Method, device, medium and equipment for quickly identifying oil liquid physical property model parameters

Abstract

This invention relates to a method, apparatus, medium, and equipment for rapid identification of oil physical property model parameters. The method includes: determining oil physical property data for hydraulic simulation; establishing a set of formulas for identifying oil physical property model parameters based on the oil physical property data; collecting three sets of oil physical property data under fixed pressure and temperature, and organizing them into a set of known data for identification; substituting the density measurement value, kinematic viscosity measurement value, and isothermal compressibility coefficient measurement value from the known data set into the set of formulas for identifying oil physical property model parameters, and respectively obtaining the isobaric thermal expansion coefficients γ, C2, C1, C3, E3, and E... 2、 E1; Based on the solved parameter set θ={E1,E2,E3,C1,C2,C3,γ}, establish an oil property model, and substitute it with the test conditions of normal temperature and pressure, normal temperature and high pressure and normal pressure and non-normal temperature, test the calculated results of oil property (ρ,ν,β) to ensure that they are consistent with the actual measured values.

Description

Technical Field

[0001] This invention relates to a method, apparatus, medium, and equipment for rapid identification of oil physical property model parameters, belonging to the field of hydraulic modeling and simulation technology for underwater control systems in marine oil and gas production. Background Technology

[0002] In deepwater hydraulic control systems used in offshore oil and gas production, water-based hydraulic oils (such as Oceanic HWxxx series and Transaqua HTx series) are commonly used as the working medium. Their physical properties, such as density, viscosity, and compressibility, vary significantly with pressure and temperature. In underwater, especially deep-water environments, the oil pressure can vary by 600-700 atmospheres due to the combined effects of the hydraulic equipment's terminal load and deep-water static pressure. Simultaneously, the oil temperature varies considerably depending on the geographical location of the oil and gas field, water depth, and the marine environment. Therefore, when designing underwater control systems and performing hydraulic response calculations and analyses in deep-water scenarios, it is essential to use a property model that accurately reflects the changes in oil properties with pressure and temperature for the relevant calculations and analyses.

[0003] Existing technologies typically model hydraulic fluid properties using data point fitting. This involves using interpolation functions to fit hydraulic fluid property (density, viscosity, compressibility) data points into curves or surfaces that change with pressure and temperature. These fitted curves or surfaces are then used in the simulation analysis of the hydraulic system. This approach places high demands on the quality of the data points, including their quantity, dimensionality, coverage of pressure and temperature variations, and data continuity. If the data quality fails to meet these requirements, calculation errors or significant inaccuracies can occur, impacting system design. In reality, the quantity, dimensionality, and coverage of data points obtainable from hydraulic fluid suppliers or through property testing are limited, and data continuity cannot be guaranteed, making it difficult to meet the requirements for hydraulic response calculations in deep-water scenarios. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method, apparatus, medium, and equipment for rapid identification of oil property model parameters in hydraulic simulation of deep-water scenarios. This method obtains a 7-parameter property model that meets the calculation requirements through rapid identification of property model parameters based on fewer data points, and has high cost-effectiveness and practicality.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for rapid identification of oil physical property model parameters includes: Under deep-water and high-pressure conditions, cavitation and air mixing effects are ignored, and the oil is kept in the liquid phase. The oil physical property data for hydraulic simulation are determined. Based on oil property data, a set of formulas for identifying parameters of the oil property model is established. The set of formulas for identifying parameters of the oil property model includes the property function formulas for the isothermal compressibility coefficient β, the property function formulas for density ρ, and the property function formulas for kinematic viscosity ν. Under fixed pressure and temperature, several sets of oil physical property data were collected and organized to obtain a set of known data for identification. Substitute the density measurements at normal pressure and extreme temperatures from the known dataset into the physical property function formula for density ρ, and solve the equation to obtain the isobaric thermal expansion coefficient γ; Substitute the kinematic viscosity measurements at room temperature and pressure and at normal pressure and extreme temperature from the known data set into the formula obtained by taking the natural logarithm of both sides of the physical property function formula for kinematic viscosity ν. Subtract the two equations to obtain a univariate explicit equation, and solve for C2. Substitute the kinematic viscosity measurements at room temperature and pressure from the known dataset into the physical property function formula for kinematic viscosity ν, and solve the univariate explicit equation to obtain C1; By substituting the measured kinematic viscosity values ​​under normal temperature and high pressure conditions from the known data set into the formula obtained by taking the natural logarithm of both sides of the physical property function formula for kinematic viscosity ν, C3 can be obtained by solving the univariate explicit equation. Substitute the measured values ​​of the isothermal compressibility coefficient at normal temperature and pressure and the measured values ​​of the isothermal compressibility coefficient at normal pressure and extreme temperature from the known data set into the physical property function formula of the isothermal compressibility coefficient β, divide the two formulas, and obtain E3 and E2. Substitute the measured values ​​of the isothermal compressibility coefficient under normal temperature and high pressure from the known data set into the physical property function formula of the isothermal compressibility coefficient β, and solve to obtain E1; Based on the parameter set θ={E1,E2,E3,C1,C2,C3,γ} obtained from the solution, an oil property model is established, and the test conditions of normal temperature and pressure, normal temperature and high pressure, and normal pressure and non-normal temperature are substituted to test the calculated results of oil property (ρ,ν,β) to ensure consistency with the actual measured values.

