Methods, apparatus and computer equipment for predicting carbon dioxide diffusion coefficient in heavy oil enhanced oil recovery

Abstract

This application provides a method, apparatus, and computer equipment for predicting the carbon dioxide diffusion coefficient in heavy oil enhanced oil recovery, relating to the field of oil and gas development technology. The method is based on Fick's law and the law of conservation of mass. It determines the saturated carbon dioxide concentration in heavy oil through carbon dioxide diffusion experiments. Simultaneously, it considers the volume expansion of heavy oil caused by carbon dioxide dissolving in it, and the oil relative flow opposite to the carbon dioxide diffusion direction caused by this volume expansion. Based on this, a partial differential equation is established to obtain a carbon dioxide diffusion model. The experimentally obtained saturated carbon dioxide concentration in heavy oil is used as the constraint condition for constructing the carbon dioxide diffusion model. The carbon dioxide diffusion model considers the volume expansion change of heavy oil as a one-dimensional expansion change, that is, the volume expansion change of heavy oil is considered through the change in the heavy oil liquid level. Finally, an iterative method is used to obtain a numerical solution. The above method achieves reliable and accurate prediction of the carbon dioxide diffusion coefficient.

Description

Technical Field

[0001] This application belongs to the field of oil and gas extraction technology, specifically relating to a method for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced oil recovery, a device for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced oil recovery, a computer device, and a machine-readable storage medium. Background Technology

[0002] Faced with the dwindling conventional oil resources, unconventional oil is gaining widespread attention. Its exploration, development, and application are of significant strategic importance for optimizing the current energy consumption structure, meeting the ever-increasing energy demand, and ensuring the secure supply of oil resources. Unconventional oil resources typically include heavy oil, tar sands, carbonate fractured-vuggy oil, shale oil, and tight oil. Among these, heavy oil, as a substitute for conventional oil, has attracted considerable attention. Heavy oil is mainly distributed in the Bohai Bay Basin, Karamay Oilfield, Fengcheng Block, and Liaohe Block, enriched in clastic rocks, volcanic rocks, metamorphic rocks, and carbonate reservoirs. Due to its high viscosity, high C / H ratio, and high heteroatom content, as well as its deep burial and thin reservoir characteristics, heavy oil resource extraction is difficult and its extraction efficiency is low.

[0003] Multi-component thermal fluid combined enhancement technology for heavy oil has gradually become an important technology for heavy oil recovery, and the diffusion of carbon dioxide, a key component, plays a dominant role in the recovery process. Therefore, accurately determining the diffusion coefficient of carbon dioxide in heavy oil is crucial for evaluating the effectiveness of multi-component thermal fluid combined enhancement. Currently, methods for determining the diffusion coefficient of carbon dioxide in heavy oil include direct and indirect methods. The indirect method involves monitoring the changes of key system parameters (such as pressure and liquid level) over time and establishing a functional relationship between these parameters and the diffusion coefficient (primarily based on Fick's second law) to calculate the diffusion coefficient. This method is simple to operate and does not interfere with the diffusion process, making it the mainstream approach for determining the diffusion coefficient of carbon dioxide in heavy oil. Indirect methods mainly include the pressure pulse method, liquid density change measurement method, nuclear magnetic resonance (NMR) method, and PVT method. However, most of these methods neglect the volume expansion of heavy oil during carbon dioxide dissolution in their diffusion coefficient calculation models. An investigation revealed that Chinese patent application CN202011441532.0 discloses a method for calculating the diffusion coefficient of a methane-carbon dioxide-propane mixture considering the volume expansion of heavy oil. This method considers the volume expansion effect of heavy oil during gas dissolution, but its calculation model does not consider the convection effect caused by volume expansion. The convection effect refers to the convection of dissolved gas accompanying the movement of the oil phase during volume expansion.

[0004] Therefore, accurately calculating the diffusion coefficient of carbon dioxide in heavy oil is a technical challenge that urgently needs to be overcome. Summary of the Invention

[0005] The purpose of this application is to provide a method for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced oil recovery, a device for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced oil recovery, a computer device, and a machine-readable storage medium, so as to overcome the technical problem in the prior art that the accuracy of the measured carbon dioxide diffusion coefficient is poor due to the failure to consider the convection effect caused by volume expansion.

[0006] To overcome the above problems, the first aspect of this application provides a method for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced recovery, comprising: The saturated carbon dioxide concentration of heavy oil under the characteristic parameters is obtained through a carbon dioxide diffusion experiment of heavy oil injection under the characteristic parameters. The characteristic parameters represent the initial state of the current heavy oil reservoir during enhanced production. Constraints are established based on the saturated carbon dioxide concentration of heavy oil, and the carbon dioxide diffusion model is solved iteratively to obtain the carbon dioxide convection velocity caused by the expansion of heavy oil and the carbon dioxide concentration in heavy oil at each time point. The carbon dioxide diffusion model is constructed based on the carbon dioxide diffusion equation that considers the expansion of heavy oil and the convection effect during expansion. The carbon dioxide convection velocity caused by the expansion of heavy oil at each time point is accumulated according to the time step of the iterative solution, and the relationship between the heavy oil level and time is obtained based on the accumulation result. The carbon dioxide concentration in the heavy oil at each time point is accumulated according to the spatial step size during the iterative solution. The amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process is obtained based on the accumulation result. The spatial step size is the change in the height of the heavy oil liquid level within one time step. The relationship between carbon dioxide bulk pressure and time, the characteristic parameters, the relationship between heavy oil level and time, and the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process are obtained from the carbon dioxide diffusion experiment in the heavy oil. These are then substituted into the first equation, and the curve of the change of carbon dioxide bulk pressure with the square root of time is fitted. The first equation is the relationship equation between the change of carbon dioxide bulk pressure, the characteristic parameters, the relationship between heavy oil level and time, and the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process. The carbon dioxide diffusion coefficient is determined based on the fitted variation curve.

[0007] In a specific embodiment of this application, the characteristic parameters include the initial carbon dioxide injection pressure, the current heavy oil reservoir temperature, the initial height of the heavy oil level, the oil-gas interface area, and the heavy oil mass.

[0008] In a specific embodiment of this application, the saturated carbon dioxide concentration of heavy oil is obtained through a carbon dioxide diffusion experiment under the aforementioned characteristic parameters, including: Under the aforementioned characteristic parameters, carbon dioxide from the reference cylinder is injected into the sample cylinder containing heavy oil via a pressurization device; Once the bulk carbon dioxide pressure in the sample cylinder has remained stable for a first preset time period, record the current heavy oil level. The saturated carbon dioxide concentration of heavy oil was calculated based on the change in the bulk carbon dioxide pressure in the reference cylinder, the equilibrium pressure of the bulk carbon dioxide in the sample cylinder at the end of the dissolution process, and the recorded heavy oil level.

