerals in the model can be obtained under different conditions, as shown in Table 2.

Table 2 The maximum adsorption amount of asphaltenes on the surface of different rock minerals under different simulation conditions

During the simulation process, the adsorption parameters of clay minerals were set according to Table 1 and Table 2, and the adsorption parameters of "other" types of clay minerals and quartz sand were selected from the adsorption parameters of 60-mesh quartz sand. In order to simulate the reservoir damage process caused by asphaltene deposition and adsorption under different conditions, the reservoir damage model caused by asphaltene deposition and adsorption process was constructed according to the process shown in Fig. 2, as shown in Fig. 8 and Fig. 9.

Figure 8a is a two-dimensional slice of the original digital core model containing various rock minerals, and Figure 8b is a two-dimensional slice of the digital core model after asphaltene adsorption, asphaltene aggregates suspended in the pore space are adsorbed on different types of rock minerals Under the influence of different adsorption capacities, it is adsorbed on the surface of rock minerals according to different adsorption capacities. Since montmorillonite and chlorite have strong adsorption capacity and large adsorption capacity, the adsorption of asphaltene on the surface of these two types of rock minerals after the simulation process is completed The adsorption amount on the surface of illite and quartz sand is relatively small; at the same time, the basic assumption of the Langmuir adsorption model is satisfied, and the asphaltene aggregates are basically attached to the surface of various rock minerals in the form of monolayer adsorption. Figure 9 (Figure 9a is the digital core model after asphaltene deposition and adsorption under dry conditions of 80°C, Figure 9b is the digital core model after asphaltene deposition and adsorption under 80°C wet conditions, and Figure 9c is the digital core model of asphaltene deposition under 180°C dry conditions Figure 9d is the digital core model after asphaltene deposition and adsorption under 180°C wet conditions) It can be seen that the asphaltene deposition and adsorption status is different under different reservoir simulation conditions, and the asphaltene deposition and adsorption status is different under dry conditions. Reservoir damage caused by sedimentation adsorption is more obvious, and a large number of asphaltene aggregates are adsorbed on the surface of rock minerals. Under the above conditions, the reservoir damage caused by the deposition and adsorption of asphaltene is not obvious.

(5) Study on the characteristics of reservoir damage caused by asphaltene deposition and adsorption

Since rock mineral types, reservoir temperature, water-humidity conditions and other factors have different effects on asphaltene deposition and adsorption process, in order to further study the influence of asphaltene deposition and adsorption process on reservoir porosity and permeability under different simulation conditions , this section studies the clay mineral content and volume changes caused by changes in the mineral properties of reservoir rocks, as well as the changes in porosity and permeability of the model.

① Changes in content and volume of asphaltene

Figure 7 and Figure 9 show the reservoir damage model caused by asphaltene deposition and adsorption under different simulation conditions. During asphaltene deposition, asphaltene is suspended in the reservoir pores in the form of dispersed aggregates, while in During the adsorption process of asphaltenes, suspended asphaltene aggregates adhere to the surface of rock minerals under the adsorption of rock minerals according to the difference of maximum adsorption capacity and adsorption capacity. Table 3 shows the changes of asphaltene content and volume in the model under different deposition conditions.

Table 3 The deposition amount and volume change of asphaltene under different conditions

It can be seen from Fig. 7 and Table 3 that during the process of asphaltene deposition, the porosity of the original model under high temperature conditions and the original model under low temperature conditions decreased from the original 26.38% to 25.52% and 25.69%, respectively. Although the precipitation of asphaltene aggregates is suspended in the crude oil system in the form of solid particles, it is regarded as a solid phase, and although the porosity decreases to a certain extent, the deposition of asphaltene aggregates does not affect the effective porosity of the reservoir. have a significant impact on permeability. Suspended asphaltene aggregates are adsorbed on the surface of different types of rock minerals under the action of hydrogen bond, dipole-dipole interaction, ion exchange, etc., so that the physical properties of the reservoir are significantly affected, as shown in Table 3. The deposition amount, adsorption amount and volume change of asphaltene after mass deposition and adsorption, among which the densities of montmorillonite, illite, chlorite, kaolinite, quartz sand, other types of rock minerals and asphaltene are respectively 2.35kg/m ³ , 2.75kg/m ³ , 2.75kg/m ³ , 2.62kg/m ³ , 2.65kg/m ³ , 2.65kg/m ³ and 1.2kg/m ³ .

