Displacement fluid and shale rock imbibition law numerical simulation method and system

Abstract

The invention relates to the technical field of model construction, in particular to a displacement fluid and shale rock imbibition law numerical simulation method and system. According to the method, by quantitatively analyzing the linear correlation degree of granularity and experimental capillary constant distribution and the disturbance degree of local feature points, regularization parameters of a regression algorithm are dynamically adjusted, and on the basis of accurately-calibrated capillary constants, a contact angle is determined by combining permeability characteristic data of probe liquid, so that the permeability of the probe liquid is determined. According to the method, shale surface free energy is obtained through surface energy component theoretical model decoupling, the interface action state of displacement fluid, shale and crude oil is reflected through a three-phase contact angle determined through a three-phase equilibrium relation, and the adhesion work needed by shale oil stripping is obtained. According to the method, the high-precision fitting model of the granularity and the capillary constant is established, and the surface energy component theory is combined, so that more accurate calculation of the shale oil stripping adhesion work is realized, and a reliable numerical simulation basis is provided for improving the shale oil recovery ratio.

Description

Technical Field

[0001] This invention relates to the field of model building technology, specifically to a numerical simulation method and system for the permeation law of displacement fluids and shale rocks. Background Technology

[0002] In shale oil reservoir development, displacement fluids are spontaneously drawn into and replace crude oil in the tiny pores of shale through percolation. The core of this process is to accurately characterize the interaction energy between the displacement fluid, shale rock, and crude oil, which directly determines the direction of displacement fluid formulation optimization and the effect of recovery rate improvement.

[0003] However, shale oil reservoirs are characterized by nanoscale pore networks, high clay mineral content, and strong heterogeneity, which makes the correlation between particle size and capillary constant significantly affected by differences in pore structure. Existing studies have mostly focused on conventional sandstone reservoirs, neglecting the special characteristics of clay-organic matter composite interfaces in shale oil reservoirs, thus limiting the applicability of existing models in shale oil reservoirs.

[0004] Existing molecular simulation techniques utilize the construction of molecular models of rock nanopores and multi-component shale oil to obtain the density distribution, adsorbed phase mass, and motion trajectory of each component within the pores under equilibrium conditions. The total interaction energy is then obtained based on trajectory analysis, enabling the evaluation and analysis of inter-component interaction energies. However, molecular simulation techniques cannot represent the strong heterogeneity of unconventional reservoirs, affecting the calculation of interaction energies and leading to significant deviations between the calculated and actual values. This could potentially exacerbate the calculation errors, failing to accurately reflect the permeation and adsorption patterns between displacing fluids and shale rocks, hindering effective guidance for displacing fluid formulation optimization, and ultimately limiting the development efficiency of shale oil reservoirs. Summary of the Invention

[0005] To address the technical problem that existing technologies often result in significant discrepancies between calculated interaction energies and actual values, potentially exacerbating these calculation errors and failing to accurately reflect the permeation behavior of displacing fluids and shale rocks, this invention aims to provide a numerical simulation method and system for the permeation behavior of displacing fluids and shale rocks. The specific technical solution adopted is as follows:

[0006] This invention provides a numerical simulation method for the permeation behavior of displacement fluids and shale rocks, the method comprising:

[0007] The experimental capillary constants of multiple sets of standard particle samples with different particle sizes were obtained;

[0008] Based on the distribution characteristics of the particle size and the experimental capillary constant, the linear correlation degree and the degree of perturbation of local feature points are analyzed; the regularization parameter of the regression algorithm is dynamically adjusted according to the linear correlation degree and the degree of perturbation of local feature points to construct a fitting relationship model between particle size and capillary constant.

[0009] Based on the particle size of the shale powder to be tested, the calibration capillary constant is determined using the fitting relationship model; the permeability characteristics of different probe liquids in the shale powder to be tested are measured, and the contact angle of each probe liquid is determined in combination with the calibration capillary constant; based on the contact angle and surface energy parameters of each probe liquid, the surface free energy of the shale powder to be tested is obtained by decoupling using the surface energy component theory model.

[0010] The contact angles between the displacing fluid and the shale powder to be tested, as well as the contact angles between shale oil and the shale powder to be tested, were measured. Combined with the surface free energy of the shale powder, the solid-liquid interfacial energy and the solid-oil interfacial energy were obtained. The oil-liquid interfacial energy between the shale oil and the displacing fluid was measured. Combined with the solid-liquid and solid-oil interfacial energies, the three-phase contact angles between the shale oil, displacing fluid, and the shale powder to be tested were determined through three-phase equilibrium relationships. Based on the three-phase contact angles, the adhesion work required for shale oil stripping was analyzed.

[0011] Furthermore, the method for obtaining the degree of linear correlation includes:

[0012] The data on particle size and experimental capillary constant are standardized to obtain two-dimensional data points. For each standardized two-dimensional data point, a difference feature vector is constructed between it and its neighboring data points. The directional similarity between two adjacent difference feature vectors is calculated, and the degree of linear correlation is determined based on the mean of the directional similarity of all adjacent vectors.