[0006] The method for rapid identification of oil property model parameters, preferably, uses the following formula for the isothermal compressibility coefficient β:

[0007] Where E1, E2, and E3 are the parameters to be identified, and p and T are the pressure and temperature variables, respectively. ref Pressure at the reference point.

[0008] The method for rapid identification of oil property model parameters preferably uses the following formula for the property function of density ρ:

[0009] Where, ρ refThe reference point density is γ, which is the isobaric thermal expansion coefficient and also the parameter to be identified. T ref This is the reference point temperature.

[0010] The method for rapid identification of oil property model parameters preferably uses the following formula for the kinematic viscosity ν:

[0011] C1, C2, and C3 are the parameters to be identified.

[0012] The method for rapid identification of oil property model parameters, preferably, includes the following known data set: D i ={ρ(p i ,T i ),υ(p i ,T i ), β(p i ,T i ), p i ,T i}, i=1,2,3, Group 1 is under normal temperature and pressure conditions, p1=p ref ,T1=T ref The following are the measured oil physical property data and test conditions: D1={ρ ref ,υ(p ref ,T ref ), β(p ref ,T ref ), p ref ,T ref}; Group 2 is at room temperature, T2=T ref Oil physical property data and test conditions obtained under high pressure: D2={υ(p2,T ref ), β(p2,T ref ), p2,T ref}; Group 3 is at normal pressure p3=p ref Oil physical property data obtained under low temperature conditions: D3={ρ(p ref ,T3),υ(p ref ,T3),β(p ref ,T3), p ref ,T3}.

[0013] The method for rapid identification of oil property model parameters, preferably, involves the following calculation process for the isobaric thermal expansion coefficient γ: The density measurements at normal pressure and extreme temperatures are the data {ρ(pref Substituting {,T3),T3}∈D3 into the property function formula for density ρ, we know {ρ ref ,T ref}∈D1, solve the linear equation in one variable to obtain γ: .

[0015] The aforementioned method for rapid identification of oil property model parameters preferably involves solving for parameters C1, C2, and C3. The specific process is as follows:

[0016]

[0017] .

[0019] The aforementioned method for rapid identification of oil property model parameters preferably involves solving for parameters E1, E2, and E3. The specific process is as follows:

[0020]

[0021] .

