Method for evaluating shale oil movable sweet spot based on shale oil macroscopic percolation model

Abstract

The present application relates to the method for evaluating shale oil movable sweet spot based on shale oil macroscopic percolation model, belongs to the technical field of evaluating shale oil movable sweet spot, including: based on shale percolation experiment, on the basis of shale oil mobility control factor research, the microcosmic percolation model of shale oil is established; through the coupling of microcosmic parameters and macroscopic geological factors, the macroscopic percolation model of shale oil is established; through the macroscopic percolation model of shale oil, the geological application of percolation model is realized, and the movable sweet spot of shale oil is predicted.The present application couples microcosmic parameters and macroscopic parameters from percolation mechanism, reveals the percolation law of shale oil from mechanism, solves the problem that existing percolation model is difficult to be applied to geology, realizes the quantitative evaluation of shale oil percolation capacity, and the geological application result of macroscopic percolation model obtained by the method can provide intuitive and effective data support for shale oil movable sweet spot optimization.

Description

Technical Field

[0001] This invention relates to a method for evaluating the movable sweet spot of shale oil based on a macroscopic seepage model of shale oil, and belongs to the technical field of evaluating the movable sweet spot of shale oil. Background Technology

[0002] In recent years, with the ongoing exploration and evaluation of shale oil, industrial-flow wells have been drilled in shale reservoirs across multiple basins / regions in China, demonstrating promising exploration prospects. However, shale oil well productivity varies greatly, with generally low production per well and rapid decline in output, making it difficult to achieve industrial-scale production. The main reason is that my country's shale reservoirs are characterized by low porosity, low permeability, and strong heterogeneity, making it difficult to grasp the flow patterns of shale oil. Research on the flowability of shale oil is crucial to determining whether and how much of the extremely abundant shale oil resources in my country's lacustrine shale can be utilized. Therefore, it is necessary to conduct in-depth research on the flow patterns of continental shale oil to provide a scientific basis for the optimal selection of exploration targets for continental shale oil.

[0003] During shale oil reservoir development, changes in reservoir pressure, pore structure, and temperature lead to variations in fluid flow characteristics, manifesting as a dynamic flow process. The main factors influencing shale oil flow mechanisms are the boundary layer effect and effective stress. The boundary layer effect refers to the formation of a boundary layer on a solid surface due to the viscosity between liquid molecules and their interaction with solid molecules. Higher pore fluid pressure results in a thinner boundary layer, while smaller pore throat radii lead to a thicker boundary layer. The abundant micro- and nano-pores in the shale matrix significantly amplify the boundary layer effect. Furthermore, the boundary layer thickness is controlled by mineral composition and the composition of the shale oil. In mudstone and shale reservoirs, relatively small pore throats mean that even minor changes in pore throat size under effective stress can lead to a significant decrease in permeability. Additionally, the highly compressible nano-scale flat pores in mudstone and shale, combined with the high compressibility of clay minerals, make shale highly pressure-sensitive. In general, effective stress causes changes in reservoir pore structure, which in turn affects the inherent permeability of shale reservoirs. The boundary layer effect, on the other hand, is the result of the interaction between the fluid and the reservoir pore walls, and is related not only to fluid dynamics but also to factors such as changes in reservoir pore structure and reservoir temperature, thus affecting the seepage characteristics of shale oil. Previous researchers have established microscopic seepage models for shale oil that consider the effects of effective stress and boundary layer effects; however, these models are all based on the study of seepage mechanisms and are built upon shale microscopic parameters, making them difficult to directly apply in shale oil exploration and development. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for evaluating the movable sweet spot of shale oil based on a macroscopic seepage model of shale oil;

[0005] This invention reveals the laws and characteristics of shale oil seepage through seepage experiments on typical shale samples. Based on the study of effective stress and boundary layer effects, and combined with the Kozeny-Carmen equation and Poiseuille equation, a mathematical model of shale oil micro-seepage is established. Microscopic parameters such as boundary layer thickness and pore size in the shale oil micro-seepage model cannot be directly evaluated using well logging methods, making geological applications in shale oil exploration and development difficult. It is necessary to establish relationships between these microscopic parameters and macroscopic parameters such as mineral composition and temperature, thereby achieving indirect evaluation through well logging or understanding geological laws. Accordingly, the micro-seepage model is transformed into a macroscopic seepage model. Then, an evaluation method for shale macroscopic parameters is established, combined with crude oil viscosity and formation pressure evaluation, to realize the geological application of the macroscopic characterization model of shale oil seepage. Based on the geological application results of the macroscopic seepage model, the favorable active shale oil layers can be depicted vertically, and the favorable seepage zones can be depicted horizontally.

[0006] The technical solution of this invention is as follows:

[0007] Methods for evaluating the movable sweet spot of shale oil based on macroscopic seepage models include:

[0008] Based on shale seepage experiments and the study of factors controlling the mobility of shale oil, a microscopic seepage model for shale oil is established.

[0009] A macroscopic seepage model for shale oil was established by coupling microscopic parameters with macroscopic geological factors.

[0010] The geological application of seepage models is realized through macroscopic seepage models of shale oil, predicting the movable sweet spot of shale oil.

[0011] According to a preferred embodiment of the present invention, a microscopic seepage model of shale oil is established based on shale seepage experiments and the study of factors controlling the mobility of shale oil; including:

[0012] When a fluid undergoes laminar flow in a horizontal circular pipe, the volumetric flow rate Q of the fluid, the pressure difference Δp between the two ends of the horizontal circular pipe, the radius R and length L of the horizontal circular pipe, and the viscosity coefficient η of the fluid conform to Poiseuille's law. Considering the effects of effective stress and boundary layer effect, it is transformed into equation (1):

[0013] (1);

[0014] In equation (1), Q is the volumetric flow rate of the fluid, m 3 / s;r e η is the effective aperture, in meters; ΔP is the pressure difference between the two ends of the horizontal circular pipe, in Pa; η is the viscosity coefficient of the fluid, in Pa·s.

[0015] The permeability K is related to the porosity Φ, the average pore throat radius r, and the pore tortuosity φ in the following ways:

[0016] (2);

[0017] In equation (2), K is the permeability, mD; Φ is the porosity, %; r is the average pore throat radius, nm; and τ is the tortuosity, dimensionless.

[0018] The changes in permeability and porosity caused by effective stress under constant temperature and confining pressure were obtained through a pressure-controlled porosity-permeability test.

[0019] (3);

[0020] (4);

[0021] In the formula, K is the permeability, mD; K0 is the initial permeability, mD; α and β are the pore-permeability compressibility coefficients, dimensionless; Φ is the porosity, %; Φ0 is the initial porosity, % For effective stress, MPa; EXP refers to an exponential function with base e; assuming the tortuosity remains constant, combining equations (2) to (4), we get:

[0022] (5);

[0023] Let (α-β)=-γ, then the formula simplifies to:

[0024] (6);

[0025] In the formula, r is the average pore throat radius, m; r0 is the initial pore throat radius, m; α, β, γ are the pore permeability compressibility coefficients, dimensionless; P e Effective stress, MPa;

[0026] From the perspective of shale oil occurrence mechanism, the boundary layer thickness was determined by nuclear magnetic resonance-centrifugation and thermogravimetric analysis, combined with the adsorption ratio equation, and the relationship between boundary layer thickness and temperature was obtained:

[0027] (7);

[0028] In the formula, H is the thickness of the adsorbed oil, i.e., the boundary layer thickness, in nm; t is the temperature, in °C.