Table 4 The deposition amount, adsorption amount and volume change of asphaltene after asphaltene deposition and adsorption under different conditions

It can be seen from Table 4 that the deposition volume of asphaltenes varies with the pore volume of the initial model, and the deposition volumes of the original model at 80°C and 180°C are 69010 voxels and 55081 voxels, respectively. Since the maximum adsorption capacity and adsorption equilibrium constant are the two most important limiting conditions in the simulation process, after the simulation, under dry conditions, except that the deposition amount of the original model at 80 °C is greater than the maximum adsorption amount of asphaltene by the model rock minerals, other Under dry conditions, the maximum adsorption capacity of rock minerals in different models is significantly higher than that of asphaltene deposition, indicating that under dry conditions, after asphaltene deposition occurs, there are more adsorption sites on the surface of reservoir rock minerals for the undisturbed. Adsorbed asphaltene aggregates. Under water-wet conditions, the adsorption process of asphaltene on the rock surface better satisfies the Langmuir isothermal adsorption model due to the existence of the water film, indicating that the existence of the water film makes the adsorption process of asphaltene on the rock mineral surface closer to monolayer adsorption. The existence of water film effectively inhibits the adsorption process of asphaltene on the surface of rock minerals. On the other hand, under water-wet conditions, the maximum adsorption capacity of various rock minerals is significantly reduced, and the effective adsorption sites provided by the surface of rock minerals in the reservoir point reduction.

② Changes in porosity and permeability

During the process of asphaltene deposition, although asphaltene aggregates, which are regarded as plugs, are continuously precipitated from the crude oil system, resulting in a decrease in porosity, the asphaltene basically exists in the reservoir pores in the form of suspension during this process, so it is called When treated as a solid phase, the asphaltene aggregates did not form an effective plug in the pores to affect the reservoir permeability; while the adsorption process of rock minerals for asphaltene, on the one hand, decreased the effective porosity of the reservoir, on the other hand Asphaltenes attached to rock mineral surfaces alter the pore-throat structure of reservoir rocks, further reducing reservoir permeability.

Table 5 Changes of model pore parameters during different types of reservoir damage

It can be seen from Table 5 that the deposition and adsorption of asphaltene has caused obvious secondary damage to the reservoir. Under the condition of dry simulated condensate at 80°C, the reservoir permeability first decreased from 589.76×10 ³ μm ² to 552.12×10 ³ μm ² , the porosity decreased from 26.38% to 23.42%, and the permeability and porosity decreased by 6.38% and 11.22% respectively after reservoir damage. The water-wet environment has a significant inhibitory effect on asphaltene deposition and adsorption. After asphaltene deposition and adsorption under the water-humidity condition of the simulated condensate model at 180 °C, the permeability decreased from 589.76×10 ³ μm ² to 582.90×10 ³ μm ² . Just 1.16%. Therefore, temperature conditions and water-humidity environment play an important role in controlling the reservoir damage process of heavy oil steam injection high-pressure wells.

Claims (6)

1. a kind of heavy crude reservoir asphaltene deposits absorption damage analogy method based on digital cores model, which is characterized in that packet Include following steps：

Step 1, it is based on true reservoir two-dimensional signal, based on improved mixed algorithm and clustering algorithm building containing a variety of rock forming minerals Digital cores model；

Step 2, it is obtained under different simulated conditions by laboratory experiment, the deposition and asphalitine of asphalitine are in different type rock The absorption situation of stone mineral surfaces；

Step 3, the deposition fraction of the different crude oils asphalitine under different simulated conditions obtained by laboratory experiment, contains original On the basis of a variety of rock forming mineral digital cores models, deposition asphalitine is placed in interstitial space occupy-place in proportion, output is heavy Digital cores model after product asphalitine；

Step 4, the adsorpting characteristic based on asphalitine under different simulated conditions on different type rock forming mineral surface, will be deposited on hole The asphalitine in gap space is placed in different types of rock forming mineral surface, output absorption according to the adsorpting characteristic that laboratory experiment obtains Digital cores model after asphalitine.