[0013] Furthermore, the method for obtaining the degree of perturbation of local feature points includes:

[0014] Calculate the sum of the directional similarities between the difference feature vector of each two-dimensional data point and the difference feature vectors of all other two-dimensional data points, and use this sum as the feature representation value of each two-dimensional data point; calculate the range of the feature representation values ​​of all two-dimensional data points, and use the ratio of the range to the linear correlation degree as the degree of perturbation of local feature points.

[0015] Furthermore, the method for obtaining the fitting relationship model includes:

[0016] The feature representation values ​​are processed using the threshold segmentation method, and the total number of two-dimensional data points whose feature representation values ​​are less than the segmentation threshold is counted. The total number is multiplied by the degree of perturbation of local feature points to obtain the fitting correction strength. The fitting correction strength is used to correct the preset initial regularization strength to obtain the adjusted regularization parameters.

[0017] The ridge regression algorithm with adjusted regularization parameters was used to fit the granularity and experimental capillary constant to obtain a fitting relationship model.

[0018] Furthermore, the method for obtaining the contact angle of the liquid in each probe includes:

[0019] The permeation rate parameters of each probe liquid in the shale powder under test were determined using the Washburn capillary rise method. Based on the permeation rate parameters, the viscosity and surface tension of each probe liquid, and the calibrated capillary constant, the contact angle of each probe liquid was obtained using the permeation kinetic equation.

[0020] Furthermore, the method for obtaining the surface free energy of the shale powder to be tested includes:

[0021] The contact angle and surface energy parameters of the probe liquid with at least two known dispersive and polar components are substituted into the OWRK equation to construct a linear regression model.

[0022] The slope and intercept of the model are determined by linear regression. The polar component of the shale powder to be tested is determined based on the slope, and the dispersion component of the shale powder to be tested is determined based on the intercept. The surface free energy of the shale powder to be tested is obtained by summing the polar component and the dispersion component.

[0023] Furthermore, the method for obtaining the solid-liquid interfacial energy and the solid-oil interfacial energy includes:

[0024] Using Young's equation, the solid-liquid interfacial energy is obtained based on the contact angle between the displacing fluid and the shale powder to be tested, the surface tension of the displacing fluid, and the surface free energy of the shale powder to be tested.

[0025] Using Young's equation, the solid-oil interfacial energy is obtained based on the contact angle between shale oil and the shale powder to be tested, the surface tension of shale oil, and the surface free energy of the shale powder to be tested.

[0026] Furthermore, the method for obtaining the three-phase contact angle includes:

[0027] Based on solid-liquid interface energy, solid-oil interface energy, and oil-liquid interface energy, the three-phase contact angles of shale oil, displacement fluid, and the shale powder under test are obtained through three-phase interface equilibrium relationship analysis.

[0028] Furthermore, the adhesion work is obtained by combining the three-phase contact angle and the oil interface energy.

[0029] The present invention also provides a numerical simulation system for the permeation law of displacement fluids and shale rocks, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the numerical simulation method for the permeation law of displacement fluids and shale rocks as described above.

[0030] The present invention has the following beneficial effects:

[0031] This invention, by quantitatively analyzing the linear correlation between particle size and experimental capillary constant distribution, as well as the degree of perturbation at local feature points, and dynamically adjusting the regularization parameters of the regression algorithm, effectively eliminates calibration errors caused by experimental noise and heterogeneity, establishing more accurate calibration data. Furthermore, based on the accurately calibrated capillary constant and the contact angle determined by the permeability characteristics data of the probe liquid, the shale surface free energy obtained through decoupling from the surface energy component theory model is comprehensively considered. Taking into account the interfacial energies of the solid-liquid, solid-oil, and oil-liquid three-phase interfaces, the three-phase contact angle determined by the three-phase equilibrium relationship reflects the interfacial interaction state between the displacing fluid, shale, and crude oil, thus more accurately deriving the adhesion work required for shale oil stripping. This invention, by establishing a high-precision fitting model between particle size and capillary constant, and combining it with surface energy component theory, achieves more accurate calculation of the adhesion work required for shale oil stripping, providing a reliable numerical simulation basis for improving shale oil recovery. Attached Figure Description

[0032] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart illustrating a numerical simulation method for the permeation behavior of displacement fluids and shale rocks, provided as an embodiment of the present invention. Detailed Implementation

[0034] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a numerical simulation method and system for the permeation law of displacement fluids and shale rocks proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0036] The following description, in conjunction with the accompanying drawings, details the specific scheme of the numerical simulation method and system for the permeation law of displacement fluids and shale rocks provided by this invention.

[0037] Please see Figure 1 The diagram illustrates a flowchart of a numerical simulation method for the permeation law of displacement fluids and shale rocks according to an embodiment of the present invention. The method includes the following steps:

[0038] S1: Obtain the experimental capillary constants of multiple sets of standard particle samples with different particle sizes.

