Method and device for evaluating CO2 flooding initial-stage injection capacity of low-permeability oil field
By establishing a three-dimensional geological model in a low-permeability oilfield and conducting numerical simulations, sensitivity analyses, and probabilistic simulations, the problem of low adaptability of traditional methods to geological complexity was solved, enabling more accurate evaluation and optimization design of CO2 flooding initial injection capacity.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-14
- Publication Date
- 2026-04-14
AI Technical Summary
Existing technologies have limited adaptability to geological complexity in low-permeability oilfields and fail to effectively account for the impact of uncertainties, resulting in inaccurate evaluation of the initial injection capacity of traditional CO2 flooding.
By acquiring reservoir geological data, establishing a three-dimensional geological model, determining the properties of carbon dioxide under reservoir conditions, modeling and numerically simulating the displacement process, conducting sensitivity analysis and probability simulation, and combining economic evaluation with optimized design, an implementation plan for carbon dioxide injection is formulated.
It more realistically reflects the heterogeneity and anisotropy of reservoirs, improves the model's adaptability to geological complexity, reduces errors caused by simplified models, improves the reliability and accuracy of simulation results, provides simulation results under multiple possibilities, and facilitates the development of comprehensive carbon dioxide injection schemes.
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Figure CN121853994A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of oil reservoir development technology, and in particular to a method and apparatus for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields. Background Technology
[0002] Low-permeability oilfields refer to oilfields with low reservoir permeability, low abundance, and low single-well productivity. The initial injection capacity of carbon dioxide flooding in low-permeability reservoirs refers to the ability to improve the recovery rate and reservoir displacement effect by injecting carbon dioxide. In low-permeability reservoirs, due to low porosity and low permeability, traditional extraction techniques are difficult to effectively promote oil production, leading to the development of carbon dioxide flooding technology.
[0003] Traditional methods for evaluating the initial injection capacity of CO2 flooding in low-permeability reservoirs improve reservoir fluidity and increase crude oil displacement efficiency through the physical and chemical effects of carbon dioxide, thereby enhancing reservoir recovery.
[0004] However, traditional methods typically employ simplified models of parameters such as reservoir permeability and porosity, which have low adaptability to geological complexity, cannot reflect the heterogeneity and anisotropy of reservoirs, and usually do not consider the impact of uncertainties. Summary of the Invention
[0005] The main objective of this invention is to provide a method and apparatus for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields, aiming to solve the technical problems of existing technologies having low adaptability to geological complexity and not considering the impact of uncertainties.
[0006] To achieve the above objectives, this invention provides a method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields. The method includes: S10, acquiring reservoir geological data; S20, establishing a three-dimensional geological model; S30, determining the properties of carbon dioxide under reservoir conditions; S40, performing displacement process modeling and numerical simulation; S50, performing sensitivity analysis to determine parameters affecting carbon dioxide injection; S60, performing probabilistic simulation based on the uncertainty of the parameters affecting carbon dioxide injection; S70, performing economic evaluation and optimization design; and S80, formulating an implementation plan for carbon dioxide injection and evaluating the initial injection capacity of carbon dioxide flooding.
[0007] In some embodiments, in step S10, the reservoir geological data includes well logging data, seismic data, and core data.
[0008] In some embodiments, step S20 includes: S210, obtaining basic information about reservoir properties, the basic information including permeability, porosity, and lithology, wherein permeability can be expressed based on the following formula: [K(x)=\bar{K}+\sum{i=1}^{n}\lambdai(K_i-\bar{K})] In the formula: K(x) is the permeability at location x, \bar{K} is the global average permeability, sum{} represents the summation function, K_i is the permeability at sample point i, and lambdai is the weight of Kriging; S220, the basic information is transformed into a three-dimensional geological model.