2. the heavy crude reservoir asphaltene deposits absorption damage analogy method based on digital cores model according to claim 1, It is characterized in that, true reservoir two-dimensional signal includes casting body flake, rock grain size distribution, clay mineral point in the step 1 Cloth, clay mineral attitude Characteristics, specific model construction step include：

The first step considers the total content of clay mineral, in deposition process when constructing fundamental digital core model using process method In, according to the size distribution situation of true reservoir, the radius of deposited particles is randomly choosed, the size of deposited particles is not only by original Deposited particles size distribution determine that while the additional ratio considered between clay mineral and reservoir sandstone particle is high meeting Deposition process is simulated on the basis of energy environment and the maximum whereabouts simulation principle of gravitional force gradient, and combines true core hole Degree, the porosity of selection compacting factor domination number word core model；

Second step, it is unstable to neighborhood by it by the space occupy-place of unit bodies pixel, i.e. point, line and face occupy-place three types Property percentage contribution assign weight, wherein face is 5, o'clock is 2 while be 3；When choosing cross-over unit body image vegetarian refreshments, the body is calculated Unstability percentage contribution S on pixel and neighborhood mass point, line and face, and based on energy value decline in simulated annealing Process, introduce cross-over unit body image vegetarian refreshments percentage contribution parameter S instable to its neighborhood_d, to the commutative of exchange point Property judged, improve the validity of cross-over unit body image vegetarian refreshments, wherein S_dFor nothing relevant to system capacity in simulation process Because of sub-value：

S_d=N × β (E₀-E_i/△E_max) (1)

In formula, N is the number for the neighborhood contact point that unit body image vegetarian refreshments influences, dimensionless；β be unit body image vegetarian refreshments to neighborhood not Stability coefficient, dimensionless；E₀For the primary power of system, dimensionless；E_iFor the energy of system after i-th cooling, dimensionless； ΔE_maxFor initial model and the energy differences of the model reference system based on reservoir rock two-dimensional signal, dimensionless, initial model Refer to the fundamental digital core model of process method building；

Third step, using improved mixed algorithm construct initial number core model the step of be：

1. the reference model based on reservoir rock two-dimensional signal is established, using fundamental digital core model that process method constructs as changing Into the initial model of hybrid algorithm, initial temperature is set, and calculates the relevant parameter of initial system, includes auto-correlation function, line Property path function, fractal characteristic function and energy value；

2. calculating the 26 space occupy-places pair of cross-over unit body image vegetarian refreshments on the basis of guaranteeing simulated annealing temperature-fall period randomness The instable percentage contribution S of neighborhood；Work as S>S_dWhen, it is believed that the unstable degree of the point is higher, can be used as the friendship of system update It changes a little；Work as S<S_dWhen, then repeatedly step is 2.；

3. calculating the relevant parameter of system after cross-over unit body image vegetarian refreshments, including single-point probability function, auto-correlation function, linear road Diameter function, fractal function and energy value calculate and the energy differences Δ E that does not exchange preceding system；As Δ E<When 0, more new system；When ΔE>When 0, judge whether system updates according to Metropolis criterion, i.e., receives system under certain Probability Condition more Newly；System update condition is unsatisfactory for after if it is determined that, then return step is 2.；

4. loop termination condition in judging judges whether system capacity difference is less than the minimum energy of setting under the conditions of same temperature Measure difference；Simultaneously to avoid system from just cooling down, system capacity rises and immediately leads to the cooling that interior circulation terminates and generates, and passes through Set the failure rate f of system update_fAvoid the appearance of the phenomenon, wherein：

In formula, N_fFor the number for the update failure for causing system capacity to be gone up；N is the total degree of system update；

Work as f_fAfter certain value, then cooling processing is carried out, temperature-fall period is taken etc. than cooling profiles, and return step is 2.；

5. being less than setting when analog process temperature is reduced to final set temperature or with the system capacity difference DELTA E of last time cooling When value, entire simulation process is terminated；

