A method for inverse calculation of condensate gas reservoir fluid composition

Abstract

The present invention discloses a method for inverse calculation of the fluid composition of a condensate gas reservoir, comprising: obtaining the phase state data of a fluid sample; obtaining an equation of state and parameters based on the phase state data; establishing a numerical simulation model of the component numbers of the condensate gas reservoir; and optimizing the numerical simulation model of the component numbers of the condensate gas reservoir based on the equation of state, parameters and actual production data to obtain the original components of the fluid sample. The method for inverse calculation of the fluid composition of the condensate gas reservoir according to the present invention obtains the phase state data of the fluid sample, establishes the equation of state, parameters and the numerical simulation model of the component numbers of the condensate gas reservoir, and through optimizing and fitting the numerical simulation of the component numbers, inversely calculates the fluid composition and parameters of the condensate gas reservoir under the original conditions, providing basic fluid data for numerical simulation and the development of the condensate gas reservoir.

Description

Technical Field

[0001] The present invention belongs to the field of reservoir engineering used in oil and gas field development, and particularly relates to a method for inversely calculating the fluid composition of a condensate gas reservoir. Background Art

[0002] The determination of the original fluid composition of a condensate gas reservoir is the basis for the study of phase behavior characteristics, numerical simulation calculations, etc. However, during the development process of a condensate gas reservoir, phase changes occur, and the composition of the well stream also changes continuously. Limited by actual situations (such as missing the sampling opportunity, etc.), the fluid samples obtained in production may not be able to represent the original fluid composition.

[0003] Under closed boundary conditions, there is a lack of fluid replenishment. After condensate oil precipitates in the formation, due to the difference in the seepage capacities of the oil and gas phases, the oil phase (heavy components) remains in the formation pores, resulting in the continuous lightening of the fluid composition in the well stream. Therefore, the compositions of the condensate gas samples obtained at different times change continuously and are difficult to represent the fluid characteristics under the original conditions of the condensate gas reservoir. There is very little research on how to inversely calculate the original fluid composition of a condensate gas reservoir through the fluid samples obtained.

[0004] Guo Ping et al. gave the general principle of phase state restoration of condensate gas reservoirs in "Research on the Theory of Phase State Restoration of Condensate Gas Reservoirs" in 2001. They studied the phase state restoration directly from the well stream produced when the formation pressure drops to different degrees and the phase state characteristics of the original fluid obtained by sampling the separator oil and separator gas at the current separator according to the original production gas-oil ratio after separating this well stream once, and also sampled according to the formation pressure equal to the dew point pressure, and compared the influence of the two different restoration methods on the representativeness of the original fluid samples.

[0005] Zhang Jialiang et al. summarized the current methods for restoring fluid samples of condensate gas reservoirs in "Method for Restoring Fluid Samples of Condensate Gas Reservoirs and Its Application" in 2005, and discussed the applicability of 3 sample restoration methods in combination with the phase state characteristics of condensate gas reservoir fluids.

[0006] Zhao Xiaoliang et al. proposed a direct phase state restoration method in "A Method for Phase State Restoration of Condensate Gas Reservoirs" published in 2009. However, during the application process, the original formation pressure cannot drop too much, otherwise it will affect the reliability of the restored fluid.

[0007] Generally speaking, the following three methods are usually adopted:

[0008] (1) Sampling according to the dew point equal to the original formation pressure;

[0009] (2) Sampling according to the original production gas-oil ratio and the molar compositions of the oil and gas in the first-stage separator under the current pressure;

[0010] (3) Simulate the process of pressure decline during depletion, find out the variation law of components with pressure, and then extrapolate back to the original dew point pressure condition to obtain the composition of the fluid at the original dew point pressure.

[0011] In actual production, factors such as the change of the operating regime of condensate gas wells and the production time will affect the sample composition. However, the current three methods do not comprehensively consider the actual production history data of condensate gas wells, resulting in a deviation between the restored fluid composition and the original conditions.

