A multi-layer heterogeneous reservoir percolation simulation analysis method based on digital cores

Abstract

The application discloses a kind of multilayer heterogeneous reservoir percolation simulation analysis methods based on digital core, including obtaining the three-dimensional digital core model of high-permeability reservoir sample, medium-permeability reservoir sample and low-permeability reservoir sample;Get the pore network model after commingling;Based on the pore network model after commingling, simulate oil-water displacement and suction process by percolation theory, calculate the corresponding capillary pressure curve and relative permeability curve, and compare and analyze percolation characteristics.The beneficial effects of the technical scheme proposed in the application include: based on the pore network model after commingling, simulate oil-water displacement and suction process by percolation theory, calculate the corresponding capillary pressure curve and relative permeability curve, and compare and analyze percolation characteristics.Thereby, the multilayer heterogeneous reservoir percolation process can be simulated, and the interlayer interference influence mechanism and residual oil distribution characteristics during multilayer commingling in the reservoir with large differences in low-permeability and heterogeneity can be truly reflected.

Description

Technical Field

[0001] This invention relates to the field of reservoir seepage research technology, and in particular to a method for simulation and analysis of seepage in multi-layered heterogeneous reservoirs based on digital cores. Background Technology

[0002] In oil and gas field development, to reduce extraction costs and improve reservoir utilization, multi-layer co-production is often employed. However, due to the significant inter-layer heterogeneity often present in reservoirs, multi-layer co-production exacerbates inter-layer conflicts due to reservoir heterogeneity and differences in oil-water two-phase flow, leading to severe inefficient water circulation, reduced water injection efficiency, and significantly impacting waterflooding effectiveness. While previous research has yielded substantial results on inter-layer interference, most multi-pipe parallel laboratory experiments only focused on co-production in medium-to-high permeability cores, resulting in incomplete interpretation of experimental data and failing to accurately reflect the inter-layer interference mechanisms and residual oil distribution characteristics in low-permeability, highly heterogeneous reservoirs undergoing multi-layer co-production. Summary of the Invention

[0003] In view of this, it is necessary to provide a seepage simulation and analysis method for multi-layer heterogeneous reservoirs based on digital cores, in order to solve the technical problem that existing multi-pipe parallel indoor experimental studies cannot truly reflect the inter-layer interference mechanism and residual oil distribution characteristics during multi-layer synergistic production in reservoirs with low permeability and large heterogeneity differences.

[0004] To achieve the above objectives, this invention provides a method for simulating and analyzing seepage in multi-layered heterogeneous oil reservoirs based on digital core data, comprising:

[0005] Three-dimensional grayscale images of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples in the study area were obtained respectively. Based on the three-dimensional grayscale images of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples, three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples were obtained.

[0006] Based on the three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples, parallel three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples are obtained. Based on the parallel three-dimensional digital core models, the pore network model after combined mining is obtained.

[0007] Based on the pore network model after comminution, calculate the absolute permeability and relative permeability of the pore network model after comminution, and plot the capillary pressure curves during the displacement and suction processes.

[0008] Based on the pore network model after joint mining, the corresponding physical properties and structural characteristic parameters are calculated.

[0009] Based on the pore network model after commingled mining, the oil-water displacement and adsorption processes are simulated using permeation theory. The corresponding capillary pressure curves and relative permeability curves are calculated, and the seepage characteristics are compared and analyzed.

[0010] In some embodiments, based on the three-dimensional grayscale images of high-permeability reservoir samples, intermediate-permeability reservoir samples, and low-permeability reservoir samples, three-dimensional digital core models of the samples are obtained, specifically including:

[0011] Binary segmentation was performed on the three-dimensional grayscale images of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples to obtain the value of each pixel in the three-dimensional grayscale images of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples.

[0012] Based on the assigned values ​​at each pixel of the high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples, the corresponding three-dimensional digital core models are obtained.