[0009] In some embodiments, step S30 includes: S310, conducting laboratory experiments to determine the solubility, density, and phase change properties of carbon dioxide under reservoir conditions, ensuring the use of accurate fluid property parameters in the numerical simulation process; S320, combining experimental data to obtain the properties of carbon dioxide and crude oil; S330, based on phase change and viscosity parameters, obtaining the fluid behavior during the carbon dioxide injection process and determining the properties of carbon dioxide under reservoir conditions.
[0010] In some embodiments, in step S310, the fluid property parameter includes the density of the mixed fluid, and the density of the mixed fluid is calculated based on the following formula: [\rhom=\phi\rhor+(1-\phi)\rho{CO2}] In the formula: rhom is the density of the mixed fluid, phi is the porosity, rhor is the density of crude oil, and rho{CO2} is the density of carbon dioxide.
[0011] In some embodiments, step S40 includes: S410, selecting a numerical simulation method based on multiphase flow; S420, modeling the displacement process; S430, performing a numerical simulation method for carbon dioxide in the displacement process based on multiphase flow and phase change, and predicting the dynamic process of carbon dioxide injection.
[0012] In some embodiments, in step S410, the numerical simulation method includes the finite element method and the finite difference method.
[0013] In some embodiments, step S430 includes: S4301, setting initial conditions for the numerical simulation method, the initial conditions including initializing field variables, initial saturation distribution, and relative permeability, wherein the relative permeability is calculated based on the following formula: [k{ro}=k{ro0}S{wi}^{nw}] [k{rw}=k{rw0}(1-S{wi})^{n{rw}}] Wherein, k{ro} is the relative permeability of the oil phase, representing the relative flow capacity of the oil phase in the porous medium, k{rw} is the relative permeability of the water phase, k{ro0} is the initial relative permeability of the oil phase, k{rw0} is the initial relative permeability of the water phase, S{wi} is the initial water saturation, {nw} is the empirical parameter of the oil phase, and n{rw} is the empirical parameter of the water phase; S4302, a numerical simulation method for the carbon dioxide displacement process is performed by matching the simulated initial state with the actual situation based on the initial conditions; S4303, based on multiphase flow, the changes in saturation, pressure, and temperature within the reservoir are simulated in real time to predict the dynamic process of carbon dioxide injection.
[0014] In some embodiments, in step S50, the parameters affecting the carbon dioxide injection include relative permeability, permeability, and porosity.
[0015] In some embodiments, after step S60, the method further includes: S610, obtaining the probability distribution of the parameters based on the results of the probability simulation; S620, representing the parameter uncertainty based on the following formula: [f(x|\mu,\sigma^2)=\frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}] In the formula: f(x|\mu,\sigma^2) is the probability density function, representing the probability density of the random variable x under the given mean mu and variance sigma^2, where mu is the mean, sigma^2 is the variance, x is the random variable, pi is pi, and e is the base of the natural logarithm; S630, based on the probability distribution of the parameters, obtain the influence of the uncertainty of the parameters on the simulation results.
[0016] In some embodiments, step S70 includes: S710, estimating various costs in the carbon dioxide injection process based on the following cost estimation formula: [Total_Cost=Fixed_Cost+Variable_Cost\times Volume]; In the formula: Total_Cost is the total cost, Fixed_Cost is the fixed cost, Variable_Cost is the variable cost per unit volume, and Volume is the injected volume; S720, based on the various costs in the carbon dioxide injection process, evaluate the improvement of oil recovery rate by carbon dioxide injection, and the impact of oil price and tax factors on the economic benefits of the project; S730, based on the evaluation results and the following formula, adjust the key parameters to optimize the design of the carbon dioxide injection process: [\theta{n+1}=\thetan-\eta\nabla J(\theta_{n})] In the formula: theta{n+1} is the updated parameter vector, theta_n is the current parameter vector, eta is the learning rate, nabla J(\theta_{n}) is the gradient of the economic objective function J(\theta_{n}) at the current parameter vector theta_n, and J(\theta_{n}) is the economic objective function.