[0023] A second aspect of the present invention provides a device for rapid identification of oil physical property model parameters, comprising: The first processing unit is used to determine the oil physical property data for hydraulic simulation under deep water and high pressure conditions, ignoring cavitation and air mixing effects, keeping the oil in a liquid phase state. The second processing unit is used to establish a set of formulas for identifying parameters of the oil property model based on the oil property data. The set of formulas for identifying parameters of the oil property model includes the property function formulas for the isothermal compressibility coefficient β, the property function formulas for density ρ, and the property function formulas for kinematic viscosity ν. The third processing unit is used to collect three sets of oil physical property data under fixed pressure and temperature, and organize them into a set of known data for identification. The fourth processing unit is used to substitute the density measurements at normal pressure and extreme temperatures in the known data set into the physical property function formula of density ρ, and solve the equation to obtain the isobaric thermal expansion coefficient γ. The fifth processing unit is used to substitute the kinematic viscosity measurements at normal temperature and pressure and at normal pressure and extreme temperature from the known data set into the formula obtained by taking the natural logarithm of both sides of the physical property function formula of kinematic viscosity ν. The two equations are subtracted to obtain a univariate explicit equation, and C2 is obtained by solving it. The sixth processing unit is used to substitute the kinematic viscosity measurements at room temperature and pressure from the known dataset into the physical property function formula of kinematic viscosity ν, and solve the univariate explicit equation to obtain C1; The seventh processing unit is used to substitute the kinematic viscosity measurement values ​​under normal temperature and high pressure conditions from the known data set into the formula obtained by taking the natural logarithm of both sides of the physical property function formula of kinematic viscosity ν, and solve the univariate explicit equation to obtain C3; The eighth processing unit is used to substitute the measured values ​​of the isothermal compressibility coefficient at normal temperature and pressure and the measured values ​​of the isothermal compressibility coefficient at normal pressure and extreme temperature in the known data set into the physical property function formula of the isothermal compressibility coefficient β, and then divide the two formulas to obtain E3 and E2. The ninth processing unit is used to substitute the measured values ​​of the isothermal compressibility coefficient under normal temperature and high pressure in the known data set into the physical property function formula of the isothermal compressibility coefficient β, and solve to obtain E1; The tenth processing unit is used to establish an oil property model based on the parameter set θ={E1,E2,E3,C1,C2,C3,γ} obtained by solving, and to test the calculated results of oil properties (ρ,ν,β) by substituting the test conditions of normal temperature and pressure, normal temperature and high pressure and normal pressure and non-normal temperature, to ensure that they are consistent with the actual measured values.

[0024] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the method for rapid identification of oil physical property model parameters described in any one of the above claims.

[0025] A fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for rapid identification of oil physical property model parameters described in any of the above-described methods.

[0026] The present invention has the following advantages due to the adoption of the above technical solutions: 1. The method of this invention can obtain a 7-parameter physical property model that meets the calculation requirements by quickly identifying the parameters of the physical property model based on a small number of data points, which has high cost performance and high practicality. Technical practice shows that for water-based hydraulic oil HW443, only 3 sets of (ρ, ν, β) physical property data need to be collected in the range of [0℃, 40℃] and [1 bar, 100 bar], and all 7 oil physical property model parameters can be quickly identified by manual calculation. This method can be further extended to other hydraulic simulation technology fields that have high requirements for the coverage of pressure and temperature changes and where it is difficult to obtain complete oil physical property data through experimental means or supplier information channels.

[0027] 2. This invention employs an algebraic expression in the form of a 7-parameter exponential function, which possesses excellent numerical smoothing characteristics and accurately reflects the changes in the physical properties of water-based hydraulic oil with pressure and temperature, as the oil's physical property model. Using only a small amount of measured data on oil properties, the model parameters can be identified and the model calibrated, thereby rapidly establishing a physical property model corresponding to the oil. This provides a 7-parameter physical property model that meets practical engineering requirements for numerical calculation of hydraulic response in deep-water scenarios. While fully utilizing the mathematical functions of the physical property model, an explicit algebraic equation containing each parameter to be identified is obtained according to a pre-planned identification method. This allows users to quickly identify all 7 parameters manually using only 3 sets of 8 measured data points, effectively reducing the requirements for oil property testing conditions and the completeness of oil supplier data. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating the rapid identification of oil property model parameters for hydraulic simulation in deep water scenarios, provided as an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0030] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," "third," "fourth," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0031] For ease of description, spatial relative terms may be used in the text to describe the relationship of one element or feature relative to another element or feature as shown in the figure. These relative terms include, for example, "inside," "outside," "middle," "outer," "below," "above," etc. Such spatial relative terms are intended to include different orientations of the device in use or operation, other than those depicted in the figure.