[0029] Combining equations (6) and (7), we obtain the formula for the effective aperture variation considering the effects of effective stress and boundary layer:

[0030] (8);

[0031] In the formula, n is the number of effective holes, which is dimensionless, and r e Effective aperture;

[0032] Combining equation (1), we obtain a microscopic seepage model for shale oil that considers the effects of effective stress and boundary layer:

[0033] (9).

[0034] According to a preferred embodiment of the present invention, a macroscopic seepage model for shale oil is established by coupling microscopic parameters with macroscopic geological factors; including:

[0035] Microscopic parameters are expressed using relevant macroscopic parameters:

[0036] (10);

[0037] (11);

[0038] (12);

[0039] In the macroscopic flow model of shale oil, the boundary layer thickness is n times that in the microscopic flow model of shale oil, where n is the number of effective pores. The calculation method for n is as follows: the adsorption layer thickness in the microscopic flow model of shale oil is equivalent to the boundary layer thickness of the macropores. The diameter of the equivalent macropores is replaced by the average pore diameter of the sample obtained by mercury intrusion porosimetry. The ratio of the equivalent macropore diameter to the average pore diameter of mercury intrusion porosimetry is n. That is, if the average pore diameter of the sample is used as the small pore, n equivalent macropores are needed to fill the diameter.

[0040] Therefore, the microscopic seepage model of shale oil is transformed into a macroscopic seepage model of shale oil based on macroscopic parameters:

[0041] (13);

[0042] In the formula, n is the number of effective holes, which is dimensionless.

[0043] According to a preferred embodiment of the present invention, after the macroscopic seepage model of shale oil is established, a calculation model for macroscopic parameters γ and r0 is established; including: using the BP neural network method to establish a calculation model for γ using the brittleness index, TOC, temperature, and confining pressure, and a calculation model for r0 using the content of clay minerals, siliceous minerals, and calcareous minerals.

[0044] According to a preferred embodiment of the present invention, the geological application of the seepage model is realized through a macroscopic seepage model of shale oil to predict the movable sweet spot of shale oil; including:

[0045] First, an organic heterogeneity logging evaluation was conducted to obtain shale geochemical parameters TOC, chloroform bitumen A, and pyrolysis S1.

[0046] Based on the resistivity of the well logging curves and the sonic transit time, the ΔlogR method was used to model and predict the results, thereby obtaining the vertically continuous shale geochemical parameters TOC, chloroform bitumen A, and pyrolysis S1.

[0047] Then, inorganic heterogeneity logging evaluation is carried out on the shale, that is, the mineral composition of the shale is evaluated by logging curves, including three categories: clay minerals, siliceous minerals, and calcareous minerals.

[0048] A calculation model for the content of clay minerals, siliceous minerals, and calcareous minerals in shale was established based on the BP neural network algorithm.

[0049] Normalize the logging curves;

[0050] Well logging curves with good correlation and response characteristics to the predicted target were selected. These included: CAL, AC, CNL, DEN, RNML, and RLML for modeling and evaluating shale clay mineral content; CAL, GR, R4, and R25 for modeling and evaluating siliceous mineral content; and CAL, SP, AC, DEN, and CNL for modeling and evaluating calcareous mineral content. Where CAL refers to well diameter (cm); AC refers to sonic transit time (μs / ft); CNL refers to neutrons (%); and DEN refers to density (g / cm³). 3 RNML refers to micropotential resistivity, Ω·m; RLML refers to microgradient resistivity, Ω·m; GR refers to natural gamma, API; R4 refers to bottom gradient resistivity at 4 meters, Ω·m; R25 refers to bottom gradient resistivity at 2.5 meters, Ω·m; SP refers to natural potential, mV.

[0051] In pressure assessment, confining pressure represents the geostress under geological conditions, and geostress is decomposed into vertical stress S. V Horizontal maximum principal stress S H and the minimum principal stress S h The effective stress is the average of the sum of the three axial stresses of the ground stress. The wellbore pressure is obtained by direct measurement, empirical formula, and numerical simulation. The pore fluid pressure is predicted by using the acoustic time-of-flight logging curve based on the Eaton acoustic model.

[0052] In the formation temperature evaluation, the surface temperature T0 is first calculated using the formation temperature T, the depth H of the measuring point, and the geothermal gradient G data known during the oil test in the study area; then, the formation temperature at different depths is evaluated.

[0053] Further optimized, based on the resistivity of the well logging curves and the sonic transit time, the ΔlogR method is used for modeling and prediction, thereby obtaining the vertically continuous shale geochemical parameters TOC, chloroform bitumen A, and pyrolysis S1; including:

[0054] ΔlogR is calculated based on the superposition of resistivity and acoustic time difference, as shown in equations (14) and (15):

[0055] (14);

[0056] (15);

[0057] In the formula, ΔlogR is the distance between the two curves; R is the measured resistivity of the logging tool, in Ω·m; R 基线 Δt represents the resistivity corresponding to the baseline, in Ω·m; Δt is the measured acoustic transit time, in μs / ft; Δt 基线 The acoustic transit time corresponding to the baseline is μs / ft; the physical meaning of the K value is the number of units of resistivity in logarithmic coordinates corresponding to each acoustic transit time, log(R / R 基线 (Δt-Δt) is dimensionless. 基线 () has dimensions, and the geological meaning of the K value is (Δt-Δt) 基线 Transform (Δt - Δt) into a dimensionless number, such that (Δt - Δt) 基线 ) and log(R / R 基线 The magnitudes are similar, together forming ΔlogR; R min That is, Δt min R is the minimum value of the resistivity curve scale when the resistivity and acoustic transit time curves are superimposed. max That is, Δt max , which is the maximum value of the resistivity curve scale when the resistivity and acoustic time difference curves are superimposed.

[0058] After assuming a baseline, we get:

[0059] (16);

[0060] Substituting equations (15) and (16) into equation (14), we can further derive equation (17):

[0061] (17);

[0062] ΔlogR is linearly correlated with organic carbon and is a function of maturity. The empirical formula for calculating organic carbon from ΔlogR is:

[0063] (18);

[0064] In the formula, TOC is the calculated organic carbon content, %; LOM reflects the maturity of organic matter; ΔTOC is the background value of organic carbon content.

