Shale oil reservoir multi-medium model mesoscopic permeability scale upgrading method

Abstract

The present disclosure provides a shale reservoir multi-medium model mesoscopic permeability scale upgrading method, comprising: obtaining the permeability of each medium of multiple scales and the cross-scale channeling factor; inputting multiple permeabilities corresponding to multiple scale media in the constructed multi-scale different medium model, and outputting the respective unit flow field of multiple scale media; inputting multiple permeabilities corresponding to multiple scale media and multiple cross-scale channeling factors in the constructed mesoscopic to macroscopic scale upgrading model, the mesoscopic to macroscopic scale upgrading model driving the permeability and the cross-scale channeling factor to obtain scale upgrading data; using the scale upgrading data and the unit flow field to solve the optimal estimation target based on the target least square norm, and obtaining the macroscopic scale medium and attribute according to the optimal estimation target. The present disclosure is based on the pore and fracture characteristics and flow mechanism of shale reservoir multi-scale media, establishes a multi-medium model macroscopic permeability parameter field, and provides input for multi-medium model numerical simulation.

Description

Technical Field

[0001] This disclosure relates to the field of shale oil permeability calculation, and in particular to a method for upgrading the mesoscopic permeability scale of a multi-medium model for shale oil reservoirs. Background Technology

[0002] Shale oil is a primary reservoir type of oil and gas reservoir, with the reservoir space mainly consisting of nano- and micron-sized pores. Generally, it can be divided into two main types: organic pores and inorganic pores, which can be further subdivided into different subcategories. Experimental and simulation studies show that the scale span of different reservoir spaces is large, and the multiphase fluid flow patterns also vary considerably. Currently, there is no unified understanding of the applicability of multi-scale mass transfer characterization methods for shale oil reservoirs.

[0003] Determining mass transfer parameters across multiple media at various scales is a significant challenge in numerical simulations of shale reservoirs. Shale exhibits extremely low porosity and permeability, making it difficult to obtain accurate parameters such as permeability using conventional experimental methods. For instance, current shale permeability experiments suffer from challenges such as large displacement pressure differentials, long flow velocity stabilization times, and insufficient flow rates for precise measurement. Many researchers have studied and obtained characteristic parameters such as intrinsic and apparent permeability of shale using direct simulation methods, including digital core analysis and Lattice Boltzmann Method (LBM) or Molecular Dynamics (MD). Ma et al., based on experimental data such as shale core scans, summarized different matrix pore distribution patterns and established mesoscopic (mm-level) characteristic parameter models using multi-point statistical methods. Based on microstructural digital models, Germanou et al. compared different flow equations, such as the Brinkman and Boltzmann equations, summarized the adaptability of different equations, and established a process for calculating rock permeability at the microscale. Bohacs et al., through their classification study of microscopic reservoir types and hydrocarbon occurrence states, found that free-state shale oil is mainly found in reservoir spaces such as intergranular pores, intragranular pores, and multigenetic fractures, while adsorbed shale oil is mainly found on the surface of mineral grains and kerogen. In addition, although intragranular pores occupy a considerable proportion of the pore size, they generally do not constitute large-scale flow channels.

[0004] The permeability obtained by the above methods is the mesoscopic permeability of shale oil reservoirs. Directly applying this mesoscopic permeability parameter to macroscopic flow simulation has certain limitations. To address this, many scholars have proposed methods to upgrade the parameter scale from mesoscopic to macroscopic. However, the resulting macroscopic permeability is a comprehensive property of pores at multiple scales. Further improvements are needed to develop a scale upgrade method based on a multi-medium model. Summary of the Invention

[0005] The embodiments of the present invention provide a method for upgrading the mesoscopic permeability scale of a multi-medium model for shale oil reservoirs, establishing a macroscopic permeability parameter field for the multi-medium model, and providing input for numerical simulation of the multi-medium model.

[0006] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0007] A method for upgrading the mesoscopic permeability scale of a multi-media model for shale oil reservoirs, comprising:

[0008] To obtain the permeability and cross-scale flow factor of media at various scales;

[0009] Input multiple permeability values ​​corresponding to various media at different scales into the constructed multi-scale media model, and output the flow field of each unit volume of the various media at different scales;

[0010] In the constructed mesoscale-to-macroscale upgrade model, multiple permeability rates and multiple cross-scale flow factors corresponding to various scale media are input. The mesoscale-to-macroscale upgrade model drives the permeability rate and the cross-scale flow factors to obtain scale upgrade data.