[0017] Furthermore, to achieve the above objectives, this application also provides a device for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields. The device includes: a data acquisition module for acquiring reservoir geological data; a model building module for establishing a three-dimensional geological model using geological modeling software; a property determination module for determining the properties of carbon dioxide under reservoir conditions; a modeling and numerical simulation method module for modeling and numerically simulating the displacement process; an analysis module for performing sensitivity analysis to determine parameters affecting carbon dioxide injection; a probability simulation module for performing probability simulations based on the uncertainty of parameters affecting carbon dioxide injection; an evaluation and optimization module for performing economic evaluation and optimization design; and an evaluation module for developing an implementation plan for carbon dioxide injection and evaluating the initial injection capacity of carbon dioxide flooding.
[0018] In addition, to achieve the above objectives, embodiments of this application also provide a computer-readable storage medium including instructions that, when run on a computer, cause the computer to execute the method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields according to any embodiment of this application.
[0019] In addition, to achieve the above objectives, this application also provides a computing device, which includes at least one processor, a memory, and an input / output unit; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the low-permeability oilfield CO2 flooding initial injection capacity evaluation method of any embodiment of this application.
[0020] This application provides a method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields. Through displacement process modeling and numerical simulation, it more realistically reflects the heterogeneity and anisotropy of the reservoir, considers more geological parameters, and helps to more accurately predict the reservoir response during CO2 injection. This improves the model's adaptability to geological complexity and reduces errors caused by simplification of the reservoir structure. Sensitivity analysis identifies parameters that affect CO2 injection, considering parameter uncertainties and improving model reliability. Probabilistic simulation provides simulation results under multiple possibilities, helping to better understand the impact of key parameters on the CO2 injection process and economic benefits, making the simulation results more credible and facilitating the development of a comprehensive CO2 injection plan. Attached Figure Description
[0021] Figure 1 A flowchart of the method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields provided in this application embodiment; Figure 2 A structural block diagram of the low-permeability oilfield CO2 flooding initial injection capacity evaluation device provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the medium provided in the embodiments of this application; Figure 4 A schematic diagram of the structure of a computing device provided in an embodiment of this application.
[0022] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0023] It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the application. Rather, these embodiments are provided to make the disclosure more thorough and complete, and to fully convey the scope of the disclosure to those skilled in the art.
[0024] Those skilled in the art will understand that the embodiments of this application can be implemented as a system, apparatus, device, method, or computer program product. Therefore, this application can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.
[0025] This application provides a method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields. This method can be executed by a computer, such as... Figure 1 As shown, the method may include the following steps: Step S10: Obtain reservoir geological data.
[0026] Specifically, the reservoir geological data includes well logging data, seismic data, and core data.
[0027] Step S20: Establish a three-dimensional geological model.
[0028] The three-dimensional geological model was created using geological modeling software.
[0029] In an exemplary embodiment, step S20 may specifically include the following steps: S210, Obtain basic information about the reservoir properties, including permeability, porosity, and lithology, wherein permeability can be expressed based on the following formula: [K(x)=\bar{K}+\sum{i=1}^{n}\lambdai(K_i-\bar{K})](1) In the formula: K(x) is the permeability at position x, \bar{K} is the global average permeability, sum{} represents the summation function, K_i is the permeability at sample point i, and lambdai is the weight of Kriging; S220, the basic information is converted into a three-dimensional geological model.
[0030] In this embodiment, permeability refers to the ability of rock to allow fluid to pass through under a certain pressure difference; it is a parameter characterizing the ability of soil or rock itself to conduct liquids. Weight refers to the degree of importance of a factor or indicator relative to a particular thing. It emphasizes the relative importance of a factor or indicator, rather than a simple percentage, and is typically used to measure the contribution or importance of different factors or indicators in the overall evaluation.
[0031] Step S30: Determine the properties of carbon dioxide under reservoir conditions.