[0032] Existing technologies typically model hydraulic fluid properties using data point fitting. This involves using interpolation functions to fit hydraulic fluid property (density, viscosity, compressibility) data points into curves or surfaces that change with pressure and temperature. These fitted curves or surfaces are then used in the simulation analysis of the hydraulic system. This approach places high demands on the quality of the data points, including their quantity, dimensionality, coverage of pressure and temperature variations, and data continuity. If the data quality fails to meet these requirements, calculation errors or significant inaccuracies can occur, impacting system design. In reality, the quantity, dimensionality, and coverage of data points obtainable from hydraulic fluid suppliers or through property testing are limited, and data continuity cannot be guaranteed, making it difficult to meet the requirements for hydraulic response calculations in deep-water scenarios.

[0033] Based on the above-mentioned technical problems, the present invention provides a method, device, medium and equipment for rapid identification of oil property model parameters for hydraulic simulation in deep water scenarios. The method obtains a 7-parameter property model that meets the calculation requirements by rapidly identifying the property model parameters based on a small number of data points, which has high cost performance and high practicality.

[0034] like Figure 1 As shown, the method for rapid identification of oil property model parameters for hydraulic simulation in deep water scenarios provided by this invention includes the following specific steps: 1. A method for rapid identification of oil physical property model parameters, characterized in that it includes: Under deep-water and high-pressure conditions, cavitation and air mixing effects can be ignored, and the oil remains in the liquid phase. The oil physical property data used for hydraulic simulation are determined, and the main considerations for the oil physical property data used for hydraulic simulation are density, compressibility coefficient and kinematic viscosity. Based on oil property data, a set of formulas for identifying parameters of the oil property model is established. The set of formulas for identifying parameters of the oil property model includes the property function formulas for the isothermal compressibility coefficient β, the property function formulas for density ρ, and the property function formulas for kinematic viscosity ν. Under fixed pressure and temperature, three sets of oil physical property data were collected and organized to obtain a set of known data for identification. Substitute the density measurements at normal pressure and extreme temperatures from the known dataset into the physical property function formula for density ρ, and solve the equation to obtain the isobaric thermal expansion coefficient γ; Substitute the kinematic viscosity measurements at room temperature and pressure and at normal pressure and extreme temperature from the known data set into the formula obtained by taking the natural logarithm of both sides of the physical property function formula for kinematic viscosity ν. Subtract the two equations to obtain a univariate explicit equation, and solve for C2. Substitute the kinematic viscosity measurements at room temperature and pressure from the known dataset into the physical property function formula for kinematic viscosity ν, and solve the univariate explicit equation to obtain C1; By substituting the measured kinematic viscosity values ​​under normal temperature and high pressure conditions from the known data set into the formula obtained by taking the natural logarithm of both sides of the physical property function formula for kinematic viscosity ν, C3 can be obtained by solving the univariate explicit equation. Substitute the measured values ​​of the isothermal compressibility coefficient at normal temperature and pressure and the measured values ​​of the isothermal compressibility coefficient at normal pressure and extreme temperature from the known data set into the physical property function formula of the isothermal compressibility coefficient β, divide the two formulas, and obtain E3 and E2. Substitute the measured values ​​of the isothermal compressibility coefficient under normal temperature and high pressure from the known data set into the physical property function formula of the isothermal compressibility coefficient β, and solve to obtain E1; Based on the parameter set θ={E1,E2,E3,C1,C2,C3,γ} obtained from the solution, an oil property model is established, and the test conditions of normal temperature and pressure, normal temperature and high pressure, and normal pressure and non-normal temperature are substituted to test the calculated results of oil property (ρ,ν,β) to ensure consistency with the actual measured values.