[0011] The optimal estimated target is obtained by using the scale-up data and the unit volume flow field based on the target least squares norm, and the macroscopic scale medium and properties are obtained based on the optimal estimated target.

[0012] In one possible implementation, obtaining the permeability and cross-scale crossflow factor of the various scale media includes:

[0013] The tortuosity and volume fraction of pores of various media at different scales in shale oil reservoirs are obtained, and the permeability of each media is calculated based on the tortuosity and volume fraction of each media.

[0014] Obtain the characteristic length of the mesoscopic characterization unit of the mesoscopic medium in shale oil reservoirs, and determine the heterogeneity coefficient between different media based on the proportion of porous media at different scales and the heterogeneity of media at different scales.

[0015] The cross-scale flow factor is obtained based on the characteristic length of the mesoscopic characterization unit and the heterogeneous system between different media.

[0016] In one possible implementation, the proportions of the different pore sizes are obtained in the following manner:

[0017] The porosity of each medium at different scales in shale oil reservoirs is obtained, and the proportion of the porous media at different scales is calculated based on the porosity.

[0018] In the mesoscopic characterization, the pore volume fractions of different media are respectively The proportions of the pore media of different scales are respectively expressed by the following formula (1):

[0019] φ m =φt χ m (1)

[0020] Where, φ t Porosity; χ m satisfy

[0021] In one possible implementation, calculating the permeability of each medium based on the tortuosity of each medium and the volume fraction of each medium includes:

[0022] The permeability of different media is calculated by introducing the analytical relationship formula (2);

[0023]

[0024] Where, ξ m χ is the proportion of connected pores in medium m. m It is the volume fraction of medium m, τ m It is the porosity tortuosity of medium m, k I It is the inherent permeability of the mesosphere;

[0025] The mesoscopic intrinsic permeability is a comprehensive property parameter of multi-scale pores, including organic pores, inorganic pores, and pore structure.

[0026] In one possible implementation, the method for constructing the multi-scale, different-medium model includes:

[0027] Build and train media models to improve the accuracy of media model usage;

[0028] Darcy's formula (3) is introduced into the medium model to obtain the unit volume flow field;

[0029]

[0030] In the formula, K is the permeability, and λ p For phase mobility, P p Let g be the phase pressure, D be the gravitational acceleration, p be the vertical depth, and u be the fluid phase. p For flow rate;

[0031] The mesoscale medium maintains the same properties, and Darcy's formula (3) is used to obtain the flow field of the mesoscale unit. The flow field of the macroscopic multi-medium unit is generated by periodic boundary conditions. The macroscopic flow field is obtained by replacing the mesoscopic parameters with macroscopic parameters in Darcy's formula (3).

[0032] In one possible implementation, the multiple scale media include: macroscale organic media, macroscale intragranular media, macroscale intergranular media, mesoscale organic media, mesoscale intragranular media, and mesoscale intergranular media.

[0033] In one possible implementation, the method for constructing the mesoscopic-to-macroscale upgrade model includes:

[0034] Build and train a scaling-up model to improve the accuracy of scaling-up model usage;

[0035] Introduce a multi-parameter optimal estimation objective function for scaling up in the scaling up model;

[0036] The objective function for the scale-up multi-parameter optimal estimation takes mesoscopic permeability and inter-medium crossflow factor as inputs and outputs macroscopic grid cell permeability and macroscopic grid cell crossflow factor as outputs.

[0037] In one possible implementation, the process of establishing the multi-parameter optimal estimation objective function for scaling up includes:

[0038] Permeability k is a spatial function described by κ(x) = cψ(x), where c ∈ R N It is a parameter vector, and ψ(x) is an N-dimensional real basis vector of the field space;

[0039] Reference data y∈R M Let x(c) be the data obtained by the mesoscopic-to-macroscale upgrade model;

[0040] The objective of the multi-parameter optimal estimation function for scaling up is to find a suitable c under the constraints of formula (4) such that the deviation of the calculated data x(c) from the reference data is minimized, i.e.:

[0041] min{norm[yx(c)]) (4)

[0042] Select inflow / outflow Q m and inter-medium flow rate Q m1，m2 As the observed data d, based on the distribution properties of the flow field and pressure field, the corresponding m(c) is obtained from formulas (2) and (5):

[0043]

[0044] Where, ξ m χ is the proportion of connected pores in medium m. m It is the volume fraction of medium m, τ m It is the porosity tortuosity of medium m, k I It is the inherent permeability of the mesosphere;

[0045] α m1，m2 =σ m1m2 k*L 2 (5)

[0046] Wherein, the heterogeneity coefficient σm1m2 It is a coefficient related to the relationship between heterogeneity and pore connectivity, and L is the characteristic length of the mesoscopic characterizing unit cell.