[0009] In a specific embodiment of this application, the saturated carbon dioxide concentration of heavy oil is calculated based on the change in the bulk pressure of carbon dioxide in the reference cylinder, the equilibrium pressure of the bulk carbon dioxide in the sample cylinder at the end of the dissolution process, and the recorded current heavy oil level. This includes: Substitute the change in carbon dioxide bulk pressure in the reference cylinder, the equilibrium pressure of carbon dioxide bulk pressure in the sample cylinder at the end of the dissolution process, and the recorded heavy oil liquid level into Equation 1 to calculate the saturated carbon dioxide concentration of the heavy oil. Equation 1 is:

[0010] ; in, Indicates the first intermediate parameter. R Represents the universal constant for gases. T This indicates the experimental temperature equal to the temperature of the heavy oil reservoir. P 1 represents the initial bulk pressure of carbon dioxide in the reference cylinder. P 2 represents the bulk equilibrium pressure of carbon dioxide in the reference cylinder. V RC Indicates the blank volume of the reference cylinder. V SC Indicates the blank volume of the sample cylinder. P 3 represents the bulk equilibrium pressure of carbon dioxide in the sample cylinder at the end of the dissolution process. A Indicates the oil-gas interface area. L eq This indicates the recorded height of the heavy oil level. Z 1 indicates that carbon dioxide is at the experimental temperature T Initial bulk pressure of carbon dioxide in the reference cylinder P Compression factor at 1 Z 2 indicates the temperature at which carbon dioxide is reacted during the experiment. T Bulk equilibrium pressure of carbon dioxide in the reference cylinder P Compression factor at 2 Z 3 indicates the experimental temperature of carbon dioxide. T And the bulk equilibrium pressure of carbon dioxide in the sample cylinder at the end of the dissolution process. PCompression factor at 3 c eq This indicates the saturated carbon dioxide concentration in heavy oil.

[0011] In a specific embodiment of this application, the carbon dioxide diffusion model is constructed based on a carbon dioxide diffusion equation that considers heavy oil expansion and the convection effect during expansion, including: Establish a carbon dioxide diffusion equation that takes into account the expansion of heavy oil and the convection effect during expansion; Determine the initial and boundary conditions for the carbon dioxide diffusion process in heavy oil; The parameters in the carbon dioxide diffusion equation are dimensionless to obtain the dimensionless parameters. The initial conditions and boundary conditions are represented by the dimensionless parameters; A carbon dioxide diffusion model is constructed, wherein the carbon dioxide diffusion equation after parameter variability is used as the diffusion equation of the fluid diffusion process, the initial conditions expressed using the parameter variability are used as the initial conditions of the fluid diffusion process, and the boundary conditions expressed using the parameter variability are used as the boundary conditions of the fluid diffusion process.

[0012] In a specific embodiment of this application, the carbon dioxide diffusion equation before dimensionality is expressed by Equation 2, the initial conditions before dimensionality are expressed by Equation 3, and the boundary conditions before dimensionality are expressed by Equation 4. Equation 2 is: Formula 3 is: , Formula four is: , ; D This represents the diffusion coefficient of carbon dioxide in heavy oil. c This indicates the concentration of carbon dioxide in heavy oil. x Indicates coordinate position, t Indicates time, u This indicates the carbon dioxide convection velocity caused by the expansion of heavy oil. c eq Indicates the saturated carbon dioxide concentration of heavy oil. L ( t )express t The height of the heavy oil level at all times. L 0 represents t=0 The height of the heavy oil level at any given time.

[0013] In a specific embodiment of this application, the implicit difference method is used to iteratively solve the carbon dioxide diffusion model to obtain the carbon dioxide convection velocity and carbon dioxide concentration in the heavy oil caused by the expansion of heavy oil at each time point.

[0014] In a specific embodiment of this application, the first equation is expressed by the following formula: ; in, ; ; P 0 indicates the initial carbon dioxide injection pressure; T Indicates the temperature of the heavy oil reservoir; H This indicates the height of the container in a carbon dioxide diffusion experiment involving heavy oil. A Indicates the oil-gas interface area; R Represents the universal constant of a gas; L 0 represents t=0 The height of the heavy oil level at any given time; An expression representing the relationship between the level of heavy oil and time; This indicates the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process; The formula representing the relationship between the bulk pressure of carbon dioxide collected in a carbon dioxide diffusion experiment in heavy oil and time; Z P(t) Indicates pressure The compression factor at that time; L eq This represents the height of the heavy oil level recorded at the end of the dissolution process in the carbon dioxide diffusion experiment. Indicates dimensionless time τ The dimensionless convection velocity at the surface of the heavy oil is dimensionless. Indicates dimensionless time τ Dimensionless coordinate position The carbon dioxide concentration in heavy oil is dimensionless; Δ τ It represents a dimensionless time step, which is dimensionless. It represents the dimensionless step size in the dimensionless space.

[0015] A second aspect of this application provides a device for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced oil recovery, comprising: The first acquisition module is used to acquire the saturated carbon dioxide concentration of heavy oil under the characteristic parameters. The saturated carbon dioxide concentration of heavy oil is obtained by a carbon dioxide diffusion experiment of heavy oil under the characteristic parameters. The characteristic parameters represent the initial state of the current heavy oil reservoir during enhanced production. The iterative solution module is used to establish constraints based on the saturated carbon dioxide concentration of the heavy oil, and iteratively solve the carbon dioxide diffusion model to obtain the carbon dioxide convection velocity caused by the expansion of the heavy oil and the carbon dioxide concentration in the heavy oil at each time point. The carbon dioxide diffusion model is constructed based on the carbon dioxide diffusion equation that considers the expansion of the heavy oil and the convection effect during the expansion. The first calculation module is used to accumulate the carbon dioxide convection velocity caused by the expansion of heavy oil at each time point according to the time step during iterative solution, and obtain the relationship between the heavy oil level and time based on the accumulation result; The second calculation module is used to accumulate the carbon dioxide concentration in the heavy oil at each time point according to the spatial step size during the iterative solution, and obtain the amount of carbon dioxide dissolved in the heavy oil when the dissolution process ends based on the accumulation result. The spatial step size is the change in the height of the heavy oil liquid level within one time step. The fitting module is used to input the relationship between carbon dioxide bulk pressure and time, the characteristic parameters, the relationship between heavy oil level and time, and the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process collected in the heavy oil carbon dioxide diffusion experiment into the first equation, and fit the change curve of carbon dioxide bulk pressure with the square root of time. The first equation is the relationship equation between the change of carbon dioxide bulk pressure, the characteristic parameters, the relationship between heavy oil level and time, and the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process. The diffusion coefficient determination module is used to determine the carbon dioxide diffusion coefficient based on the fitted variation curve.

[0016] A third aspect of this application provides a computer device, comprising: The memory is configured to store instructions; and The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the heavy oil enhanced recovery carbon dioxide diffusion coefficient prediction method according to the first aspect of this application.

[0017] A fourth aspect of this application provides a machine-readable storage medium storing instructions for causing a machine to execute the heavy oil enhanced recovery carbon dioxide diffusion coefficient prediction method according to a first aspect of this application.

