Multi-scale productivity prediction method and system for complex lithofacies shale oil reservoir

Abstract

The invention discloses a complex lithofacies shale oil reservoir multi-scale productivity prediction method and system. The method comprises the following steps: establishing a complex lithofacies shale oil reservoir model with a multi-scale aperture structure; representing the complex lithofacies shale oil reservoir model by adopting a dual-medium model and an embedded discrete fracture model; establishing a complex lithofacies shale oil reservoir seepage model and a numerical solution algorithm considering the starting pressure gradient and the stress sensitive effect, and forming a complex lithofacies shale multi-scale numerical simulation method; the complex lithofacies shale multi-scale numerical simulation method is adopted to carry out complex lithofacies shale oil reservoir seepage numerical simulation, and the shale oil reservoir oil and gas yield is obtained through calculation. According to the method, the seepage simulation and productivity calculation of the complex lithofacies shale oil reservoir can be quickly and accurately realized by establishing the seepage numerical simulation method of the complex lithofacies shale oil reservoir considering the multi-scale aperture characteristics, the starting pressure gradient and the stress sensitive effect.

Description

Technical Field

[0001] This application belongs to the field of oil and gas field development technology, specifically involving a multi-scale production capacity prediction method and system for complex lithofacies shale oil reservoirs. Background Technology

[0002] Complex lithofacies shale oil reservoirs have complex reservoir media, including micro- and nano-scale pores, micron-scale bedding and natural fractures, and meter-scale hydraulically pressure fractures, spanning multiple spatial scales. Influenced by factors such as the multi-scale heterogeneity of the reservoir and the unique fluid seepage mechanisms within the dense matrix pores, characterizing the fluid flow patterns and laws in complex lithofacies shale oil reservoirs is challenging. Corresponding numerical simulation methods are still immature, making it difficult to accurately assess the productivity of complex lithofacies shale oil reservoirs.

[0003] Currently, most traditional numerical simulation methods for shale oil reservoirs use single models such as dual-medium and local mesh refinement to characterize fractures in the reservoir. They do not fully consider complex factors such as shale oil initiation pressure gradient and reservoir stress sensitivity effects. As a result, multi-scale seepage simulation and production prediction technology suitable for complex lithofacies shale oil reservoirs has not yet been developed, which restricts the efficient and accurate formulation of development plans for complex lithofacies shale. Summary of the Invention

[0004] To address the issue that traditional numerical simulation techniques fail to fully consider complex factors such as the initiation pressure gradient and reservoir stress sensitivity effects in shale oil reservoirs, resulting in inaccurate flow simulation and production capacity prediction for complex lithofacies shale oil reservoirs, this application proposes a multi-scale production capacity prediction method and system for complex lithofacies shale oil reservoirs. This application constructs a mathematical model of flow in complex lithofacies shale oil reservoirs by comprehensively considering multi-scale pore characteristics, initiation pressure gradient, and stress sensitivity effects, enabling efficient numerical simulation. This allows for rapid and accurate flow simulation and production capacity prediction of complex lithofacies shale oil reservoirs, providing a theoretical basis for production capacity evaluation and scheme optimization design in the development of complex lithofacies shale oil.

[0005] This application is achieved through the following technical solution:

[0006] A multi-scale production capacity prediction method for complex lithofacies shale reservoirs, the method comprising:

[0007] Establish a reservoir model for complex lithofacies shale oil reservoirs with multi-scale structures including matrix, bedding, natural fractures, and hydraulic fracturing fractures;

[0008] The complex lithofacies shale oil reservoir model was characterized using a dual-medium model and an embedded discrete fracture model.

[0009] Establish a seepage model and numerical solution algorithm for complex lithofacies shale reservoirs that consider the initiation pressure gradient and stress sensitivity effects, and form a multi-scale numerical simulation method for complex lithofacies shale;

[0010] Based on the porosity, permeability, initiation pressure gradient, and stress sensitivity coefficient obtained from core tests of different lithofacies strata, as well as fluid phase parameters, the multi-scale numerical simulation method for complex lithofacies shale was used to conduct numerical simulation of seepage in complex lithofacies shale reservoirs, and the oil and gas production of shale reservoirs was calculated.