[0012] For the above reasons, there is an urgent need to establish a method that can consider the physical properties of condensate gas reservoirs and the actual production history data, and use the fluid samples obtained at different stages to inversely calculate the fluid composition under the original conditions. Summary of the Invention

[0013] The object of the present invention is to propose an inversion calculation method for the fluid composition of a condensate gas reservoir that can consider the physical properties of the condensate gas reservoir and the actual production history data, and use the fluid samples obtained at different stages to inversely calculate the fluid composition under the original conditions.

[0014] To achieve the above object, the present invention provides an inversion calculation method for the fluid composition of a condensate gas reservoir, including: obtaining the phase state data of the fluid sample; based on the phase state data, obtaining the equation of state and parameters; establishing a numerical simulation model of the component of the condensate gas reservoir; and optimizing the numerical simulation model of the component of the condensate gas reservoir based on the equation of state and parameters and the actual production data to obtain the original components of the fluid sample.

[0015] Optionally, the step of optimizing the numerical simulation model of the component of the condensate gas reservoir based on the equation of state and parameters and the actual production data to obtain the original components of the fluid sample includes: filling the equation of state and parameters into the numerical simulation model of the component, and using the genetic algorithm to optimize the numerical simulation model of the component of the condensate gas reservoir to obtain the original components of the fluid sample.

[0016] Optionally, set the parameters of the genetic algorithm, and use the selection method, replication method, crossover method or mutation method in the genetic algorithm to obtain the first optimization index and the second optimization index of fitness evaluation.

[0017] Optionally, when the first optimization index of fitness evaluation is greater than the first preset threshold or the second optimization index is greater than the second preset threshold, reset the parameters of the genetic algorithm.

[0018] Optionally, when the first optimization index of fitness evaluation is less than the first preset threshold and the second optimization index is less than the second preset threshold, use the set parameters of the genetic algorithm as the original components of the fluid sample.

[0019] Optionally, the first optimization index and the second optimization index of the fitness evaluation are obtained through the following formula

[0020]

[0021]

[0022] where Fitness1 is the first optimization index of the fitness evaluation, N c is the number of component groups, is the molar percentage of the actual components, is the molar percentage of the simulated components, Fitness2 is the second optimization index of the fitness evaluation, is the actual cumulative oil production, is the simulated cumulative oil production.

[0023] Optionally, an indoor PVT physical simulation experiment is performed on the fluid sample to obtain the phase state data of the fluid sample.

[0024] Optionally, based on the phase state data, obtaining the equation of state and parameters includes: establishing an equation of state using phase state simulation software, and setting the parameters of the equation of state so that the phase state data of the equation of state is the same as the phase state data of the fluid sample.

[0025] Optionally, using numerical simulation software, a component numerical simulation model of the condensate gas reservoir is established by using the geological data of the condensate gas reservoir and the actual production data.

[0026] Optionally, the phase state data includes composition and bubble and dew points.

[0027] The beneficial effects of the present invention are as follows: The method for inverse calculation of the fluid composition of the condensate gas reservoir of the present invention obtains the phase state data of the fluid sample, establishes the equation of state and parameters and the component numerical simulation model of the condensate gas reservoir, and through optimizing and fitting the component numerical simulation, inversely calculates the fluid composition and parameters of the condensate gas reservoir under the original conditions, providing basic fluid data for numerical simulation and the development of the condensate gas reservoir.

[0028] The present invention has other characteristics and advantages, which will be obvious from the accompanying drawings incorporated herein and the subsequent specific embodiments, or will be described in detail in the accompanying drawings incorporated herein and the subsequent specific embodiments, and these accompanying drawings and specific embodiments are jointly used to explain the specific principles of the present invention. Description of the Drawings

[0029] By describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features and advantages of the present invention will become more obvious, wherein, in the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0030] Figure 1 The flowchart of an inversion calculation method for condensate gas reservoir fluid composition according to an embodiment of the present invention is shown.

[0031] Figure 2 The optimized Pareto front diagram of an inversion calculation method for condensate gas reservoir fluid composition according to an embodiment of the present invention is shown.