[0047] In one possible implementation, the process of establishing the multi-parameter optimal estimation objective function for scaling up further includes:

[0048] The minimum value of formula (4) is obtained using the weighted objective least squares norm of formula (6) as follows:

[0049] min{norm[yx(c)]}=min{[yx(c)] T w[yx(c)]} (6)

[0050] Where W is the observation data weight vector, for a specific observation data y i The corresponding weight value y i The larger it is, the greater ||y| i -x i The smaller;

[0051] For multi-scale media models, crossflow between different media is greatly affected by the heterogeneity of micropore structure, so the weight value is reduced.

[0052] Solving for the extreme value of formula (6) yields c. * Thus, we obtain k * and inter-medium crossflow factor α m1*，m2* The asterisk (*) indicates macroscopic media and their properties.

[0053] This disclosure has at least the following technical effects or advantages:

[0054] The embodiments of the present invention establish a macroscopic permeability parameter field for a multi-medium model based on the pore and fracture characteristics and flow mechanism of multi-scale media in shale oil reservoirs, providing input for numerical simulation of the multi-medium model. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments of the present invention or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 Here is a flowchart of a method for upgrading the mesoscopic permeability scale of a multi-medium model for shale oil reservoirs according to some embodiments of this disclosure;

[0057] Figure 2This is a schematic diagram illustrating the proportion of different pore types in a mesoscopic characterization body provided according to some embodiments of the present disclosure;

[0058] Figure 3 A diagram showing the relationship between mesoscopic and macroscopic parameters according to some embodiments of this disclosure;

[0059] Figure 4 A schematic diagram of a mesoscopic model flow field provided according to some embodiments of this disclosure;

[0060] Figure 5 This is a diagram showing the pressure field distribution in different media according to some embodiments of this disclosure;

[0061] Figure 6 A flowchart illustrating the mesoscopic-to-macroscopic parameter scale upgrade process provided according to some embodiments of this disclosure;

[0062] Figure 7 This is a schematic diagram of a conceptual model provided according to some embodiments of the present disclosure;

[0063] Figure 8 This is a schematic diagram of the mechanism model parameters provided according to some embodiments of the present disclosure;

[0064] Figure 9 This is a comparison chart of flow simulation results based on some embodiments of the present disclosure. Detailed Implementation

[0065] The present disclosure will now be described in detail with reference to the embodiments shown in the accompanying drawings. However, it should be noted that these embodiments are not intended to limit the present disclosure. Any equivalent changes or substitutions in function, method, or structure made by those skilled in the art based on these embodiments are within the scope of protection of the present disclosure.

[0066] To address the current problem of difficulty in determining permeability parameters in multi-medium models, embodiments of this disclosure provide a method for upgrading the mesoscopic permeability scale of a multi-medium model for shale oil reservoirs, thereby establishing a macroscopic permeability parameter field for the multi-medium model and providing input for numerical simulation of the multi-medium model.

[0067] The embodiments of this disclosure provide a method for upgrading the mesoscopic permeability scale of a multi-medium model for shale oil reservoirs, including:

[0068] To obtain the permeability and cross-scale flow factor of media at various scales;

[0069] Input multiple permeability rates corresponding to various media at different scales into the constructed multi-scale media model, and output the flow field of each unit cell of the various media at different scales;

[0070] In the constructed mesoscale-to-macroscale upgrade model, multiple permeability and multiple cross-scale flow factors corresponding to various scale media are input. The mesoscale-to-macroscale upgrade model drives the permeability and cross-scale flow factors to obtain scale upgrade data.

[0071] The optimal estimated target is obtained by using scale-up data and unit volume flow field based on the target least squares norm, and the macro-scale medium and properties are obtained based on the optimal estimated target.