[0039] In the process of solving for the interaction energy, the contact angle between the liquid and the solid is a key parameter characterizing wettability and directly affects the accuracy of the interfacial energy calculation. The contact angle characterizes the ability of a liquid to spread on a solid surface; the smaller the contact angle, the better the wettability.

[0040] In this embodiment of the invention, a gas-liquid-solid macroscopic contact angle experiment is first used to measure the contact angle between a standard material, such as quartz, and a standard probe liquid, such as deionized water. Specifically, a smooth quartz sheet sample, typically 1cm × 1cm in size, is selected. After cleaning and hydrophilication treatment, the sample is measured multiple times at different locations using a contact angle measuring instrument, and the average value is taken. The number of measurements is no less than 5. In this embodiment, the average contact angle between the quartz sheet and deionized water is measured to be 15°.

[0041] Furthermore, the pore structure is characterized using quartz particles of different particle sizes, i.e., mesh sizes. Multiple sets of standard particle samples with different particle sizes are selected, such as quartz sand to simulate shale matrix with different pore structures. In this embodiment of the invention, the particle size range covers 50 to 400 mesh, and a total of 15 sets of quartz samples are prepared at 25-mesh intervals. A fixed mass, typically 3g, of each set of quartz sand samples is loaded into the sample tube and compacted to a fixed height of 3cm, ensuring that the powder height is consistent for each set of quartz sand samples. When the height of the quartz powder in that set no longer changes, the sample loading is complete, thus constructing a powder bed with a specific capillary structure.

[0042] The capillary constant was determined using the Washburn capillary rise method: For each group of quartz sand samples, deionized water was pumped into the bottom of the sample tube using the inlet pump in the experimental setup. Timing began when the deionized water contacted the bottom of the powder bed and continued until the deionized water completely wetted the entire powder bed. The wetting time t was recorded, along with the volume V of the pumped deionized water. Using the known physical properties of deionized water (viscosity 1.002 mPa·s, surface tension 72.8 mJ / m²), V, t, and contact angle were substituted into the Washburn equation to calculate the experimental capillary constant corresponding to the standard particle size sample of that group.

[0043] Please refer to Table 1, which shows the viscosity and interfacial tension coefficients of different solvents in the laboratory according to an embodiment of the present invention.

[0044] Table 1. Viscosity and interfacial tension coefficients of different solvents in the laboratory.

[0045] This completes the basic data collection for particle size and experimental capillary constant.

[0046] S2: Based on the distribution characteristics of particle size and experimental capillary constant, analyze the degree of linear correlation and the degree of perturbation of local feature points; dynamically adjust the regularization parameter of the regression algorithm according to the degree of linear correlation and the degree of perturbation of local feature points, and construct a fitting relationship model between particle size and capillary constant.

[0047] In the process of solving for interaction energy, the capillary constant is a key parameter that determines the balance between the liquid's interfacial tension and gravity in the solid. Since the capillary constant is only related to the solid's pore structure, it is often obtained through fitting a relationship. When there is a fitting deviation, the calculated interaction energy will be distorted, leading to significant errors in the exploitation of the oil reservoir.

[0048] Solid particles of individual sizes are compacted to simulate the effect of different pore structures on the capillary constant. Generally, the capillary constant increases with the increase of particle size, and the two are basically positively correlated. However, due to the limitations of compaction conditions and measurement deviations, the two do not show a completely linear relationship. Therefore, it is necessary to analyze the linear relationship between the two based on the scatter plot data of the two, that is, the changes of particle size and corresponding capillary constant.

[0049] In this embodiment of the invention, the data on particle size and experimental capillary constant are standardized to obtain two-dimensional data points. In one specific embodiment, Z-score standardization can be performed to eliminate computational interference caused by the difference in dimensions between the two. A two-dimensional spatial coordinate system is constructed with particle size and experimental capillary constant as the two dimensions respectively, and two-dimensional data points are mapped to obtain the data points. In the two-dimensional spatial coordinate system, particle size is the horizontal axis and experimental capillary constant is the vertical axis.

[0050] For each standardized 2D data point, a difference feature vector is constructed between it and its neighboring data points. Specifically, the coordinates of the (i+1)th standardized data point are subtracted from the coordinates of the ith standardized data point, resulting in the difference feature vector of the ith data point. This vector represents the local variation trend of the data. Then, the directional similarity between two adjacent difference feature vectors is calculated, and the degree of linear correlation is determined based on the mean of the directional similarities of all adjacent vectors. Specifically, the cosine of the angle between two adjacent difference feature vectors is calculated as the directional similarity, and then the arithmetic mean of the directional similarities of all adjacent points is calculated as the degree of linear correlation.

