A method for establishing a deep high-temperature high-pressure natural gas deviation factor prediction model

Abstract

This invention discloses a method for establishing a prediction model for deviation factors in deep, high-temperature, and high-pressure natural gas, relating to the field of oil and gas field development technology. Based on experimental data of the Z-factor from actual gas reservoirs, this invention calculates the deviation factor Z under set formation temperature and different pressure conditions using existing methods. By analyzing and comparing the fitting and error results of various methods, a suitable quadratic parabolic mathematical model with critical pressure and critical temperature as independent variables is selected. Through appropriate experimental fitting steps, a prediction model for the Z-factor of specific gas reservoirs is obtained. This invention has undergone extensive experimental simulation and field application, demonstrating high feasibility and accuracy. It effectively solves the high error rate of current methods for calculating deviation factors in deep and ultra-deep gas reservoirs, enabling optimized calculation of deviation factors in ultra-deep, high-temperature, and high-pressure gas wells in the Shuangyushi Qixia Formation in northern Sichuan, providing important theoretical basis for field oil and gas field development.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, and more specifically to a method for establishing a prediction model for deviation factors of deep, high-temperature and high-pressure natural gas. Background Technology

[0002] With the accelerated pace of oil and gas exploration, the development of deep and ultra-deep gas reservoirs has become one of the most important areas of gas reservoir development. Due to the characteristics of these gas reservoirs, such as deep burial and high temperature, the accuracy of their gas deviation factor calculation directly affects the accuracy of gas reservoir reserve calculation and the reliability of dynamic analysis results.

[0003] In the calculation of deviation factors for high-temperature and high-pressure gas reservoirs, previous researchers have proposed various methods based on conventional charts and empirical formulas. These mainly include the HY (Hall-Yarbough), DPR (Dranchuk-Purvis-Obinson), and DAK (Dranchuk-Abou-Kassem) methods. However, since these methods are based on state-based methods grounded in phase theory, the calculated results often fall short of the required accuracy for the Z-factor in the early stages of high-temperature and high-pressure gas reservoirs. This results in dynamic reserves calculations that are 1 to 1.5 times higher than actual values, making rapid improvement and adaptation difficult. Accurate calculation of early reserves and production capacity in actual high-temperature and high-pressure mines like Shuangyushi is crucial, especially for high-pressure stages where Z-factor prediction accuracy is critical for early reserve calculations. Summary of the Invention

[0004] To overcome the defects and shortcomings of existing technologies, this invention provides a method for establishing a prediction model for deviation factors in deep, high-temperature, and high-pressure natural gas. The purpose of this invention is to address the problem that existing deviation factor calculation methods cannot be quickly improved to meet the accuracy requirements of the Z-factor in the early stages of high-temperature and high-pressure gas reservoirs. Based on actual gas reservoir Z-factor experimental data, this invention calculates the deviation factor Z under set formation temperature and different pressure conditions using various commonly used methods. By analyzing and comparing the fitting results of various methods and the aforementioned error results, a suitable quadratic parabolic mathematical model with critical pressure and critical temperature as independent variables is selected. Through appropriate experimental fitting steps, a prediction model for the Z-factor of specific gas reservoirs is obtained. This invention has been experimentally simulated and applied in the field in Sichuan's ultra-deep natural gas production area, demonstrating high feasibility and accuracy. It effectively solves the high error rate of current deviation factor calculation methods for deep and ultra-deep gas reservoirs, enabling optimized calculation of deviation factors for ultra-deep, high-temperature, and high-pressure gas wells in the Shuangyushi Qixia Formation in northern Sichuan, providing important theoretical basis for field oil and gas field development.

[0005] To address the problems existing in the prior art, the present invention is achieved through the following technical solution:

[0006] A method for establishing a prediction model for deviation factors of deep, high-temperature, and high-pressure natural gas includes the following steps:

[0007] Step A: Based on experimental data of the Z-factor of a certain gas reservoir, and according to various commonly used methods for calculating the Z-factor, calculate the deviation factor under different pressure conditions under a set formation temperature.