[0065] Interpretation models are established for different depth segments, where 10 in equation (18) (2.297-0.1688LOM)Treating it as a constant, denoted as A, equation (18) is modified within the depth range of establishing the explanatory model as follows:

[0066] (19);

[0067] Substituting equation (17) into equation (19), we get:

[0068] (20);

[0069] In the formula, A and Δt max R min If ΔTOC is a constant, then the theoretical model of ΔlogR based on the resistivity of the well logging curve and the sonic transit time to predict TOC is obtained:

[0070] (twenty one);

[0071] In the formula, a, b, and c are the coefficients of the fitting formula;

[0072] The application process of the ΔlogR theoretical model is as follows:

[0073] First, select one or several exploration wells in the study area that have a large number of TOC, chloroform bitumen A, and pyrolysis S1 experimental data points, and that have continuous lithological profiles and complete logging data.

[0074] Then, based on TOC, chloroform bitumen A, and pyrolysis S1, and combined with well logging resistivity and sonic transit time data, the ΔlogR model coefficients for predicting TOC, chloroform bitumen A, and pyrolysis S1 were determined, thereby establishing ΔlogR models for predicting TOC, chloroform bitumen A, and pyrolysis S1 in the study area.

[0075] Finally, after the three prediction models, including the ΔlogR model for predicting TOC, chloroform bitumen A, and pyrolysis S1, were established, they were applied to wells with logging data in the study area to predict TOC, chloroform bitumen A, and pyrolysis S1.

[0076] A further preferred method is to normalize the logging curves; including:

[0077] For curves with approximately linear characteristics, the linear normalization formula (22) is used for processing; for curves with nonlinear logarithmic characteristics such as resistivity, the logarithmic normalization formula (23) is used for processing.

[0078] (twenty two);

[0079] (twenty three);

[0080] In the formula, X i ,Y i represents the normalized logging curve value; X i * , Y i * These are the original well logging values; X max * , Y max * To study the maximum value of the logging curve in the target formation; X min * , Y min * The minimum value of the logging curve for the target layer is determined by the research.

[0081] A further preferred method is to predict pore fluid pressure using the acoustic time-of-flight logging curve based on the Eaton acoustic model, as shown in equation (24):

[0082] (twenty four);

[0083] In the formula, P B P is the pore fluid pressure; O The pressure gradient of the overlying strata; P N The hydrostatic pressure gradient; ΔT n The acoustic transit time value for normal compaction; ΔT c is the actual acoustic time difference; C is the Eaton index, which is derived from the known pore fluid pressure in the study area.

[0084] In a further preferred manner, the surface temperature T0 is first calculated using Equation (25) with the known formation temperature T, measurement depth H, and geothermal gradient G data from the oil test in the study area; then, the formation temperature at different depths is evaluated using Equation (25):

[0085] (25);

[0086] In the formula, G is the geothermal gradient, °C / 100m; T is the ground temperature, °C; T0 is the surface temperature, °C; and H is the depth of the measuring point, m.

[0087] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the above-described method for evaluating the movable sweet spot of shale oil based on a macroscopic seepage model of shale oil.

[0088] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for evaluating the movable sweet spot of shale oil based on a macroscopic seepage model of shale oil.

[0089] The beneficial effects of this invention are as follows:

[0090] The macroscopic seepage model for shale oil, starting from the seepage mechanism, couples microscopic and macroscopic parameters. It not only reveals the seepage law of shale oil from a mechanistic perspective, but also solves the problem that existing seepage models are difficult to apply geologically. It realizes the quantitative evaluation of the seepage capacity of shale oil. The geological application results of the macroscopic seepage model obtained by this method can provide intuitive and effective data support for the selection of mobile sweet spots for shale oil. Attached Figure Description

[0091] Figure 1 This is a schematic diagram of the effective aperture model;

[0092] Figure 2 A neural network model for calculating r0 and a flowchart of its application are established for the BP neural network method.

[0093] Figure 3 The figure shows the fitting effect between the pore permeability compressibility coefficient γ and the initial pore throat radius r0 predicted by the BP neural network method and the experimental values.

[0094] Figure 4 The figure shows the fitting effect between the flow rate (Q) calculated by the macroscopic seepage model of shale oil and the experimental data of shale oil seepage.

[0095] Figure 5 This is a graph showing the fitting effect between the clay mineral, silica mineral, and calcareous mineral content data predicted by well logging curves using the BP neural network method and the experimental data. Detailed Implementation

[0096] The present invention will be further defined below with reference to the accompanying drawings and embodiments, but is not limited thereto.

[0097] Example 1

[0098] Methods for evaluating the movable sweet spot of shale oil based on macroscopic seepage models include:

[0099] Based on shale seepage experiments and the study of factors controlling the mobility of shale oil, a microscopic seepage model for shale oil is established.

[0100] A macroscopic seepage model for shale oil was established by coupling microscopic parameters with macroscopic geological factors.

[0101] The geological application of seepage models is realized through macroscopic seepage models of shale oil, predicting the movable sweet spot of shale oil.

[0102] Example 2

[0103] The method for evaluating the movable sweet spot of shale oil based on the macroscopic seepage model of shale oil described in Example 1 differs in that:

[0104] Based on shale seepage experiments and the study of factors controlling the mobility of shale oil, a microscopic seepage model for shale oil is established, including:

[0105] During shale oil seepage, the effective pore size is affected by the combined effects of effective stress and boundary layer. In actual geological conditions, these two effects manifest as the interaction between formation pressure and pore fluid. These two effects lead to changes in the reservoir pore structure, affecting the effective seepage channels of shale oil. Therefore, determining the change in effective pore size also involves the effects of effective stress and boundary layer. Studies have shown that when fluid moves in laminar flow in a horizontal circular pipe, the volumetric flow rate Q of the fluid, the pressure difference Δp between the two ends of the horizontal circular pipe, the radius R and length L of the horizontal circular pipe, and the viscosity coefficient η of the fluid conform to Poiseuille's law. Considering the effects of effective stress and boundary layer, it can be transformed into equation (1):

[0106] (1);

[0107] In equation (1), Q is the volumetric flow rate of the fluid, m 3 / s;r e η is the effective aperture, m; ΔP is the pressure difference between the two ends of the horizontal circular pipe, Pa; η is the viscosity coefficient of the fluid, Pa·s; Equation (1) is a modified formula established after considering the effects of effective stress and boundary layer effect. The parameter "radius" will change due to the effects of effective stress and boundary layer effect, and is expressed as the effective radius r. e .

[0108] The Kozeny-Carmen equation is the most well-known semi-empirical formula in the field of porous media seepage, capable of coupling physical properties and microstructural parameters to characterize the influence of effective stress. Through empirical statistical analysis, the permeability K and porosity Φ, along with the average pore throat radius r and pore tortuosity φ, have the following relationships:

[0109] (2);

[0110] In equation (2), K is the permeability, mD; Φ is the porosity, %; r is the average pore throat radius, nm; and τ is the tortuosity, dimensionless.

[0111] The changes in permeability and porosity caused by effective stress under constant temperature and confining pressure were obtained through a pressure-controlled porosity-permeability test.