[0051] The stronger the collinearity between the particle size to be fitted and the capillary constant during the current measurement process, the higher the similarity of the feature vectors constructed from two adjacent data points. This indicates a stronger linear correlation, suggesting a near-linear relationship between the particle size and the capillary constant, fewer overall feature deviation points, and a smaller corresponding regularization strength. Conversely, poorer collinearity between the two data points leads to a greater deviation in the difference feature vectors between two adjacent data points, resulting in a smaller linear correlation strength.

[0052] The collinearity of capillary constant and all particle sizes was initially assessed by considering their overall distribution. However, during the experiment, as the particle size increases, inflection points or other characteristic points may appear in the capillary constant. These characteristic points cannot be evaluated solely through linear correlation. For inflection points or points of significant change, their local trends often deviate from the overall trend, leading to reduced consistency in the data distribution before and after that point.

[0053] In this embodiment of the invention, the sum of the directional similarities between the difference feature vector of each two-dimensional data point and the difference feature vectors of all other two-dimensional data points is calculated as the feature representation value of each two-dimensional data point. This feature representation value reflects the degree of consistency between the local change trend of the current data point and the change trend of other global data points. For normal points that conform to the overall linear law, their change trend is consistent with most points, resulting in a larger feature representation value. However, for feature points that are inflection points or exhibit abnormal fluctuations, their change trend deviates from most points, leading to a smaller feature representation value.

[0054] Then, the range of the feature values ​​of all two-dimensional data points is calculated. The ratio of the range to the linear correlation is used as the degree of local feature point perturbation. The range quantifies the dispersion of the feature values. The larger the range, the more significant the difference between the feature points and normal points. The degree of local feature point perturbation combines the magnitude of the difference between the feature points and normal points with the overall data linearity, reflecting the intensity of the interference of the feature points on the overall linear distribution. The larger the value, the more prominent the perturbation effect of the feature points, and the higher the risk of disrupting the fitting relationship. It should be noted that if there is a case where the linear correlation is zero, the minimum perturbation value is used as the degree of local feature point perturbation. The minimum perturbation value can be set to 0.01, and the specific value can be adjusted by the implementer.

[0055] When analyzing local perturbations, the focus is on measuring the distribution deviation of the feature vectors of data points from the feature vectors of other scatter points to be fitted. A larger range of feature values ​​at a data point indicates the presence of feature points that significantly deviate from the overall trend, and a greater interference from the data points on the linear distribution. Simultaneously, a smaller corresponding linear correlation indicates a weaker linear basis for the overall data, making it more difficult for the perturbation of feature points to be offset by the overall linear trend, thus resulting in a larger value for the degree of perturbation at local feature points. Conversely, a smaller range of feature values ​​and a larger linear correlation indicate fewer feature points and weaker interference in the data, resulting in a smaller value for the degree of perturbation at local feature points, and allowing for a corresponding reduction in the adjustment range of the regularization parameter during the fitting process.

[0056] In this embodiment of the invention, the regularization parameter is dynamically adjusted based on the degree of linear correlation and the degree of perturbation of local feature points. A threshold segmentation method is used to process the feature representation values, and the total number of two-dimensional data points with feature representation values ​​less than the segmentation threshold is counted. Specifically, the Otsu thresholding method is used to calculate the feature representation values ​​of all data points to obtain a segmentation threshold that distinguishes normal points from feature points. Data points with feature representation values ​​less than the segmentation threshold are identified as potential feature points, and the number of such points is counted. It should be noted that the two-dimensional data points counted here correspond one-to-one with the adjacent data point groups corresponding to the aforementioned constructed difference feature vectors. That is, each difference feature vector corresponds to the position of a two-dimensional data point to be evaluated, ensuring consistency between the feature point count and the source of perturbation. Furthermore, the Otsu thresholding method is a well-known technique, and its segmentation results are not affected by the data dimensions or distribution range, making it suitable for standard particle sample datasets with different particle size ranges and pore structures. Further details are omitted here.

[0057] The total number of statistically significant points is further multiplied by the degree of perturbation of local feature points to obtain the fitting correction strength. This fitting correction strength is then used to adjust the preset initial regularization strength, resulting in the adjusted regularization parameter. Specifically, the total number quantifies the scale of feature points, and the degree of perturbation of local feature points quantifies the perturbation strength of a single feature point. The multiplication of these values ​​comprehensively characterizes the total interference of feature points on the fitted relationship. A larger fitting correction strength indicates stronger total interference from feature points, requiring a greater adjustment of the regularization parameter to suppress this interference. Therefore, in a specific embodiment of this invention, the preset initial regularization strength is set to 0.1, which is selected based on industry experience and serves as a basic reference value for regularized linear regression. The weighted sum of the fitting correction strength and the initial regularization strength yields the adjusted regularization parameter. The sensitivity of the adjustment is controlled by weighting, with the weight set to 0.5. The specific value can be adjusted by the implementer.