[0072] Please see Figure 2 This disclosure calculates the proportion of porous media at different scales in the mesoscopic characterization volume, and the pore volume fractions of different media are as follows: The proportions of porous media with different pore sizes are respectively represented by the following formula (1):

[0073] φ m =φ t χ m (1)

[0074] Where, φ t Porosity; χ m satisfy

[0075] The preferred method is to obtain the permeability and cross-scale flow factor of media at various scales, including:

[0076] Obtain the tortuosity and volume fraction of pores of various media at different scales in shale oil reservoirs, and calculate the permeability of each media based on the tortuosity and volume fraction of each media.

[0077] Obtain the characteristic length of the mesoscopic characterization unit of the mesoscopic medium in shale oil reservoirs, and determine the heterogeneity coefficient between different media based on the proportion of porous media at different scales and the heterogeneity of media at different scales.

[0078] The cross-scale flow factor is obtained based on the characteristic length of the mesoscopic characterizing unit and the heterogeneous system between different media.

[0079] Based on the above scheme, the optimal proportion of porous media with different pore sizes is obtained through the following method:

[0080] Obtain the porosity of each medium at different scales in shale oil reservoirs, and calculate the proportion of porous media at different scales based on the porosity;

[0081] In the mesoscopic characterization, the pore volume fractions of different media are respectively The proportions of porous media with different pore sizes are respectively represented by the following formula (1):

[0082] φ m =φ t χ m (1)

[0083] Where, φ t Porosity; χ m satisfy

[0084] When calculating the inherent permeability of different porous media, parameters such as shale permeability are often obtained based on direct simulation methods such as MD / LBM using digital cores (µm to mm scale), and are also called inherent permeability. The inherent permeability obtained by direct simulation methods such as digital core stress-strain models and shale gas seepage LBM models is a comprehensive property of multi-scale pores, including organic pores, inorganic pores, and foliation fractures. Therefore, it is first necessary to distinguish the permeability of different media. (See also...) Figure 3 For the mesoscopic intrinsic permeability k I Directly calculating the effective permeability of different media is difficult. Considering the porosity and volume fraction of different media, the permeability of different media can be calculated using analytical formula (2).

[0085]

[0086] Where, ξ m χ is the proportion of connected pores in medium m. m It is the volume fraction of medium m, τ m It is the porosity tortuosity of medium m, k I It is the inherent permeability of the mesoscopic level.

[0087] Based on the above scheme, it is preferable to calculate the permeability of each medium according to its respective tortuosity and volume fraction, including:

[0088] The permeability of different media is calculated by introducing the analytical relationship formula (2);

[0089]

[0090] Where, ξ m χ is the proportion of connected pores in medium m. m It is the volume fraction of medium m, τ m It is the porosity tortuosity of medium m, k I It is the inherent permeability of the mesosphere;

[0091] Mesoscopic intrinsic permeability is a comprehensive property parameter of multi-scale pores, including organic pores, inorganic pores, and pore structure.

[0092] Preferred methods for constructing multi-scale models with different media include:

[0093] Build and train media models to improve the accuracy of media model usage;

[0094] Darcy's formula (3) is introduced into the medium model to obtain the unit volume flow field;

[0095]

[0096] In the formula, K is the permeability, and λ p For phase mobility, P p Let g be the phase pressure, D be the gravitational acceleration, p be the vertical depth, and u be the fluid phase. p For flow rate;

[0097] The mesoscale medium maintains the same properties, and Darcy's formula (3) is used to obtain the flow field of the mesoscale unit. The flow field of the macroscopic multi-medium unit is generated by periodic boundary conditions. The macroscopic flow field is obtained by replacing the mesoscopic parameters with macroscopic parameters in Darcy's formula (3).

[0098] Based on the above scheme, multiple scale media are preferred, including: macroscale organic media, macroscale intragranular media, macroscale intergranular media, mesoscale organic media, mesoscale intragranular media, and mesoscale intergranular media.

[0099] Based on the above scheme, the preferred methods for constructing the mesoscopic-to-macroscale upgrade model include:

[0100] Build and train a scaling-up model to improve the accuracy of scaling-up model usage;

[0101] Introduce a multi-parameter optimal estimation objective function for scaling up in the scaling up model;

[0102] The objective function for the scale-up multi-parameter optimal estimation takes mesoscopic permeability and inter-medium crossflow factor as inputs and outputs macroscopic grid cell permeability and macroscopic grid cell crossflow factor as outputs.