[0011] In some embodiments, the process of establishing the complex lithofacies shale oil reservoir model specifically includes:

[0012] Based on the exploration data and drilling core observation data of the target shale oil reservoir, the reservoir structure and physical property parameters are obtained;

[0013] Based on imaging logging and core sampling data, bedding and natural fracture development characteristic parameters of each well were obtained, and seismic data constraints were used to obtain bedding and natural fracture development characteristic parameters for the entire area.

[0014] Based on the monitoring results obtained during the hydraulic fracturing process, characteristic parameters of hydraulic fracturing fracture development in different sections were acquired.

[0015] Based on reservoir structure and physical property parameters, combined with bedding and natural fracture development parameters as well as hydraulic fracturing fracture development parameters, a complex lithofacies shale oil reservoir model with multi-scale structures of matrix, bedding, natural fractures and hydraulic fracturing fractures was comprehensively established.

[0016] In some embodiments, the physical property parameters include porosity, permeability, and fluid saturation.

[0017] In some embodiments, the bedding and natural fracture development characteristic parameters include the orientation, aperture, and development density of the bedding and natural fractures.

[0018] And / or, the hydraulic fracturing fracture development characteristic parameters include the number of hydraulic fracturing fracture clusters, length, and height.

[0019] In some implementations, a dual-medium model and an embedded discrete fracture model are used to characterize the complex lithofacies shale oil reservoir model, specifically including:

[0020] Two sets of meshes are established for regions with developed bedding and natural cracks. One set is a matrix mesh, and the other set is a mesh for bedding and natural cracks. The two sets of meshes share coordinate positions in space, and there is fluid flow between the two meshes at the same position.

[0021] To address the morphology of hydraulic fracturing fractures, the geometric surface of the hydraulic fracturing fractures is embedded into two sets of meshes. The geometric surface of the hydraulic fracturing fractures is then cut by the two sets of meshes to form an embedded discrete fracture mesh. Fluid flow exists between the embedded discrete fracture mesh and the two sets of meshes.

[0022] In some implementations, a seepage model and numerical solution algorithm for complex lithofacies shale reservoirs considering initiation pressure gradient and stress-sensitive effects are established, specifically including:

[0023] A flow model for complex lithofacies shale reservoirs was established, including the mass conservation equations for components in the matrix and fractures, the three-phase motion equations of oil, gas and water considering the oil-water initiation pressure gradient, and auxiliary equations.

[0024] The numerical discrete equations of the seepage model of the complex lithofacies shale oil reservoir were established using the finite element volume method, and the residual form of the numerical discrete equations was constructed.

[0025] A numerical solution algorithm for complex lithofacies shale oil reservoirs is established by solving the residual equation.

[0026] In some implementations, the mass conservation equation is:

[0027]

[0028] The three-phase motion equations for oil, gas, and water are as follows:

[0029]

[0030] The auxiliary equation is:

[0031]

[0032] S o +S g +S w =1

[0033] p w =p g -p cwg (S w )

[0034] p o =p g -p cog (S w ,S o )

[0035] In the formula, the subscripts o, g, and w represent the three phases of oil, gas, and water, respectively; N c For groupings; x i y i ρ represents the mole fraction of hydrocarbon component i in the oil phase and gas phase, respectively; o ρ g ρ w These are the molar densities of the oil phase, gas phase, and water phase, respectively; v o v g v wThese represent the seepage velocities of the oil phase, gas phase, and water phase, respectively; S o S g S w These represent the saturation levels of the oil, gas, and water phases, respectively; φ and k represent reservoir porosity and permeability; q i and q w Represent the source and sink terms of hydrocarbon component i and water component respectively; μ β The viscosity of the β phase (oil phase or water phase); μ g The viscosity of the β phase (oil phase or water phase); k rβ The relative permeability of the β phase (oil phase or water phase), k rg denoted as ρ, where ρ is the relative permeability of the gas phase; p is the phase pressure; ψ is the flow potential; ζ is the fluid specific weight; D represents the reservoir depth; G is the initial pressure gradient; k0 is the initial permeability; p0 is the initial pressure; c is the stress sensitivity coefficient; p cwg and p cog These represent the capillary forces of water vapor and oil vapor, respectively.