[0032] In an exemplary embodiment, step S30 may specifically include the following steps: S310, conduct laboratory experiments to determine the solubility, density, and phase change properties of carbon dioxide under reservoir conditions, ensuring the use of accurate fluid property parameters in the numerical simulation process; S320, combined with experimental data, to obtain the properties of carbon dioxide and crude oil; S330, based on phase change and viscosity parameters, obtains the fluid behavior during carbon dioxide injection and determines the properties of carbon dioxide under reservoir conditions.
[0033] In an exemplary embodiment, in step S310, the fluid property parameter includes the density of the mixed fluid, and the density of the mixed fluid is calculated based on the following formula: [\rhom=\phi\rhor+(1-\phi)\rho{CO2}](2) In the formula: rhom is the density of the mixed fluid, phi is the porosity, rhor is the density of crude oil, and rho{CO2} is the density of carbon dioxide.
[0034] Specifically, phase, or state of matter (or simply phase, also called physical state), refers to a set of states possessed by a macroscopic physical system. Phase change refers to a change in the state of an object. Viscosity, also known as viscosity, refers to the resistance exhibited by a fluid to flow. Viscosity parameters are parameters representing the resistance exhibited by a fluid to flow, and are divided into absolute viscosity parameters and relative viscosity parameters. The mixed fluid density here refers to the average density of the fluid formed after carbon dioxide mixes with crude oil in the reservoir during carbon dioxide injection. This density is calculated based on the individual densities of carbon dioxide and crude oil, as well as their volume fractions (i.e., porosity) in the mixed fluid. In other words, the mixed fluid density is a weighted average of the carbon dioxide density and crude oil density according to their proportions in the mixed fluid. By calculating the mixed fluid density, the physical properties of the fluid during carbon dioxide injection can be described more accurately, which is crucial for numerical simulation methods and for predicting and controlling fluid behavior in actual operations. Calculating the mixed fluid density helps ensure the accuracy of the fluid property parameters used in numerical simulation methods, thereby improving the reliability of simulation results.
[0035] Step S40: Model and numerically simulate the displacement process.
[0036] In an exemplary embodiment, step S40 may include the following steps: S410, based on multiphase flow, select numerical simulation method; S420, to model the displacement process; S430 is a numerical simulation method for carbon dioxide displacement processes based on multiphase flow and phase changes, and it predicts the dynamic process of carbon dioxide injection.
[0037] Multiphase flow refers to the simultaneous flow of two or more fluids. Numerical simulation, also known as numerical analysis, uses computer programs to solve approximate solutions to mathematical models; it is also called computer simulation. Before modeling the displacement process, a model needs to be selected, taking into account multiphase flow and physicochemical reactions. The parameters of the numerical model are set, including relative permeability curves, phase change parameters, and geological parameters. The values of these parameters are determined through literature review and experimental data.
[0038] Specifically, in step S410, the numerical simulation method includes the finite element method and the finite difference method.
[0039] Furthermore, the finite element method generally employs a method similar to the finite difference method, discretizing the domain and using only finitely small discretized elements to make the trial function satisfy the requirements of the boundary value problem, and integrating within the elements to replace the conditions for satisfying the requirements and the integration over the entire domain. The finite element method is essentially a finite element variational method. The finite difference method is a method for solving numerical solutions to differential equations. By discretizing the continuous spatial and time domains, it uses difference formulas to approximate differential operators, thus forming difference equations, and obtaining numerical solutions by solving these equations.
[0040] In an exemplary embodiment, step S430 may include the following steps: S4301, Set the initial conditions for the numerical simulation method. The initial conditions include initializing the field variables, initial saturation distribution, and relative permeability, wherein the relative permeability is calculated based on the following formula: [k{ro}=k{ro0}S{wi}^{nw}](3) [k{rw}=k{rw0}(1-S{wi})^{n{rw}}](4) Where k{ro} is the relative permeability of the oil phase, representing the relative flow capacity of the oil phase in the porous medium, k{rw} is the relative permeability of the water phase, k{ro0} is the initial relative permeability of the oil phase, k{rw0} is the initial relative permeability of the water phase, S{wi} is the initial water saturation, {nw} is the empirical parameter of the oil phase, and n{rw} is the empirical parameter of the water phase. S4302, A numerical simulation method for the carbon dioxide displacement process that matches the simulated initial state with the actual situation based on the initial conditions. S4303, based on multiphase flow, simulates changes in saturation, pressure, and temperature within the reservoir in real time to predict the dynamic process of carbon dioxide injection.