[0112] (3);

[0113] (4);

[0114] In the formula, K is the permeability, mD; K0 is the initial permeability, mD; α and β are the pore-permeability compressibility coefficients, dimensionless; Φ is the porosity, %; Φ0 is the initial porosity, % Effective stress, MPa; EXP refers to an exponential function with base e; for example, EXP(-βP) e That is, e^(-βP) e Assuming the tortuosity remains constant, combining equations (2) to (4), we obtain:

[0115] (5);

[0116] Let (α-β)=-γ, then the formula simplifies to:

[0117] (6);

[0118] In the formula, r is the average pore throat radius, m; r0 is the initial pore throat radius, m; α, β, γ are the pore permeability compressibility coefficients, dimensionless; P e Effective stress, MPa;

[0119] Based on the hypothetical model ( Figure 1 ), Figure 1 This is a schematic diagram of the effective pore size model, where r is the average pore throat radius. When shale oil flows in pores or throats, the oil interacts with the pore throat walls, causing some oil to be adsorbed on the pore throat surface, forming a boundary layer with a thickness of H. The effective pore size is r. e This represents the difference between the average pore throat radius and the boundary layer thickness. The effective pore size equals the pore size under effective stress minus the boundary layer thickness, making the determination of the boundary layer crucial. Molecular simulations can analyze the influencing factors and mechanisms of the boundary layer from a mechanistic perspective. Results show that the boundary layer thickness is unaffected by fluid properties, pore size, and pore fluid pressure, but only by temperature. However, molecular simulations can only explain the mechanism and cannot be applied to the macroscopic scale. In this invention, from the perspective of shale oil occurrence mechanisms, the boundary layer thickness is determined using NMR-centrifugation and thermogravimetric analysis, combined with the adsorption ratio equation, yielding the relationship between boundary layer thickness and temperature:

[0120] (7);

[0121] In the formula, H is the thickness of the adsorbed oil, i.e., the boundary layer thickness, in nm; t is the temperature, in °C.

[0122] Combining equations (6) and (7), we obtain the formula for the effective aperture variation considering the effects of effective stress and boundary layer:

[0123] (8);

[0124] In the formula, n is the number of effective holes, which is dimensionless, and r e Effective aperture;

[0125] Combining equation (1), we obtain a microscopic seepage model for shale oil that considers the effects of effective stress and boundary layer:

[0126] (9).

[0127] A macroscopic seepage model for shale oil is established by coupling microscopic parameters with macroscopic geological factors; including:

[0128] Because the parameters r0, γ, and H in the microscopic seepage model are difficult to obtain in practical applications, yet these parameters are indispensable for calculating reservoir seepage capacity, the microscopic seepage model for shale oil is difficult to apply in exploration and development. The main problem addressed in this study is to establish a coupling relationship between readily available macroscopic physical property parameters and microscopic physical property parameters, thereby building a macroscopic seepage model for shale oil and enabling its geological application.

[0129] Microscopic physical properties are influenced by various macroscopic physical properties. Screening revealed that r0 has a good correlation with mineral composition, specifically a negative correlation with felsic and calcareous minerals, and a positive correlation with clay minerals. γ is affected by shale mineral composition, organic matter content, temperature, and confining pressure. H is affected by temperature and can be characterized by temperature, as shown in equation (7). Thus, the microscopic parameters are expressed using the relevant macroscopic parameters:

[0130] (10);

[0131] (11);

[0132] (12);

[0133] Equation (10) refers to the function of r0 (initial pore throat radius) under the combined influence of felsic minerals, calcareous minerals, and clay minerals. f 1.

[0134] Equation (11) refers to the function of γ (porosity-permeability compressibility coefficient) as a result of the combined effects of mineral composition, organic matter content, temperature, and confining pressure. f 2.

[0135] Equation (12) refers to the effect of temperature on H (boundary layer thickness). f 3.

[0136] In the micro-flow model, the boundary layer thickness is the thickness of a single pore size. This is obviously not applicable to the macro-flow model. Therefore, the boundary layer thickness in the macro-flow model of shale oil is n times the boundary layer thickness in the micro-flow model of shale oil, where n is the number of effective pores. The calculation method for n is as follows: the adsorption layer thickness in the micro-flow model of shale oil is equivalent to the boundary layer thickness of the macropores. The diameter of the equivalent macropores is replaced by the average pore diameter of the sample obtained by mercury intrusion porosimetry. Then, the ratio of the equivalent macropore diameter to the average pore diameter of mercury intrusion porosimetry is n. That is, if the average pore diameter of the sample is used as the small pore, n equivalent macropores are needed to fill the diameter.

[0137] Therefore, the microscopic seepage model of shale oil is transformed into a macroscopic seepage model of shale oil based on macroscopic parameters:

[0138] (13);

[0139] In the formula, n is the number of effective holes, which is dimensionless.

[0140] After the macroscopic seepage model of shale oil is established, calculation models for macroscopic parameters γ and r0 are established, including: using the BP neural network method to establish a calculation model for γ based on brittleness index, TOC, temperature, and confining pressure, and a calculation model for r0 based on the content of clay minerals, silica minerals, and calcareous minerals. Figure 2 This paper establishes a model for calculating r0 using the BP neural network method and outlines its application process. The process includes: First, selecting representative shale samples for experiments to obtain data on the contents of clay minerals, silica minerals, and calcareous minerals, as well as the initial pore throat radius r0. Then, using the clay mineral, silica mineral, and calcareous mineral content data and the r0 data, a neural network model for calculating r0 is established. Finally, using the clay mineral, silica mineral, and calcareous mineral content data at any given point, the r0 value at that point can be calculated using the established neural network model.

[0141] The process of modeling and applying the BP neural network method is as follows:

[0142] ① Select some shale samples for experiments to obtain input parameters and target parameters, and the input parameters correspond one-to-one with the target parameters;

[0143] ②In IBM SPSS Modeler The software imports input parameters and target parameters, and uses the BP neural network method to model and obtain a neural network model for predicting the target parameters.

[0144] ③ Import the input parameters of any shale point, and run the neural network model for predicting the target parameters established in step ② to calculate the target parameters for that point. For example, when modeling and predicting r0, first import the clay mineral, silica mineral, and calcareous mineral content data of the experimental sample (3 input parameters) and the r0 data (target parameter). Then, use the BP neural network method to model based on these experimental data. After the modeling is completed, the calculation model for r0 can be applied. When applying, import the clay mineral, silica mineral, and calcareous mineral content data of any point, and the r0 value for that point can be obtained through model calculation.

[0145] The modeling results show that the correlation coefficient between the measured γ and the calculated γ is 0.9886, and the correlation coefficient between the measured r0 and the calculated r0 is 1.00, indicating good correlation. The model has high calculation accuracy, and the calculated values ​​are consistent with the experimental values. Figure 3 The figure shows the fitting effect between the pore permeability compressibility coefficient γ and the initial pore throat radius r0 predicted by the BP neural network method and the experimental values. Figure 3 Figure (1) shows the fitting effect between the porosity compressibility coefficient γ predicted by the BP neural network method and the experimental value. Figure 3 (2) is a fitting effect diagram of the initial throat radius r0 predicted by the BP neural network method and the experimental value; the correlation between the predicted value and the experimental value is good, indicating that it is feasible to couple the micro parameters and macro parameters through this method.