[0035] Specifically, the formula for the physical property function of the isothermal compressibility coefficient β is as follows (1):

[0036] Where E1, E2, and E3 are the parameters to be identified, and p and T are the pressure and temperature variables, respectively. ref The pressure is taken as a reference point. The units of all variables, constants, and parameters are as follows: β [Pa] -1 E1[-], E2[Pa], E3[K] -1 ]、p ref [Pa].

[0037] Furthermore, the formula for the physical property function of density ρ is as follows (2):

[0038] Where, ρ ref The reference point density is γ, which is the isobaric thermal expansion coefficient and also the parameter to be identified. T ref The reference point temperature is used. The units and dimensions of all variables, constants, and parameters are as follows: ρ ref [kg m -3 ], γ[K -1 ], T ref [K]

[0039] Furthermore, the formula for the physical property function of kinematic viscosity ν is as follows (3):

[0040] Where C1, C2, and C3 are the parameters to be identified. The units of each variable, constant, and parameter are as follows: C1 [mm] 2 s -1 C2 [℃], C3 [bar] -1 ].

[0041] The formula set for identifying parameters of the oil physical property model is θ={E1,E2,E3,C1,C2,C3,γ}, corresponding to formulas (1), (2), and (3). ref ,T ref As a public benchmark value, T is not included in the identification as a constant. ref Typically, a room temperature of 15°C is used, which is 288.15 K. ref The standard atmospheric pressure is typically taken as 1.01325 bar, or 101325 Pa. Pressure and temperature under other test conditions are treated similarly.

[0042] Solving the mathematically processed explicit equations by following these steps will allow you to identify all parameters at once, without the need for regression calculations or other iterative solution algorithms.

[0043] Step S1: Data Preparation With fixed p i and T i Below, three sets of physical property data (density ρ, kinematic viscosity υ, and isothermal compressibility coefficient β) were collected and organized to obtain a set of known data for identification: D i ={ρ(p i ,T i ),υ(p i ,T i ), β(p i ,T i ), p i ,T i}, i=1,2,3, Group 1 is under normal temperature and pressure conditions, p1=p ref ,T1=T ref The following are the measured oil physical property data and test conditions: D1={ρ ref ,υ(p ref ,T ref ), β(p ref ,T ref ), p ref ,T ref}; Group 2 is at room temperature, T2=T ref Oil physical property data and test conditions obtained under high pressure: D2={υ(p2,T ref ), β(p2,T ref ), p2,T ref}; Group 3 is at normal pressure p3=p ref Oil physical property data obtained under low temperature conditions: D3={ρ(p ref ,T3),υ(p ref ,T3),β(p ref ,T3), p ref ,T3}.

[0044] The three different test conditions are ambient temperature and atmospheric pressure, ambient temperature and high pressure, and ambient pressure and extreme temperature. For the extreme temperature test condition, only one point needs to be selected within the range of [0℃, 40℃]. For the high pressure test condition, only one point needs to be selected within the range of [50bar, 100bar].

[0045] Step S2: Parameter Identification Step S21: Identify the isobaric thermal expansion coefficient γ: The density measurements at normal pressure and extreme temperatures are the data {ρ(p ref Substituting {,T3),T3}∈D3 into the property function formula for density ρ, we know {ρ ref ,T ref}∈D1, solve the linear equation in one variable to obtain γ: .

[0047] Step S22: Identify parameters C1, C2, and C3 Taking the natural logarithm of both sides of the formula (3) for the physical property function of kinematic viscosity υ, we get the following formula (4). (4) The kinematic viscosity measurement value under normal temperature and pressure, i.e., the data {υ(p ref ,T ref ),T ref}∈D1 and the kinematic viscosity measurements at normal pressure and extreme temperature, i.e., the data {υ(p ref Substituting T3 into formula (4), subtracting the two equations yields a univariate explicit equation, which can be solved to obtain C2:

[0048] The kinematic viscosity measurement value under normal temperature and pressure, i.e., the data {υ(p ref ,T ref ),T ref Substituting}∈D1 into formula (3), solving the univariate explicit equation yields C1:

[0049] The kinematic viscosity measurement value under normal temperature and high pressure conditions, i.e., the data {υ(p2,T refSubstituting p2}∈D2 into formula (4), we can solve the univariate explicit equation to obtain C3: .