[0041] In this embodiment, relative permeability is the ratio of the effective permeability to the absolute permeability of each phase when multiple phases coexist.
[0042] Step S50: Perform sensitivity analysis to determine the parameters that affect the carbon dioxide injection, including relative permeability, permeability, and porosity.
[0043] Specifically, sensitivity analysis is a method used to assess the impact of uncertainties on the output of a model or system. It determines which parameters have the greatest impact on the results by changing the values of the input parameters in the model and observing the changes in the output.
[0044] In sensitivity analysis, probabilistic statistical methods are used to determine the probability distribution of parameters. Sensitivity analysis can identify the key parameters that have the greatest impact on the carbon dioxide injection process, such as relative permeability, permeability, and porosity. Assessing the uncertainty of these key parameters and determining their probability distribution using probabilistic statistical methods helps to account for the impact of uncertainty on simulation results, thereby improving the reliability and accuracy of the model.
[0045] S60, Based on the uncertainty of the parameter that has the greatest impact on the carbon dioxide injection, a probabilistic simulation is performed.
[0046] Specifically, by using the parameters of the probability distribution to perform multiple probability simulations, we can obtain a series of simulation results under different probabilities, which helps to understand the variability and uncertainty of the model results.
[0047] In an exemplary embodiment, after step S60, the following steps may also be included: S610, Based on the results of the probability simulation, obtain the probability distribution of the parameters; S620, based on the following formula, expresses the parameter uncertainty: [f(x|\mu,\sigma^2)=\frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}] (5) In the formula: f(x|\mu,\sigma^2) is the probability density function, which represents the probability density of random variable x under the given mean mu and variance sigma^2, where mu is the mean, sigma^2 is the variance, x is the random variable, pi is pi, and e is the base of the natural logarithm.
[0048] Furthermore, frac is the mathematical symbol for fractions. In mathematics, frac is used to represent fractions; for example, 4 / 3 can be written as 4frac3. sqrt{} is a function used to calculate the square root; for example, sqrt{16} = 4.
[0049] S630, Based on the probability distribution of the parameters, obtain the impact of the parameter uncertainty on the simulation results.
[0050] S70, conduct economic assessment and optimize design.
[0051] In an exemplary embodiment, step S70 may include the following steps: S710 estimates the various costs involved in the carbon dioxide injection process based on the following cost estimation formula: [Total_Cost=Fixed_Cost+Variable_Cost\times Volume] (6) In the formula: Total_Cost is the total cost, Fixed_Cost is the fixed cost, Variable_Cost is the variable cost per unit volume, and Volume is the injection volume; S720, based on the various costs in the carbon dioxide injection process, assess the improvement of oil recovery by carbon dioxide injection, as well as the impact of oil prices and taxes on the economic benefits of the project. Based on the evaluation results and the following formula, key parameters of S730 were adjusted to optimize the design of the carbon dioxide injection process: [\theta{n+1}=\thetan-\eta\nabla J(\theta_{n})](7) In the formula: theta{n+1} is the updated parameter vector, theta_n is the current parameter vector, eta is the learning rate, nabla J(\theta_{n}) is the gradient of the economic objective function J(\theta_{n}) at the current parameter vector theta_n, and J(\theta_{n}) is the economic objective function.
[0052] Specifically, the costs involved in the carbon dioxide injection process include equipment purchase, operating costs, and monitoring fees.
[0053] S80, formulate the implementation plan for the carbon dioxide injection and evaluate the injection capacity in the initial stage of carbon dioxide flooding.