[0008] Step B: Analyze and compare the calculation results of Step A. Based on the evaluation of the calculation accuracy of the deviation factor Z obtained in Step A, use the curve fitting function in Excel to fit the experimental data of the measured gas reservoir Z factor in Step A and establish the corresponding mathematical model. Select a suitable quadratic parabolic mathematical model with critical pressure and critical temperature as independent variables, and obtain the initial value prediction model through appropriate experimental fitting steps.

[0009] Step C: Remove the first-order term from the initial value prediction model obtained in Step B, add the second-order term and the constant term together and take the square root to obtain a new formula for calculating the Z factor. Use this formula to calculate the deviation coefficient and determine whether the trend between the calculated value and the measured Z value is similar. If they are similar, use the error value to establish the first-order term.

[0010] Step D: Subtract the calculated value of the Z factor from the center calculated in Step C from the measured Z value to obtain the error value. Perform regression fitting on the error value curve and use the obtained regression equation as the first-order term function of the initial value prediction model in Step B to obtain the natural gas deviation factor prediction model.

[0011] Furthermore, step E involves using the natural gas deviation factor prediction model obtained in step D to calculate the deviation coefficient values ​​at the same pressure from 137℃ to 177℃, and then obtaining the error between the calculated deviation coefficient values ​​and the measured values ​​at that temperature. The error values ​​at at least a few different temperatures under the same pressure are then arranged to obtain a function of temperature and pressure as two independent variables. Finally, surface fitting or multi-parameter fitting is used to establish a new function expression for the error curve. This new function expression for the error curve is then integrated into the natural gas deviation factor prediction model obtained in step D to obtain a deviation factor prediction model for deep, high-temperature, and high-pressure natural gas.

[0012] Furthermore, in step A, the deviation factors under different pressure conditions are calculated based on the HY model, DPR model, and DAK model under a set formation temperature.

[0013] Furthermore, the set formation temperature is 157°C.

[0014] In step B, the corresponding mathematical model is established as follows: In the formula, Z represents the deviation factor, p pr denoted by pressure; a represents the coefficient of the quadratic term, b represents the coefficient of the linear term, and c represents the coefficient of the constant term;

[0015] According to the Katz chart, the Z-factor for natural gas is the relative pressure p. pr and temperature T pr If the coefficients a, b, and c in equation (1) are also relative to the pressure p, then the coefficients a, b, and c are also relative to the pressure p. pr and temperature T pr If the function is , then a, b, and c can be expressed as:

[0016]

[0017]

[0018]

[0019] Substituting a, b, and c into equation (1), we obtain the initial value prediction model as follows:

[0020]

[0021] In step D, the error value curve is fitted by regression, and the resulting regression equation is as follows:

[0022]

[0023] Substituting the obtained regression equation into equation (1), we obtain the natural gas deviation factor prediction model, as shown in the following expression:

[0024]

[0025] In step E, the deviation coefficients at the same pressure for 137℃, 147℃, 157℃, 167℃ and 177℃ are calculated using equation (2), and the error between the calculated value and the measured Z value at the same temperature is obtained. The error values ​​of the five different temperatures under the same pressure are arranged accordingly to obtain a value related to T. pr and p pr The function of two independent variables is finally fitted with a surface or multi-parameter fitting to establish a new error curve function, as shown in the following equation:

[0026]

[0027] Integrating equation (3) into equation (2), we obtain the prediction model for the deviation factor of deep high-temperature and high-pressure natural gas, as shown below:

[0028]

[0029] Compared with the prior art, the beneficial technical effects of the present invention are as follows:

[0030] The method and steps for establishing an accurate prediction model of the Z-factor for special high-temperature and high-pressure gas reservoirs based on experiments provided by this invention have been tested and applied in a large number of experimental simulations and field practice in ultra-deep natural gas production in Sichuan. It has high feasibility and accuracy, and effectively solves the problem of high error in the current methods for calculating deviation factors in deep and ultra-deep gas reservoirs. It can realize the optimized calculation of deviation factors for ultra-deep high-temperature and high-pressure gas wells in the Shuangyushi Qixia Formation in northern Sichuan, and provides important theoretical basis for the development of oil and gas fields in the field. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0032] Figure 1 This is a comparison chart of the calculation results of the initial value prediction model of this invention and the experimental Z factor;

[0033] Figure 2 Error curves provided for embodiments of the present invention;

[0034] Figure 3 A comparison chart of calculation results and measured data of the prediction model for deviation factors of deep high-temperature and high-pressure natural gas at 137℃ provided in the embodiments of the present invention;

[0035] Figure 4 A comparison chart of calculation results and measured data of the prediction model for deviation factors of deep high-temperature and high-pressure natural gas at 147℃ provided in the embodiments of the present invention;

[0036] Figure 5 A comparison chart of calculation results and measured data of the prediction model for deviation factors of deep high-temperature and high-pressure natural gas at 157℃ provided in the embodiments of the present invention;

[0037] Figure 6 A comparison chart of calculation results and measured data of the prediction model for deviation factors of deep high-temperature and high-pressure natural gas at 167℃ provided in the embodiments of the present invention;

[0038] Figure 7 A comparison chart of the calculation results and measured data of the prediction model for deviation factors of deep high-temperature and high-pressure natural gas at 177℃ provided in the embodiments of the present invention. Detailed Implementation

[0039] The present invention will be further described and illustrated in detail below with reference to the accompanying drawings and embodiments. To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0040] Example 1

[0041] As a preferred embodiment of the present invention, this embodiment provides a method for establishing a prediction model for deviation factors of deep high-temperature and high-pressure natural gas, including the following steps:

[0042] Step A: Based on experimental data of the Z-factor of a certain gas reservoir, such as the Shuangyushi gas reservoir in Sichuan, which is 8000 meters deep, in this embodiment as the research object; according to the existing commonly used methods for calculating the Z-factor, the deviation factor under different pressure conditions under the set formation temperature is calculated.

[0043] Step B: Analyze and compare the calculation results of Step A. Based on the evaluation of the calculation accuracy of the deviation factor Z obtained in Step A, use the curve fitting function in Excel to fit the experimental data of the measured gas reservoir Z factor in Step A and establish the corresponding mathematical model. Select a suitable quadratic parabolic mathematical model with critical pressure and critical temperature as independent variables, and obtain the initial value prediction model through appropriate experimental fitting steps.

[0044] Step C: Remove the first-order term from the initial value prediction model obtained in Step B, add the second-order term and the constant term together and take the square root to obtain a new formula for calculating the Z factor. Use this formula to calculate the deviation coefficient and determine whether the trend between the calculated value and the measured Z value is similar. If they are similar, use the error value to establish the first-order term.

[0045] Step D: Subtract the calculated value of the Z factor from the center calculated in Step C from the measured Z value to obtain the error value. Perform regression fitting on the error value curve and use the obtained regression equation as the first-order term function of the initial value prediction model in Step B to obtain the natural gas deviation factor prediction model.

[0046] Furthermore, to investigate the combined effects of temperature and pressure, the following experiments were conducted: Step E involved using the natural gas deviation factor prediction model obtained in Step D to calculate deviation coefficient values ​​at the same pressure from 137℃ to 177℃, and then determining the error between the calculated deviation coefficient values ​​and the measured values ​​at that temperature. Error values ​​at at least several different temperatures under the same pressure were then arranged to obtain a function of temperature and pressure as independent variables. Finally, surface fitting or multi-parameter fitting was used to establish a new function expression for the error curve. This new function expression for the error curve was then integrated into the natural gas deviation factor prediction model obtained in Step D to obtain a deviation factor prediction model for deep, high-temperature, and high-pressure natural gas.