[0146] Although errors are unavoidable in coupling macroscopic parameters with microscopic model parameters, the macroscopic seepage model can still maintain high computational accuracy. The calculation results of the shale oil macroscopic seepage model are compared with the results of shale oil seepage experiments. Figure 4 The figure shows the fitting effect between the flow rate (Q) calculated by the macroscopic seepage model of shale oil and the experimental data of shale oil seepage. Figure 4 (1) shows the fitting effect between the flow rate (Q) calculated by the macroscopic seepage model of shale oil and the experimental data of shale oil seepage. Figure 1 , Figure 4 (2) shows the fitting effect between the flow rate (Q) calculated by the macroscopic seepage model of shale oil and the experimental data of shale oil seepage. Figure 2Shale oil seepage experiments were conducted using cylindrical shale samples with a diameter of 2.5 cm. Considering the effects of effective stress and boundary layer, different confining pressures (i.e., effective stress), temperatures, and displacement pressures (i.e., pore fluid pressures) were set in the experiments. The pressure difference (ΔP) was the difference between the displacement pressure and atmospheric pressure. It was found that the calculated values ​​maintained a good fit with the measured values, reflecting the high accuracy of the macroscopic seepage model for shale oil. This indicates that after coupling microscopic and macroscopic parameters, the macroscopic seepage model can maintain high computational accuracy, laying the foundation for its geological applications. During shale oil exploration and development, evaluating the various parameters in the macroscopic seepage model allows for the calculation of the seepage capacity of shale oil in different strata.

[0147] Geological applications of shale oil seepage models are implemented through macroscopic seepage models to predict mobile sweet spots in shale oil; including:

[0148] First, an organic heterogeneity logging evaluation was conducted to obtain shale geochemical parameters TOC, chloroform bitumen A, and pyrolysis S1.

[0149] Based on the resistivity of the well logging curves and the sonic transit time, the ΔlogR method was used to model and predict the results, thereby obtaining the vertically continuous shale geochemical parameters TOC, chloroform bitumen A, and pyrolysis S1.

[0150] Then, inorganic heterogeneity logging evaluation of shale is carried out, that is, the mineral composition of shale is evaluated by logging curves, including three categories: clay minerals, siliceous minerals (quartz, feldspar), and calcareous minerals (calcite, dolomite);

[0151] A calculation model for the content of clay minerals, siliceous minerals, and calcareous minerals in shale was established based on the BP neural network algorithm; including:

[0152] The input parameters are well logging curves, and the target parameters are the contents of clay minerals, silica minerals, and calcareous minerals, respectively. Three models are established, and then applied to predict the contents of clay minerals, silica minerals, and calcareous minerals. Because experimental data is often limited and requires sampling and experimentation, while well logging data is readily available, abundant, and continuous across formation profiles, neural network models can be used to calculate the continuous mineral content characteristics across formation profiles based on well logging data.

[0153] To eliminate the influence of dimensions and orders of magnitude of logging curves and make the curves comparable, logging curves need to be normalized before modeling and prediction.

[0154] Different logging curves exhibit varying logging response characteristics and correlations for clay minerals, silica minerals, and calcareous minerals. Therefore, when modeling, it is essential to first select logging curves that demonstrate good logging response characteristics and correlations with the predicted object. Specifically, for modeling and evaluating shale clay mineral content, the selected logging curves are CAL, AC, CNL, DEN, RNML, and RLML; for modeling and evaluating silica mineral content, the selected logging curves are CAL, GR, R4, and R25; and for modeling and evaluating calcareous mineral content, the selected logging curves are CAL, SP, AC, DEN, and CNL. These results have yielded good fitting performance. Figure 5 This is a graph showing the fitting effect between the clay mineral, silica mineral, and calcareous mineral content data predicted by well logging curves using the BP neural network method and the experimental data. Figure 5 Figure (1) shows the fitting effect between the clay mineral content data predicted by well logging curves using the BP neural network method and the experimental data. Figure 5 Figure (2) shows the fitting effect between the siliceous mineral content data predicted by well logging curves using the BP neural network method and the experimental data. Figure 5 Figure (3) shows the fitting effect between the calcareous mineral content data predicted by the BP neural network method using well logging curves and the experimental data; its good fitting effect reflects that exploration and development can use easily obtainable and abundant well logging curve data to evaluate macroscopic parameters and obtain reliable evaluation results, thus making it easier to apply the macroscopic seepage model to geology. Among them, CAL refers to well diameter, cm; AC refers to sonic transit time, μs / ft; CNL refers to neutrons, %; DEN refers to density, g / cm³. 3 RNML refers to micropotential resistivity, Ω·m; RLML refers to microgradient resistivity, Ω·m; GR refers to natural gamma, API; R4 refers to bottom gradient resistivity at 4 meters, Ω·m; R25 refers to bottom gradient resistivity at 2.5 meters, Ω·m; SP refers to natural potential, mV.

[0155] In pressure assessment, the confining pressure in the experiment represents the in-situ stress under geological conditions, reflecting the effective stress. Therefore, when applying the macroscopic seepage model of shale oil to geological applications, it is necessary to evaluate the in-situ stress. Methods for evaluating in-situ stress include the core-and-hole method, wellbore collapse method, hydraulic fracturing method, and rock acoustic emission method. In-situ stress is decomposed into vertical stress S. V Horizontal maximum principal stress S H and the minimum principal stress S hIn the geological application of the shale oil macro-flow model, the average of the three axial stresses is taken as the effective stress. In addition, pore fluid pressure and wellbore pressure need to be evaluated, with ΔP representing the difference between the pore fluid pressure and the wellbore pressure. Wellbore pressure is obtained through direct measurement, empirical formulas, and numerical simulation. Pore fluid pressure is predicted using the Eaton acoustic logging model based on acoustic time-of-flight logging curves. Thus, the pressure evaluation in the geological application of the shale oil macro-flow model is completed.

[0156] In the formation temperature evaluation, the surface temperature T0 is first calculated using the formation temperature T, the depth H of the measuring point, and the geothermal gradient G data known during the oil test in the study area; then, the formation temperature at different depths is evaluated.

[0157] Based on the above methods, all parameters in the macroscopic seepage model (13) of shale oil are obtained through macroscopic physical property parameter evaluation, and then the corresponding seepage capacity Q is calculated. This provides data support for the selection of the optimal movable sweet spot of shale oil.