[0051] Step S23: Identify parameters E1, E2, and E3 The measured value of the isothermal compressibility coefficient under normal temperature and pressure is the data {β(p ref ,T ref ),p ref ,T ref}∈D1 and the measured values ​​of the isothermal compressibility at constant pressure and extreme temperature, i.e., the data {β(p ref ,T3), p ref Substituting T3 into formula (1) and dividing the two equations, we get:

[0052] Solving the above equation yields E3:

[0053] E2 can also be obtained at the same time:

[0054] The measured isothermal compressibility coefficient under normal temperature and high pressure, i.e., the data {β(p2,T], will be used. ref ),p2,T ref Substituting}∈D2 into formula (1) yields:

[0055] From the above formula, we can obtain E1: .

[0057] Step S3: Physical property model calibration Based on the identified parameter set θ={E1,E2,E3,C1,C2,C3,γ}, a physical property model is established, namely formulas (1), (2), and (3). Substituting the test conditions of normal temperature and pressure, normal temperature and high pressure, and normal pressure and non-normal temperature, the calculated results of oil physical properties (ρ,ν,β) are tested to ensure consistency with the measured values.

[0058] A second aspect of the present invention provides a device for rapid identification of oil physical property model parameters, comprising: The first processing unit is used to determine the oil physical property data for hydraulic simulation under deep water and high pressure conditions, ignoring cavitation and air mixing effects, keeping the oil in a liquid phase state. The second processing unit is used to establish a set of formulas for identifying parameters of the oil property model based on the oil property data. The set of formulas for identifying parameters of the oil property model includes the property function formulas for the isothermal compressibility coefficient β, the property function formulas for density ρ, and the property function formulas for kinematic viscosity ν. The third processing unit is used to collect three sets of oil physical property data under fixed pressure and temperature, and organize them into a set of known data for identification. The fourth processing unit is used to substitute the density measurements at normal pressure and extreme temperatures in the known data set into the physical property function formula of density ρ, and solve the equation to obtain the isobaric thermal expansion coefficient γ. The fifth processing unit is used to substitute the kinematic viscosity measurements at normal temperature and pressure and at normal pressure and extreme temperature from the known data set into the formula obtained by taking the natural logarithm of both sides of the physical property function formula of kinematic viscosity ν. The two equations are subtracted to obtain a univariate explicit equation, and C2 is obtained by solving it. The sixth processing unit is used to substitute the kinematic viscosity measurements at room temperature and pressure from the known dataset into the physical property function formula of kinematic viscosity ν, and solve the univariate explicit equation to obtain C1; The seventh processing unit is used to substitute the kinematic viscosity measurement values ​​under normal temperature and high pressure conditions from the known data set into the formula obtained by taking the natural logarithm of both sides of the physical property function formula of kinematic viscosity ν, and solve the univariate explicit equation to obtain C3; The eighth processing unit is used to substitute the measured values ​​of the isothermal compressibility coefficient at normal temperature and pressure and the measured values ​​of the isothermal compressibility coefficient at normal pressure and extreme temperature in the known data set into the physical property function formula of the isothermal compressibility coefficient β, and then divide the two formulas to obtain E3 and E2. The ninth processing unit is used to substitute the measured values ​​of the isothermal compressibility coefficient under normal temperature and high pressure in the known data set into the physical property function formula of the isothermal compressibility coefficient β, and solve to obtain E1; The tenth processing unit is used to establish an oil property model based on the parameter set θ={E1,E2,E3,C1,C2,C3,γ} obtained by solving, and to test the calculated results of oil properties (ρ,ν,β) by substituting the test conditions of normal temperature and pressure, normal temperature and high pressure and normal pressure and non-normal temperature, to ensure that they are consistent with the actual measured values.