[0013] In some embodiments, based on the three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples, a parallel three-dimensional digital core model of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples is obtained. Based on the parallel three-dimensional digital core model, a pore network model after combined mining is obtained, specifically including:

[0014] Based on the three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples, parallel three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples are obtained.

[0015] Based on the parallel three-dimensional digital core model, the pore network model after combined mining is obtained by the maximum sphere method.

[0016] In some embodiments, the method for calculating the absolute permeability of the pore network model after commingling is as follows:

[0017] The model is saturated with a fluid, a driving pressure is applied to the model, the fluid flow rate is calculated, and the absolute permeability of the pore network model after combined mining is obtained based on the driving pressure and fluid flow rate.

[0018] In some embodiments, the formula for calculating the absolute permeability of the post-harvest pore network model is:

[0019]

[0020] In the formula, K is the absolute permeability; μ i Let Q be the viscosity of phase i; i P is the flow rate under the applied pressure difference when the model is fully saturated with phase i fluid; A is the cross-sectional area of ​​the model; P is the flow rate under the applied pressure difference. I For inlet pressure; PO L represents the export pressure; L represents the model length.

[0021] In some embodiments, the method for calculating the relative permeability of the pore network model after commingling specifically includes:

[0022] Calculate the total flow rate of the pore network model after combined mining when the entire model is a single-phase flow in a certain phase.

[0023] The flow rate of a phase is calculated when the pore network model after commingled mining is a multiphase flow.

[0024] The relative permeability of a phase is obtained by considering the total flow rate of a single-phase flow in the overall pore network model after commingled mining and the flow rate of that phase in the multiphase flow model after commingled mining.

[0025] In some embodiments, the relative permeability of a phase is obtained based on the total flow rate when the overall pore network model after commingled mining is a single-phase flow and the flow rate of that phase when the pore network model after commingled mining is a multiphase flow. The specific formula is as follows:

[0026]

[0027] In the formula, K rp Q represents the relative penetration rate. tmp Q represents the flow rate of phase p in multiphase flow. tsp The total flow rate of the entire model is for a single-phase flow of phase p.

[0028] In some embodiments, the specific method for plotting capillary pressure curves during displacement and aspiration processes is as follows:

[0029] The capillary inlet pressure that needs to be overcome when oil enters a water-filled unit is calculated using the Young-Laplace equation.

[0030] Calculate the water saturation of the entire model;

[0031] The capillary pressure curve of the entire model is obtained based on the capillary inlet pressure and the water saturation of the entire model.

[0032] In some embodiments, the Young-Laplace equation is:

[0033]

[0034] In the formula, P cow σ is the capillary inlet pressure that oil needs to overcome when entering a water-filled unit. ow r1 and r2 are the two principal radii of the interface surface, representing the oil-water interfacial tension.

[0035] In some embodiments, the water saturation S of the entire model wCalculate using the following formula:

[0036]

[0037] In the formula, n is the total number of pores and pore throats; V i V is the volume of the pores or pore throats. iw This represents the volume of water contained in the corresponding pore throat.

[0038] Compared with the prior art, the beneficial effects of the technical solution proposed in this invention include:

[0039] (1) Based on micron CT, three-dimensional grayscale images of high-permeability reservoir, medium-permeability reservoir and low-permeability reservoir samples were obtained respectively. Binary segmentation was performed by the maximum inter-class distance algorithm to obtain the corresponding three-dimensional digital cores. Based on the three-dimensional digital cores of high-permeability reservoir, medium-permeability reservoir and low-permeability reservoir samples, a parallel digital core model was constructed. According to the parallel digital core model, the pore network model after joint mining can be obtained.