[0018] The above technical solution is based on Fick's law and the law of conservation of mass. It determines the saturated carbon dioxide concentration in heavy oil through carbon dioxide diffusion experiments. Simultaneously, it considers the volume expansion of heavy oil caused by carbon dioxide dissolving in it, as well as the corresponding effect of oil relative flow opposite to the carbon dioxide diffusion direction caused by this volume expansion. Based on this, a partial differential equation is established to obtain a carbon dioxide diffusion model. The experimentally obtained saturated carbon dioxide concentration in heavy oil serves as the constraint condition for constructing the carbon dioxide diffusion model. Furthermore, the carbon dioxide diffusion model considers the volume expansion change of heavy oil as a one-dimensional expansion change, that is, the volume expansion change of heavy oil is considered through the change in the heavy oil liquid level. Finally, an iterative method is used to obtain a numerical solution. Based on this, the above technical solution achieves a reliable and accurate prediction of the carbon dioxide diffusion coefficient.

[0019] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1 A flowchart illustrating a method for predicting the carbon dioxide diffusion coefficient of heavy oil recovery according to an embodiment of this application is shown schematically. Figure 2 A schematic diagram of a heavy oil carbon dioxide diffusion experimental apparatus according to an embodiment of this application is shown. Figure 3 This diagram illustrates the principle of calculating the carbon dioxide diffusion coefficient in heavy oil. Figure 4 The schematic diagram illustrates the calculation process of the carbon dioxide diffusion model; Figure 5 The illustration shows the bulk pressure of carbon dioxide in a specific application example 1. P Over time t The decay curve; Figure 6 The diagram illustrates the variation curve of carbon dioxide bulk pressure with the square root of time obtained from the fitting in Example 1 of a specific application. Figure 7 This illustration shows the bulk pressure of carbon dioxide in a specific application example two. P Over time t The decay curve; Figure 8 The diagram illustrates the variation curve of carbon dioxide bulk pressure with the square root of time obtained from the fitting in Example 2 of a specific application. Figure 9 The carbon dioxide pressure in Comparative Example 1 is shown schematically. P Over time t The decay curve; Figure 10 The curve illustrating the change of carbon dioxide bulk pressure with the square root of time obtained from the fitting in Comparative Example 1 is shown. Figure 11 The carbon dioxide pressure in Comparative Example 2 is shown schematically. P Over time t The decay curve; Figure 12 The curve illustrating the variation of carbon dioxide bulk pressure with the square root of time obtained from the fitting in Comparative Example 2 is shown schematically. Figure 13 This schematic diagram illustrates a block diagram of a heavy oil enhanced recovery carbon dioxide diffusion coefficient prediction device according to an embodiment of this application; Figure 14 A schematic block diagram of a computer device according to an embodiment of this application is shown. Detailed Implementation

[0021] The specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the embodiments of this application.

[0022] If the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0023] Multi-component thermal fluid combined recovery technology for heavy oil has gradually become an important technology for enhancing heavy oil recovery, and the diffusion of carbon dioxide, a key component, plays a dominant role in the recovery process. Therefore, accurately determining the diffusion coefficient of carbon dioxide in heavy oil is crucial for evaluating the effectiveness of multi-component thermal fluid combined recovery technology. However, in existing technologies, the volume expansion and convection effects of heavy oil are not considered, leading to errors in the predicted carbon dioxide diffusion coefficient and reducing the effectiveness of multi-component thermal fluid combined recovery. Accordingly, this application provides a method for predicting the carbon dioxide diffusion coefficient in heavy oil recovery, which predicts the carbon dioxide diffusion coefficient in the following manner: The saturated carbon dioxide concentration of heavy oil under the characteristic parameters is obtained through a carbon dioxide diffusion experiment of heavy oil injection under these characteristic parameters. The characteristic parameters characterize the initial state of the current heavy oil reservoir during enhanced production. Constraints were established based on the saturated carbon dioxide concentration of heavy oil, and the carbon dioxide diffusion model was solved iteratively to obtain the carbon dioxide convection velocity caused by the expansion of heavy oil and the carbon dioxide concentration in heavy oil at each time point. The carbon dioxide diffusion model was constructed based on the carbon dioxide diffusion equation that takes into account the expansion of heavy oil and the convection effect during expansion. The carbon dioxide convection velocity caused by the expansion of heavy oil at each time point is accumulated according to the time step of the iterative solution, and the relationship between the heavy oil level and time is obtained based on the accumulation result. The carbon dioxide concentration in the heavy oil at each time point is accumulated according to the spatial step size during the iterative solution. The amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process is obtained based on the accumulation result. The spatial step size is the change in the height of the heavy oil liquid within one time step. The relationship between carbon dioxide bulk pressure and time, characteristic parameters, relationship between heavy oil level and time, and amount of carbon dioxide dissolved in heavy oil at the end of the dissolution process were collected in the carbon dioxide diffusion experiment of heavy oil. These were then substituted into the first equation, and the curve of carbon dioxide bulk pressure changing with the square root of time was fitted. The first equation is the relationship equation between the change in carbon dioxide bulk pressure, characteristic parameters, relationship between heavy oil level and time, and amount of carbon dioxide dissolved in heavy oil at the end of the dissolution process. The carbon dioxide diffusion coefficient is determined based on the variation curve obtained from the fitting.

[0024] In the embodiments described above, after collecting characteristic parameters representing the initial state of the current heavy oil reservoir, the saturated carbon dioxide concentration of the heavy oil is first obtained through a heavy oil carbon dioxide diffusion experiment under these characteristic parameters. Based on the constraints constructed using this saturated carbon dioxide concentration, numerical calculations of the carbon dioxide diffusion process are performed. Furthermore, the model used for numerical calculation (the heavy oil carbon dioxide diffusion model) considers heavy oil expansion and its convection effects. Compared to comparative implementations that rely solely on implicit difference methods to numerically determine the carbon dioxide diffusion coefficient, this application reduces the uncertainty of the numerical calculation process and improves the accuracy of the calculation results. Simultaneously, in the numerical calculation process using the heavy oil carbon dioxide diffusion model, heavy oil expansion is quantified as one-dimensional expansion, i.e., the change in heavy oil expansion volume is characterized by the change in heavy oil liquid level height. Compared to quantifying the expansion volume using three-dimensional volume in the numerical calculation process, the calculation process is simpler, thereby improving the reliability of the entire numerical calculation process and the accuracy of the calculation results.

[0025] In summary, the embodiments of this application, through the combination of experimental methods and improved numerical calculation models, improve the accuracy and reliability of predicting the carbon dioxide diffusion coefficient in heavy oil recovery, thereby providing more accurate guidance for evaluating the effect of multi-element thermal fluid combined recovery of heavy oil and improving injection and production parameters.

[0026] Figure 1 A schematic flowchart illustrating the method for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced recovery according to an embodiment of this application is shown. Figure 1 As shown, the heavy oil enhanced recovery carbon dioxide diffusion coefficient prediction method provided in this application embodiment includes steps 102 to 114.

[0027] Step 102: Obtain characteristic parameters that characterize the initial state of the current heavy oil reservoir during enhanced oil recovery.

[0028] Specifically, in this application, the characteristic parameters include the initial carbon dioxide injection pressure, the current heavy oil reservoir temperature, the initial height of the heavy oil level, the oil-gas interface area, and the heavy oil mass.

[0029] Step 104: Conduct a carbon dioxide diffusion experiment on heavy oil under characteristic parameters to obtain the saturated carbon dioxide concentration of heavy oil. The saturated carbon dioxide concentration of heavy oil refers to the concentration of heavy oil when it is saturated with carbon dioxide.