[0054] The detailed implementation plan for carbon dioxide injection can include the following: determining the injection volume, injection cycle, and monitoring plan; ensuring that the implementation plan meets geological, engineering, and economic considerations; determining how to conduct real-time monitoring during the carbon dioxide injection process; collecting measured data to facilitate model correction based on the measured data; and ensuring the control and optimization of the injection process.
[0055] This application provides a method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields through one or more embodiments. By modeling the displacement process and using numerical simulation, it more realistically reflects the heterogeneity and anisotropy of the reservoir, considers more geological parameters, and helps to more accurately predict the reservoir response during CO2 injection. This improves the model's adaptability to geological complexity and reduces errors caused by simplification of the reservoir structure. Sensitivity analysis identifies parameters that affect CO2 injection, considering parameter uncertainties and improving model reliability. Probabilistic simulation provides simulation results under multiple possibilities, helping to better understand the impact of key parameters on the CO2 injection process and economic benefits, making the simulation results more credible and facilitating the development of a comprehensive CO2 injection plan.
[0056] Based on the above embodiments, refer to Figure 2 Another embodiment of this application also provides a device for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields. The device 200 for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields may include the following modules: Data acquisition module 210 is used to acquire reservoir geological data; Model building module 220 is used to build three-dimensional geological models using geological modeling software; Property determination module 230 is used to determine the properties of carbon dioxide under reservoir conditions; Modeling and numerical simulation methods module 240 is used for modeling and numerical simulation methods of displacement processes; Analysis module 250 is used to perform sensitivity analysis to determine parameters that affect the carbon dioxide injection; The probability simulation module 260 is used to perform probability simulations based on the uncertainty of parameters that affect the carbon dioxide injection; The evaluation and optimization module 270 is used for economic evaluation and optimization design. Evaluation module 280 is used to formulate the implementation plan for the carbon dioxide injection and to evaluate the injection capacity in the initial stage of carbon dioxide flooding.
[0057] Based on the above embodiments, this application also provides a computer-readable storage medium, see reference. Figure 3 The computer-readable storage medium shown is an optical disc 50, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will implement the steps described in the above-described method implementation, such as: acquiring reservoir geological data; establishing a three-dimensional geological model using geological modeling software; determining the properties of carbon dioxide under reservoir conditions; performing displacement process modeling and numerical simulation methods; performing sensitivity analysis to determine parameters that affect the carbon dioxide injection; performing probabilistic simulation based on the uncertainty of the parameters affecting the carbon dioxide injection; performing economic evaluation and optimization design; formulating an implementation plan for the carbon dioxide injection; and evaluating the injection capacity in the initial stage of the carbon dioxide displacement. The specific implementation methods of each step will not be repeated here.
[0058] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.
[0059] Furthermore, based on the above embodiments, this application also provides a computing device. Figure 4 A block diagram is shown of an exemplary computing device 60 suitable for implementing embodiments of the present application. The computing device 60 may be a computer system or a server. Figure 4 The computing device 60 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.
[0060] like Figure 4 As shown, the components of computing device 60 may include, but are not limited to: one or more processors or processing units 601, system memory 602, and bus 603 connecting different system components (including system memory 602 and processing unit 601).
[0061] The computing device 60 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 60, including volatile and non-volatile media, removable and non-removable media.
[0062] System memory 602 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 6021 and / or cache memory 6022. Computing device 60 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 6023 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 4 (Not shown in the image, usually referred to as "hard drive"). Although not shown in... Figure 4 The diagram illustrates that a disk drive for reading and writing to removable non-volatile disks (e.g., "floppy disks") and an optical disk drive for reading and writing to removable non-volatile optical disks (e.g., CD-ROMs, DVD-ROMs, or other optical media) can be provided. In these cases, each drive can be connected to a bus 603 connecting different system components via one or more data media interfaces. The system memory 602 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.