[0032] Figure 3 The cumulative production fitting result diagram of an inversion calculation method for condensate gas reservoir fluid composition according to an embodiment of the present invention is shown.

[0033] Figure 4 The bottom-hole flowing pressure and production gas-oil ratio fitting result diagram of an inversion calculation method for condensate gas reservoir fluid composition according to an embodiment of the present invention is shown.

[0034] Figure 5 The fitting result diagram of the first group of sample fluid compositions of an inversion calculation method for condensate gas reservoir fluid composition according to an embodiment of the present invention is shown.

[0035] Figure 6 The fitting result diagram of the second group of sample fluid compositions of an inversion calculation method for condensate gas reservoir fluid composition according to another embodiment of the present invention is shown. Detailed implementation manners

[0036] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0037] An inversion calculation method for condensate gas reservoir fluid composition according to the present invention includes: obtaining the phase state data of a fluid sample; obtaining the state equation and parameters based on the phase state data; establishing a component numerical simulation model of the condensate gas reservoir; and optimizing the component numerical simulation model of the condensate gas reservoir based on the state equation, parameters and actual production data to obtain the original components of the fluid sample.

[0038] Specifically, based on the phase state data of the fluid sample, the state equation and parameters are obtained, the state equation and parameters are substituted into the component numerical simulation model of the condensate gas reservoir, and the component numerical simulation model of the condensate gas reservoir is optimized and fitted using an algorithm to obtain the original components of the fluid sample.

[0039] According to an exemplary embodiment, for the inversion calculation method of the condensate gas reservoir fluid composition of the present invention, the phase state data of the obtained fluid sample is used to establish an equation of state and parameters and a numerical simulation model of the component number of the condensate gas reservoir. By optimizing and fitting the numerical simulation of the component number, the fluid composition and parameters of the condensate gas reservoir under the original conditions are inversely calculated, providing basic fluid data for numerical simulation and the development of condensate gas reservoirs.

[0040] As an alternative, based on the equation of state and parameters and actual production data, the numerical simulation model of the component number of the condensate gas reservoir is optimized to obtain the original components of the fluid sample, including: filling the equation of state and parameters into the numerical simulation model of the component number, and using the genetic algorithm to optimize the numerical simulation model of the component number of the condensate gas reservoir to obtain the original components of the fluid sample.

[0041] Specifically, the equation of state and related parameters need to be given in the numerical simulation model of the component number. The equation of state and related parameters are filled into the numerical simulation file in the format required by the numerical simulator. The genetic algorithm is used to optimize and fit the numerical simulation model of the component number of the condensate gas reservoir. The optimized result is compared with the actual production data. When the optimized data is the same as or less than the preset error range of the actual production data, the optimization is stopped, and the optimized result is used as the original components of the fluid sample.

[0042] Using the numerical simulation model of the component number to simulate the production and development process, the fluid components at any time during the development process can be obtained. By continuously fitting and adjusting the components of the numerical simulation model of the component number, the fluid composition produced at a certain time is made consistent with the fluid composition of the obtained sample (obtained by PVT), and it is determined that the given original fluid composition in the numerical simulation model of the component number is correct. The genetic algorithm is used in this fitting and adjusting process, and the variables set by the genetic algorithm are the original components.

[0043] The genetic algorithm is a computational model that simulates the natural selection of Darwin's theory of biological evolution and the biological evolution process of genetic mechanisms. It is a method for searching for the optimal solution by simulating the natural evolution process. The following clarifies the key terms and meanings in the genetic algorithm:

[0044] Population and Generation: The set of all individuals in each generation is called a population.

[0045] Fitness: An index for evaluating the quality of individuals.

[0046] Selection: Selecting several individuals as parents from the population in a certain way and probability.

[0047] Reproduction: Directly inheriting from parent individuals to offspring individuals.

[0048] Crossover: Combine two parents in a certain way to generate offspring.

[0049] Mutation: Apply random changes to parent individuals to generate offspring.

[0050] As an alternative, set the parameters of the genetic algorithm and use the selection method, replication method, crossover method, or mutation method in the genetic algorithm to obtain the first optimization index and the second optimization index of fitness evaluation.