[0030] As an optional embodiment of this application, the heavy oil carbon dioxide diffusion experiment under the characteristic parameters is as follows: Under the characteristic parameters, carbon dioxide from the reference cylinder is injected into the sample cylinder containing heavy oil through a pressurization device; Once the bulk carbon dioxide pressure in the sample cylinder has remained stable for the first preset time period, record the current heavy oil level. The saturated carbon dioxide concentration of the heavy oil is calculated based on the change in the bulk carbon dioxide pressure in the reference cylinder, the equilibrium pressure of the bulk carbon dioxide in the sample cylinder at the end of the dissolution process, and the heavy oil level recorded in the previous step.

[0031] As an example, Figure 2 A schematic diagram of a heavy oil carbon dioxide diffusion experimental apparatus according to an embodiment of this application is shown. Figure 2 As shown, the heavy oil carbon dioxide diffusion experimental apparatus includes a reference cylinder, a sample cylinder, a booster pump, a vacuum pump, a pressure sensor, a level gauge, a needle valve, a signal receiver, and a data acquisition system. The booster pump pressurizes the carbon dioxide gas source, and the pressurized carbon dioxide enters the reference cylinder, then the sample cylinder, which contains a heavy oil sample. Pressure sensors are installed at the top of both the reference and sample cylinders. The level gauge is connected to the sample cylinder and is used to monitor the heavy oil level within it. The pressure sensor, reference cylinder, sample cylinder, needle valve, and level gauge are all housed within a temperature-controlled device. The pressure data collected by the pressure sensor is transmitted to the signal receiver outside the temperature-controlled device, and the data acquisition system receives the data from the signal receiver. Figure 2 The heavy oil carbon dioxide diffusion experiment mainly includes steps A1 to A4.

[0032] Step A1: Determine the initial carbon dioxide injection pressure P 0. Heavy oil reservoir temperature T Height of the experimental container in the diffusion of carbon dioxide into heavy oil H Initial height of oil level L 0. Oil-gas interface area A And the quality of heavy oil m .

[0033] Step A2 involves conducting a carbon dioxide diffusion experiment on heavy oil. The injected carbon dioxide comes into contact with the heavy oil and gradually diffuses and dissolves. The bulk pressure of the carbon dioxide is recorded using a data acquisition system. P Over time t The decay curve.

[0034] Step A3, waiting for the bulk carbon dioxide pressure in the sample cylinder P Maintain stability for 200 minutes ( P The fluctuation range was 0.1 psi, assuming that carbon dioxide was fully dissolved and diffused in the heavy oil. The total experimental time and the height of the heavy oil level were recorded. L eq The value of 200 minutes is a preferred value in this embodiment of the application, and can be optimized and adjusted according to specific application scenarios. Therefore, this value is not used as a limitation on the first preset duration.

[0035] Step A4: Calculate the carbon dioxide concentration when the heavy oil is saturated with carbon dioxide using formulas (1) and (2). c eq The unit is mol / m 3 ; (1) (2); In the above formulas (1) and (2): Indicates the first intermediate parameter; R This represents the universal gas constant, with a value of 8.314 J / mol / K; T This indicates the experimental temperature equal to the temperature of the heavy oil reservoir. P 1 represents the initial bulk pressure of carbon dioxide in the reference cylinder, in Pa; P 2 represents the bulk equilibrium pressure of carbon dioxide in the reference cylinder, in Pa. V RC This represents the blank volume of the reference cylinder, in meters (m). 3 ; V SC The blank volume of the sample cylinder is expressed in cubic meters (m³). 3 ; P 3 represents the bulk equilibrium pressure of carbon dioxide in the sample cylinder at the end of the dissolution process, in Pa. A Indicates the oil-gas interface area; L eq This indicates the recorded height of the heavy oil level; Z 1 indicates that carbon dioxide is at the experimental temperature T Initial bulk pressure of carbon dioxide in the reference cylinder P The compressibility factor at 1, dimensionless; Z 2 indicates the temperature at which carbon dioxide is reacted during the experiment. T Bulk equilibrium pressure of carbon dioxide in the reference cylinder P The compressibility factor at 2 is dimensionless. Z 3 indicates the experimental temperature of carbon dioxide. T And the bulk equilibrium pressure of carbon dioxide in the sample cylinder at the end of the dissolution process. PThe compressibility factor at 3 is dimensionless. c eq This represents the saturated carbon dioxide concentration in heavy oil. The compressibility factor of the gas can be calculated using the Span-Wagner equation of state.

[0036] Step 106: Construct constraints based on the heavy oil saturated carbon dioxide concentration obtained in Step 104, and iteratively solve the carbon dioxide diffusion model to obtain the carbon dioxide convection velocity caused by heavy oil expansion at each time point and the carbon dioxide concentration in the heavy oil at each time point.

[0037] In this application, a carbon dioxide diffusion model is constructed based on a carbon dioxide diffusion equation that considers the expansion of heavy oil and the convection effect during expansion. Specifically, the carbon dioxide diffusion equation that considers the expansion of heavy oil and the convection effect during expansion is an improved diffusion equation proposed in this application.

[0038] It should be understood that, in conjunction with the carbon dioxide diffusion equation in the ordinary embodiments, in order to take into account the expansion of heavy oil and its convection effect during the diffusion process, those skilled in the art should know how to construct the improved diffusion equation proposed in this application and the carbon dioxide diffusion model described in this application.

[0039] As an example, combined Figure 3 The calculation principle of carbon dioxide diffusion coefficient in heavy oil is shown below. A carbon dioxide diffusion model is constructed through the following steps: Step B1: Establish a carbon dioxide diffusion equation that takes into account the expansion of heavy oil and the convection effect during expansion; Step B2: Determine the initial and boundary conditions for the carbon dioxide diffusion process in heavy oil; Step B3: Degenerate the parameters in the carbon dioxide diffusion equation to obtain the degenerate parameters. Step B4: Express the initial and boundary conditions determined in step B2 using the dimensionless parameters obtained in step B3; Step B5: Construct a carbon dioxide diffusion model, wherein the carbon dioxide diffusion model uses the carbon dioxide diffusion equation after parameter variability as the diffusion equation of the fluid diffusion process, the initial conditions expressed using the parameter variability as the initial conditions of the fluid diffusion process, and the boundary conditions expressed using the parameter variability as the boundary conditions of the fluid diffusion process.

[0040] Specifically, in this example, the carbon dioxide diffusion equation considering the expansion of heavy oil and the convection effect during expansion is expressed by equation (3) before dimensionless transformation, the initial conditions before dimensionless transformation are expressed by equations (4) and (5), and the boundary conditions before dimensionless transformation are expressed by equations (6) and (7), where: (3) (4) (5) (6) (7); In the above formula (3) ~ formula (7): D This represents the diffusion coefficient of carbon dioxide in heavy oil, i.e., the carbon dioxide diffusion coefficient described in this application, with units of m. 2 / s; c This indicates the concentration of carbon dioxide in heavy oil, expressed in mol / m³. 3 ; x Indicates coordinate position, in meters (m); t Indicates time, in seconds; u This indicates the convection velocity of carbon dioxide caused by the expansion of heavy oil, expressed in m / s. c eq This indicates the saturated carbon dioxide concentration of heavy oil, expressed in mol / m³. 3 ; L ( t )express t The height of the heavy oil level at any given time, in meters (m). L 0 represents t=0 The height of the heavy oil level at any given time.