[0158] Based on the resistivity and sonic transit time of the well logging curves, the ΔlogR method was used for modeling and prediction, thus obtaining the vertically continuous shale geochemical parameters TOC, chloroform bitumen A, and pyrolysis S1; including:

[0159] ΔlogR is calculated based on the superposition of resistivity and acoustic time difference, as shown in equations (14) and (15):

[0160] (14);

[0161] (15);

[0162] In the formula, ΔlogR is the distance between the two curves; R is the measured resistivity of the logging tool, in Ω·m; R 基线 Δt represents the resistivity corresponding to the baseline, in Ω·m; Δt is the measured acoustic transit time, in μs / ft; Δt 基线 The acoustic transit time corresponding to the baseline is μs / ft; the physical meaning of the K value is the number of units of resistivity in logarithmic coordinates corresponding to each acoustic transit time (1 μs / ft), log(R / R 基线 (Δt-Δt) is dimensionless. 基线 () has dimensions, and the geological meaning of the K value is (Δt-Δt) 基线 Transform (Δt - Δt) into a dimensionless number, such that (Δt - Δt) 基线 ) and log(R / R 基线 The magnitudes are similar, together forming ΔlogR; R min That is, Δt min R is the minimum value of the resistivity (sound transit time) curve scale when the resistivity and sound transit time curves are superimposed. max That is, Δtmax , is the maximum value of the resistivity (sound wave transit time) curve scale when the resistivity and sound wave transit time curves are superimposed.

[0163] After assuming a baseline, we get:

[0164] (16);

[0165] Substituting equations (15) and (16) into equation (14), we can further derive equation (17):

[0166] (17);

[0167] ΔlogR is linearly correlated with organic carbon and is a function of maturity. The empirical formula for calculating organic carbon from ΔlogR is:

[0168] (18);

[0169] In the formula, TOC represents the calculated organic carbon content (%); LOM reflects the maturity of organic matter and can be determined based on the analysis of a large number of samples (e.g., vitrinite reflectance R). o Thermal distortion index, T max The value is obtained from analysis or from evaluation of burial history and thermal history; ΔTOC is the background value of organic carbon content.

[0170] Since a well often has multiple baseline values, interpretation models need to be established for different depth segments. Within the depth range where the interpretation model is established, the change in organic matter maturity is generally not large. In equation (18), 10 (2.297-0.1688LOM) Treating it as a constant, denoted as A, equation (18) is modified within the depth range of establishing the explanatory model as follows:

[0171] (19);

[0172] Substituting equation (17) into equation (19), we get:

[0173] (20);

[0174] In the formula, A and Δt max R min ΔTOC is a constant. The calculation of organic carbon content is affected by the superposition coefficient K. Let K take the optimal value (the optimal K value can make the correlation between ΔlogR and the measured TOC R) 2 (Maximum), then the theoretical model of ΔlogR based on the resistivity of the well logging curve and the sonic transit time to predict TOC is obtained:

[0175] (twenty one);

[0176] In the formula, a, b, and c are the coefficients of the fitting formula. This principle model can also be used to evaluate the content of chloroform bitumen “A” or S1 in shale by measuring the resistivity of the logging curve and the sonic transit time. Simply replace TOC in the model with chloroform bitumen “A” or S1. When calibrating the undetermined parameters of the model, replace them with the measured values ​​of chloroform bitumen “A” or S1.

[0177] The application process of the ΔlogR theoretical model is as follows:

[0178] First, select one or several exploration wells in the study area that have a large number of TOC, chloroform bitumen A, and pyrolysis S1 experimental data points, and that have continuous lithological profiles and complete logging data.

[0179] Then, based on TOC, chloroform bitumen A, and pyrolysis S1, and combined with well logging resistivity and sonic transit time data, the ΔlogR model coefficients for predicting TOC, chloroform bitumen A, and pyrolysis S1 were determined, thereby establishing ΔlogR models for predicting TOC, chloroform bitumen A, and pyrolysis S1 in the study area.

[0180] Finally, after establishing the three prediction models, including the ΔlogR model for predicting TOC, chloroform bitumen A, and pyrolysis S1, they were applied to wells with logging data in the study area to predict TOC, chloroform bitumen A, and pyrolysis S1. This enabled well logging evaluation of organic heterogeneity based on the ΔlogR method.

[0181] Normalize the logging curves; including:

[0182] For curves with approximately linear characteristics, the linear normalization formula (22) is used for processing; for curves with nonlinear logarithmic characteristics such as resistivity, the logarithmic normalization formula (23) is used for processing.

[0183] (twenty two);

[0184] (twenty three);

[0185] In the formula, X i , Y i represents the normalized logging curve value; X i * , Y i * These are the original well logging values; X max * , Y max * To study the maximum value of the logging curve in the target formation; Xmin * , Y min * The minimum value of the logging curve for the target layer is determined by the research.

[0186] Pore ​​fluid pressure is predicted using the acoustic time-of-flight logging curve based on the Eaton acoustic model, as shown in equation (24).

[0187] (twenty four);

[0188] In the formula, P B P is the pore fluid pressure; O The pressure gradient of the overlying strata; P N The hydrostatic pressure gradient; ΔT n The acoustic transit time value for normal compaction; ΔT c is the actual acoustic time difference; C is the Eaton index, which is derived from the known pore fluid pressure in the study area.

[0189] First, the surface temperature T0 is calculated using Equation (25) based on the known formation temperature T, measurement depth H, and geothermal gradient G data from the oil test in the study area. During the oil test, some formation temperature data and geothermal gradients are measured, and the depths corresponding to these formation temperatures are known. Therefore, T0 can be calculated using Equation (25). Then, the formation temperature at different depths is evaluated using Equation (25): T0 can be calculated based on Equation (25) using the oil test data from the study area. Since the geothermal gradient data is also known, the formation temperature at any depth in the study area can be calculated using Equation (25), thus achieving an evaluation of the formation temperature.

[0190] (25);

[0191] In the formula, G is the geothermal gradient, °C / 100m; T is the ground temperature, °C; T0 is the surface temperature, °C; and H is the depth of the measuring point, m.

[0192] Formation shale oil viscosity data can be obtained during oil testing or calculated based on empirical formulas relating surface crude oil density and formation crude oil viscosity.

[0193] Using the methods described above, the permeability Q of shale oil at different depths can be calculated. Permeability reflects the mobility of shale oil. A high permeability indicates good mobility of the shale oil; the "mobile sweet spot" refers to the strata with good mobility. Therefore, by obtaining the permeability Q of shale oil at different depths, the mobile sweet spot of shale oil can be predicted and evaluated.

[0194] Based on the experimental law of shale seepage, a micro-seepage model of shale oil is established by using Poiseuille's law and considering the effects of effective stress and boundary layer (Equation (9)).

[0195] By coupling microscopic parameters (r0, γ, H) with macroscopic geological factors, a function that uses macroscopic parameters to characterize microscopic parameters is established. f 1. f 2. f 3, i.e., equations (10) to (12), thus establishing a macroscopic seepage model for shale oil (equation (13)).

[0196] Determine function f 1. f 2. f 3. Based on the influence relationship between micro-parameters and macro-parameters, a calculation model for γ using the brittleness index, TOC, temperature, and effective stress, and a calculation model for r0 using the content of clay minerals, silica minerals, and calcareous minerals were established using the BP neural network method. H is affected by temperature (Equation (7)). Thus, it is possible to characterize micro-parameters (r0, γ, H) using macro-parameters (brittleness index, TOC, temperature, effective stress, clay mineral content, silica mineral content, and calcareous mineral content), and then calculate the shale seepage capacity. Comparing the calculation results with shale seepage experimental data, it was found that the two have a high degree of agreement ( Figure 4 This indicates that the macroscopic seepage model for shale oil is feasible and has high accuracy.