[0040] (2) Based on the pore network model after joint mining, calculate the corresponding physical properties and structural characteristic parameters;

[0041] (3) Based on the pore network model after synergistic production, the oil-water displacement and intake process is simulated through permeability theory, and the corresponding capillary pressure curve and relative permeability curve are calculated and compared to analyze the seepage characteristics. Thus, the seepage process of multi-layer heterogeneous reservoirs can be simulated, and the inter-layer interference mechanism and residual oil distribution characteristics during multi-layer synergistic production of low-permeability reservoirs with large heterogeneity differences can be realistically reflected. Attached Figure Description

[0042] Figure 1 This is a schematic flowchart of an embodiment of the seepage simulation and analysis method for multi-layer heterogeneous oil reservoirs based on digital cores provided by the present invention.

[0043] Figure 2 These are micron-sized CT scan images of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples.

[0044] Figure 3 yes Figure 1 A flowchart illustrating step S1;

[0045] Figure 4 yes Figure 2 Three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples and low-permeability reservoir samples;

[0046] Figure 5 yes Figure 1 A flowchart illustrating step S2;

[0047] Figure 6 It is Figure 4The parallel three-dimensional digital core model obtained by merging the three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples and low-permeability reservoir samples, and the pore network model after combined production obtained based on the parallel three-dimensional digital core model.

[0048] Figure 7 yes Figure 1 A flowchart illustrating the method for calculating the relative permeability of the pore network model after commingled mining in step S1.

[0049] Figure 8 yes Figure 6 Comparison of structural features of pore network models after joint mining;

[0050] Figure 9 It simulates the capillary pressure curves obtained during oil-water displacement and suction processes;

[0051] Figure 10 It is a relative permeability curve simulating the oil-water displacement process;

[0052] Figure 11 It is a relative permeability curve simulating the inhalation process;

[0053] Figure 12 It is a visualization process for simulating oil-water distribution through parallel displacement simulation. Detailed Implementation

[0054] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0055] Please refer to Figure 1 This invention provides a method for simulating and analyzing seepage in multi-layered heterogeneous oil reservoirs based on digital cores, comprising the following steps:

[0056] S1. Obtain three-dimensional grayscale images of high-permeability reservoir samples, medium-permeability reservoir samples and low-permeability reservoir samples in the study area respectively. Based on the three-dimensional grayscale images of high-permeability reservoir samples, medium-permeability reservoir samples and low-permeability reservoir samples, obtain three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples and low-permeability reservoir samples.

[0057] When selecting high-permeability, medium-permeability, and low-permeability reservoir samples for the study area, representative high-permeability, medium-permeability, and low-permeability reservoir samples should be chosen. Inappropriate sample selection may lead to a decrease in the accuracy of the final experimental results.

[0058] In this embodiment, CT scanning was used to acquire three-dimensional grayscale images of high-permeability, medium-permeability, and low-permeability reservoir samples from the study area. CT scanning has the advantages of true three-dimensional imaging and non-destructive features. X-ray micron-level CT utilizes cone-shaped X-rays to penetrate the object, magnifying the image through objectives of different magnifications. A three-dimensional model is reconstructed from a large number of attenuated X-ray images obtained by 360-degree rotation. The characteristic of using micron-level CT for core scanning is that it can comprehensively display very small feature surfaces through a large amount of image data without damaging the sample.

[0059] In this embodiment, as Figure 2 As shown, three-dimensional grayscale images of high-permeability, medium-permeability, and low-permeability reservoir samples were obtained based on micron-scale CT. The voxel resolution was 4 μm, the voxel size was 300×200×200, and the physical size was 1.2 mm×0.8 mm×0.8 mm.

[0060] Please refer to the example below. Figure 3 In step S1, based on the three-dimensional grayscale images of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples, three-dimensional digital core models of the samples are obtained, specifically including:

[0061] S11. Perform binary segmentation on the three-dimensional grayscale images of high-permeability reservoir samples, medium-permeability reservoir samples and low-permeability reservoir samples to obtain the value of each pixel in the three-dimensional grayscale images of high-permeability reservoir samples, medium-permeability reservoir samples and low-permeability reservoir samples. In this embodiment, the value is assigned to 0 or 1, where 0 represents pore voxels and 1 represents particle voxels.