[0059] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the method for rapid identification of oil physical property model parameters described in any one of the above claims.

[0060] A fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for rapid identification of oil physical property model parameters described in any of the above-described methods.

[0061] This invention is described based on flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to specific embodiments. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the flowcharts and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0062] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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; and these 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 the present invention.

Claims

1. A method for rapid identification of oil physical property model parameters, characterized in that, include: Under deep-water and high-pressure conditions, cavitation and air mixing effects are ignored, and the oil is kept in the liquid phase. The oil physical property data for hydraulic simulation are determined. Based on oil property data, a set of formulas for identifying parameters of the oil property model is established. The set of formulas for identifying parameters of the oil property model includes the property function formulas for the isothermal compressibility coefficient β, the property function formulas for density ρ, and the property function formulas for kinematic viscosity ν. Under fixed pressure and temperature, several sets of oil physical property data were collected and organized to obtain a set of known data for identification. Substitute the density measurements at normal pressure and extreme temperatures from the known dataset into the physical property function formula for density ρ, and solve the equation to obtain the isobaric thermal expansion coefficient γ; Substitute the kinematic viscosity measurements at room temperature and pressure and at normal pressure and extreme temperature from the known data set into the formula obtained by taking the natural logarithm of both sides of the physical property function formula for kinematic viscosity ν. Subtract the two equations to obtain a univariate explicit equation, and solve for C2. Substitute the kinematic viscosity measurements at room temperature and pressure from the known dataset into the physical property function formula for kinematic viscosity ν, and solve the univariate explicit equation to obtain C1; By substituting the measured kinematic viscosity values ​​under normal temperature and high pressure conditions from the known data set into the formula obtained by taking the natural logarithm of both sides of the physical property function formula for kinematic viscosity ν, C3 can be obtained by solving the univariate explicit equation. Substitute the measured values ​​of the isothermal compressibility coefficient at normal temperature and pressure and the measured values ​​of the isothermal compressibility coefficient at normal pressure and extreme temperature from the known data set into the physical property function formula of the isothermal compressibility coefficient β, divide the two formulas, and obtain E3 and E2. Substitute the measured values ​​of the isothermal compressibility coefficient under normal temperature and high pressure from the known data set into the physical property function formula of the isothermal compressibility coefficient β, and solve to obtain E1; Based on the parameter set θ={E1,E2,E3,C1,C2,C3,γ} obtained from the solution, an oil property model is established, and the test conditions of normal temperature and pressure, normal temperature and high pressure, and normal pressure and non-normal temperature are substituted to test the calculated results of oil property (ρ,ν,β) to ensure consistency with the actual measured values.

2. The method for rapid identification of oil physical property model parameters according to claim 1, characterized in that, The formula for the isothermal compressibility coefficient β is as follows: Where E1, E2, and E3 are the parameters to be identified, and p and T are the pressure and temperature variables, respectively. ref Pressure at the reference point.

3. The method for rapid identification of oil physical property model parameters according to claim 2, characterized in that, The formula for the physical property function of density ρ is as follows: Where, ρ ref The reference point density is γ, which is the isobaric thermal expansion coefficient and also the parameter to be identified. T ref This is the reference point temperature.

4. The method for rapid identification of oil physical property model parameters according to claim 3, characterized in that, The formula for the kinematic viscosity ν is as follows: C1, C2, and C3 are the parameters to be identified.