[0047] Example 2

[0048] As another preferred embodiment of the present invention, please refer to the appendix to the specification. Figure 1 and Figure 2 This embodiment discloses:

[0049] A method for establishing a prediction model for deviation factors of deep, high-temperature, and high-pressure natural gas includes the following steps:

[0050] Step A: Based on experimental data of the Z factor of a specific gas reservoir, such as the Shuangyushi gas reservoir in Sichuan, which is 8000 meters deep, this application uses three commonly used methods (HY model, DPR model, and DAK model) to calculate the deviation factor under different pressure conditions at formation temperature (157℃).

[0051] Step B: Analyze and compare the results of Step A. Based on evaluating the accuracy of the deviation factor calculations obtained by the three calculation methods in Step A under different pressure conditions at formation temperature (157℃), firstly, use the curve fitting function in Excel to fit the experimental data of the Z-factor of the 8000-meter-deep Shuangyushi gas reservoir in Sichuan to establish a corresponding mathematical model. The steps of this modeling method are similar. If we consider that the Z-factor curve resembles a parabolic mathematical model, let's assume:

[0052]

[0053] According to the Katz chart, the Z-factor for natural gas is the relative pressure p. pr and T prIf the function is a function of the two parameters, then the parameters a, b, and c in equation (1) should also be functions of the two parameters. In fact, when performing the initial fitting, the parameters a, b, and c have many possible forms. For example, when performing the optimization fitting, there are at least 30 forms to choose from. To avoid a large number of trial calculations, we can set it as the mathematical model form of DAR, where the coefficients of equation (1) can be expressed as:

[0054]

[0055]

[0056]

[0057] This calculation result can be obtained from Figure 1 It can be seen that the initial values ​​of the model building method are similar.

[0058] Step C: Subtract the sum of the linear term and the constant term in equation (1) (Z-(b+c)) from the experimentally measured Z value. The difference obtained is the error caused by the quadratic term. Perform regression fitting on the error curve. Considering that the quadratic term is mainly caused by the change in pressure, select the form related to the comparative pressure to obtain the following regression equation:

[0059]

[0060] Taking this regression equation as the quadratic term in equation (1), we get:

[0061]

[0062] The deviation coefficient Z was recalculated using equation (3). There was still an error between the calculated value and the measured value. The prediction results showed that the result was worse.

[0063] D. Due to the fact that at any comparison temperature (T) pr Under these conditions, the Z-factor changes parabolicly with formation pressure; therefore, we consider trying a second quadratic regression equation:

[0064]

[0065]

[0066] Calculations and comparisons revealed significant errors, indicating problems with both forms. After multiple comparisons and verifications using Z-factor data from other high-temperature, high-pressure gas reservoirs, it was determined that the power-law form for the quadratic term is the most reasonable, as the linear term introduces even greater errors.

[0067] Step E: Remove the first term in equation (1), add the second term and the constant term together and take the square root to obtain a new formula for calculating the Z factor. Calculate the Z factor using the new formula and compare it with the experimental value. The calculated value and the experimental value show that the trend is roughly similar. Comparing the error shows that it still does not have good adaptability.

[0068] Step F: Further improve the model by subtracting the calculated value from step E from the experimentally measured Z value to obtain the error between the two equations. Perform regression fitting on this error curve, select the linear term form with good fitting accuracy, and obtain the regression equation as follows:

[0069]

[0070] Through steps A to F above, the obtained bp pr Substituting into equation (1), we obtain the prediction expression for the deviation coefficient Z for a specific gas reservoir:

[0071]

[0072] The Z value calculated by equation (7) fits the measured value of Shuangyushi very well, indicating that equation (7) is very suitable for predicting the deviation coefficient of natural gas in the Qixia Formation area at a temperature of 137℃.