[0036] In some implementations, the residual equation is:

[0037]

[0038] In the formula, subscripts n and m represent grid numbers; i represents component number; t represents the previous time step; t+1 represents the current time step; η n Let n be the set of all adjacent grids of grid n; V is the average value at the interface between grids n and m; V is the grid volume. This represents the residual of grid n in the mass conservation equation for component i at the current time step; This represents the residual of grid n in the mass conservation equation for the aqueous phase at the current time step; These represent the flow potentials of the oil, gas, and water phases within grid m, respectively. λ represents the flow potential of the oil, gas, and water phases within grid n, respectively; Δt is the time step; λ o , λ g , λ w These represent the fluid mobility in the oil phase, gas phase, and water phase, respectively. Let n be the conduction rate between grids n and m at the current time step; Let be the equivalent conductance of the starting pressure gradient between grids n and m at the current time step.

[0039] In some implementations, the multi-scale numerical simulation method for complex lithofacies shale is used to conduct numerical simulations of seepage in complex lithofacies shale reservoirs, and the oil and gas production of the shale reservoirs is calculated, specifically including:

[0040] Based on core samples obtained from drilling and coring of different rock facies, basic physical property tests, fluid displacement and fluid-structure interaction experiments were conducted to obtain physical property parameters such as porosity, permeability, starting pressure gradient and stress sensitivity coefficient of different rock facies.

[0041] Based on the wellhead fluid mixture, underground crude oil samples were obtained, and high-pressure fluid property experiments were carried out. The results of the high-pressure fluid property experiments were fitted using the three-parameter Peng-Robinson equation of state to obtain the fluid phase parameters.

[0042] The physical properties and fluid phase parameters of different lithofacies are substituted into the reservoir model of the complex lithofacies shale oil reservoir, and the numerical simulation calculation is performed using the numerical solution algorithm of the complex lithofacies shale oil reservoir to obtain the oil and gas production of the shale oil reservoir.

[0043] On the other hand, this application also proposes a multi-scale production capacity prediction system for complex lithofacies shale reservoirs, the production capacity prediction system comprising:

[0044] The reservoir model building module is used to build a complex lithofacies shale oil reservoir model with multi-scale structures including matrix, bedding, natural fractures and hydraulic fracturing fractures.

[0045] A multi-scale pore and fracture simulation module is used to characterize the complex lithofacies shale oil reservoir model using a dual-medium model and an embedded discrete fracture model.

[0046] The numerical simulation module establishes a seepage model and numerical solution algorithm for complex lithofacies shale reservoirs that consider the initiation pressure gradient and stress sensitivity effects, thus forming a multi-scale numerical simulation method for complex lithofacies shale.

[0047] And a calculation module, which uses the porosity, permeability, initiation pressure gradient and stress sensitivity coefficient obtained from core tests of different lithofacies strata, as well as fluid phase parameters, to carry out numerical simulation of seepage in complex lithofacies shale reservoirs using the multi-scale numerical simulation method for complex lithofacies shale, and calculates the oil and gas production of shale reservoirs.

[0048] This application proposes a multi-scale production capacity prediction method and system for complex lithofacies shale oil reservoirs. By establishing a numerical simulation method for seepage in complex lithofacies shale oil reservoirs that considers multi-scale pore and fracture characteristics, initiation pressure gradient, and stress sensitivity effects, it is possible to quickly and accurately simulate seepage and calculate production capacity in complex lithofacies shale oil reservoirs.

[0049] This application proposes a multi-scale production capacity prediction method and system for complex lithofacies shale oil reservoirs, which can provide theoretical basis and technical support for production capacity evaluation and scheme optimization design of complex lithofacies shale oil development, and has guiding significance for the efficient development of complex lithofacies shale oil reservoirs. Attached Figure Description

[0050] The accompanying drawings, which are included to provide a further understanding of the embodiments of this application and form part of this application, do not constitute a limitation on the embodiments of this application. In the drawings:

[0051] Figure 1 This is a flowchart illustrating the method of an embodiment of this application;

[0052] Figure 2 This is a system principle block diagram of an embodiment of this application;

[0053] Figure 3 This is a reservoir model structure of a complex lithofacies shale oil reservoir according to an embodiment of this application;

[0054] Figure 4 The reservoir model described in this application is characterized by a dual-extraction medium and an embedded discrete fracture model.

[0055] Figure 5 The numerical simulations obtained in this application show the reservoir pressure, saturation distribution, and oil and gas production after the development of a shale oil reservoir. (a) represents the reservoir pressure distribution; (b) represents the oil saturation distribution; (c) represents the oil production; and (d) represents the gas production. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this application are only for explaining this application and are not intended to limit this application.