[0063] A program / utility 6025 having a set (at least one) of program modules 6024 may be stored, for example, in system memory 602, and such program modules 6024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment. Program modules 6024 typically perform the functions and / or methods described in the embodiments of this application.
[0064] The computing device 60 can also communicate with one or more external devices 604 (such as a keyboard, pointing device, display, etc.). This communication can be performed via input / output (I / O) interface 605. Furthermore, the computing device 60 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 606. Figure 4 As shown, network adapter 606 communicates with other modules of computing device 60 (such as processing unit 601, etc.) via bus 603, which connects different system components. It should be understood that, although... Figure 4 As not shown, it can be used in conjunction with computing device 60 with other hardware and / or software modules.
[0065] The processing unit 601 executes various functional applications and data processing by running programs stored in the system memory 602. For example, it acquires reservoir geological data; establishes a three-dimensional geological model using geological modeling software; determines the properties of carbon dioxide under reservoir conditions; performs displacement process modeling and numerical simulation; conducts sensitivity analysis to determine parameters affecting carbon dioxide injection; performs probabilistic simulation based on the uncertainty of parameters affecting carbon dioxide injection; conducts economic evaluation and optimization design; formulates an implementation plan for carbon dioxide injection; and evaluates the initial injection capacity of the carbon dioxide displacement. In fact, according to embodiments of this application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules for embodiment.
[0066] In the description of this application, it should be noted that the terms "first", "second", and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.
[0067] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0068] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0069] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0070] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0071] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0072] Finally, it should be noted that the above embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the technical scope disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.
[0073] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.
Claims
1. A method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields, characterized in that, The method includes the following steps: S10, Obtain reservoir geological data; S20, Establish a three-dimensional geological model; S30, determine the properties of carbon dioxide under reservoir conditions; S40, a method for modeling and numerically simulating the displacement process; S50, Perform sensitivity analysis to determine the parameters that affect the carbon dioxide injection; S60, Based on the uncertainty of the parameters that affect the carbon dioxide injection, a probabilistic simulation is performed; S70, conduct economic evaluation and optimize design; S80, formulate the implementation plan for the carbon dioxide injection and evaluate the injection capacity in the initial stage of carbon dioxide flooding.
2. The method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields according to claim 1, characterized in that, In step S10, the reservoir geological data includes well logging data, seismic data, and core data.
3. The method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields according to claim 1, characterized in that, Step S20 includes: S210, obtaining basic information about reservoir properties, including permeability, porosity, and lithology, wherein permeability can be expressed based on the following formula: [K(x)=\bar{K}+\sum{i=1}^{n}\lambdai(K_i-\bar{K})] In the formula: K(x) is the permeability at position x, \bar{K} is the global average permeability, sum{} represents the summation function, K_i is the permeability at sample point i, and lambdai is the weight of Kriging; S220, the basic information is converted into a three-dimensional geological model.
4. The method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields according to claim 1, characterized in that, Step S30 includes: S310, conduct laboratory experiments to determine the solubility, density, and phase change properties of carbon dioxide under reservoir conditions, ensuring the use of accurate fluid property parameters in the numerical simulation process; S320, combined with experimental data, to obtain the properties of carbon dioxide and crude oil; S330, based on phase change and viscosity parameters, obtains the fluid behavior during carbon dioxide injection and determines the properties of carbon dioxide under reservoir conditions.
5. The method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields according to claim 4, characterized in that, In step S310, the fluid property parameters include the density of the mixed fluid, and the density of the mixed fluid is calculated based on the following formula: [\rhom=\phi\rhor+(1-\phi)\rho{CO2}] In the formula: rhom is the density of the mixed fluid, phi is the porosity, rhor is the density of crude oil, and rho{CO2} is the density of carbon dioxide.
6. The method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields according to claim 1, characterized in that, Step S40 includes: S410, based on multiphase flow, select numerical simulation method; S420, to model the displacement process; S430 is a numerical simulation method for carbon dioxide displacement processes based on multiphase flow and phase changes, and it predicts the dynamic process of carbon dioxide injection.