As constraint condition, statistical function used in simulated annealing includes：Single-point probability function P (r), auto-correlation letter Number, linear path function and fractal function, carry out annealing simulation to initial system using auto-correlation function and linear path function, After model has certain fractal characteristic, the further constraint reestablishing model of fractal function is introduced；

4th step, class ball rock particles after hybrid algorithm is rebuild in initial number core model and construct in process method The original spherical rock particles of fundamental digital core model compares and takes the two supplementary set, and initial number core model is tentatively drawn It is divided into rock matrix phase, hole phase and clay mineral phase three categories；

5th step counts the clay mineral group in initial number core model by Hoshen-Kopelman algorithm And division, wherein the probability occupied by M phase is c, the probability occupied by T-phase is 1-c, for each of lattice occupy-place i, when When it is occupied by M phase, then a group label is assigned to the occupy-placeWherein α is the characteristic symbol of group label, and t is group The label of the number of label, a certain discrete point is indicated by a series of natural numbers：

Only one natural number is the accurate marker of group α in this set of natural numbers, this is labeled asAnd the value is set (3) minimum value of all natural numbers in, the relationship between other each groups labels are then provided by following set of integers：

Wherein, onlyIt is positive integer element, which is the number of M phase in group, is clocked when carrying out t deutero-albumose, if group Middle M phase number is less than the M phase number of last time labeling process group α, then the difference is expressed as to the T-phase of corresponding t times group α Number, other elements in (4) are all negative integer, are reflectedIt is marked with other groupsRelationship,WithRelationship use Formula (5) indicates：

Inspection is judged whether discrete point has the adjacent discrete point being scanned, if adjacent discrete point is T-phase, will currently be judged to Dialysis scatterplot assigns the label of new group；If there is an adjacent discrete point assigned group label, then by current grid with Adjacent discrete point assigns identical label；If there is more than one adjacent discrete point has assigned group label, and group mark Remember different, then assign discrete points all in group to identical label, finally clay mineral phase in statistics and partitioning model The number and size of group；

6th step, biggish connection group are the clay mineral base that group size is greater than neighboring matrix particle size in clay phase Group, by K-means algorithm to the larger-size clay mineral group of clay mineral phase group in initial number core model into Row divides, and specific step is as follows：

1. reading the set of data sample；

2. setting the number k of sample clustering, random selection k number is according to sample as initial data sample cluster centre；

3. calculating Euclidean distance, each data are calculated in data sample to the European geometric distance of each cluster centre, then basis Data are divided into cluster corresponding to corresponding different cluster centres according to far and near distance by minimal error sum-of-squares criterion function In the middle；

4. cluster centre is updated, using the mean value of data all in each cluster center new as each cluster, and accidentally with minimum Poor sum-of-squares criterion recalculates the value of new cluster centre；

5. iteration differentiates, by step 4. in the numerical value that is calculated compare with the preceding numerical value being once calculated, if the two Difference is less than or equal to preset critical value, then stops iteration, otherwise re-start step and be 3. iterated；

6. output data sample and cluster result, cluster centre, size including each cluster；

7th step, when the discrete point on clay mineral group boundary is single rock particles, then by the clay mineral group division To hand over form, explanation form is distributed mainly in rock particles, in the formal distribution of single discrete point；When clay mineral group It is then particle surface by the clay mineral phase group division when adjacent discrete point on boundary is single rock matrix particle and hole Filling form；

When the adjacent discrete point on clay mineral group boundary is multiple rock matrix particles and hole, then by the clay mineral base Group is divided into intergranular filling form；

The clay mineral group of explanation form, particle surface filling form and intergranular filling form is respectively labeled as A, B, C；Most The distribution of different structure clay mineral group and the distribution of different types of clay mineral group are obtained eventually；

8th step obtains clay mineral in initial number core model based on Hoshen-Kopelman algorithm and K-means algorithm Group size and distributed number, and the clay mineral types of radicals and distributed number that are divided by structure, in conjunction with true storage Layer clay content and distribution and main clay mineral design feature, by clay mineral phase group size and design feature by mould Clay mineral in type assigns corresponding clay property, obtains the digital cores model of the distribution of rock forming mineral containing multicomponent.