[0057] Example 1:

[0058] Traditional numerical simulation techniques for shale oil reservoirs do not fully consider complex factors such as shale oil initiation pressure gradients and reservoir stress sensitivity effects, thus failing to accurately achieve multi-scale seepage simulation and production capacity prediction for complex lithofacies shale oil reservoirs. This hinders the efficient and accurate formulation of development plans for complex lithofacies shale oil reservoirs. Therefore, this embodiment proposes a multi-scale production capacity prediction method for complex lithofacies shale oil reservoirs. The proposed method, by constructing a mathematical model of seepage in amplitude lithofacies shale oil reservoirs that comprehensively considers multi-scale pore characteristics, high initiation pressure gradients, and stress sensitivity effects, along with an efficient numerical simulation method, can quickly and accurately achieve seepage simulation and production capacity prediction for complex lithofacies shale oil reservoirs.

[0059] like Figure 1 As shown, the method proposed in this embodiment specifically includes the following steps:

[0060] Step 100: Establish a complex lithofacies shale oil reservoir model with multi-scale structures including matrix, bedding, natural fractures, and hydraulic fracturing fractures.

[0061] Step 100 can establish a complex lithofacies shale reservoir model with multi-scale structures including matrix, bedding, natural fractures, and hydraulically fractured fractures, based on the seismic, logging, drilling, fracturing design, and monitoring interpretation results of the target shale reservoir. This step specifically includes the following processes:

[0062] Step 101: Based on geophysical exploration data such as seismic and well logging data and core drilling observation data of the target shale oil reservoir, obtain parameter information reflecting the reservoir structure and physical properties (such as porosity, permeability and fluid saturation).

[0063] Step 102: Based on imaging logging, core drilling observation and other data, obtain the bedding and natural fracture development characteristic parameters of each well, including the direction, aperture and development density of bedding and natural fractures, and use seismic data constraints to obtain the distribution of bedding and natural fracture development characteristic parameters of the whole area.

[0064] Step 103: Based on the monitoring results of microseismic and downhole television operations implemented during the hydraulic fracturing process, obtain the development characteristics of hydraulic fracturing fractures in different sections, including the number of hydraulic fracturing fracture clusters, length, and height.

[0065] Step 104: Based on reservoir characteristic parameters, combined with the distribution of bedding and natural fracture development characteristic parameters and the geometric parameters of hydraulic fracturing fractures, a complex lithofacies shale oil reservoir model with multi-scale pore and fracture structure of matrix, bedding, natural fractures and hydraulic fracturing fractures is comprehensively established.

[0066] Step 200: The complex lithofacies shale oil reservoir model is characterized by a dual-medium model and an embedded discrete fracture model, so as to realize efficient simulation of multi-scale pores and fractures in complex lithofacies shale oil reservoirs.

[0067] Step 200 employs a dual-medium model to characterize the influence of bedding and natural fractures on seepage, and an embedded discrete fracture model to characterize hydraulic fracturing fractures, achieving efficient and accurate simulation of multi-scale porosity and fractures in complex lithofacies shale reservoirs. Specifically, the process includes the following steps:

[0068] Step 201: A dual-medium model is used to characterize the bedding and natural fractures in the reservoir. Specifically, two sets of meshes are established for the regions with developed bedding and natural fractures: one set is the matrix mesh, and the other is the mesh for the bedding and natural fractures. The two sets of meshes share coordinate positions in space, and fluid flow exists between two meshes at the same position. The fluid flow rate can be calculated using equation (1):

[0069]

[0070] In the formula, q nfα is the flux between the matrix and bedding / natural fractures; α is the shape factor, related to the density of bedding and natural fracture development; k m p represents matrix permeability. m p represents the pressure within the matrix. nf ρ represents the pressure within bedding and natural fractures; μ represents the fluid viscosity.

[0071] Step 202: For large-scale hydraulic fracturing fractures, an embedded discrete fracture model is used to characterize the hydraulic fracturing fractures. Specifically, based on the morphology of the hydraulic fracturing fractures, the geometric surface of the hydraulic fracturing fractures is embedded into the matrix mesh, bedding, and natural fracture mesh from step 201. The reservoir mesh is then used to cut the geometric surface of the hydraulic fracturing fractures through the matrix mesh, bedding, and natural fracture mesh, forming an embedded discrete fracture mesh. Fluid flow exists between the embedded discrete fracture mesh and the matrix mesh, bedding, and natural fracture mesh. The fluid flow rate can be calculated using equation (2):

[0072]

[0073] In the formula, q hf p is the flow rate between the hydraulic fracturing fracture and the matrix. hf Here, represents the pressure within the hydraulic fracturing fracture; T represents the conductivity between the hydraulic fracturing fracture and the matrix, calculated using the following formula:

[0074]

[0075] In the formula, A f d represents the area of ​​the hydraulically fractured mesh; fm This represents the average distance between the hydraulically fractured crack and the matrix.