[0073] Step G: In order to study the effect of the combined effects of temperature and pressure, the following experiment was conducted: The deviation coefficient at 147℃ was calculated using equation (7), and then compared with the measured value. When the temperature increases, the calculated value is relatively lower than the measured value, indicating that the last term in equation (7) only considers temperature correction. The calculation formula still has problems and should consider the combined effect of the comparative temperature and comparative pressure. The following steps were adopted: First, the deviation coefficient value at the same pressure from 137℃ to 177℃ (interval of 10℃) was calculated using equation (7), and then the error between the calculated value and the measured value at that temperature was obtained. Then, the error values ​​of five different temperatures under the same pressure were arranged to obtain a function of the two independent variables Tpr and Ppr. Finally, a new function expression (8) of the error curve was established by surface fitting or multi-parameter fitting.

[0074]

[0075] In summary, the Z-accuracy prediction method for the characteristics of Shuangyushi natural gas is expressed as follows:

[0076]

[0077] Example 3

[0078] As another preferred embodiment of the present invention, please refer to the appendix to the specification. Figure 2-7As shown, this embodiment discloses:

[0079] A method for establishing a prediction model for deviation factors of deep, high-temperature, and high-pressure natural gas includes the following steps:

[0080] Step A: Taking Shuangyu 001-1 well as the research object, the deviation factor Z is calculated under different pressure conditions at formation temperature (157℃) based on three commonly used methods (HY model, DPR model, DAK model).

[0081] Step B involves analyzing and comparing the fitting results of various methods with the error results of Step A. It is believed that by selecting a suitable quadratic parabolic mathematical model with critical pressure (ppr) and critical temperature (Tpr) as independent variables and through appropriate experimental fitting steps, a high-precision prediction model for the Z-factor of a specific gas reservoir can be obtained.

[0082] Step C: Subtract the sum of the linear term and constant term obtained in step B from the experimentally measured Z value. The difference is the prediction result corresponding to the quadratic term. Perform regression fitting on the difference curve to obtain multiple regression equations. Substitute them into different quadratic term regression equations to recalculate the deviation coefficient. There is still an error between the calculated value and the measured value. Evaluate the fitness of the quadratic term and select a suitable quadratic term.

[0083] Step D: Remove the first-order term from the initial value model, add the second-order term and the constant term together and take the square root to obtain a new formula for calculating the Z factor. Investigate whether the trend between the calculated value and the experimental value is roughly similar. If they are similar, then use the error value to establish the first-order term.

[0084] Step E: Predict the Z-factor of the model obtained in step D and calculate the difference between the Z-value and the experimentally measured Z-value. Use this difference for regression fitting and use the obtained regression equation as the first-order term function of the quadratic equation to obtain an accurate mathematical model for predicting the Z-factor of high-temperature and high-pressure natural gas at a specific temperature. The model established using Shuangyushi as an example shows that it is very suitable for predicting the deviation coefficient of natural gas in the Qixia Formation area.

[0085] Step F: To study the effect of temperature, the Z-factor prediction mathematical model obtained in Step E is used to calculate the deviation coefficients at different temperatures. These coefficients are then compared with measured values ​​to further improve the function form of the constant term. The constant term should be the combined effect of Ppr and Tpr. In practice, the deviation coefficient values ​​at the same pressure from 137℃ to 177℃ (10℃ intervals) are first calculated, and then the error between the calculated value and the measured value at that temperature is obtained. The error values ​​at five different temperatures under the same pressure are arranged accordingly to obtain a table function of the two independent variables, Tpr and Ppr. Fitting this table function yields the expression form of the constant term.

[0086] In the embodiments provided by the present invention, in steps A to F above, it is required that the error between the prediction result and the measured value of the mathematical model for calculating the Z factor for a specific gas reservoir established through these steps is smaller than the prediction error of the three commonly used methods: HY model, DPR model and DAK model.