[0062] Based on the 3D grayscale image obtained from CT scans, the Otsu algorithm is used to perform binary segmentation on the image. The Otsu method, proposed by Otsu in 1975, is also known as the Otsu method or the maximum inter-class variance method. It dynamically determines the image binarization threshold by maximizing the inter-class variance between the foreground and background.

[0063] The specific steps for determining the threshold for image binarization include:

[0064] (1) Define the variance between classes:

[0065]

[0066] (2) Define population variance:

[0067]

[0068] (3) Select the optimal threshold t* to satisfy the maximum decision criterion:

[0069]

[0070] In the formula, L represents the gray level of the image; For inter-class variance; This represents the overall variance.

[0071] S12. Based on the assigned values ​​of each pixel point for the high-permeability reservoir sample, medium-permeability reservoir sample, and low-permeability reservoir sample, obtain the three-dimensional digital core models corresponding to the high-permeability reservoir sample, medium-permeability reservoir sample, and low-permeability reservoir sample.

[0072] like Figure 4 As shown, in this embodiment, the corresponding three-dimensional digital core is obtained by binary segmentation using the maximum inter-class spacing algorithm.

[0073] S2. Based on the three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples and low-permeability reservoir samples, parallel three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples and low-permeability reservoir samples are obtained. Based on the parallel three-dimensional digital core models, the pore network model after joint production is obtained.

[0074] Please refer to Figure 5 Step S2 specifically includes:

[0075] S21. Based on the three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples, a parallel three-dimensional digital core model of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples is obtained.

[0076] Specifically, this involves three-dimensional digital cores based on high-permeability, medium-permeability, and low-permeability reservoir samples. The data volume of the three-dimensional digital core is represented using binary 0s and 1s, where 0 represents rock pores and 1 represents the rock skeleton. Therefore, the data volume files can be directly spliced ​​together according to the order of each layer based on closed boundaries to construct a parallel digital core.

[0077] S22. Based on the parallel three-dimensional digital core model, the pore network model after comminution is obtained by the maximum sphere method.

[0078] Since the computational cost of seepage simulation based on digital cores is very high, it is necessary to simplify the digital cores by extracting a pore network model composed of simple-shaped pore throats that can reflect the characteristics of the actual rock sample as a research platform.

[0079] The concept of the maximum sphere was initially proposed by Silin et al. and used to analyze the pore space of rock samples. Later, it was used by Blunt et al. to study the pore space of sandstone and limestone. The "maximum sphere" is defined as a sphere that is contained within the pore space but not contained by other spheres, and the local maximum sphere is defined as a "pore". Hu Dong et al. improved this method, and the main steps of their search for the maximum sphere are as follows: ① For each point in the pore of the three-dimensional digital core model, expand outward from that point to generate a sphere until the sphere touches the rock skeleton; ② Store all these sphere data in a list, arranged in descending order of radius; ③ Starting from the first sphere in the list, delete the spheres that intersect with or are contained by the first sphere to its right; ④ Finally, the remaining spheres in the list are all non-intersecting spheres that fill the entire pore space. These non-intersecting spheres constitute a set of maximum spheres, and some of the largest local "maximum spheres" in space are defined as "pores". After the pore units are determined, the space occupied by the pore bodies is continuously expanded until they intersect with other "maximum sphere" units. The intersecting maximum spheres are defined as the center of the "throat" in the pore structure of the rock.

[0080] The pore network model after comminution defines the pore space by using the maximum inscribed sphere region, which accurately divides the pore space in the comminution model into the space occupied by the pore and throat units.

[0081] The specific steps for extracting the pore network model are as follows: (1) Optimize the parallel three-dimensional digital core model; (2) Construct and optimize the central axis of the pore space of the parallel three-dimensional digital core model; (3) Establish a pore network model with the topological structure and geometric features of the pore space of the true core, which is the pore network model.