[0058] During the regularization correction process, the initial values ​​are adjusted based on the actual granularity and capillary constant distribution. If the linear relationship between the two deviates significantly, and the influence and interference of feature points are substantial, a larger fitting correction strength is obtained, thereby amplifying the regularization correction strength and allowing the model to focus on the detailed information of feature points during the fitting process. If the linear relationship between granularity and capillary constant is strong, the fitting correction strength is close to zero, and the model will pay more attention to the linear relationship between the two.

[0059] Finally, a ridge regression algorithm incorporating adjusted regularization parameters is used to fit the particle size and experimental capillary constant, resulting in a fitted relationship model. In this embodiment of the invention, the particle size data and experimental capillary constant data are substituted into the ridge regression model, and the dynamically adjusted regularization parameters are used to solve the problem, outputting a linear regression equation between particle size and capillary constant. It can be understood that this fitted relationship model calibrates the influence of particle size on pore structure parameters, i.e., capillary constant, under specific experimental conditions.

[0060] Thus, the construction of the capillary constant fitting model considering data distribution characteristics and local disturbances provides a data foundation for subsequent accurate capillary constant determination of the shale powder to be tested.

[0061] S3: Based on the particle size of the shale powder to be tested, the calibration capillary constant is determined using a fitting relationship model; the permeability characteristics of different probe liquids in the shale powder to be tested are measured, and the contact angle of each probe liquid is determined in combination with the calibration capillary constant; based on the contact angle and surface energy parameters of each probe liquid, the surface free energy of the shale powder to be tested is obtained by decoupling using the surface energy component theoretical model.

[0062] By analyzing the obtained model of the fitting relationship between particle size and capillary constant, the capillary constant of shale powder with unknown pore structure can be accurately calibrated. The fitting model is essentially based on the correlation law between particle size and capillary constant of standard particle samples to establish a mapping relationship between the particle size and pore structure characteristics of the shale powder to be tested, avoiding the errors caused by the complex composition and uneven pores when directly measuring the capillary constant of shale powder.

[0063] The shale powder to be tested needs to simulate the mineral composition and pore characteristics of actual shale oil reservoirs. Optional components include quartz sand, kaolinite, illite, feldspar, montmorillonite, oil sand, and mixed sand of quartz sand and oil sand. Mixing ratios can be selected as 30:1, 10:1, 5:1, 3:1, 2:1, 1:1, 1:2, 1:3, 1:5, 1:10, and 1:30 to ensure its physical properties are consistent with real shale. A single mesh size within the range of 50 to 400 mesh is selected to match the particle size range of the standard particle sample, avoiding extrapolation errors. A fixed mass of 3g of the shale powder to be tested is loaded into the sample tube and compacted to a height of 3cm with weights, maintaining the same loading conditions as the standard particle sample to eliminate interference from the compaction degree in subsequent permeability experiments. Then, the actual particle size value of the shale powder to be tested is substituted into the fitting relationship model to directly obtain the calibrated capillary constant of the shale powder to be tested. This value reflects the comprehensive characteristics of its pore structure.

[0064] The permeation characteristics of different probe liquids in the shale powder to be tested are then measured. The contact angle of each probe liquid is determined by calibrating the capillary constant. In this embodiment of the invention, at least two solvents with known physical properties are selected as probe liquids, such as deionized water, diethylene glycol, and n-hexane, to ensure coverage of different polarities and surface energy characteristics, meeting the subsequent surface free energy decoupling requirements. Permeation characteristic data are measured using the Washburn capillary rise method: the target probe liquid is pumped into the bottom of the sample tube containing the shale powder to be tested using a pump. Timing begins when the liquid contacts the bottom of the powder bed and stops when the liquid completely wets the entire powder bed. The wetting time t is recorded, and the volume of liquid absorbed by the powder, V (i.e., the permeation volume), is recorded simultaneously. t and V together constitute the permeation rate-related parameters.

[0065] Based on the permeation rate parameters, the known physical properties of each probe liquid (viscosity and surface tension), and the calibrated capillary constant, substituting these into the permeation kinetics equation, as an example, the expression is: In the formula, the contact angle of the shale powder surface to be tested is... , This is expressed as the viscosity of the probe liquid. This is expressed as the calibration capillary constant. This is expressed as the surface tension of the probe liquid. The contact angle between the probe liquid and the shale powder being tested is derived by inversely using the equation. This angle directly reflects the wettability of the probe liquid on the surface of the shale powder. It should be noted that the Washburn equation is a well-known technique to those skilled in the art, and its meaning will not be further elaborated here.

[0066] As an example, please refer to Table 2, which shows the contact angle results of a probe liquid on the surface of shale mineral powder according to an embodiment of the present invention.

[0067] Table 2. Contact angle results of probe liquid on shale mineral powder surface.

[0068] In one specific embodiment of the present invention, the contact angle measurement of each probe liquid is repeated at least three times. After removing outliers deviating from the mean by ±10%, the average value is taken to ensure measurement accuracy. The difference in contact angle between shale powders of different mesh sizes essentially reflects the influence of particle size, i.e., pore size, on wettability, providing multi-dimensional data support for subsequent surface free energy calculations.