[0103] The embodiments of this disclosure calculate the crossflow factor between different media, including:

[0104] α m1，m2 =σ m1m2 k*L 2 (5)

[0105] Wherein, the heterogeneity coefficient σ m1m2 It is a coefficient related to the relationship between heterogeneity and pore connectivity, and L is the characteristic length of the mesoscopic characterizing unit cell.

[0106] The permeability and crossflow factor obtained above are still mesoscopic parameters. Directly applying them to macroscopic flows will result in large errors, and the parameters need to be scaled up.

[0107] This embodiment establishes the flow field of a macroscopic multi-medium unit under periodic boundary conditions. According to Darcy's formula, the flow velocity of fluid phase p is:

[0108]

[0109] In the formula, K is the permeability, and λ p For phase mobility, P p Let g be the phase pressure, D be the gravitational acceleration, p be the vertical depth, and u be the fluid phase. p For flow rate;

[0110] Please see Figure 6 A multi-parameter optimal estimation objective function for scale-up is established. The inputs to scale-up are mesoscopic permeability and inter-medium crossflow factor, and the outputs are macroscopic grid cell permeability and crossflow factor. The essence of scale-up is multi-parameter optimal estimation. The permeability k is a spatial function described by κ(x)=cψ(x), where c∈R N Here, ψ(x) is a parameter vector, and ψ(x) is an N-dimensional real basis vector of the field space; the reference data y∈R M Let x(c) be the data obtained driven by the mesoscopic-to-macroscale scaling model. Let x(c) be the data obtained by the model (called the computational data). The goal of scaling is to find a suitable C under certain constraints such that the deviation of the computational data x(c) from the observed data is minimized. That is:

[0111] min{norm[yx(c)]} (4)

[0112] Select inflow / outflow Q m and inter-medium flow rate Q m1，m2 As the observed data d, based on the distribution properties of the flow field and pressure field, the corresponding m(c) is obtained from formulas (2) and (5):

[0113]

[0114] Where, ξ m χ is the proportion of connected pores in medium m. m It is the volume fraction of medium m, τ m It is the porosity tortuosity of medium m, k I It is the inherent permeability of the mesosphere;

[0115] α m1，m2 =σ m1m2 k*L 2 (5)

[0116] Wherein, the heterogeneity coefficient σ m1m2 It is a coefficient related to the relationship between heterogeneity and pore connectivity, and L is the characteristic length of the mesoscopic characterizing unit cell.

[0117] Based on the above scheme, the process of establishing the optimal estimation objective function for the multi-parameter scaling upgrade includes:

[0118] Permeability k is a spatial function described by κ(x) = cψ(x), where c ∈ R N It is a parameter vector, and ψ(x) is an N-dimensional real basis vector of the field space;

[0119] Reference data y∈R M Let x(c) be the data obtained by the mesoscopic-to-macroscale upgrade model;

[0120] In the objective function of the multi-parameter optimal estimation of scaling, the goal of scaling is to find a suitable C under the constraints of formula (4) such that the deviation of the calculated data x(c) from the reference data is minimized, i.e.:

[0121] min{norm[yx(c)]} (4)

[0122] Select inflow / outflow Q m and inter-medium flow rate Q m1，m2 As the observed data d, based on the distribution properties of the flow field and pressure field, the corresponding m(c) is obtained from formulas (2) and (5):

[0123]

[0124] Where, ξ m χ is the proportion of connected pores in medium m. m It is the volume fraction of medium m, τ m It is the porosity tortuosity of medium m, k I It is the inherent permeability of the mesosphere;

[0125] α m1，m2 =σ m1m2 k*L 2 (5)

[0126] Wherein, the heterogeneity coefficient σ m1m2 It is a coefficient related to the relationship between heterogeneity and pore connectivity, and L is the characteristic length of the mesoscopic characterizing unit cell.

[0127] This disclosure also provides an embodiment for solving the optimal estimated objective based on the objective least squares norm. The minimum value of formula (4) is obtained using the weighted objective least squares norm of formula (6) below:

[0128] min{norm[yx(c)]}=min{[yx(c)] T w[yx(c)]} (6)

[0129] Where W is the observation data weight vector, for a specific observation data y i The corresponding weight value y i The larger |y is, the more i -xi |The smaller the value;For multi-scale media models, the crossflow between different media is greatly affected by the heterogeneity of the micro-pore structure, so the weight value is reduced;The extreme value of formula (6) is obtained to get c. * Thus, we obtain k * and inter-medium crossflow factor α m1*，m2* The asterisk (*) indicates macroscopic media and their properties.