[0082] In this embodiment, as Figure 6 As shown, the corresponding pore network model was extracted by parallel digital cores, with physical dimensions of 1.2mm × 0.8mm × 2.4mm.

[0083] S3. Based on the pore network model after comminution, calculate the absolute permeability and relative permeability of the pore network model after comminution, and plot the capillary pressure curves during the displacement and suction processes.

[0084] In this invention, the pore network model is considered a quasi-steady-state model, meaning the flow is entirely controlled by capillary forces, and the pressure drop caused by viscous forces is negligible compared to the capillary pressure. According to the infiltration-percolation theory, the fluid flow from one pore to another is instantaneous, and the flow process in the pore throat is not considered. The fluid is an incompressible Newtonian fluid. The pore-level network model can be used to simulate the displacement and suction processes of percolation.

[0085] In step S3, the method for calculating the absolute permeability of the pore network model after commingled mining is as follows:

[0086] The model is saturated with a fluid, a driving pressure is applied to the model, the fluid flow rate is calculated, and the absolute permeability of the pore network model after combined mining is obtained based on the driving pressure and fluid flow rate.

[0087] In this embodiment, it is assumed that the driving pressure is (P) I -P O Then, the absolute permeability of the pore network model after combined mining is solved using Darcy's formula:

[0088]

[0089] In the formula, K is the absolute permeability, in μm. 2 μ i Q is the viscosity of the i-phase fluid, in mPa·s; i The flow rate (cm) is the flow rate under the applied pressure difference when the i-phase fluid is fully saturated in the model. 3 / s; A is the cross-sectional area of ​​the model, cm 2 ;P I For inlet pressure, 10 -1 MPa; P O Due to export pressure, 10 -1 MPa; L is the model length, cm.

[0090] In formula (4), the flow rate Q is the flow rate Q under the applied pressure difference when the model is fully saturated with phase i fluid. i The calculation process is as follows:

[0091] Let P k P j Let q be the pressure in the two pores connected by the throat, then the flow rate q between these two pores is... kj for:

[0092]

[0093] In the formula, L kj The distance between the two pores is m; g kj The total conductivity between the two pores is the harmonic average of the conductivity coefficients of the two pores and the throat between them. For each pore in the model, the amount of fluid entering through the inlet throat should be equal to the amount flowing out through the outlet throat, which can be obtained from the conservation of flow rate:

[0094]

[0095] In the formula, Z k This is the number of pore throats connected to pore k, i.e., the coordination number.

[0096] Applying formulas (5) and (6) to all pores of the pore network model after commingled mining yields a set of linear equations. Solving these equations provides the pressure in each pore, which in turn allows us to calculate the total flow rate under the pressure difference across the pore network model after commingled mining, which is Q. i .

[0097] Please refer to Figure 7 In step S3, the method for calculating the relative permeability of the pore network model after commingled mining specifically includes:

[0098] S31. Calculate the total flow rate when the pore network model after combined mining is a single-phase flow in a certain phase.

[0099] Step S31 can be calculated according to formulas (5) and (6).

[0100] S32. Calculate the flow rate of the phase when the pore network model after commingling is a multiphase flow;

[0101] When oil and water flow simultaneously, the pressure field is solved using the same method as when it is a single phase, except that the conductivity coefficient needs to be the conductivity coefficient of the corresponding fluid phase.

[0102] S33. Based on the total flow rate of a single-phase flow in the overall pore network model after commingled mining and the flow rate of that phase in the multiphase flow in the pore network model after commingled mining, the relative permeability of that phase is obtained.

[0103] In this embodiment, after determining the flow rate of each phase, the calculation can be performed.

[0104]

[0105] In the formula, K rp Q represents the relative penetration rate. tmp Q represents the flow rate of phase p in multiphase flow. tsp The total flow rate (cm) for the entire model is the p-phase single-phase flow. 3 / s.