[0076] This embodiment integrates characterization methods for bedding, natural fractures, and hydraulic fracturing fractures to achieve efficient and accurate simulation of fluid flow in multi-scale pore spaces of complex lithofacies shale oil reservoirs.

[0077] Step 300: Establish a seepage model and numerical solution algorithm for complex lithofacies shale reservoirs that consider the initiation pressure gradient and stress sensitivity effects, thus forming a multi-scale numerical simulation method for complex lithofacies shale reservoirs.

[0078] This step 300 specifically includes the following processes:

[0079] Step 301: Establish a flow model for complex lithofacies shale reservoirs, including mass conservation equations for components in the matrix and fractures, three-phase flow equations considering the oil-water initiation pressure gradient, and auxiliary equations. The component mass conservation equation is expressed as:

[0080]

[0081] The three-phase motion equations of oil, gas, and water considering the oil-water initiation pressure gradient and stress sensitivity are expressed as follows:

[0082]

[0083] The auxiliary equation is expressed as:

[0084]

[0085] S o +S g +S w =1(9)

[0086] p w =p g -p cwg (S w (10)

[0087] p o =p g -p cog (S w ,S o (11)

[0088] In the formula, the subscripts o, g, and w represent the three phases of oil, gas, and water, respectively; N c For groupings; x i y i ρ represents the mole fraction of hydrocarbon component i in the oil phase and gas phase, respectively; o ρ g ρ w These are the molar densities of the oil phase, gas phase, and water phase, respectively; v o v g v w These represent the seepage velocities of the oil phase, gas phase, and water phase, respectively; S o S g S w These represent the saturation levels of the oil, gas, and water phases, respectively; φ and k represent reservoir porosity and permeability; q i and q w Represent the source and sink terms of hydrocarbon component i and water component respectively; μ β The viscosity of the β phase (oil phase or water phase); μ g The viscosity of the β phase (oil phase or water phase); k rβ The relative permeability of the β phase (oil phase or water phase), k rg ψ is the relative permeability of the gas phase; p is the phase pressure; ψ is the flow potential (the flow potential of different phases is calculated according to the flow potential calculation formula in equation (6) based on the corresponding parameters); ζ is the fluid specific weight; D represents the reservoir depth; G is the starting pressure gradient; k0 is the initial permeability; p0 is the initial pressure; c is the stress sensitivity coefficient; p cwg and pcog These represent the capillary forces of water vapor and oil vapor, respectively.

[0089] Step 302: Establish the numerical discretization equations of the seepage model using the finite volume method, and construct the residual form of the numerical discretization equations. The residual form is specifically expressed as follows:

[0090]

[0091] In the formula, subscripts n and m represent grid numbers; i represents component number; t represents the previous time step; t+1 represents the current time step; η n Let n be the set of all adjacent grids of grid n; V is the average value at the interface between grids n and m; V is the grid volume. This represents the residual of grid n in the mass conservation equation for component i at the current time step; This represents the residual of grid n in the mass conservation equation for the aqueous phase at the current time step; These represent the flow potentials of the oil, gas, and water phases within grid m, respectively. λ represents the flow potential of the oil, gas, and water phases within grid n, respectively; Δt is the time step; λ o , λ g , λ w These represent the fluid mobility in the oil phase, gas phase, and water phase, respectively. Let n be the conduction rate between grids n and m at the current time step; Let be the equivalent conductance of the starting pressure gradient between grids n and m at the current time step.

[0092] Step 303: The Newton-Raphson method is used to solve the above residual equations to establish a numerical solution algorithm for complex lithofacies shale oil reservoirs.

[0093] Step 400: Based on the porosity, permeability, initiation pressure gradient, and stress sensitivity coefficient obtained from core tests of different lithofacies strata, as well as fluid phase parameters, a multi-scale numerical simulation method for complex lithofacies shale oil reservoirs is used to conduct a numerical simulation of seepage in complex lithofacies shale oil reservoirs, and the oil and gas production of the shale oil reservoir is calculated.