[0051] As an alternative, when the first optimization index of fitness evaluation is greater than the first preset threshold or the second optimization index is greater than the second preset threshold, reset the parameters of the genetic algorithm.

[0052] As an alternative, when the first optimization index of fitness evaluation is less than the first preset threshold and the second optimization index is less than the second preset threshold, use the set parameters of the genetic algorithm as the original components of the fluid sample.

[0053] In one example, the first preset threshold and the second preset threshold are set according to actual production data.

[0054] Specifically, calculate the first optimization index and the second optimization index of fitness evaluation through formula (1) and formula (2), and judge whether the optimization index meets the requirements. When the first optimization index of fitness evaluation is less than the first preset threshold and the second optimization index is less than the second preset threshold, that is, it meets the requirements, use the set variable parameters of the genetic algorithm as the original components of the fluid sample; when the first optimization index of fitness evaluation is greater than the first preset threshold or the second optimization index is greater than the second preset threshold, that is, it does not meet the requirements, then reset the variable parameters of the genetic algorithm, recalculate the first optimization index and the second optimization index of fitness evaluation, and re-judge whether it meets the requirements. If it does not meet the requirements, reset the variable parameters of the genetic algorithm again, recalculate the first optimization index and the second optimization index of fitness evaluation, and re-judge whether it meets the requirements until the first optimization index of fitness evaluation is less than the first preset threshold and the second optimization index is less than the second preset threshold, that is, it meets the requirements, and use the variable parameters of the genetic algorithm reset when it meets the requirements as the original components of the fluid sample.

[0055] As an alternative, obtain the first optimization index and the second optimization index of fitness evaluation through the following formula

[0056]

[0057]

[0058] where Fitness1 is the first optimization index of fitness evaluation, N c is the number of component types is the mole percentage of the actual components, is the mole percentage of the simulated components, and Fitness2 is the second optimization index of fitness evaluation. is the actual cumulative oil production, is the simulated cumulative oil production.

[0059] Specifically, set the parameters of the genetic algorithm: such as the number of components, the upper and lower limits of components, constraint equations, population size, fitness evaluation, and iteration stop conditions, etc. The specific settings are as follows:

[0060] Component: Z i 0 , the original mole percentage of components, i = 1, 2,..., N c ;

[0061] Number of components: N c , the number of components;

[0062] Upper and lower limits of components: 0 - 100 mol%;

[0063] Component constraint equation:

[0064]

[0065] Population size: 200 (recommended);

[0066] Limit algebra: 100 (recommended);

[0067] Initial population: the composition of the sampled sample (recommended);

[0068] Selection criterion: Tournament league algorithm (recommended);

[0069] Replication / crossover ratio: 20% / 80% (recommended);

[0070] Crossover criterion: Intermediate weighted average (recommended);

[0071] Mutation probability: 1% (recommended);

[0072] Mutation criterion: Adaptive constraint (recommended);

[0073] Optimization indices: ① Well stream composition, ② Cumulative oil production;

[0074] Fitness evaluation equation:

[0075]

[0076]

[0077] In the formula, Fitness1 is the first optimization index for fitness evaluation, and N c is the number of components, is the mole percentage of the actual components, is the mole percentage of the simulated components, Fitness2 is the second optimization index for fitness evaluation, is the actual cumulative oil production, is the simulated cumulative oil production.

[0078] By means of selection, replication, crossover, mutation, etc. in the genetic algorithm, the parameters of the algorithm are repeatedly set, and the changes in the fluid composition under different initial composition conditions until a specific time period are calculated, until the obtained fluid components are known to be the same as those of the known fluid sample, and finally the variable combination that most conforms to the actual conditions is evolved and calculated, and the component parameters set by the algorithm are used as the original components of the fluid.

[0079] As an alternative, an indoor PVT physical simulation experiment is carried out on the fluid sample to obtain the phase state data of the fluid sample.

[0080] Specifically, the fluid sample obtained on site is subjected to an indoor PVT physical simulation experiment to determine its phase state data such as composition, bubble point and dew point.