[0197] Geological Applications of Macroscopic Seepage Models for Shale Oil. Applying macroscopic seepage models geologically requires a large number of vertically continuous macroscopic parameters. Since these parameters cannot all be obtained experimentally, a cost-effective and feasible method is needed. The following method is adopted in this invention:

[0198] ①Based on the resistivity of the logging curve and the sonic transit time, the ΔlogR method is used to model and predict, and continuous TOC data in the vertical direction is obtained.

[0199] ② The mineral composition of shale, including the content of clay minerals, siliceous minerals, and calcareous minerals, is evaluated using a backpropagation (BP) neural network method based on well logging curves. The brittleness index can be calculated from the mineral composition.

[0200] ③The formation temperature can be calculated using the geothermal gradient formula (Equation (25)).

[0201] ④ Effective stress evaluation methods include core-sleeve method, wellbore collapse method, hydraulic fracturing method, and rock acoustic emission method.

[0202] ⑤ ΔP is the difference between pore fluid pressure and wellbore pressure. Wellbore pressure evaluation methods include direct measurement, empirical formulas, and numerical simulation. Pore fluid pressure is evaluated and predicted using the Eaton method based on the Eaton acoustic model (Equation 24) and acoustic time-of-flight logging curves.

[0203] ⑥ Formation shale oil viscosity data can be obtained during oil testing, or calculated based on empirical formulas relating surface crude oil density and formation crude oil viscosity.

[0204] Based on the above methods, all parameters in the macroscopic seepage model of shale oil (Equation (13)) are obtained by evaluating macroscopic physical property parameters, and then the corresponding seepage capacity Q is calculated to evaluate the movable sweet spot of shale oil.

[0205] The main problem this invention addresses is establishing a coupling relationship between readily available macroscopic physical property parameters and microscopic physical property parameters, thereby creating a macroscopic seepage model for shale oil and enabling its geological application.

[0206] Example 3

[0207] A computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method for evaluating the movable sweet spot of shale oil based on a macroscopic seepage model of shale oil as described in Embodiment 1 or 2.

[0208] Example 4

[0209] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for evaluating the movable sweet spot of shale oil based on a macroscopic seepage model of shale oil as described in Embodiment 1 or 2.

Claims

1. A method for evaluating the movable sweet spot of shale oil based on a macroscopic seepage model of shale oil, characterized in that, include: Based on shale seepage experiments and the study of factors controlling the mobility of shale oil, a microscopic seepage model for shale oil is established. A macroscopic seepage model for shale oil was established by coupling microscopic parameters with macroscopic geological factors. Geological applications of seepage models are realized through macroscopic seepage models of shale oil, predicting the movable sweet spot of shale oil. Based on shale seepage experiments and the study of factors controlling the mobility of shale oil, a microscopic seepage model for shale oil is established, including: When a fluid undergoes laminar flow in a horizontal circular pipe, the volumetric flow rate Q of the fluid, the pressure difference Δp between the two ends of the horizontal circular pipe, the radius R and length L of the horizontal circular pipe, and the viscosity coefficient η of the fluid conform to Poiseuille's law. Considering the effects of effective stress and boundary layer effect, it is transformed into equation (1): （1）； In equation (1), Q is the volumetric flow rate of the fluid, m 3 / s;r e η is the effective aperture, in meters; ΔP is the pressure difference between the two ends of the horizontal circular pipe, in Pa; η is the viscosity coefficient of the fluid, in Pa·s. The permeability K is related to the porosity Φ, the average pore throat radius r, and the pore tortuosity φ in the following ways: （2）； In equation (2), K is the permeability, mD; Φ is the porosity, %; r is the average pore throat radius, nm; and τ is the tortuosity, dimensionless. The changes in permeability and porosity caused by effective stress under constant temperature and confining pressure were obtained through a pressure-controlled porosity-permeability test. （3）； （4）； In the formula, K is the permeability, mD; K0 is the initial permeability, mD; α and β are the pore-permeability compressibility coefficients, dimensionless; Φ is the porosity, %; Φ0 is the initial porosity, % For effective stress, MPa; EXP refers to an exponential function with base e; assuming the tortuosity remains constant, combining equations (2) to (4), we get: （5）； Let (α-β)=-γ, then the formula simplifies to: （6）； In the formula, r is the average pore throat radius, m; r0 is the initial pore throat radius, m; α, β, γ are the pore permeability compressibility coefficients, dimensionless; P e Effective stress, MPa; From the perspective of shale oil occurrence mechanism, the boundary layer thickness was determined by nuclear magnetic resonance-centrifugation and thermogravimetric analysis, combined with the adsorption ratio equation, and the relationship between boundary layer thickness and temperature was obtained: （7）； In the formula, H is the thickness of the adsorbed oil, i.e., the boundary layer thickness, in nm; t is the temperature, in °C. Combining equations (6) and (7), we obtain the formula for the effective aperture variation considering the effects of effective stress and boundary layer: （8）； In the formula, n is the number of effective holes, which is dimensionless, and r e Effective aperture; Combining equation (1), we obtain a microscopic seepage model for shale oil that considers the effects of effective stress and boundary layer: （9）； A macroscopic seepage model for shale oil is established by coupling microscopic parameters with macroscopic geological factors; including: Microscopic parameters are expressed using relevant macroscopic parameters: （10）； （11）； （12）； In the macroscopic flow model of shale oil, the boundary layer thickness is n times that in the microscopic flow model of shale oil, where n is the number of effective pores. The calculation method for n is as follows: the adsorption layer thickness in the microscopic flow model of shale oil is equivalent to the boundary layer thickness of the macropores. The diameter of the equivalent macropores is replaced by the average pore diameter of the sample obtained by mercury intrusion porosimetry. The ratio of the equivalent macropore diameter to the average pore diameter of mercury intrusion porosimetry is n. That is, if the average pore diameter of the sample is used as the small pore, n equivalent macropores are needed to fill the diameter. Therefore, the microscopic seepage model of shale oil is transformed into a macroscopic seepage model of shale oil based on macroscopic parameters: （13）； In the formula, n is the number of effective holes, which is dimensionless.

2. The method for evaluating the movable sweet spot of shale oil based on a macroscopic seepage model of shale oil according to claim 1, characterized in that, After the macroscopic seepage model of shale oil is established, calculation models for macroscopic parameters γ and r0 are established, including: using the BP neural network method to establish a calculation model for γ based on brittleness index, TOC, temperature, and confining pressure, and a calculation model for r0 based on the content of clay minerals, silica minerals, and calcareous minerals.