[0087] In the embodiments provided by the present invention, in step B above, a suitable binomial initial value function form is selected during the comparison prediction. For example, the initial value function in the method of the present invention is selected in the following form.

[0088]

[0089] In the embodiments provided by the present invention, in step C above, a suitable form of quadratic term is studied, evaluated and selected. The most reasonable form of quadratic term is that the coefficient function of the quadratic term is a power function of temperature, which at the same time reflects the main structure of the Z-factor mathematical model under the influence of temperature on pressure.

[0090] In the embodiments provided by the present invention, in steps D to E above, the improvement of the linear term of the model is achieved by subtracting the calculated value of the model obtained in step C from the experimentally measured Z value, obtaining the error between the two equations, and performing regression fitting on this error curve to establish the basic structure of the linear term, equation (2).

[0091]

[0092] Therefore, we can obtain Equation 3, and thus obtain... Figure 3 Good forecast results;

[0093]

[0094] Since Equation (3) is based on experimental test values ​​at a constant temperature and is established through the method of the invention in steps A to E above, the Z value calculated by Equation (3) has a very high degree of fit with the measured value. It is very suitable for the accurate prediction of the deviation coefficient of natural gas in the Qixia Formation area, and the accuracy reaches the prediction level of commonly used HY model, DPR model and DAK model.

[0095] In step F of the method provided in this example, in order to make the constant term of the quadratic function also reflect the influence of the comparative temperature and comparative pressure, the deviation coefficient value at the same pressure from 137℃ to 177℃ (interval of 10℃) is first calculated, and then the error between the calculated value and the measured value at that temperature is obtained. The error values ​​of five different temperatures at the same pressure are arranged accordingly to obtain a table function of the two independent variables Tpr and Ppr. This table function is fitted to obtain the function expression of the error curve (4):

[0096]

[0097] Therefore, in this example, by adopting the steps of the invention described above, the new Z-factor high-precision prediction model (5) of the present invention for the Shuangyushi deep high temperature and high pressure gas reservoir (over 8000 meters) is implemented in this specific case, and the prediction accuracy is much higher than that of the three commonly used models.

[0098]

[0099] Implementation example:

[0100] Taking the Shuangyu 001-1 well in the Qixia Formation of Shuangyushi in northern Sichuan as an example, the new Z-factor prediction model (5) obtained by the new model establishment method for natural gas deviation factor provided by this invention was compared. The results show that the prediction accuracy of the Shuangyushi natural gas deviation factor calculated by this model is higher than that of the HY model, DPR model and DAK model, especially in the early pressure drop stage, as shown in Table 1 and 2. Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 As shown.

[0101] Table 1 compares the calculated results with the actual results to show the error.

[0102]

[0103]

[0104] This invention provides a novel method for establishing a high-precision prediction algorithm for deviation factors. After extensive experimental simulation and field application, it has demonstrated high feasibility and accuracy. It effectively solves the problem of high error rates in current deviation factor calculation methods for deep and ultra-deep gas reservoirs. This method can optimize the calculation of deviation factors for ultra-deep, high-temperature, and high-pressure gas wells in the Shuangyushi Qixia Formation in northern Sichuan, providing important theoretical support for oil and gas field development.