[0081] As an alternative, based on the phase state data, the equation of state and parameters are obtained, including: using phase state simulation software to establish the equation of state and setting the parameters of the equation of state so that the phase state data of the equation of state is the same as the phase state data of the fluid sample.

[0082] Specifically, using phase state simulation software to establish the equation of state, fitting the phase state parameters obtained from the PVT experiment, and repeatedly setting the parameters of the equation of state until the phase state data of the equation of state is the same as the phase state data of the fluid sample, and the equation of state and parameters required for component simulation are obtained.

[0083] As an alternative, using numerical simulation software, a numerical simulation model of the number of components of the condensate gas reservoir is established by using the geological data of the condensate gas reservoir and the actual production data.

[0084] Specifically, using commercial numerical simulation software, a numerical simulation model of the number of components of the condensate gas reservoir is established by using the geological data of the condensate gas reservoir and the on-site actual production data.

[0085] As an alternative, the phase state data includes composition and bubble point and dew point.

[0086] Embodiment

[0087] Figure 1 shows a flowchart of a method for inverse calculation of the fluid composition of a condensate gas reservoir according to an embodiment of the present invention.

[0088] AsFigure 1 As shown in Figure 1 , the inversion calculation method for the fluid composition of a condensate gas reservoir includes:

[0089] S102: Obtain the phase state data of the fluid sample;

[0090] Among them, conduct indoor PVT physical simulation experiments on the fluid sample to obtain the phase state data of the fluid sample.

[0091] Among them, the phase state data includes composition and bubble and dew points.

[0092] Specifically, conduct indoor PVT physical simulation experiments on the fluid sample obtained on-site to determine its phase state data such as composition, bubble and dew points.

[0093] S104: Based on the phase state data, obtain the equation of state and parameters;

[0094] Among them, obtaining the equation of state and parameters based on the phase state data includes: using phase state simulation software to establish the equation of state, setting the parameters of the equation of state so that the phase state data of the equation of state is the same as the phase state data of the fluid sample.

[0095] Specifically, use phase state simulation software to establish the equation of state, fit the phase state parameters obtained from the PVT experiment, repeatedly set the parameters of the equation of state until the phase state data of the equation of state is the same as the phase state data of the fluid sample, and obtain the equation of state and parameters required for component simulation.

[0096] S106: Establish a component numerical simulation model for the condensate gas reservoir;

[0097] Among them, use numerical simulation software and utilize the geological data and actual production data of the condensate gas reservoir to establish a component numerical simulation model for the condensate gas reservoir.

[0098] Specifically, use commercial numerical simulation software and utilize the geological data of the condensate gas reservoir and the on-site actual production data to establish a component numerical simulation model for this condensate gas reservoir.

[0099] S108: Based on the equation of state and parameters and actual production data, optimize the component numerical simulation model of the condensate gas reservoir to obtain the original components of the fluid sample.

[0100] Among them, optimizing the component numerical simulation model of the condensate gas reservoir based on the equation of state and parameters and actual production data to obtain the original components of the fluid sample includes: filling the equation of state and parameters into the component numerical simulation model, and using the genetic algorithm to optimize the component numerical simulation model of the condensate gas reservoir to obtain the original components of the fluid sample.

[0101] Among them, the parameters of the genetic algorithm are set, and the first optimization index and the second optimization index of fitness evaluation are obtained by using the selection method, replication method, crossover method or mutation method in the genetic algorithm.

[0102] Among them, when the first optimization index of fitness evaluation is greater than the first preset threshold or the second optimization index is greater than the second preset threshold, the parameters of the genetic algorithm are reset.

[0103] Among them, when the first optimization index of fitness evaluation is less than the first preset threshold and the second optimization index is less than the second preset threshold, the set parameters of the genetic algorithm are used as the original components of the fluid sample.

[0104] Among them, the first optimization index and the second optimization index of fitness evaluation are obtained through the following formula

[0105]

[0106]

[0107] Among them, Fitness1 is the first optimization index of fitness evaluation, N c is the number of components, is the mole percentage of the actual components, is the mole percentage of the simulated components, Fitness2 is the second optimization index of fitness evaluation, is the actual cumulative oil production, is the simulated cumulative oil production.