3. The method for evaluating the movable sweet spot of shale oil based on the macroscopic seepage model of shale oil according to claim 1, characterized in that, Geological applications of shale oil seepage models are implemented through macroscopic seepage models to predict mobile sweet spots in shale oil; including: First, an organic heterogeneity logging evaluation was conducted to obtain shale geochemical parameters TOC, chloroform bitumen A, and pyrolysis S1. Based on the resistivity of the well logging curves and the sonic transit time, the ΔlogR method was used to model and predict the results, thereby obtaining the vertically continuous shale geochemical parameters TOC, chloroform bitumen A, and pyrolysis S1. Then, inorganic heterogeneity logging evaluation is carried out on the shale, that is, the mineral composition of the shale is evaluated by logging curves, including three categories: clay minerals, siliceous minerals, and calcareous minerals. A calculation model for the content of clay minerals, siliceous minerals, and calcareous minerals in shale was established based on the BP neural network algorithm. Normalize the logging curves; Well logging curves with good correlation and response characteristics to the predicted target were selected. These included: CAL, AC, CNL, DEN, RNML, and RLML for modeling and evaluating shale clay mineral content; CAL, GR, R4, and R25 for modeling and evaluating siliceous mineral content; and CAL, SP, AC, DEN, and CNL for modeling and evaluating calcareous mineral content. Where CAL refers to well diameter (cm); AC refers to sonic transit time (μs / ft); CNL refers to neutrons (%); and DEN refers to density (g / cm³). 3 RNML refers to micropotential resistivity, Ω·m; RLML refers to microgradient resistivity, Ω·m; GR refers to natural gamma, API; R4 refers to bottom gradient resistivity at 4 meters, Ω·m; R25 refers to bottom gradient resistivity at 2.5 meters, Ω·m; SP refers to natural potential, mV. In pressure assessment, confining pressure represents the geostress under geological conditions, and geostress is decomposed into vertical stress S. V Horizontal maximum principal stress S H and the minimum principal stress S h The effective stress is the average of the sum of the three axial stresses of the ground stress. The wellbore pressure is obtained by direct measurement, empirical formula, and numerical simulation. The pore fluid pressure is predicted by using the acoustic time-of-flight logging curve based on the Eaton acoustic model. In the formation temperature evaluation, the surface temperature T0 is first calculated using the formation temperature T, the depth H of the measuring point, and the geothermal gradient G data known during the oil test in the study area; then, the formation temperature at different depths is evaluated.

4. The method for evaluating the movable sweet spot of shale oil based on the macroscopic seepage model of shale oil according to claim 3, characterized in that, Based on the resistivity and sonic transit time of the well logging curves, the ΔlogR method was used for modeling and prediction, thus obtaining the vertically continuous shale geochemical parameters TOC, chloroform bitumen A, and pyrolysis S1; including: ΔlogR is calculated based on the superposition of resistivity and acoustic time difference, as shown in equations (14) and (15): （14）； （15）； In the formula, ΔlogR is the distance between the two curves; R is the measured resistivity of the logging tool, in Ω·m; R 基线 Δt represents the resistivity corresponding to the baseline, in Ω·m; Δt is the measured acoustic transit time, in μs / ft; Δt 基线 The acoustic transit time corresponding to the baseline is μs / ft; the physical meaning of the K value is the number of units of resistivity in logarithmic coordinates corresponding to each acoustic transit time, log(R / R 基线 (Δt-Δt) is dimensionless. 基线 () has dimensions, and the geological meaning of the K value is (Δt-Δt) 基线 Transform (Δt - Δt) into a dimensionless number, such that (Δt - Δt) 基线 ) and log(R / R 基线 The magnitudes are similar, together forming ΔlogR; R min That is, Δt min R is the minimum value of the resistivity curve scale when the resistivity and acoustic transit time curves are superimposed. max That is, Δt max , is the maximum value of the resistivity curve scale when the resistivity and acoustic transit time curves are superimposed; After assuming a baseline, we get: （16）； Substituting equations (15) and (16) into equation (14), we can further derive equation (17): （17）； ΔlogR is linearly correlated with organic carbon and is a function of maturity. The empirical formula for calculating organic carbon from ΔlogR is: （18）； In the formula, TOC is the calculated organic carbon content, %; LOM reflects the maturity of organic matter; ΔTOC is the background value of organic carbon content. Interpretation models are established for different depth segments, where 10 in equation (18) (2.297-0.1688LOM) Treating it as a constant, denoted as A, equation (18) is modified within the depth range of establishing the explanatory model as follows: （19）； Substituting equation (17) into equation (19), we get: （20）； In the formula, A and Δt max R min If ΔTOC is a constant, then the theoretical model of ΔlogR based on the resistivity of the well logging curve and the sonic transit time to predict TOC is obtained: （21）； In the formula, a, b, and c are the coefficients of the fitting formula; The application process of the ΔlogR theoretical model is as follows: First, select one or several exploration wells in the study area that have a large number of TOC, chloroform bitumen A, and pyrolysis S1 experimental data points, and that have continuous lithological profiles and complete logging data. Then, based on TOC, chloroform bitumen A, and pyrolysis S1, and combined with well logging resistivity and sonic transit time data, the ΔlogR model coefficients for predicting TOC, chloroform bitumen A, and pyrolysis S1 were determined, thereby establishing ΔlogR models for predicting TOC, chloroform bitumen A, and pyrolysis S1 in the study area. Finally, after the three prediction models, including the ΔlogR model for predicting TOC, chloroform bitumen A, and pyrolysis S1, were established, they were applied to wells with logging data in the study area to predict TOC, chloroform bitumen A, and pyrolysis S1.

5. The method for evaluating the movable sweet spot of shale oil based on the macroscopic seepage model of shale oil according to claim 3, characterized in that, Normalize the logging curves; including: For curves with approximately linear characteristics, the linear normalization formula (22) is used for processing; for curves with nonlinear logarithmic resistivity characteristics, the logarithmic normalization formula (23) is used for processing. （22）； （23）； In the formula, X i , Y i represents the normalized logging curve value; X i * , Y i * These are the original well logging values; X max * , Y max * To study the maximum value of the logging curve in the target formation; X min * , Y min * The minimum value of the logging curve for the target layer is determined by the research.

6. The method for evaluating the movable sweet spot of shale oil based on a macroscopic seepage model of shale oil according to claim 1, characterized in that, Pore ​​fluid pressure is predicted using the acoustic time-of-flight logging curve based on the Eaton acoustic model, as shown in equation (24). （24）； In the formula, P B P is the pore fluid pressure; O The pressure gradient of the overlying strata; P N The hydrostatic pressure gradient; ΔT n The acoustic transit time value for normal compaction; ΔT c is the actual acoustic transit time; C is the Eaton exponent, which is derived from the known pore fluid pressure in the study area. First, using the known formation temperature T, measurement depth H, and geothermal gradient G data from the oil test in the study area, the surface temperature T0 is calculated using equation (25); then, the formation temperature at different depths is evaluated using equation (25): （25）； In the formula, G is the geothermal gradient, °C / 100m; T is the ground temperature, °C; T0 is the surface temperature, °C; and H is the depth of the measuring point, m.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for evaluating the movable sweet spot of shale oil based on the macroscopic seepage model of shale oil as described in any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for evaluating the movable sweet spot of shale oil based on the macroscopic seepage model of shale oil as described in any one of claims 1-6.

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