[0041] In this example, the parameters in formula (3) are dimensionless according to the following formulas (8) and (9) to obtain formula (10). Formula (10) is the dimensionless carbon dioxide diffusion equation. Formulas (4) to (7) are then expressed using formulas (8) and (9) to obtain formulas (11) to (14), where: (8) (9) (10) (11) (12) (13) (14); in, τ It represents dimensionless time; It represents dimensionless coordinate position and is dimensionless. This represents dimensionless convection velocity; This represents the dimensionless concentration of carbon dioxide.

[0042] As an optional embodiment of this application, the carbon dioxide diffusion model is solved iteratively using the implicit difference method to obtain the carbon dioxide convection velocity and carbon dioxide concentration in the heavy oil caused by the expansion of the heavy oil at each time point.

[0043] Specifically, as an example, the process of solving the above formula (10) using the implicit difference method is as follows: In step C1, when solving formula (10) using the implicit difference method, formula (15) is obtained by combining the initial conditions expressed by formulas (11) and (12) and the boundary conditions expressed by formulas (13) and (14), and the differential operator used is shown in formula (16). During iterative solution, the carbon dioxide concentration in the previous time step is used as the initial value of the carbon dioxide concentration in the new time step, and the convection velocity distribution in the new time step is calculated, so as to update the carbon dioxide concentration distribution according to the new convection velocity distribution. Specifically, formulas (15) and (16) are as follows: (15) (16); In formulas (15) and (16), a i , b i , e i and f i All of these represent formula parameters and are dimensionless. Indicates the first n +1 time i The dimensionless carbon dioxide concentration in the -1 grid is dimensionless. Indicates the first n +1 time i The dimensionless carbon dioxide concentration in the grid is dimensionless. Indicates the first n +1 time i The dimensionless carbon dioxide concentration in grid +1 is dimensionless; Δ τ It represents a dimensionless time step, which is dimensionless. This represents the dimensionless spatial step size; Indicates the first n At this moment i The dimensionless carbon dioxide concentration in the grid is dimensionless. Indicates the first n +1 time i The dimensionless convection velocity of heavy oil in the +1 grid; Indicates the first n +1 time iThe dimensionless convection velocity of heavy oil in the grid -1; This represents the error term in the function approximation.

[0044] Step C2: Construct formula (15) into a matrix, which is represented by formula (17). Solve the matrix using the Gauss-Selder iteration method to calculate the carbon dioxide concentration and convection velocity distribution in the heavy oil at each time step. It can be seen that other iteration methods can also be used to solve the matrix. The dimensionless convection velocity is obtained by formulas (18) and (19), as follows: (17) (18) (19); In formulas (17) to (19), f The coefficient of thermal expansion of heavy oil is represented by formula (20); Indicates the first n +1 time i The change in convection velocity of heavy oil within a unit dimensionless time step in the +1 grid is dimensionless. Indicates the first n +1 time i The change in convection velocity of heavy oil within a unit dimensionless time step in the grid is dimensionless. Formula (20) is expressed as follows: (20).

[0045] Step C3: Repeat the iteration in step C2 until the maximum relative error of carbon dioxide concentration in each volume element is less than the first preset value, and obtain the calculation results, that is, output the carbon dioxide convection velocity caused by heavy oil expansion at each time point and the carbon dioxide concentration in heavy oil at each time point. Preferably, the first preset value can be 10. -4 .

[0046] Step 108: Accumulate the carbon dioxide convection velocity caused by the expansion of heavy oil at each time point according to the time step during iterative solution, and obtain the relationship between the heavy oil level and time based on the accumulation result.

[0047] Step 110: Accumulate the carbon dioxide concentration in the heavy oil at each time point according to the spatial step size during the iterative solution. Based on the accumulation result, obtain the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process. The spatial step size is the change in the height of the heavy oil liquid within one time step.

[0048] Step 112: Substitute the relationships between carbon dioxide bulk pressure and time, characteristic parameters, heavy oil level and time, and the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process collected during the heavy oil carbon dioxide diffusion experiment into the first equation, and fit the curve of the change in carbon dioxide bulk pressure with the square root of time. The first equation is the relationship equation between the change in carbon dioxide bulk pressure, characteristic parameters, heavy oil level and time, and the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process.

[0049] Step 114: Determine the carbon dioxide diffusion coefficient based on the variation curve obtained by fitting in step 112.

[0050] As an optional embodiment of this application, the first equation is expressed by formula (21), the relationship between the heavy oil level and time is obtained by formula (22), and the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process is obtained by formula (23), wherein: (twenty one) (twenty two) (twenty three); In formulas (21) to (23), P 0 indicates the initial carbon dioxide injection pressure; T Indicates the temperature of the heavy oil reservoir; H This indicates the height of the container in a carbon dioxide diffusion experiment involving heavy oil. A Indicates the oil-gas interface area; R Represents the universal constant of a gas; L 0 represents t=0 The height of the heavy oil level at any given time; An expression representing the relationship between the level of heavy oil and time; This indicates the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process; The formula representing the relationship between the bulk pressure of carbon dioxide collected in the carbon dioxide diffusion experiment in heavy oil and time is the aforementioned bulk pressure of carbon dioxide. P Over time t The decay curve; Z P(t) Indicates pressure The compression factor at that time; Indicates dimensionless time τ The dimensionless convection velocity at the surface of the heavy oil is dimensionless. Indicates dimensionless time τ Dimensionless coordinate position The carbon dioxide concentration in heavy oil is dimensionless; Δ τ It represents a dimensionless time step, which is dimensionless. It represents the dimensionless step size in the dimensionless space.

[0051] The following specific application examples illustrate the predictive effectiveness of the carbon dioxide diffusion coefficient prediction methods for heavy oil enhanced recovery provided in the above embodiments.

[0052] In the specific application example one, the following is adopted: Figure 2 The apparatus shown was used to conduct a carbon dioxide diffusion experiment on heavy oil. The principle of the carbon dioxide diffusion model is as follows: Figure 3 As shown, the calculation process of the carbon dioxide diffusion model is as follows: Figure 4 As shown. Based on this, the carbon dioxide diffusion coefficient prediction process specifically includes steps S1 to S5.

[0053] Step S1: Determine the initial carbon dioxide injection pressure P 0. Heavy oil reservoir temperature T The height of the container for the carbon dioxide diffusion experiment in heavy oil. H Initial height of oil level L 0. Oil-gas interface area A And the quality of heavy oil m .