[0108] Specifically, set the parameters of the genetic algorithm: such as the number of components, the upper and lower limits of components, constraint equations, population size, fitness evaluation, and iteration stop conditions, etc. The specific settings are as follows:

[0109] Components: The mole percentage of the original components, i = 1, 2,..., N c ;

[0110] Number of components: N c , the number of components;

[0111] Upper and lower limits of components: 0 - 100 mol%;

[0112] Component constraint equation:

[0113]

[0114] Population size: 200 (recommended);

[0115] Limit algebra: 100 (recommended);

[0116] Initial population: the composition of the sampled sample (recommended);

[0117] Selection criterion: Tournament algorithm (recommended);

[0118] Copy / crossover ratio: 20% / 80% (recommended);

[0119] Crossover criterion: Intermediate weighted average (recommended);

[0120] Mutation probability: 1% (recommended);

[0121] Mutation criterion: Adaptive constraint (recommended);

[0122] Optimization indicators: ① Well stream composition, ② Cumulative oil production;

[0123] Fitness evaluation equation:

[0124]

[0125]

[0126] In the formula, Fitness1 is the first optimization index of fitness evaluation, N c is the number of components, is the molar percentage of the actual component, is the molar percentage of the simulated component, Fitness2 is the second optimization index of fitness evaluation, is the actual cumulative oil production, is the simulated cumulative oil production.

[0127] By using methods such as selection, replication, crossover, and mutation in the genetic algorithm, repeatedly set the parameters of the algorithm, and calculate the changes in fluid composition under different initial composition conditions until a specific time period, until the obtained fluid components are known to be the same as those of the known fluid sample, and finally evolve and calculate the variable combination that best meets the actual conditions, and use the component parameters set by the algorithm as the original components of the fluid.

[0128] Figure 2 Shows the optimized Pareto front diagram of a method for inverse calculation of condensate gas reservoir fluid composition according to an embodiment of the present invention. Figure 3 Shows the cumulative production fitting result diagram of a method for inverse calculation of condensate gas reservoir fluid composition according to an embodiment of the present invention. Figure 4 Shows the fitting result diagram of bottom hole flowing pressure and production gas-oil ratio of a method for inverse calculation of condensate gas reservoir fluid composition according to an embodiment of the present invention. Figure 5 Shows the fitting result diagram of the first group of sample fluid compositions of a method for inverse calculation of condensate gas reservoir fluid composition according to an embodiment of the present invention. Figure 6Shows the fitting result diagram of the second group of sample fluid compositions of a condensate gas reservoir fluid composition inversion calculation method according to another embodiment of the present invention.

[0129] The basic parameters of a certain condensate gas reservoir are shown in Table 1 as follows:

[0130] Table 1 Basic parameters of L condensate gas reservoir

[0131] Original formation pressure Formation temperature Porosity Formation permeability Perforated interval 31.41 MPa 110.4℃ 6.76％ 0.99 mD 3140.8-3167.4m

[0132] There is a well in this gas reservoir that was put into production on September 20, 2014; the first sampling was on March 17, 2015; the second sampling was on August 16, 2016, and the cumulative oil production was 5995 m3.

[0133] The fluid components and parameters of the second sampling are shown in Table 2 as follows:

[0134] Table 2 Fluid composition and parameter table of the second sampling

[0135]

[0136] According to the established numerical simulation model of the condensate gas reservoir component and the actual production data of the condensate gas well, the Pareto front diagram is finally optimized according to the condensate gas reservoir fluid composition inversion calculation method of the present invention, as Figure 2 shown.

[0137] As Figure 2 shown, the final optimization reaches the 137th generation and stops due to too small change in iteration parameters. The optimal target point Optim(6.93, 162.69) on the Pareto front is selected, indicating that the optimization error of the fluid composition of the second sampling is 6.93 mol%, and the optimization error of the cumulative oil production is 162.69 m3.