[0106] In step S3, the specific method for plotting the capillary pressure curves during displacement and absorption processes is as follows:

[0107] The capillary inlet pressure that oil needs to overcome to enter a water-filled unit is calculated using the Young-Laplace equation. The formula is as follows:

[0108]

[0109] In the formula, P cow σ is the capillary inlet pressure that oil needs to overcome when entering a water-filled unit. ow R0 represents the interfacial tension between oil and water, in m N / m; r1 and r2 are the two principal radii of the interfacial surface, in m.

[0110] Once the shape of the pores and the contact angle of the oil-water interface are determined, the corresponding capillary inlet pressure can be calculated.

[0111] The model uses regular geometric shapes to represent the pore space, so the oil-water distribution in each pore throat can be quantitatively solved using elementary geometry. After calculating the oil-water content in each pore throat of the model, the water saturation S of the entire model is obtained. w The following formula can be used to calculate:

[0112]

[0113] In the formula, n is the total number of pores and pore throats; V i The volume of the pores or pore throats, in cm. 3 V iw This represents the volume of water contained in the corresponding pore throat, in cm. 3 .

[0114] Since the pores at the inlet face of the model are connected to the reservoir through the pore throat, a certain reservoir pressure corresponds to a capillary pressure in the network model. After calculating the water saturation of the entire model using the above formula, the capillary pressure curve of the entire model can be plotted.

[0115] It should be noted that during the construction of the pore network model, the real pore space is divided into pore and throat units in a general sense, and characterized using columnar units with circular, square, and arbitrary triangular cross-sectional shapes. Statistical analysis of the structural parameters of these regular units allows for a detailed evaluation of the pore network structure. The coordination number Z refers to the number of throats connected to the pores, used to characterize the configuration relationship between pores and throats, and is a parameter characterizing the degree of reservoir connectivity.

[0116] S4. Based on the pore network model after joint mining, calculate the corresponding physical properties and structural characteristic parameters;

[0117] like Figure 8As shown, based on the pore network model after combined mining, the corresponding physical and structural characteristic parameters are calculated. It can be found that the porosity of the high-permeability layer is 31.8%, and the permeability is 1296 mD; the porosity of the medium-permeability layer is 26.8%, and the permeability is 402 mD; the porosity of the low-permeability layer is 21%, and the permeability is 124 mD; the overall porosity of combined mining is 26.5%, and the overall permeability is 556 mD. The average pore throat radius of the high-permeability layer is 57.3 μm, and the average coordination number is 3.76; the average pore throat radius of the medium-permeability layer is 66.4 μm, and the average coordination number is 3.68; the average pore throat radius of the low-permeability layer is 46.8 μm, and the average coordination number is 3.54; the average pore throat radius of the stratified combined mining is 66.5 μm, and the average coordination number is 3.64. The permeability difference between the high, medium, and low-permeability layers is approximately 3, indicating that the pore network model after combined mining can simultaneously encompass the physical and structural characteristics of the high, medium, and low-permeability layers.

[0118] S5. Based on the pore network model after commingled mining, the oil-water displacement and adsorption process is simulated through permeation theory, and the corresponding capillary pressure curve and relative permeability curve are calculated and compared to analyze the seepage characteristics.

[0119] The methods for obtaining capillary pressure curves and relative permeability curves can be found in step S3.

[0120] In this embodiment, the pore network model after commingled production is used to simulate the oil-water displacement and intake process through percolation theory, and the corresponding capillary pressure curves are calculated as follows: Figure 9 As shown, the relative permeability curve in the simulated oil-water displacement is as follows: Figure 10 As shown, the relative permeability curve during the simulated inhalation process is as follows: Figure 11 As shown, the bound water saturation in the high-permeability layer is 0.13, and the residual oil saturation is 0.28; in the medium-permeability layer, the bound water saturation is 0.21, and the residual oil saturation is 0.31; in the low-permeability layer, the bound water saturation is 0.32, and the residual oil saturation is 0.4; and in the combined-production layer, the bound water saturation is 0.22, and the residual oil saturation is 0.33. With the continuous decrease in permeability, the relative permeability curve of the oil phase shifts continuously to the right during the intake process, while the relative permeability curve of the water phase remains relatively unchanged, indicating a reduction in the oil-water co-permeability zone.