[0069] Furthermore, the surface free energy of the shale powder under test can be obtained using a surface energy component theory model. Surface free energy is a core thermodynamic parameter characterizing the interaction between a solid surface and other substances. It consists of a dispersive component, reflecting nonpolar interactions such as van der Waals forces and London forces between molecules, and a polar component, reflecting polar interactions such as electrostatic forces, hydrogen bonds, and dipole interactions. In a specific embodiment of this invention, the OWRK (Owens-Wendt-Rabel-Kaelble) equation is used as the surface energy component theory model. This model establishes a quantitative relationship between the contact angle between the liquid and the solid, and between the liquid surface energy component and the solid surface energy component, thereby decoupling the solid surface free energy and adapting it to the surface energy analysis scenario of porous solids such as shale powder.

[0070] In this embodiment of the invention, the contact angles and surface energy parameters of at least two probe liquids with known dispersive and polar components are substituted into the OWRK equation to construct a linear regression model. This requires selecting at least two probe liquids with known dispersive and polar components, and substituting the measured contact angles between each probe liquid and the shale powder to be tested, as well as the surface energy parameters of each probe liquid, into the OWRK equation. The core logic of the OWRK equation is that the interfacial interaction energy between a liquid and a solid is contributed by both dispersive and polar interactions. By transforming the equation into a linear form, the two unknown components of the solid surface energy can be solved through regression analysis.

[0071] In one specific embodiment of the present invention, the OWRK equation is transformed into a linear regression model of y = kx + b, where y represents the compatibility of the interaction between the probe liquid and the shale surface, x represents the polarity and dispersion component ratio of the probe liquid, b is the intercept of the linear regression model, corresponding to the square root of the dispersion component of the surface tension of the shale powder to be tested, and k is the slope of the linear regression model, corresponding to the square root of the polar component of the surface tension of the shale powder to be tested. By transforming the two unknown components, namely the dispersion component and the polar component of the surface tension of the shale powder to be tested, into the intercept and slope of the linear equation, the solution is achieved through fitting multiple sets of probe liquid data, avoiding calculation bias caused by single liquid data.

[0072] Please refer to Table 3, which shows the dispersive and polar components of different probe liquids under laboratory conditions according to an embodiment of the present invention.

[0073] Table 3. Results of dispersive and polar components of different probe liquids under laboratory conditions.

[0074] The slope and intercept of the model were determined by linear regression. The polar component of the shale powder to be tested was determined based on the slope, and the dispersion component of the shale powder to be tested was determined based on the intercept. The magnitude of the two values ​​directly reflects the interaction characteristics of the shale surface. If the dispersion component accounts for a higher proportion, it indicates that the shale surface is dominated by non-polar interactions and is more likely to be adsorbed by non-polar liquids, such as crude oil. If the polar component accounts for a higher proportion, it indicates that the shale surface is dominated by polar interactions and is more likely to be wetted by polar liquids, such as deionized water and surfactant solutions.

[0075] Finally, the polar and dispersive components of the shale powder to be tested are summed to obtain the surface free energy of the shale powder to be tested, which comprehensively characterizes the total ability of the surface of the shale powder to interact with other substances.

[0076] This completes the quantitative characterization of shale surface free energy, from experimental measurement to theoretical decoupling.

[0077] S4: Determine the contact angle between the displacing fluid and the shale powder to be tested, as well as the contact angle between shale oil and the shale powder to be tested. Combine this with the surface free energy of the shale powder to be tested to obtain the solid-liquid interfacial energy and the solid-oil interfacial energy. Determine the oil-liquid interfacial energy between the shale oil and the displacing fluid. Combine this with the solid-liquid interfacial energy and the solid-oil interfacial energy to determine the three-phase contact angle between the shale oil, the displacing fluid, and the shale powder to be tested through the three-phase equilibrium relationship. Analyze the adhesion work required for shale oil stripping based on the three-phase contact angle.

[0078] Displacing fluids are functional fluids used for crude oil replacement in shale oil reservoirs. Their core function is to weaken the adsorption between crude oil and shale by adjusting the interfacial properties. It should be noted that preferred displacing fluids are surfactants, such as petroleum sulfonates, heavy alkylbenzene sulfonates, alkyl alcohol polyether sulfates, alkyl alcohol polyether sulfonates, alkyl alcohol polyether carboxylates, alkyl sulfonates, alkyl sulfates, alkyl carboxylates, alkylbenzene carboxylates, alkyl naphthalene sulfonates, alkyl naphthalene carboxylates, water, polymers, polymer-surfactant composite systems, polymer-salt-surfactant composite systems, and polymer-alkali-surfactant composite systems.

[0079] The Washburn capillary rise method is used to wet the powder with the displacing fluid through the inlet pump, and the contact angle between the displacing fluid and the shale powder to be tested can be recorded. Similarly, the contact angle between shale oil and the shale powder to be tested can be obtained. The specific process is the same as the previous method, and will not be described in detail here.