[0138] The fitting effect of the production index after optimization is good, and the error of the cumulative oil production is 2.71%, as Figure 3 shown; the simulation results of the bottom-hole flowing pressure and the production gas-oil ratio also conform to the measured production data as Figure 4 shown.

[0139] The first sampling date was March 17, 2015. The production gas-oil ratio had no obvious fluctuation, but the corresponding bottom-hole pressure was lower than the dew point pressure, and the sampling opportunity had been missed. The sampled sample could not effectively represent the original condensate gas reservoir fluid. The simulation well flow component error of the first sampling node was 10.03 mol%, and the fitting result was as Figure 5 , at Figure 5The horizontal coordinate component is the component, and the vertical coordinate mole percent is the mole percentage. The simulated well stream component error at the second sampling node is 6.93 mol%, which is better than the simulated well stream component error of 10.03 mol% at the first sampling node. The fitting result is good and meets the application requirements. Figure 6 As shown in Figure 6 the horizontal coordinate component is the component, and the vertical coordinate mole percent is the mole percentage.

[0140] Comparing the simulated and experimental results of the well stream components at the two sampling nodes, and verifying the effectiveness of the method by fitting the optimized simulated results with the production history data.

[0141] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for inverse calculation of condensate gas reservoir fluid composition, characterized in that Including: Obtaining the phase state data of the fluid sample; Based on the phase state data, obtaining the equation of state and parameters; Establishing a compositional numerical simulation model of the condensate gas reservoir; Based on the equation of state, parameters and actual production data, optimizing the compositional numerical simulation model of the condensate gas reservoir to obtain the original components of the fluid sample; Among them, the step of optimizing the compositional numerical simulation model of the condensate gas reservoir based on the equation of state, parameters and actual production data to obtain the original components of the fluid sample includes: filling the equation of state and parameters into the compositional numerical simulation model, using the genetic algorithm to optimize the compositional numerical simulation model of the condensate gas reservoir, comparing the optimized result with the actual production data, and stopping the optimization when the optimized data is the same as or less than the preset error range, and taking the optimized result as the original components of the fluid sample; Among them, setting the parameters of the genetic algorithm, and using the selection method, replication method, crossover method or mutation method in the genetic algorithm to obtain the first optimization index and the second optimization index of fitness evaluation; Among them, the first optimization index and the second optimization index of fitness evaluation are obtained through the following formula Among them, Fitness1 is the first optimization index for fitness evaluation, and N c is the number of components, is the molar percentage of the actual components, is the molar percentage of the simulated components, Fitness2 is the second optimization index for fitness evaluation, is the actual cumulative oil production, is the simulated cumulative oil production.

2. The inversion calculation method for the condensate gas reservoir fluid composition according to claim 1, wherein When the first optimization index of fitness evaluation is greater than the first preset threshold or the second optimization index is greater than the second preset threshold, reset the parameters of the genetic algorithm.

3. The inversion calculation method for the fluid composition of a condensate gas reservoir according to claim 1, characterized in that When the first optimization index of fitness evaluation is less than the first preset threshold and the second optimization index is less than the second preset threshold, take the set parameters of the genetic algorithm as the original components of the fluid sample.

4. The inversion calculation method for the condensate gas reservoir fluid composition according to claim 1, wherein Conducting an indoor PVT physical simulation experiment on the fluid sample to obtain the phase state data of the fluid sample.

5. The inversion calculation method for the condensate gas reservoir fluid composition according to claim 1, characterized in that The step of obtaining the equation of state and parameters based on the phase state data includes: Using phase state simulation software to establish the equation of state and setting the parameters of the equation of state so that the phase state data of the equation of state is the same as the phase state data of the fluid sample.

6. The inversion calculation method for the condensate gas reservoir fluid composition according to claim 1, wherein Using numerical simulation software and utilizing the geological data and actual production data of the condensate gas reservoir to establish the compositional numerical simulation model of the condensate gas reservoir.

7. The inversion calculation method for the condensate gas reservoir fluid composition according to claim 1, wherein The phase state data includes composition and bubble and dew points.

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