[0121] In this embodiment, by Figures 9-11 It can be seen that in the initial stage of the water drive process after commingled mining, the aqueous phase first enters the medium-to-high permeability layer, and the change in the seepage curve at this time mainly reflects the seepage characteristics of the medium-to-high permeability layer. As the water saturation continues to increase, the aqueous phase gradually enters the medium-to-low permeability layer, and the change in the seepage curve at this time mainly reflects the seepage characteristics of the medium-to-low permeability layer. The pore network model after commingled mining can simultaneously include the seepage characteristics of high, medium, and low permeability layers.

[0122] like Figure 12As shown, this invention can also visualize the oil-water distribution in saturated water, bound water, and residual oil states. Digital core analysis technology can clearly show the initial flow path of injected water. In the combined production model, bound water is mainly concentrated in the low-permeability reservoir area, and the residual oil after combined production is also mainly concentrated in the low-permeability reservoir area. The residual oil distribution is uneven, and the dominant channels are mainly in the medium-to-high permeability areas.

[0123] In summary, the beneficial effects of the technical solution provided by this invention include:

[0124] (1) Based on micron CT, three-dimensional grayscale images of high-permeability reservoir, medium-permeability reservoir and low-permeability reservoir samples were obtained respectively. Binary segmentation was performed by the maximum inter-class distance algorithm to obtain the corresponding three-dimensional digital cores. Based on the three-dimensional digital cores of high-permeability reservoir, medium-permeability reservoir and low-permeability reservoir samples, a parallel digital core model was constructed. According to the parallel digital core model, the pore network model after joint mining can be obtained.

[0125] (2) Based on the pore network model after joint mining, calculate the corresponding physical properties and structural characteristic parameters;

[0126] (3) Based on the pore network model after synergistic production, the oil-water displacement and intake process is simulated through permeability theory, and the corresponding capillary pressure curve and relative permeability curve are calculated and compared to analyze the seepage characteristics. Thus, the seepage process of multi-layer heterogeneous reservoirs can be simulated, and the inter-layer interference mechanism and residual oil distribution characteristics during multi-layer synergistic production of low-permeability reservoirs with large heterogeneity differences can be realistically reflected.

[0127] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for simulating and analyzing seepage in multi-layered heterogeneous oil reservoirs based on digital cores, characterized in that, include: Three-dimensional grayscale images of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples in the study area were obtained respectively. Based on the three-dimensional grayscale images of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples, three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples were obtained. Based on the three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples, parallel three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples are obtained. Based on the parallel three-dimensional digital core models, the pore network model after combined mining is obtained. Based on the pore network model after comminution, calculate the absolute permeability and relative permeability of the pore network model after comminution, and plot the capillary pressure curves during the displacement and suction processes. Based on the pore network model after joint mining, the corresponding physical properties and structural characteristic parameters are calculated. Based on the pore network model after commingled mining, the oil-water displacement and adsorption processes are simulated using permeation theory. The corresponding capillary pressure curves and relative permeability curves are calculated, and the seepage characteristics are compared and analyzed.

2. The method for simulating and analyzing seepage in multi-layered heterogeneous reservoirs based on digital cores according to claim 1, characterized in that, Based on the three-dimensional grayscale images of high-permeability, medium-permeability, and low-permeability reservoir samples, three-dimensional digital core models of these samples were obtained, specifically including: Binary segmentation was performed on the three-dimensional grayscale images of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples to obtain the value of each pixel in the three-dimensional grayscale images of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples. Based on the assigned values ​​at each pixel of the high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples, the corresponding three-dimensional digital core models are obtained.