[0054] In this application example, the initial carbon dioxide injection pressure was 10 MPa, the heavy oil reservoir temperature was 100 °C, the diffusion experiment vessel height was 8.00 cm, the initial oil level was 3.60 cm, and the oil-gas interface area was 13.85 cm². 2 The weight of the heavy oil is 45g; Step S2, conduct a heavy oil carbon dioxide diffusion experiment: the injected carbon dioxide comes into contact with the heavy oil and gradually diffuses and dissolves, through... Figure 2 The data acquisition system shown records the bulk pressure of carbon dioxide. P Over time t The decay curve, such as Figure 5 As shown; Step S3, waiting for the carbon dioxide pressure in the sample cylinder P Maintain stability for 200 minutes ( P The fluctuation range was 0.1 psi, assuming that carbon dioxide was fully dissolved and diffused in the heavy oil. The total experimental duration was recorded as 5 days, and the heavy oil level was recorded. L eq It is 4.14 cm; Step S4: Calculate the concentration of heavy oil saturated with carbon dioxide using formulas (1) and (2). c eq Calculations show that the concentration of heavy oil saturated with carbon dioxide in this application example is... c eq It is 0.0017 mol / cm 3 ; Step S5: Using formulas (3) to (23), predict the carbon dioxide diffusion coefficient. The prediction results are as follows: the heavy oil expansion coefficient is 1.15; the fitted curve of the change of carbon dioxide bulk pressure with the square root of time is shown below. Figure 6 As shown, the expression for the fitted curve is: As shown in the figure, the correlation coefficient between the fitted curve and the true curve is 0.9855, indicating a good fit; the carbon dioxide diffusion coefficient is 1.8058 × 10⁻⁶. -9 m 2 / s.

[0055] The difference between Specific Application Example 2 and Specific Application Example 1 is that the initial carbon dioxide injection pressure and temperature were changed. The initial injection pressure was set to 20 MPa, the temperature to 150°C, the height of the diffusion experiment vessel was 8.00 cm, the initial height of the heavy oil surface was 3.60 cm, and the oil-gas interface area was 13.85. The mass of the heavy oil was 45 g. The total experimental duration and the height of the heavy oil level were determined through a carbon dioxide diffusion experiment using heavy oil. L The eq value is 4.104 cm, and the saturated carbon dioxide concentration of heavy oil is... c The eq value is 0.0035. carbon dioxide bulk pressure P Over time t The decay curve is as follows Figure 7 As shown. The prediction results are as follows: the expansion coefficient of heavy oil is 1.14; the curve of the change of carbon dioxide bulk pressure with the square root of time obtained by fitting is shown below. Figure 8 As shown, the expression for the fitted curve is: The correlation coefficient between the fitted curve and the true curve was 0.9808, indicating a good fit; the carbon dioxide diffusion coefficient was 2.2334 × 10⁻⁹. .

[0056] To illustrate the accuracy of the prediction results of the above-mentioned application example 1 and application example 2, this application further clarifies comparative example 1 and comparative example 2.

[0057] In Comparative Example 1, the carbon dioxide diffusion coefficient is predicted through the following steps SS1 to SS6.

[0058] Step SS1: Determine the initial carbon dioxide injection pressure. Heavy oil reservoir temperature T Volume of diffusion experimental container V Initial height of oil level Oil-gas interface area A And the quality of heavy oil m ; In this comparative example, the initial carbon dioxide injection pressure was 10 MPa, the heavy oil reservoir temperature was 100°C, and the diffusion experiment vessel volume was 161.24 cm³. 3 The oil and gas interface area is 13.85 cm². 2 The weight of the heavy oil is 45 g.

[0059] Step SS2 involves conducting a carbon dioxide diffusion experiment on heavy oil: the injected carbon dioxide comes into contact with the heavy oil and gradually diffuses and dissolves, through... Figure 2 The data acquisition system shown records carbon dioxide pressure. P Over time t The decay curve, such as Figure 9 As shown.

[0060] Step SS3, wait for the carbon dioxide pressure in the sample cylinder P Maintain stability for 200 minutes ( P The fluctuation range is 0.1 psi), assuming that carbon dioxide is fully dissolved and diffused in heavy oil, and the volume in the sample cylinder when it is fully dissolved and diffused is recorded.

[0061] Step SS4: Construct the carbon dioxide diffusion equation without considering heavy oil expansion according to Fick's second law, as shown in equation (24). Determine its initial conditions, as shown in equation (25). Determine its boundary conditions, as shown in equations (26) to (27), where: (twenty four) (25) (26) (27); Step SS5: Substitute the parameters in formula (24) into formulas (25), (26) and (27) to obtain formula (28): (28); By combining the system pressure and diffusion process through material balance calculations, we can obtain formula (29): (29).

[0062] Step SS6, by integration, formula (29) can be transformed into formula (30): (30); in, Represents the square root of time; A parameter indicating a linear relationship with pressure changes; diffusion coefficient. It can be obtained by calculating the slope of the straight line; V The free volume of the sample cylinder is expressed in units of 1. ; P ( t )and These represent the pressure values ​​in the oil and gas system at any time and at the initial time, respectively, in MPa; Z g represents the gas in P ( t The compressibility factor of a gas at a given temperature is calculated using the Span-Wagner equation of state. R It is the universal gas constant, with a value of 8.314 J / mol / K; T It is the diffusion experiment temperature, in Kelvin (K). x eq represents the equilibrium concentration at the oil-gas interface, in units of... ; A This represents the bottom area of ​​the diffusion container, in units of... The curve showing the change of carbon dioxide bulk pressure over the square root of time obtained from the fitting is shown below. Figure 10 As shown, the expression for the fitted curve is: The correlation coefficient between the fitted curve and the true curve was 0.9562. The carbon dioxide diffusion coefficient of this comparative example was calculated to be 2.0136 × 10⁻¹¹. .

[0063] As described in Comparative Example 1, which does not consider the volume expansion of heavy oil, the method for measuring the carbon dioxide diffusion coefficient in Comparative Example 2 differs from Comparative Example 1 in that the initial carbon dioxide injection pressure and temperature are changed. The initial injection pressure is set to 20 MPa, the temperature to 150°C, and the volume and height of the diffusion experiment container are 161.24 mm. The initial oil level was 3.60 cm, and the oil-gas interface area was 13.85 cm. The mass of heavy oil is 45 g. Calculate the carbon dioxide diffusion coefficient and carbon dioxide pressure following the calculation steps in Comparative Example 1. P Over time t The decay curve is as follows Figure 11 As shown, the fitted curve is as follows Figure 12 As shown, the expression for the fitted curve is: The correlation coefficient between the fitted curve and the true curve was 0.9796. The carbon dioxide diffusion coefficient of this comparative example was calculated to be 4.1157 × 10⁻¹¹. .

[0064] Record the diffusion experiment temperature, initial carbon dioxide bulk pressure, and carbon dioxide diffusion coefficient for Application Example 1, Application Example 2, Comparative Example 1, and Comparative Example 2. The specific results are shown in Table 1. Comparative Example 1 and Comparative Example 2 correspond to Application Example 1 and Application Example 2, respectively. The diffusion experiment temperature for Application Example 1 was 100°C, and the initial carbon dioxide pressure was 10 MPa. The diffusion experiment temperature for Application Example 2 was 150°C, and the initial carbon dioxide pressure was 15 MPa. According to the data in Table 1, the carbon dioxide diffusion coefficient in Application Example 1 is significantly higher than that in Comparative Example 1, and the carbon dioxide diffusion coefficient in Application Example 2 is significantly higher than that in Comparative Example 2. It is evident that the diffusion coefficient obtained by the carbon dioxide diffusion coefficient prediction method proposed in this application, which considers the volume expansion of heavy oil and its convection effect, is significantly higher than the result of the carbon dioxide diffusion coefficient prediction method that does not consider the volume expansion of heavy oil, thus proving the necessity of the prediction method provided in this application.