[0094] Step 400 can utilize the porosity, permeability, initiation pressure gradient, and stress sensitivity coefficient obtained from core tests of different lithofacies strata, as well as the crude oil composition and phase parameters obtained from high-pressure fluid property tests, to conduct numerical simulations of seepage in complex lithofacies shale oil reservoirs using the numerical simulation method of step 300, and calculate the oil and gas production of the shale oil reservoir, specifically including the following components:

[0095] Step 401: Based on the core samples obtained from drilling and coring of different rock facies strata, conduct basic physical property tests, fluid displacement and fluid-structure interaction experiments to obtain the porosity, permeability, starting pressure gradient and stress sensitivity coefficient of different rock facies.

[0096] Step 402: Based on the wellhead fluid mixture, underground crude oil samples are obtained, and high-pressure fluid property experiments are carried out, including fluid composition determination, isocomponent expansion, constant volume exhaustion, differential separation and other experiments. The three-parameter Peng-Robinson equation of state is used to fit the high-pressure property experimental results to obtain fluid phase parameters, including critical pressure, critical temperature, eccentricity factor, critical volume, etc.

[0097] Step 403: Substitute the physical properties and fluid phase parameters of different lithofacies into the complex lithofacies shale oil reservoir model established in step 100, and use the numerical solution algorithm for complex lithofacies shale oil reservoirs to perform numerical simulation calculations.

[0098] Step 404: Output the numerical simulation results, including shale oil reservoir production, gas production, production of different components, reservoir pressure distribution, and oil, gas and water saturation distribution.

[0099] Based on the same technical concept described above, this embodiment also proposes a multi-scale production capacity prediction system for complex lithofacies shale reservoirs, such as... Figure 2 As shown, the system proposed in this embodiment specifically includes:

[0100] The reservoir model building module is used to build complex lithofacies shale oil reservoir models with multi-scale structures including matrix, bedding, natural fractures, and hydraulic fracturing fractures.

[0101] The multi-scale pore and fracture simulation module uses a dual-medium model and an embedded discrete fracture model to characterize the reservoir model of complex lithofacies shale oil reservoirs, achieving efficient simulation of multi-scale pores and fractures in complex lithofacies shale oil reservoirs.

[0102] The numerical simulation module establishes a seepage model and numerical solution algorithm for complex lithofacies shale reservoirs that considers the initiation pressure gradient and stress sensitivity effects, thus forming a multi-scale numerical simulation method for complex lithofacies shale reservoirs.

[0103] In addition, there is a calculation module, which uses the porosity, permeability, initiation pressure gradient and stress sensitivity coefficient obtained from core tests of different lithofacies strata, as well as fluid phase parameters, to carry out numerical simulation of seepage in complex lithofacies shale oil reservoirs using a multi-scale numerical simulation method, and calculates the oil and gas production of shale oil reservoirs.

[0104] It should be noted that the specific implementation process of each module unit in the system proposed in this embodiment is as described in the above method, and will not be repeated here.

[0105] Example 2:

[0106] This embodiment further illustrates the technology proposed in the above embodiments using a shale oil well in a certain region. The specific process is as follows:

[0107] First, based on seismic, well logging, and core sampling data obtained from early-stage shale reservoir exploration, characteristic parameters such as the length, density, orientation, and aperture of bedding and natural fractures in the reservoir are extracted. These parameters are then combined with the geometric parameters of hydraulically fractured fractures obtained from monitoring data during the fracturing process to construct a complex lithofacies shale reservoir model with multi-scale pore and fracture structures, encompassing matrix, bedding, natural fractures, and hydraulically fractured fractures. Figure 3 As shown.

[0108] Second, a two-layer medium model and an embedded discrete fracture model are used to characterize complex lithofacies shale oil reservoirs, such as... Figure 4 As shown.

[0109] Third, establish a seepage model and numerical solution algorithm for complex lithofacies shale oil reservoirs that takes into account the initiation pressure gradient and stress sensitivity effects, thus forming a multi-scale numerical simulation method for complex lithofacies shale oil reservoirs.

[0110] Fourth, substitute the porosity, permeability, initiation pressure gradient, and stress sensitivity coefficient parameters obtained from the physical experiments of a certain lithofacies core shown in Table 1, and the fluid phase parameters shown in Table 2, into the numerical simulation method to calculate the pressure and saturation values ​​on each grid, and plot the reservoir pressure, saturation distribution, and oil and gas production during the development of complex lithofacies shale oil reservoirs, such as... Figure 5 As shown.