5. The method for rapid identification of oil physical property model parameters according to claim 4, characterized in that, The known data set specifically includes: With fixed p i and T i Below, three sets of physical property data (density ρ, kinematic viscosity υ, and isothermal compressibility coefficient β) were collected and organized to obtain a set of known data for identification: D i ={ρ(p i ,T i ),υ(p i ,T i ),β(p i ,T i ),p i ,T i },i=1,2,3, Group 1 is under normal temperature and pressure conditions, p1=p ref ,T1=T ref The following are the measured oil physical property data and test conditions: D1={ρ ref ,υ(p ref ,T ref ), β(p ref ,T ref ),p ref ,T ref }; Group 2 is at room temperature, T2=T ref Oil physical property data and test conditions obtained under high pressure: D2={υ(p2,T ref ),β(p2,T ref ),p2,T ref }; Group 3 is at normal pressure p3=p ref Oil physical property data obtained under low temperature conditions: D3={ρ(p ref ,T3),υ(p ref ,T3), β(p ref ,T3),p ref ,T3}。 6. The method for rapid identification of oil physical property model parameters according to claim 5, characterized in that, The calculation process for the isobaric thermal expansion coefficient γ is as follows: The density measurements at normal pressure and extreme temperatures are the data {ρ(p ref Substituting {,T3),T3}∈D3 into the property function formula for density ρ, we know {ρ ref ,T ref }∈D1, solve the linear equation in one variable to obtain γ: 。 7. The method for rapid identification of oil physical property model parameters according to claim 5, characterized in that, The specific process for solving parameters C1, C2, and C3 is as follows: 。 8. The method for rapid identification of oil physical property model parameters according to claim 5, characterized in that, The specific process for solving parameters E1, E2, and E3 is as follows: 。 9. A device for rapid identification of oil physical property model parameters, characterized in that, include: The first processing unit is used to determine the oil physical property data for hydraulic simulation under deep water and high pressure conditions, ignoring cavitation and air mixing effects, keeping the oil in a liquid phase state. The second processing unit is used to establish a set of formulas for identifying parameters of the oil property model based on the oil property data. The set of formulas for identifying parameters of the oil property model includes the property function formulas for the isothermal compressibility coefficient β, the property function formulas for density ρ, and the property function formulas for kinematic viscosity ν. The third processing unit is used to collect three sets of oil physical property data under fixed pressure and temperature, and organize them into a set of known data for identification. The fourth processing unit is used to substitute the density measurements at normal pressure and extreme temperatures in the known data set into the physical property function formula of density ρ, and solve the equation to obtain the isobaric thermal expansion coefficient γ. The fifth processing unit is used to substitute the kinematic viscosity measurements at normal temperature and pressure and at normal pressure and extreme temperature from the known data set into the formula obtained by taking the natural logarithm of both sides of the physical property function formula of kinematic viscosity ν. The two equations are subtracted to obtain a univariate explicit equation, and C2 is obtained by solving it. The sixth processing unit is used to substitute the kinematic viscosity measurements at room temperature and pressure from the known dataset into the physical property function formula of kinematic viscosity ν, and solve the univariate explicit equation to obtain C1; The seventh processing unit is used to substitute the kinematic viscosity measurement values ​​under normal temperature and high pressure conditions from the known data set into the formula obtained by taking the natural logarithm of both sides of the physical property function formula of kinematic viscosity ν, and solve the univariate explicit equation to obtain C3; The eighth processing unit is used to substitute the measured values ​​of the isothermal compressibility coefficient at normal temperature and pressure and the measured values ​​of the isothermal compressibility coefficient at normal pressure and extreme temperature in the known data set into the physical property function formula of the isothermal compressibility coefficient β, and then divide the two formulas to obtain E3 and E2. The ninth processing unit is used to substitute the measured values ​​of the isothermal compressibility coefficient under normal temperature and high pressure in the known data set into the physical property function formula of the isothermal compressibility coefficient β, and solve to obtain E1; The tenth processing unit is used to establish an oil property model based on the parameter set θ={E1,E2,E3,C1,C2,C3,γ} obtained by solving, and to test the calculated results of oil properties (ρ,ν,β) by substituting the test conditions of normal temperature and pressure, normal temperature and high pressure and normal pressure and non-normal temperature, to ensure that they are consistent with the actual measured values.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for rapid identification of oil physical property model parameters as described in any one of claims 1-8.

11. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for rapid identification of oil physical property model parameters as described in any one of claims 1-8.

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