3. The method for simulating and analyzing seepage in multi-layered heterogeneous reservoirs based on digital cores according to claim 1, characterized in that, Based on the three-dimensional digital core models of high-permeability, medium-permeability, and low-permeability reservoir samples, parallel three-dimensional digital core models of these samples were obtained. Based on these parallel three-dimensional digital core models, a pore network model after combined mining was derived, specifically including: Based on the three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples, parallel three-dimensional digital core models of high-permeability reservoir samples, medium-permeability reservoir samples, and low-permeability reservoir samples are obtained. Based on the parallel three-dimensional digital core model, the pore network model after combined mining is obtained by the maximum sphere method.

4. The method for simulating and analyzing seepage in multi-layered heterogeneous reservoirs based on digital cores according to claim 1, characterized in that, The specific method for calculating the absolute permeability of the pore network model after commingled mining is as follows: The model is saturated with a fluid, a driving pressure is applied to the model, the fluid flow rate is calculated, and the absolute permeability of the pore network model after combined mining is obtained based on the driving pressure and fluid flow rate.

5. The method for simulating and analyzing seepage in multi-layered heterogeneous reservoirs based on digital cores according to claim 4, characterized in that, The formula for calculating the absolute permeability of the pore network model after commingled mining is as follows: In the formula, K is the absolute permeability; μ i Let Q be the viscosity of phase i; i P is the flow rate under the applied pressure difference when the model is fully saturated with phase i fluid; A is the cross-sectional area of ​​the model; P is the flow rate under the applied pressure difference. I For inlet pressure; P O L represents the export pressure; L represents the model length.

6. The method for simulating and analyzing seepage in multi-layered heterogeneous reservoirs based on digital cores according to claim 1, characterized in that, The specific methods for calculating the relative permeability of the pore network model after commingled mining include: Calculate the total flow rate of the pore network model after combined mining when the entire model is a single-phase flow in a certain phase. The flow rate of a phase is calculated when the pore network model after commingled mining is a multiphase flow. The relative permeability of a phase is obtained by considering the total flow rate of a single-phase flow in the overall pore network model after commingled mining and the flow rate of that phase in the multiphase flow model after commingled mining.

7. The method for simulating and analyzing seepage in multi-layered heterogeneous reservoirs based on digital cores according to claim 6, characterized in that, Based on the total flow rate when the overall pore network model after commingled mining is a single-phase flow and the flow rate of that phase when the pore network model after commingled mining is a multiphase flow, the relative permeability of that phase is obtained. The specific formula is as follows: In the formula, K rp Q represents the relative penetration rate. tmp Q represents the flow rate of phase p in multiphase flow. tsp The total flow rate of the entire model is for a single-phase flow of phase p.

8. The method for simulating and analyzing seepage in multi-layered heterogeneous reservoirs based on digital cores according to claim 1, characterized in that, The specific method for plotting capillary pressure curves during displacement and absorption processes is as follows: The capillary inlet pressure that needs to be overcome when oil enters a water-filled unit is calculated using the Young-Laplace equation. Calculate the water saturation of the entire model; The capillary pressure curve of the entire model is obtained based on the capillary inlet pressure and the water saturation of the entire model.

9. The method for simulating and analyzing seepage in multi-layered heterogeneous reservoirs based on digital cores according to claim 8, characterized in that, The Young-Laplace equation is: In the formula, P cow σ is the capillary inlet pressure that oil needs to overcome when entering a water-filled unit. ow r1 and r2 are the two principal radii of the interface surface, representing the oil-water interfacial tension.

10. The method for simulating and analyzing seepage in multi-layered heterogeneous reservoirs based on digital cores according to claim 8, characterized in that, The water saturation S of the entire model w Calculate using the following formula: In the formula, n is the total number of pores and pore throats; V i V is the volume of the pores or pore throats. iw This represents the volume of water contained in the corresponding pore throat.

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