[0130] Based on the above scheme, the process of establishing the optimal estimation objective function for the multi-parameter scaling upgrade also includes:

[0131] The minimum value of formula (4) is obtained using the weighted objective least squares norm of formula (6) as follows:

[0132] min{norm[yx(c)]}=min{[yx(c)] T w[yx(c)]} (6)

[0133] Where W is the observation data weight vector, for a specific observation data y i The corresponding weight value y i The larger |y is, the more i -x i The smaller;

[0134] For multi-scale media models, crossflow between different media is greatly affected by the heterogeneity of micropore structure, so the weight value is reduced.

[0135] Solving for the extreme value of formula (6) yields c. * Thus, we obtain k * and inter-medium crossflow factor α m1*，m2* The asterisk (*) indicates macroscopic media and their properties.

[0136] The embodiments disclosed herein establish a macroscopic permeability parameter field for a multi-medium model based on the pore and fracture characteristics and flow mechanism of multi-scale media in shale oil reservoirs, providing input for numerical simulation of the multi-medium model.

[0137] Please see Figure 7 The mechanism model shown assumes that the organic medium, interparticle medium, and intraparticle medium are uniformly distributed in sequence. Please refer to Table 1 for the mechanism model parameters. The porosity, permeability, and cross-scale flow factor of different media are obtained according to the above formulas (1) to (6). The multi-scale media model of this disclosure is used to perform flow simulation. At the same time, flow simulation based directly on the mesoscopic unit characterization is carried out as a reference solution. Meanwhile, two comparative models are designed: a non-upgraded model (mesoscopic parameters are directly used for macroscopic purposes) and a single-medium model (without distinguishing media). Please refer to the simulation results. Figure 9The horizontal axis represents the injection pore volume multiple (PVI), the left vertical axis represents the daily oil production (cubic meters / day), and the right vertical axis represents the cumulative oil production (cubic meters). The results show that the model proposed in this embodiment has high accuracy, while the non-upgraded model and the single-medium model overestimate the connectivity inside the medium, resulting in an overestimation of production capacity. Among them, the single-medium model considers all media as a fully connected model, resulting in the largest production capacity error.

[0138] The detailed descriptions listed above are merely specific descriptions of feasible implementations of this disclosure and are not intended to limit the scope of protection of this disclosure. All equivalent implementations or modifications made without departing from the spirit of the art of this disclosure should be included within the scope of protection of this disclosure.

[0139] It will be apparent to those skilled in the art that this disclosure is not limited to the details of the exemplary embodiments described above, and that this disclosure can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of this disclosure is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this disclosure. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0140] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for upgrading the mesoscopic permeability scale of a multi-media model for shale oil reservoirs, characterized in that, include: To obtain the permeability and cross-scale flow factor of media at various scales; Input multiple permeability values ​​corresponding to various media at different scales into the constructed multi-scale media model, and output the flow field of each unit volume of the various media at different scales; In the constructed mesoscale-to-macroscale upgrade model, multiple permeability rates and multiple cross-scale flow factors corresponding to various scale media are input. The mesoscale-to-macroscale upgrade model drives the permeability rate and the cross-scale flow factors to obtain scale upgrade data. Using the scale-up data and the unit volume flow field, the optimal estimated target is solved based on the target least squares norm, and the macro-scale medium and properties are obtained based on the optimal estimated target. The method for constructing the multi-scale, different media model includes: Construct and train a medium model to improve the accuracy of the medium model; introduce Darcy's formula (3) into the medium model to obtain the unit flow field; In the formula, K is the permeability, and λ p For phase mobility, P p Let g be the phase pressure, D be the gravitational acceleration, p be the vertical depth, and u be the fluid phase. p For the flow velocity; the mesoscale medium maintains the same properties, and Darcy's formula (3) is used to obtain the flow field of the mesoscale unit cell. The flow field of the macroscopic multi-medium unit cell is generated by periodic boundary conditions. The macroscopic flow field is obtained by replacing the mesoscopic parameters with macroscopic parameters in Darcy's formula (3). The method for constructing the mesoscopic-to-macroscale upgrade model includes: Build and train a scaling-up model to improve the accuracy of scaling-up model usage; Introduce a multi-parameter optimal estimation objective function for scaling up in the scaling up model; The objective function for the scale-up multi-parameter optimal estimation takes mesoscopic permeability and inter-medium crossflow factor as inputs and outputs macroscopic grid cell permeability and macroscopic grid cell crossflow factor as outputs.