[0111] Table 1

[0112]

[0113] Table 2

[0114]

[0115] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above description is only a specific embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A multi-scale productivity prediction method for complex lithofacies shale reservoirs, characterized in that, The capacity forecasting method includes: Establish a reservoir model for complex lithofacies shale oil reservoirs with multi-scale structures including matrix, bedding, natural fractures, and hydraulic fracturing fractures; The complex lithofacies shale oil reservoir model was characterized using a dual-medium model and an embedded discrete fracture model. Establish a seepage model and numerical solution algorithm for complex lithofacies shale reservoirs that consider the initiation pressure gradient and stress sensitivity effects, and form a multi-scale numerical simulation method for complex lithofacies shale; Based on the porosity, permeability, initiation pressure gradient, and stress sensitivity coefficient obtained from core tests of different lithofacies strata, as well as fluid phase parameters, the multi-scale numerical simulation method for complex lithofacies shale was used to conduct numerical simulation of seepage in complex lithofacies shale reservoirs, and the oil and gas production of shale reservoirs was calculated.

2. The method for multi-scale production capacity prediction of complex lithofacies shale reservoirs according to claim 1, characterized in that, The process of establishing the complex lithofacies shale oil reservoir model specifically includes: Based on the exploration data and drilling core observation data of the target shale oil reservoir, the reservoir structure and physical property parameters are obtained; Based on imaging logging and core sampling data, bedding and natural fracture development characteristic parameters of each well were obtained, and seismic data constraints were used to obtain bedding and natural fracture development characteristic parameters for the entire area. Based on the monitoring results obtained during the hydraulic fracturing process, characteristic parameters of hydraulic fracturing fracture development in different sections were acquired. Based on reservoir structure and physical property parameters, combined with bedding and natural fracture development parameters as well as hydraulic fracturing fracture development parameters, a complex lithofacies shale oil reservoir model with multi-scale structures of matrix, bedding, natural fractures and hydraulic fracturing fractures was comprehensively established.

3. The method for multi-scale production capacity prediction of complex lithofacies shale reservoirs according to claim 2, characterized in that, The physical property parameters include: porosity, permeability, and fluid saturation.

4. The method for multi-scale production capacity prediction of complex lithofacies shale reservoirs according to claim 2, characterized in that, The characteristic parameters of bedding and natural fracture development include the orientation, aperture, and development density of bedding and natural fractures. And / or, the hydraulic fracturing fracture development characteristic parameters include the number of hydraulic fracturing fracture clusters, length, and height.

5. A multi-scale productivity prediction method for complex lithofacies shale oil reservoirs according to any one of claims 1-4, characterized in that, The complex lithofacies shale oil reservoir model is characterized using a dual-medium model and an embedded discrete fracture model, specifically including: Two sets of meshes are established for regions with developed bedding and natural cracks. One set is a matrix mesh, and the other set is a mesh for bedding and natural cracks. The two sets of meshes share coordinate positions in space, and there is fluid flow between the two meshes at the same position. To address the morphology of hydraulic fracturing fractures, the geometric surface of the hydraulic fracturing fractures is embedded into two sets of meshes. The geometric surface of the hydraulic fracturing fractures is then cut by the two sets of meshes to form an embedded discrete fracture mesh. Fluid flow exists between the embedded discrete fracture mesh and the two sets of meshes.

6. The method for multi-scale production capacity prediction of complex lithofacies shale reservoirs according to claim 5, characterized in that, Establish a flow model and numerical solution algorithm for complex lithofacies shale oil reservoirs that considers the initiation pressure gradient and stress sensitivity effects, specifically including: A flow model for complex lithofacies shale reservoirs was established, including the mass conservation equations for components in the matrix and fractures, the three-phase motion equations of oil, gas and water considering the oil-water initiation pressure gradient, and auxiliary equations. The numerical discrete equations of the seepage model of the complex lithofacies shale oil reservoir were established using the finite element volume method, and the residual form of the numerical discrete equations was constructed. A numerical solution algorithm for complex lithofacies shale oil reservoirs is established by solving the residual equation.

