A method and related equipment for predicting asphaltene deposition during gas injection.

Abstract

This invention relates to the field of reservoir gas injection development technology, and discloses a method and related equipment for predicting asphaltene deposition during the gas injection process. The method includes acquiring crude oil samples after degassing at different times, and testing the crude oil samples to obtain the contents of wax, gum, and asphaltene; performing a full-group analysis of the crude oil sample components to obtain the carbon number distribution test results, and dividing them into multiple pseudo-components; testing the asphaltene solid phase deposition points of the crude oil samples to obtain the asphaltene solid phase deposition points of each crude oil sample; calculating the grey correlation degree between each pseudo-component and the asphaltene solid phase deposition points of each crude oil sample, and selecting the two pseudo-components with the high grey correlation; constructing an empirical formula based on the contents of the crude oil sample and the two selected pseudo-components to obtain the asphaltene solid phase deposition point prediction formula, thus completing the prediction of asphaltene deposition during the gas injection process. This invention improves the accuracy of asphaltene deposition prediction during the gas injection process.

Description

Technical Field

[0001] This invention relates to the field of reservoir gas injection development technology, specifically to a method and related equipment for predicting bituminous deposition during the gas injection process. Background Technology

[0002] In the field of oil and gas extraction, the accumulation, precipitation, and deposition of asphaltene have always been key factors affecting production efficiency and safety. As an important component of crude oil, the unique physicochemical properties of asphaltene make it highly susceptible to external conditions such as temperature, pressure, and compositional changes during crude oil extraction, transportation, and post-processing.

[0003] Specifically, during crude oil extraction, as crude oil is extracted from the ground, its environmental conditions (such as temperature and pressure) change significantly, directly affecting the stability of the colloidal system within the crude oil. The colloidal system is a complex system formed by the interaction of polar substances such as asphaltenes and gums with non-polar substances such as hydrocarbons in crude oil. When external conditions change, this equilibrium may be disrupted, leading to enhanced interactions between asphaltenes molecules, resulting in aggregation and precipitation.

[0004] The precipitation of asphaltenes not only reduces the quality of crude oil, but more importantly, it deposits in critical areas such as pipelines and formation pore throats as the crude oil flows, causing blockages in production pipelines and a decrease in formation permeability. This problem is particularly prominent during enhanced oil recovery (EOR) injection. EOR technology injects gases (such as carbon dioxide and nitrogen) into the formation, utilizing the expansion and extraction properties of the gases to improve the fluidity and recovery rate of crude oil. However, in the later stages of EOR injection, as gas is continuously injected, lighter components in the crude oil are gradually extracted, causing the crude oil to become heavier, and the percentage of resins and asphaltenes increases accordingly.

[0005] At this point, if the pressure during crude oil extraction gradually decreases, the natural gas dissolved in the crude oil will further extract the lighter components, increasing the content of heavier components. This change promotes a decrease in the deposition point of asphaltene, thereby accelerating the precipitation and deposition process of asphaltene. The deposited asphaltene not only reduces the pipeline's transport capacity but may also severely damage formation pores and throats, leading to serious consequences such as decreased well production or even production stoppage.

[0006] Traditional prediction methods for asphaltene deposition during gas injection have several shortcomings. On the one hand, while "one well, one measurement" or "one sample, one measurement" methods can provide relatively accurate measurements for specific wells or samples, they cannot adapt to the constantly changing fluid composition during gas injection. On the other hand, prediction methods based on complex calculation models, although able to consider more factors, are cumbersome in their calculations and have limited accuracy, making it difficult to meet the needs of real-time prediction of asphaltene deposition in production sites. Summary of the Invention

[0007] The purpose of this invention is to provide a method and related equipment for predicting asphalt deposition during the gas injection process, so as to solve the technical problem of how to improve the prediction accuracy of asphalt deposition during the gas injection process in the prior art.

[0008] This invention is achieved through the following technical solution: In a first aspect, the present invention provides a method for predicting asphaltene deposition during gas injection, comprising: Crude oil samples were obtained after degassing at different times, and the contents of wax, gum and asphaltenes in the crude oil samples were tested to obtain the contents of wax, gum and asphaltenes in the crude oil samples. A complete analysis of the components of crude oil samples was performed to obtain the carbon number distribution test results of the crude oil samples, and the carbon number distribution test results were divided into multiple pseudo-components; The asphaltene solid phase deposition points of each crude oil sample were obtained by testing the asphaltene solid phase deposition points of each crude oil sample. Grey relational theory was used to calculate the grey relational degree between each pseudo-component and the asphaltene solid phase deposition point of each crude oil sample, and the two pseudo-components with the larger grey relational degree were selected based on the magnitude of the grey relational degree. An empirical formula was constructed based on the contents of wax, gum, and asphaltenes in crude oil samples and two pseudo-components with high correlation. The empirical formula was then fitted with nonlinear regression to obtain a formula for predicting asphaltenes solid deposition points. The prediction of asphalt deposition during the gas injection process is completed based on the obtained formula for predicting asphalt solid deposition points.