2. The method for upgrading the mesoscopic permeability scale of a multi-medium model for shale oil reservoirs according to claim 1, characterized in that, The process of obtaining the permeability and cross-scale crossflow factor of media at various scales includes: The tortuosity and volume fraction of pores of various media at different scales in shale oil reservoirs are obtained, and the permeability of each media is calculated based on the tortuosity and volume fraction of each media. Obtain the characteristic length of the mesoscopic characterization unit of the mesoscopic medium in shale oil reservoirs, and determine the heterogeneity coefficient between different media based on the proportion of porous media at different scales and the heterogeneity of media at different scales. The cross-scale flow factor is obtained based on the characteristic length of the mesoscopic characterization unit and the heterogeneous system between different media.

3. The method for upgrading the mesoscopic permeability scale of a multi-medium model for shale oil reservoirs according to claim 2, characterized in that, The proportions of the porous media with different scales were obtained through the following method: The porosity of each medium at different scales in shale oil reservoirs is obtained, and the proportion of the porous media at different scales is calculated based on the porosity. In the mesoscopic characterization, the pore volume fractions of different media are respectively The proportions of the different pore sizes of the medium are respectively expressed by the following formula (1): f m =φ t x m (1) Where, φ t Porosity; χ m satisfy .

4. The method for upgrading the mesoscopic permeability scale of a multi-medium model for shale oil reservoirs according to claim 2, characterized in that, The permeability of each medium is calculated based on the tortuosity and volume fraction of each medium, including: The permeability of different media is calculated by introducing the analytical relationship formula (2); Where, ξ m χ is the proportion of connected pores in medium m. m It is the volume fraction of medium m. It is the porosity tortuosity of medium m. It is the mesoscopic intrinsic permeability; the mesoscopic intrinsic permeability is a comprehensive property parameter of multi-scale pores, including organic pores, inorganic pores, and pore structure.

5. The method for upgrading the mesoscopic permeability scale of a multi-medium model for shale oil reservoirs according to claim 1, characterized in that, The various scale media include: macroscopic organic media, macroscopic intragranular media, macroscopic intergranular media, mesoscopic organic media, mesoscopic intragranular media, and mesoscopic intergranular media.

6. The method for upgrading the mesoscopic permeability scale of a multi-medium model for shale oil reservoirs according to claim 1, characterized in that, The process of establishing the multi-parameter optimal estimation objective function for the scaling-up includes: the permeability k is a spatial function described by κ(x)=cψ(x), where c∈R N Here, ψ(x) is a parameter vector, and ψ(x) is an N-dimensional real basis vector of the field space; the reference data y∈R M Let x(c) be the data obtained by the mesoscopic-to-macroscale scaling model; the objective of the scaling multi-parameter optimal estimation objective function is to find a suitable c under the constraints of formula (4) so ​​that the deviation of the calculated data x(c) from the reference data is minimized, i.e.: Select inflow / outflow Q m and inter-medium flow rate Q m1，m2 As the observed data d, based on the distribution properties of the flow field and pressure field, the corresponding m(c) is obtained from formulas (2) and (5): Where, ξ m χ is the proportion of connected pores in medium m. m It is the volume fraction of medium m, τ m It is the porosity tortuosity of medium m, k I It is the inherent permeability of the mesosphere; a m1，m2 =s m1m2 k*L 2 (5) Wherein, the heterogeneity coefficient σ m1m2 It is a coefficient related to the relationship between heterogeneity and pore connectivity, and L is the characteristic length of the mesoscopic characterizing unit cell. .

7. The method for upgrading the mesoscopic permeability scale of a multi-medium model for shale oil reservoirs according to claim 6, characterized in that, The process of establishing the target function for the multi-parameter optimal estimation of scaling also includes: The minimum value of formula (4) is obtained using the weighted objective least squares norm of formula (6) as follows: Where w is the observation data weight vector, for a specific observation data y i The corresponding weight value y i The larger |y is, the more i -x i |The smaller the value; For multi-scale media models, the crossflow between different media is greatly affected by the heterogeneity of the micro-pore structure, so the weight value should be reduced; Solve the extreme value of formula (6) to obtain c. * Thus, we obtain k * and inter-medium crossflow factor α m1*，m2* The asterisk (*) indicates macroscopic media and their properties.

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