[0065] Table 1

[0066] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0067] Corresponding to the heavy oil enhanced recovery carbon dioxide diffusion coefficient prediction method in the above embodiments, Figure 13 The diagram illustrates the composition of a heavy oil enhanced recovery carbon dioxide diffusion coefficient prediction device according to an embodiment of this application. For ease of explanation, only the parts relevant to the embodiments of this application are shown.

[0068] like Figure 13 As shown, the heavy oil enhanced recovery carbon dioxide diffusion coefficient prediction device 400 includes: The first acquisition module 410 is used to acquire the saturated carbon dioxide concentration of heavy oil under the characteristic parameters. The saturated carbon dioxide concentration of heavy oil is obtained by a carbon dioxide diffusion experiment of heavy oil under the characteristic parameters. The characteristic parameters represent the initial state of the current heavy oil reservoir during enhanced production. The iterative solution module 420 is used to establish constraints based on the saturated carbon dioxide concentration of the heavy oil, and iteratively solve the carbon dioxide diffusion model to obtain the carbon dioxide convection velocity caused by the expansion of the heavy oil and the carbon dioxide concentration in the heavy oil at each time point. The carbon dioxide diffusion model is constructed based on the carbon dioxide diffusion equation that considers the expansion of the heavy oil and the convection effect during the expansion. The first calculation module 430 is used to accumulate the carbon dioxide convection velocity caused by the expansion of heavy oil at each time point according to the time step during iterative solution, and obtain the relationship between the heavy oil level and time based on the accumulation result; The second calculation module 440 is used to accumulate the carbon dioxide concentration in the heavy oil at each time point according to the spatial step size during the iterative solution, and obtain the amount of carbon dioxide dissolved in the heavy oil when the dissolution ends based on the accumulation result. The spatial step size is the change in the height of the heavy oil liquid level within one time step. The fitting module 450 is used to input the relationship between carbon dioxide bulk pressure and time, the characteristic parameters, the relationship between heavy oil level and time, and the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process collected in the heavy oil carbon dioxide diffusion experiment into the first equation, and fit the change curve of carbon dioxide bulk pressure with the square root of time. The first equation is the relationship equation between the change in carbon dioxide bulk pressure, the characteristic parameters, the relationship between heavy oil level and time, and the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process. The diffusion coefficient determination module 460 is used to determine the carbon dioxide diffusion coefficient based on the fitted change curve.

[0069] As an embodiment of this application, the heavy oil enhanced recovery carbon dioxide diffusion coefficient prediction device 400 can achieve the following: Figure 1 The embodiments shown and other related method embodiments.

[0070] The process by which each module of the heavy oil enhanced oil recovery carbon dioxide diffusion coefficient prediction device 400 provided in this application implements its respective function can be found in the foregoing. Figure 1 The descriptions of the embodiments shown and other related method embodiments are not repeated here.

[0071] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. Their specific functions and technical effects can be found in the method embodiments section, and will not be repeated here.

[0072] Figure 14 A schematic block diagram of a computer device according to an embodiment of the present application is shown. In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown. Figure 14As shown in the figure, the computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05, and a memory (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A06. The network interface A02 is used for communication with external terminals via a network connection. When the computer program is executed by the processor A01, it implements a method for predicting the carbon dioxide diffusion coefficient of heavy oil recovery. The display screen A04 can be a liquid crystal display (LCD) or an e-ink display. The input device A05 can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0073] Those skilled in the art will understand that Figure 14 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0074] In one embodiment, the heavy oil enhanced recovery carbon dioxide diffusion coefficient prediction device 400 provided in this application can be implemented as a computer program, which can be implemented in the form of, for example... Figure 14 The computer device shown operates on the computer. The computer device's memory can store various program modules that constitute the heavy oil enhanced oil recovery carbon dioxide diffusion coefficient prediction device 400. The computer program, composed of these program modules, causes the processor to execute the steps in the heavy oil enhanced oil recovery carbon dioxide diffusion coefficient prediction methods of the various embodiments of this application described in this specification.

[0075] In one embodiment, this application also provides a machine-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the heavy oil enhanced recovery carbon dioxide diffusion coefficient prediction method in the above embodiments.

[0076] The device embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0077] 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.) that include computer-usable program code.

[0078] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications 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.

Claims

1. A method for predicting the carbon dioxide diffusion coefficient in heavy oil enhanced oil recovery, characterized in that, include: The saturated carbon dioxide concentration of heavy oil under the characteristic parameters is obtained through a carbon dioxide diffusion experiment of heavy oil injection under the characteristic parameters. The characteristic parameters represent the initial state of the current heavy oil reservoir during enhanced production. Constraints are established based on the saturated carbon dioxide concentration of heavy oil, and the carbon dioxide diffusion model is solved iteratively to obtain the carbon dioxide convection velocity caused by the expansion of heavy oil and the carbon dioxide concentration in heavy oil at each time point. The carbon dioxide diffusion model is constructed based on the carbon dioxide diffusion equation that considers the expansion of heavy oil and the convection effect during expansion. The carbon dioxide convection velocity caused by the expansion of heavy oil at each time point is accumulated according to the time step of the iterative solution, and the relationship between the heavy oil level and time is obtained based on the accumulation result. The carbon dioxide concentration in the heavy oil at each time point is accumulated according to the spatial step size during the iterative solution. The amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process is obtained based on the accumulation result. The spatial step size is the change in the height of the heavy oil liquid level within one time step. The relationship between carbon dioxide bulk pressure and time, the characteristic parameters, the relationship between heavy oil level and time, and the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process are obtained from the carbon dioxide diffusion experiment in the heavy oil. These are then substituted into the first equation, and the curve of the change of carbon dioxide bulk pressure with the square root of time is fitted. The first equation is the relationship equation between the change of carbon dioxide bulk pressure, the characteristic parameters, the relationship between heavy oil level and time, and the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process. The carbon dioxide diffusion coefficient is determined based on the fitted variation curve.

2. The method for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced oil recovery according to claim 1, characterized in that, The characteristic parameters include the initial carbon dioxide injection pressure, the current heavy oil reservoir temperature, the initial height of the heavy oil level, the oil-gas interface area, and the heavy oil mass.

3. The method for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced oil recovery according to claim 2, characterized in that, The saturated carbon dioxide concentration of the heavy oil was obtained through a carbon dioxide diffusion experiment under the aforementioned characteristic parameters, including: Under the aforementioned characteristic parameters, carbon dioxide from the reference cylinder is injected into the sample cylinder containing heavy oil via a pressurization device; Once the bulk carbon dioxide pressure in the sample cylinder has remained stable for a first preset time period, record the current heavy oil level. The saturated carbon dioxide concentration of heavy oil was calculated based on the change in the bulk carbon dioxide pressure in the reference cylinder, the equilibrium pressure of the bulk carbon dioxide in the sample cylinder at the end of the dissolution process, and the recorded heavy oil level.