7. The method for multi-scale production capacity prediction of complex lithofacies shale reservoirs according to claim 6, characterized in that, The mass conservation equation is: The three-phase motion equations for oil, gas, and water are as follows: The auxiliary equation is: S o +S g +S w =1 p w =p g -p cwg (S w ) p o =p g -p cog (S w ,S o ) In the formula, the subscripts o, g, and w represent the three phases of oil, gas, and water, respectively; N c For group numbers; x i y i ρ represents the mole fraction of hydrocarbon component i in the oil phase and gas phase, respectively; o ρ g ρ w These are the molar densities of the oil phase, gas phase, and water phase, respectively; v o v g v w These represent the seepage velocities of the oil phase, gas phase, and water phase, respectively; S o S g S w These represent the saturation levels of the oil, gas, and water phases, respectively; φ and k represent reservoir porosity and permeability; q i and q w Represent the source and sink terms of hydrocarbon component i and water component respectively; μ β The viscosity of the β phase; μ g The viscosity of the β phase; k rβ The relative permeability of the β phase, k rg denoted as: relative permeability of the gas phase; p as phase pressure; ψ as flow potential; ζ as fluid specific weight; D as reservoir depth; G as starting pressure gradient; k0 as initial permeability; p0 as initial pressure; c as stress sensitivity coefficient. p cwg and p cog These represent the capillary forces of water vapor and oil vapor, respectively.

8. The method for multi-scale production capacity prediction of complex lithofacies shale reservoirs according to claim 7, characterized in that, The residual equation is: In the formula, the subscripts n and m represent grid numbers; i represents component numbers; t represents the previous time step; and t+1 represents the current time step. η n Let n be the set of all adjacent grids of grid n; V is the average value at the interface between grids n and m; V is the grid volume. This represents the residual of grid n in the mass conservation equation for component i at the current time step; This represents the residual of grid n in the mass conservation equation for the aqueous phase at the current time step; These represent the flow potentials of the oil, gas, and water phases within grid m, respectively. λ represents the flow potential of the oil, gas, and water phases within grid n, respectively; Δt is the time step; λ o , λ g , λ w These represent the fluid mobility in the oil phase, gas phase, and water phase, respectively. Let n be the conduction rate between grids n and m at the current time step; Let be the equivalent conductance of the starting pressure gradient between grids n and m at the current time step.

9. A multi-scale productivity prediction method for complex lithofacies shale reservoirs according to any one of claims 6-8, characterized in that, The multi-scale numerical simulation method for complex lithofacies shale was used to conduct numerical simulation of seepage in complex lithofacies shale reservoirs, and the oil and gas production of the shale reservoirs was calculated, specifically including: Based on core samples obtained from drilling and coring of different rock facies, basic physical property tests, fluid displacement and fluid-structure interaction experiments were conducted to obtain physical property parameters such as porosity, permeability, starting pressure gradient and stress sensitivity coefficient of different rock facies. Based on the wellhead fluid mixture, underground crude oil samples were obtained, and high-pressure fluid property experiments were carried out. The results of the high-pressure fluid property experiments were fitted using the three-parameter Peng-Robinson equation of state to obtain the fluid phase parameters. The physical properties and fluid phase parameters of different lithofacies are substituted into the reservoir model of the complex lithofacies shale oil reservoir, and the numerical simulation calculation is performed using the numerical solution algorithm of the complex lithofacies shale oil reservoir to obtain the oil and gas production of the shale oil reservoir.

10. A multi-scale productivity prediction system for complex lithofacies shale reservoirs, characterized in that, The capacity forecasting system includes: The reservoir model building module is used to build a complex lithofacies shale oil reservoir model with multi-scale structures including matrix, bedding, natural fractures and hydraulic fracturing fractures. A multi-scale pore and fracture simulation module is used to characterize the complex lithofacies shale oil reservoir model using a dual-medium model and an embedded discrete fracture model. The numerical simulation module establishes a seepage model and numerical solution algorithm for complex lithofacies shale reservoirs that consider the initiation pressure gradient and stress sensitivity effects, thus forming a multi-scale numerical simulation method for complex lithofacies shale. And a calculation module, which uses the porosity, permeability, initiation pressure gradient and stress sensitivity coefficient obtained from core tests of different lithofacies strata, as well as fluid phase parameters, to carry out numerical simulation of seepage in complex lithofacies shale reservoirs using the multi-scale numerical simulation method for complex lithofacies shale, and calculates the oil and gas production of shale reservoirs.

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