3. the heavy crude reservoir asphaltene deposits absorption damage analogy method based on digital cores model according to claim 1, It is characterized in that, laboratory experiment includes under simulation initial reservoir and different working conditions, when reservoir temperature, pressure in the step 2 When fluid and injection fluid properties change in power, layer, the variation of the asphaltene deposits amount in crude oil；Asphalitine is in rock mine The Adsorption law and adsorpting characteristic on object surface, adsorbance, absorption constant including asphalitine on different type rock forming mineral surface And maximum adsorption capacity.

4. the heavy crude reservoir asphaltene deposits absorption damage analogy method based on digital cores model according to claim 1, It is characterized in that, obtaining the deposition that the digital cores model after output deposition asphalitine refers to simulation asphalitine in the step 3 Process, steps are as follows：

The first step obtains the pore volume of digital cores by the original digital cores model containing a variety of rock forming minerals；

Second step, deposition fraction and step 1 of the crude oil studies on asphaltene obtained based on step 2 under different simulated conditions are obtained The pore volume containing a variety of rock forming mineral component number core models, calculate it is original contain a variety of rock forming mineral digital cores models Asphaltene deposits amount in hole；

Third step is containing the smallest unit bodies pixel in a variety of rock forming mineral component number core models with what step 1 obtained Basic deposition analogue unit, will need the asphalitine deposited to deposit analogue unit substantially as maximum analog unit, randomly places Deposition process in interstitial space occupy-place, until completing all asphalitines.

5. the heavy crude reservoir asphaltene deposits absorption damage analogy method based on digital cores model according to claim 1, It is characterized in that, obtaining the absorption that the digital cores model after output adsorptive pitch matter refers to simulation asphalitine in the step 4 Process, steps are as follows：

The first step, the digital cores model containing a variety of rock forming minerals after reading asphaltene deposits；

Second step, the laboratory experiment obtained based on step 2 is as a result, the absorption of input different type rock forming mineral at different conditions The equilibrium constant and maximum adsorption capacity parameter；

Third step is determined original containing a variety of rock forming mineral groups by the Hoshen-Kopelman group division and statistic algorithm The radical amount and size of different type rock forming mineral in score word core model, by different type rock forming mineral to asphalitine Adsorpting characteristic relationship determine the adsorbance of different type rock forming mineral surface asphalitine；

4th step, in conjunction with original containing all kinds of rock forming mineral group sizes and reality in a variety of rock forming mineral component number core models The maximum adsorption capacity of different type rock forming mineral surface asphalitine under difference simulated conditions obtained in testing, rock in computation model The total adsorption capacity of stone ore object group；

5th step, when the maximum adsorption capacity of rock forming mineral group is greater than the deposition quality of asphalitine, rock forming mineral surface Asphaltene adsorption ratio determines that the asphalitine on all kinds of rock forming mineral surfaces is always adsorbed according to adsorpting characteristic constant under simulated conditions Amount is controlled by the deposition of asphalitine；When the maximum adsorption capacity of rock forming mineral group is less than or equal to the deposition quality of asphalitine When, the Asphaltene adsorption ratio on rock forming mineral surface is come true according to the maximum adsorption capacity of rock forming minerals all kinds of under simulated conditions Fixed, the adsorbance that the asphalitine on all kinds of rock forming mineral surfaces is total is then controlled by maximum adsorption capacity；

6th step calculates " the absorption distance " of asphalitine and clay and sorts, wherein the absorption ratio of " absorption distance " with each clay Example is related；

7th step calculates the adjacent pores occupy-place of rock forming mineral group boundary according to the Convenient stable criterion of the space occupy-place Stability, asphalitine is placed in the higher hole occupy-place of priority level by " absorption distance ", if clay reaches maximum Adsorption capacity and simulation process terminates when having met total adsorbance, otherwise continues to be simulated by the above process.

6. the heavy crude reservoir asphaltene deposits absorption damage analogy method based on digital cores model according to claim 1, It is characterized in that, damaging pitch in the digital cores model of front and back by comparing heavy crude reservoir asphaltene deposits under different simulated conditions Matter is further ground in the variation of the adsorption volume on rock forming mineral surface, the variation of the porosity and permeability of digital cores model Study carefully influence of the heavy crude reservoir asphalitine damage front and back to Microstructure of Reservoirs.