[0080] Furthermore, in this embodiment of the invention, Young's equation is used to obtain the solid-liquid interfacial energy based on the contact angle between the displacing fluid and the shale powder to be tested, the surface tension of the displacing fluid, and the surface free energy of the shale powder to be tested. Young's equation is the core equation describing the equilibrium of the gas-liquid-solid three-phase interface. As an example, the expression is: In the formula, This is expressed as the surface free energy of the shale powder to be tested. Expressed as solid-liquid interfacial energy, This is expressed as the surface tension of the displacing fluid. This represents the contact angle between the displacing fluid and the shale powder being tested.

[0081] Similarly, using Young's equation, the solid-oil interfacial energy is obtained based on the contact angle between shale oil and the shale powder to be tested, the surface tension of the shale oil, and the surface free energy of the shale powder to be tested. As an example, the expression is: In the formula, This is expressed as the surface free energy of the shale powder to be tested. Represented as solid-oil interfacial energy, This is expressed as the surface tension of shale oil. This represents the contact angle between shale oil and the shale powder being tested.

[0082] Furthermore, in this embodiment of the invention, the oil-liquid interfacial energy between shale oil and the displacing fluid is determined and analyzed using the spin drop method. Based on the solid-liquid interfacial energy, solid-oil interfacial energy, and oil-liquid interfacial energy, the three-phase contact angles of shale oil, displacing fluid, and the shale powder under test are obtained through three-phase interfacial equilibrium analysis. As an example, the expression is: In the formula, Expressed as solid-liquid interfacial energy, Represented as solid-oil interfacial energy, Expressed as oil-liquid interfacial energy, It is expressed as the three-phase contact angle.

[0083] The adhesion work required for shale oil stripping is calculated based on the three-phase contact angle. In this embodiment of the invention, the adhesion work is obtained by combining the three-phase contact angle and the oil-liquid interfacial energy. The adhesion work is a core indicator characterizing the adsorption strength between crude oil and the shale surface; the smaller the value, the easier it is for the crude oil to be stripped by the displacing fluid. In this embodiment of the invention, the adhesion work is obtained by combining the three-phase contact angle and the oil-liquid interfacial energy, based on the thermodynamic calculation principle of interfacial adhesion work. In this embodiment of the invention, the expression is: , Expressed as adhesion work, Expressed as oil-liquid interfacial energy, It is expressed as the three-phase contact angle, which can be solved directly. The result intuitively reflects the stripping efficiency of the displacing fluid on crude oil.

[0084] As an example, please refer to Table 4, which shows the test results of adhesion work required for shale oil to peel off from the surface of shale rock powder according to an embodiment of the present invention.

[0085] Table 4. Test results of adhesion work required for shale oil to peel off from the surface of shale mineral powder.

[0086] Finally, by reviewing the adhesion work calculations under different particle sizes and displacement fluid formulations, the optimal displacement agent system for specific heterogeneous shale reservoirs can be selected, thus achieving the goal of using numerical simulation to guide engineering development.

[0087] In summary, this invention effectively eliminates calibration errors caused by experimental noise and heterogeneity by quantitatively analyzing the linear correlation between particle size and experimental capillary constant distribution, as well as the degree of perturbation of local feature points, and dynamically adjusting the regularization parameters of the regression algorithm, thus establishing more accurate calibration data. Furthermore, based on the accurately calibrated capillary constant and the contact angle determined by the permeability characteristics data of the probe liquid, the shale surface free energy obtained through decoupling from the surface energy component theory model is comprehensively considered. Taking into account the interfacial energies of the solid-liquid, solid-oil, and oil-liquid three-phase interfaces, the three-phase contact angle determined by the three-phase equilibrium relationship reflects the interfacial interaction state between the displacing fluid, shale, and crude oil, thus more accurately deriving the adhesion work required for shale oil stripping. This invention, by establishing a high-precision fitting model between particle size and capillary constant, and combining it with surface energy component theory, achieves more accurate calculation of the adhesion work required for shale oil stripping, providing a reliable numerical simulation basis for improving shale oil recovery.

[0088] The present invention also provides a numerical simulation system for the permeation law of displacement fluids and shale rocks, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the numerical simulation method for the permeation law of displacement fluids and shale rocks as described above.