4. The method for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced oil recovery according to claim 3, characterized in that, The saturated carbon dioxide concentration of heavy oil is calculated based on the change in the bulk carbon dioxide pressure in the reference cylinder, the equilibrium pressure of the bulk carbon dioxide in the sample cylinder at the end of the dissolution process, and the recorded current heavy oil level. This includes: Substitute the change in carbon dioxide bulk pressure in the reference cylinder, the equilibrium pressure of carbon dioxide bulk pressure in the sample cylinder at the end of the dissolution process, and the recorded heavy oil liquid level into Equation 1 to calculate the saturated carbon dioxide concentration of the heavy oil. Equation 1 is: ； in, Indicates the first intermediate parameter. R Represents the universal constant for gases. T This indicates the experimental temperature equal to the temperature of the heavy oil reservoir. P 1 represents the initial bulk pressure of carbon dioxide in the reference cylinder. P 2 represents the bulk equilibrium pressure of carbon dioxide in the reference cylinder. V RC Indicates the blank volume of the reference cylinder. V SC Indicates the blank volume of the sample cylinder. P 3 represents the bulk equilibrium pressure of carbon dioxide in the sample cylinder at the end of the dissolution process. A Indicates the oil-gas interface area. L eq This indicates the recorded height of the heavy oil level. Z 1 indicates that carbon dioxide is at the experimental temperature T Initial bulk pressure of carbon dioxide in the reference cylinder P Compression factor at 1 Z 2 indicates the temperature at which carbon dioxide is reacted during the experiment. T Bulk equilibrium pressure of carbon dioxide in the reference cylinder P Compression factor at 2 Z 3 indicates the experimental temperature of carbon dioxide. T And the bulk equilibrium pressure of carbon dioxide in the sample cylinder at the end of the dissolution process. P Compression factor at 3 c eq This indicates the saturated carbon dioxide concentration in heavy oil.

5. The method for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced oil recovery according to claim 1, characterized in that, The carbon dioxide diffusion model is constructed based on the carbon dioxide diffusion equation that considers the expansion of heavy oil and the convection effect during expansion, including: Establish a carbon dioxide diffusion equation that takes into account the expansion of heavy oil and the convection effect during expansion; Determine the initial and boundary conditions for the carbon dioxide diffusion process in heavy oil; The parameters in the carbon dioxide diffusion equation are dimensionless to obtain the dimensionless parameters. The initial conditions and boundary conditions are represented by the dimensionless parameters; A carbon dioxide diffusion model is constructed, wherein the carbon dioxide diffusion equation after parameter variability is used as the diffusion equation of the fluid diffusion process, the initial conditions expressed using the parameter variability are used as the initial conditions of the fluid diffusion process, and the boundary conditions expressed using the parameter variability are used as the boundary conditions of the fluid diffusion process.

6. The method for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced oil recovery according to claim 5, characterized in that, The carbon dioxide diffusion equation before dimensionless transformation is expressed by Equation 2, the initial conditions before dimensionless transformation are expressed by Equation 3, and the boundary conditions before dimensionless transformation are expressed by Equation 4. Equation 2 is: Formula 3 is: , Formula four is: , ; D This represents the diffusion coefficient of carbon dioxide in heavy oil. c This indicates the concentration of carbon dioxide in heavy oil. x Indicates coordinate position, t Indicates time, u This indicates the carbon dioxide convection velocity caused by the expansion of heavy oil. c eq Indicates the saturated carbon dioxide concentration of heavy oil. L ( t )express t The height of the heavy oil level at all times. L 0 represents t=0 The height of the heavy oil level at any given time.

7. The method for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced oil recovery according to claim 1, characterized in that, The carbon dioxide diffusion model was solved iteratively using the implicit difference method to obtain the carbon dioxide convection velocity and carbon dioxide concentration in the heavy oil caused by the expansion of the heavy oil at each time point.

8. The method for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced oil recovery according to claim 1, characterized in that, The first equation is expressed by the following formula: ； in, ; ; P 0 indicates the initial carbon dioxide injection pressure; T Indicates the temperature of the heavy oil reservoir; H This indicates the height of the container in a carbon dioxide diffusion experiment involving heavy oil. A Indicates the oil-gas interface area; R Represents the universal constant of a gas; L 0 represents t=0 The height of the heavy oil level at any given time; An expression representing the relationship between the level of heavy oil and time; This indicates the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process; The formula representing the relationship between the bulk pressure of carbon dioxide collected in a carbon dioxide diffusion experiment in heavy oil and time; Z P(t) Indicates pressure The compression factor at that time; L eq This represents the height of the heavy oil level recorded at the end of the dissolution process in the carbon dioxide diffusion experiment. Indicates dimensionless time τ The dimensionless convection velocity at the surface of the heavy oil is dimensionless. Indicates dimensionless time τ Dimensionless coordinate position The carbon dioxide concentration in heavy oil is dimensionless; Δ τ It represents a dimensionless time step, which is dimensionless. It represents the dimensionless step size in the dimensionless space.

9. A device for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced oil recovery, characterized in that, include: The first acquisition module is used to acquire the saturated carbon dioxide concentration of heavy oil under the characteristic parameters. The saturated carbon dioxide concentration of heavy oil is obtained by a carbon dioxide diffusion experiment of heavy oil under the characteristic parameters. The characteristic parameters represent the initial state of the current heavy oil reservoir during enhanced production. The iterative solution module is used to establish constraints based on the saturated carbon dioxide concentration of the heavy oil, and iteratively solve the carbon dioxide diffusion model to obtain the carbon dioxide convection velocity caused by the expansion of the heavy oil and the carbon dioxide concentration in the heavy oil at each time point. The carbon dioxide diffusion model is constructed based on the carbon dioxide diffusion equation that considers the expansion of the heavy oil and the convection effect during the expansion. The first calculation module is used to accumulate the carbon dioxide convection velocity caused by the expansion of heavy oil at each time point according to the time step during iterative solution, and obtain the relationship between the heavy oil level and time based on the accumulation result; The second calculation module is used to accumulate the carbon dioxide concentration in the heavy oil at each time point according to the spatial step size during the iterative solution, and obtain the amount of carbon dioxide dissolved in the heavy oil when the dissolution process ends based on the accumulation result. The spatial step size is the change in the height of the heavy oil liquid level within one time step. The fitting module is used to input the relationship between carbon dioxide bulk pressure and time, the characteristic parameters, the relationship between heavy oil level and time, and the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process collected in the heavy oil carbon dioxide diffusion experiment into the first equation, and fit the change curve of carbon dioxide bulk pressure with the square root of time. The first equation is the relationship equation between the change of carbon dioxide bulk pressure, the characteristic parameters, the relationship between heavy oil level and time, and the amount of carbon dioxide dissolved in the heavy oil at the end of the dissolution process. The diffusion coefficient determination module is used to determine the carbon dioxide diffusion coefficient based on the fitted variation curve.

10. A computer device, characterized in that, include: The memory is configured to store instructions; as well as The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the method for predicting the carbon dioxide diffusion coefficient of heavy oil enhanced recovery according to any one of claims 1 to 8.

11. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the heavy oil enhanced recovery carbon dioxide diffusion coefficient prediction method according to any one of claims 1 to 8.

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