7. The method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields according to claim 6, characterized in that, In step S410, the numerical simulation method includes the finite element method and the finite difference method.
8. The method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields according to claim 6, characterized in that, Step S430 includes: S4301, Set the initial conditions for the numerical simulation method. The initial conditions include initializing the field variables, initial saturation distribution, and relative permeability, wherein the relative permeability is calculated based on the following formula: [k{ro}=k{ro0}S{wi}^{nw}] [k{rw}=k{rw0}(1-S{wi})^{n{rw}}] Where k{ro} is the relative permeability of the oil phase, representing the relative flow capacity of the oil phase in the porous medium, k{rw} is the relative permeability of the water phase, k{ro0} is the initial relative permeability of the oil phase, k{rw0} is the initial relative permeability of the water phase, S{wi} is the initial water saturation, {nw} is the empirical parameter of the oil phase, and n{rw} is the empirical parameter of the water phase. S4302, A numerical simulation method for the carbon dioxide displacement process that matches the simulated initial state with the actual situation based on the initial conditions. S4303, based on multiphase flow, simulates changes in saturation, pressure, and temperature within the reservoir in real time to predict the dynamic process of carbon dioxide injection.
9. The method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields according to claim 1, characterized in that, In step S50, the parameters that affect the carbon dioxide injection include relative permeability, permeability, and porosity.
10. The method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields according to claim 1, characterized in that, Following step S60, the method further includes: S610, Based on the results of the probability simulation, obtain the probability distribution of the parameters; S620, based on the following formula, expresses the parameter uncertainty: [f(x|\mu,\sigma^2)=\frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}] In the formula: f(x|\mu,\sigma^2) is the probability density function, which represents the probability density of random variable x under the given mean mu and variance sigma^2, where mu is the mean, sigma^2 is the variance, x is the random variable, pi is pi, and e is the base of the natural logarithm. S630, Based on the probability distribution of the parameters, obtain the impact of the parameter uncertainty on the simulation results.
11. The method for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields according to claim 1, characterized in that, Step S70 includes: S710 estimates the various costs involved in the carbon dioxide injection process based on the following cost estimation formula: [Total_Cost=Fixed_Cost+Variable_Cost\times Volume]; In the formula: Total_Cost is the total cost, Fixed_Cost is the fixed cost, Variable_Cost is the variable cost per unit volume, and Volume is the injection volume; S720, based on the various costs in the carbon dioxide injection process, assess the improvement of oil recovery by carbon dioxide injection, as well as the impact of oil prices and taxes on the economic benefits of the project. Based on the evaluation results and the following formula, key parameters of S730 were adjusted to optimize the design of the carbon dioxide injection process: [\theta{n+1}=\thetan-\eta\nabla J(\theta_{n})] In the formula: theta{n+1} is the updated parameter vector, theta_n is the current parameter vector, eta is the learning rate, nabla J(\theta_{n}) is the gradient of the economic objective function J(\theta_{n}) at the current parameter vector theta_n, and J(\theta_{n}) is the economic objective function.
12. A device for evaluating the initial injection capacity of CO2 flooding in low-permeability oilfields, characterized in that, include: The data acquisition module is used to acquire reservoir geological data; The model building module is used to create three-dimensional geological models using geological modeling software. The property determination module is used to determine the properties of carbon dioxide under reservoir conditions. The Modeling and Numerical Simulation Methods module is used for modeling and numerical simulation methods of the displacement process. The analysis module is used to perform sensitivity analysis to determine the parameters that affect the carbon dioxide injection. The probability simulation module is used to perform probability simulations based on the uncertainties of parameters that affect the carbon dioxide injection. The evaluation and optimization module is used for economic evaluation and optimization design. The evaluation module is used to formulate the implementation plan for the carbon dioxide injection and to evaluate the injection capacity in the initial stage of carbon dioxide flooding.