[0089] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0090] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A numerical simulation method for the permeation law of displacement fluids and shale rock, characterized in that, The method includes: The experimental capillary constants of multiple sets of standard particle samples with different particle sizes were obtained; Based on the distribution characteristics of the particle size and the experimental capillary constant, the linear correlation degree and the degree of perturbation of local feature points are analyzed; the regularization parameter of the regression algorithm is dynamically adjusted according to the linear correlation degree and the degree of perturbation of local feature points to construct a fitting relationship model between particle size and capillary constant. Based on the particle size of the shale powder to be tested, the calibration capillary constant is determined using the fitting relationship model; the permeation characteristics of different probe liquids in the shale powder to be tested are measured, and the contact angle of each probe liquid is determined in combination with the calibration capillary constant; based on the contact angle and surface energy parameters of each probe liquid, the surface free energy of the shale powder to be tested is obtained by decoupling using the surface energy component theory model. The contact angles between the displacing fluid and the shale powder to be tested, as well as the contact angles between shale oil and the shale powder to be tested, were measured. Combined with the surface free energy of the shale powder, the solid-liquid interfacial energy and the solid-oil interfacial energy were obtained. The oil-liquid interfacial energy between the shale oil and the displacing fluid was measured. Combined with the solid-liquid and solid-oil interfacial energies, the three-phase contact angles between the shale oil, displacing fluid, and the shale powder to be tested were determined through three-phase equilibrium relationships. Based on the three-phase contact angles, the adhesion work required for shale oil stripping was analyzed.

2. The numerical simulation method for the permeation law of displacement fluid and shale rock according to claim 1, characterized in that, The method for obtaining the degree of linear correlation includes: The data on particle size and experimental capillary constant are standardized to obtain two-dimensional data points. For each standardized two-dimensional data point, a difference feature vector is constructed between it and its neighboring data points. The directional similarity between two adjacent difference feature vectors is calculated, and the degree of linear correlation is determined based on the mean of the directional similarity of all adjacent vectors.

3. The numerical simulation method for the permeation law of displacement fluid and shale rock according to claim 2, characterized in that, The method for obtaining the degree of perturbation of local feature points includes: Calculate the sum of the directional similarities between the difference feature vector of each two-dimensional data point and the difference feature vectors of all other two-dimensional data points, and use this sum as the feature representation value of each two-dimensional data point; calculate the range of the feature representation values ​​of all two-dimensional data points, and use the ratio of the range to the linear correlation degree as the degree of perturbation of local feature points.

4. The numerical simulation method for the permeation law of displacement fluid and shale rock according to claim 3, characterized in that, The method for obtaining the fitting relationship model includes: The feature representation values ​​are processed using the threshold segmentation method, and the total number of two-dimensional data points whose feature representation values ​​are less than the segmentation threshold is counted. The total number is multiplied by the degree of perturbation of local feature points to obtain the fitting correction strength. The fitting correction strength is used to correct the preset initial regularization strength to obtain the adjusted regularization parameters. The ridge regression algorithm with adjusted regularization parameters was used to fit the granularity and experimental capillary constant to obtain a fitting relationship model.

5. The numerical simulation method for the permeation law of displacement fluid and shale rock according to claim 1, characterized in that, The method for obtaining the contact angle of each probe liquid includes: The permeation rate parameters of each probe liquid in the shale powder under test were determined using the Washburn capillary rise method. Based on the permeation rate parameters, the viscosity and surface tension of each probe liquid, and the calibrated capillary constant, the contact angle of each probe liquid was obtained using the permeation kinetic equation.

6. The numerical simulation method for the permeation law of displacement fluid and shale rock according to claim 1, characterized in that, The method for obtaining the surface free energy of the shale powder to be tested includes: The contact angle and surface energy parameters of the probe liquid with at least two known dispersive and polar components are substituted into the OWRK equation to construct a linear regression model. The slope and intercept of the model are determined by linear regression. The polar component of the shale powder to be tested is determined based on the slope, and the dispersion component of the shale powder to be tested is determined based on the intercept. The surface free energy of the shale powder to be tested is obtained by summing the polar component and the dispersion component.

7. The numerical simulation method for the permeation law of displacement fluid and shale rock according to claim 1, characterized in that, The methods for obtaining the solid-liquid interface energy and the solid-oil interface energy include: Using Young's equation, the solid-liquid interfacial energy is obtained based on the contact angle between the displacing fluid and the shale powder to be tested, the surface tension of the displacing fluid, and the surface free energy of the shale powder to be tested. Using Young's equation, the solid-oil interfacial energy is obtained based on the contact angle between shale oil and the shale powder to be tested, the surface tension of shale oil, and the surface free energy of the shale powder to be tested.

8. The numerical simulation method for the permeation law of displacement fluid and shale rock according to claim 1, characterized in that, The method for obtaining the three-phase contact angle includes: Based on solid-liquid interface energy, solid-oil interface energy, and oil-liquid interface energy, the three-phase contact angles of shale oil, displacement fluid, and the shale powder under test are obtained through three-phase interface equilibrium relationship analysis.

9. The numerical simulation method for the permeation law of displacement fluid and shale rock according to claim 1, characterized in that, The adhesion work is obtained by combining the three-phase contact angle and the oil-liquid interface energy.

10. A numerical simulation system for the permeation law of displacement fluids and shale rocks, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the numerical simulation method for the permeation law of displacement fluid and shale rock as described in any one of claims 1 to 9.

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