[0105] The above description is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for establishing a prediction model for deviation factors of deep, high-temperature, and high-pressure natural gas, characterized in that: Includes the following steps: Step A: Based on the experimental data of the Z factor of a certain gas reservoir, calculate the deviation factor under different pressure conditions under a set formation temperature. Step B: Analyze and compare the calculation results of Step A. Based on the evaluation of the calculation accuracy of the deviation factor obtained in Step A, use the curve fitting function in Excel to fit the experimental data of the measured gas reservoir deviation factor in Step A and establish the corresponding mathematical model. Select a quadratic parabolic mathematical model with critical pressure and critical temperature as independent variables, and obtain the initial value prediction model through the experimental fitting step. Step C: Remove the first-order term from the initial value prediction model obtained in Step B, add the quadratic term and the constant term together and take the square root to obtain a new formula for calculating the deviation factor. Use this formula to calculate the deviation coefficient and determine whether the trend between the calculated value and the measured deviation factor value is similar. If they are similar, then use the error value to establish the first-order term. Step D: Subtract the calculated value obtained from the calculation formula of the deviation factor in step C from the measured deviation factor value to obtain the error value. Perform regression fitting on this error value curve and use the obtained regression equation as the linear term function of the initial value prediction model in step B to obtain the natural gas deviation factor prediction model. Step E: Using the natural gas deviation factor prediction model obtained in Step D, calculate the deviation coefficient values ​​at the same pressure from 137℃ to 177℃, and then obtain the error between the calculated deviation coefficient values ​​and the measured values ​​at that temperature; arrange the error values ​​of at least several different temperatures at the same pressure to obtain a function with two independent variables, temperature and pressure; finally, use surface fitting or multi-parameter fitting to establish a new error curve function expression; integrate the established new error curve function expression into the natural gas deviation factor prediction model obtained in Step D to obtain a deviation factor prediction model for deep, high-temperature, and high-pressure natural gas.

2. The method for establishing a prediction model for deviation factors of deep, high-temperature, and high-pressure natural gas as described in claim 1, characterized in that: In step A, the deviation factors under different pressure conditions are calculated based on the HY model, DPR model and DAK model under the given formation temperature.

3. A method for establishing a prediction model for deviation factors of deep, high-temperature, and high-pressure natural gas as described in claim 1 or 2, characterized in that: The set formation temperature is 157°C.

4. A method for establishing a prediction model for deviation factors of deep, high-temperature, and high-pressure natural gas as described in claim 1 or 2, characterized in that: In step B, the corresponding mathematical model is established as follows: （1）； In the formula, Z represents the deviation factor. Indicates pressure, denoted by , b represents the coefficient of the quadratic term, c represents the coefficient of the linear term, and c represents the coefficient of the constant term.

5. The method for establishing a prediction model for deviation factors of deep, high-temperature, and high-pressure natural gas as described in claim 4, characterized in that: According to the Katz chart, the natural gas deviation factor is the relative pressure. and temperature The function of equation (1) is then... The coefficients b and c are also relative pressures. and temperature The function; then b and c can be represented as: ； ； ； The above Substituting b and c into equation (1), we obtain the initial value prediction model as follows: 。 6. The method for establishing a prediction model for deviation factors of deep, high-temperature, and high-pressure natural gas as described in claim 5, characterized in that: In step D, the error value curve is subjected to regression fitting to obtain the regression equation. The process is as follows: 。 7. The method for establishing a prediction model for deviation factors of deep, high-temperature, and high-pressure natural gas as described in claim 6, characterized in that: Substituting the obtained regression equation into equation (1), we obtain the natural gas deviation factor prediction model, as shown in the following expression: （2）。 8. The method for establishing a prediction model for deviation factors of deep, high-temperature, and high-pressure natural gas as described in claim 7, characterized in that: In step E, the deviation coefficients at the same pressure for 137℃, 147℃, 157℃, 167℃ and 177℃ are calculated using equation (2), and then the results are compared with those at the same temperature. The error between the measured deviation factors.

9. The method for establishing a prediction model for deviation factors of deep, high-temperature, and high-pressure natural gas as described in claim 8, characterized in that: Arranging the error values ​​at five different temperatures under the same pressure will yield a result regarding... and The function of two independent variables is finally fitted with a surface or multi-parameter fitting to establish a new error curve function, as shown in the following equation: （3）； Integrating equation (3) into equation (2), we obtain the prediction model for deviation factors of deep, high-temperature and high-pressure natural gas, as shown in the following equation: （4）。

Images