[0009] Preferably, the step of using grey relational analysis to calculate the correlation between each pseudo-component and the asphaltene solid phase deposition points of each crude oil sample, and selecting the two pseudo-components with the highest correlation based on the magnitude of the correlation, is as follows: A reference sequence was constructed from the asphaltene solid phase deposition points of each crude oil sample, and a comparison sequence was constructed from the predefined pseudo-components; the reference sequence and the comparison sequence were combined to form a data matrix. The data matrix is ​​subjected to infinitesimal hardening, the correlation coefficient is calculated, and the grey relational degree is calculated based on the correlation coefficient. Based on the obtained grey relational degree, select the two pseudo-components with the larger grey relational degree.

[0010] Furthermore, the expression for the reference sequence is as follows: ; The expression for the comparison sequence is as follows: ; in, Representing the first of the matrix OK; Representing the first of the matrix List.

[0011] Furthermore, the expression for quantizing the data matrix is ​​as follows:

[0012] in, It is an indicator The The attribute values ​​of each sample; , Distribution as an indicator The mean and standard deviation in the data.

[0013] Furthermore, the expression for calculating the correlation coefficient is as follows:

[0014] in, It is the correlation coefficient; The resolution coefficient is typically between 0 and 1.

[0015] Furthermore, the grey relational degree is calculated based on the correlation coefficient, and the calculation expression is as follows:

[0016] in: The calculated correlation value is where correlation value The closer a value is to 1, the stronger the correlation between the sequences; correlation score The closer a value is to 0, the weaker the correlation between the sequences.

[0017] Preferably, in the step of constructing an empirical formula based on the contents of wax, resin, and asphaltenes in the crude oil sample and two selected pseudo-components with high correlation, and then performing nonlinear regression fitting on the empirical formula to obtain the asphaltenes solid phase deposition point prediction formula, the expression of the empirical formula is as follows:

[0018] in, These are asphaltene solid deposition points. o C; , and These represent wax content, resin content, and asphaltene content, respectively, in wt%; , They are respectively and The total mass percentage of all components, % , , , , and These are the coefficients to be fitted.

[0019] Furthermore, a nonlinear regression fitting is performed on the empirical formula, and the fitting coefficients are obtained. , , , , and The formula for calculating the nonlinear regression fit is as follows:

[0020] in, For the coefficients to be solved, For input matrices or vectors, To output a matrix or vector, Is with Functions that are matrix or vector values ​​of the same size.

[0021] Secondly, the present invention also provides a prediction system for asphaltene deposition during the gas injection process, comprising: The crude oil sample content testing module is used to obtain crude oil samples after degassing at different times, and to test the contents of wax, gum and asphaltenes in the crude oil samples. The data partitioning module is used to perform a full group analysis of the components of crude oil samples to obtain the carbon number distribution test results of crude oil samples, and to divide the carbon number distribution test results into multiple pseudo-components; The asphaltene solid deposition point test module is used to test the asphaltene solid deposition point of crude oil samples to obtain the asphaltene solid deposition point of each crude oil sample. The grey relational degree calculation module is used to calculate the grey relational degree between each pseudo-component and the asphaltene solid phase deposition point of each crude oil sample using grey relational theory, and select the two pseudo-components with the larger grey relational degree based on the magnitude of the grey relational degree. The empirical calculation module is used to construct empirical formulas based on the contents of wax, gum and asphaltenes in crude oil samples and two selected pseudo-components with high correlation, and to obtain the prediction formula for asphaltenes solid phase deposition points by performing nonlinear regression fitting on the empirical formulas. The asphalt solids deposition point prediction module is used to predict asphalt deposition during the gas injection process based on the obtained asphalt solids deposition point prediction formula.

[0022] Thirdly, the present invention also provides a mobile terminal, 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 predicting asphaltene deposition during the gas injection process as described above.

[0023] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for predicting asphalt deposition during the gas injection process as described above.

[0024] Fifthly, the present invention also provides a computer program product, including computer instructions that instruct a computing device to perform operations corresponding to the prediction method for asphalt deposition during the gas injection process as described above.

[0025] Compared with the prior art, the present invention has the following beneficial technical effects: This invention provides a method for predicting asphaltene deposition during gas injection. First, crude oil samples after degassing at different times are obtained, and detailed component analysis is performed on these samples, including the determination of wax, resin, and asphaltene content. This ensures the accuracy and reliability of subsequent analyses. By performing a full-group analysis of the crude oil samples and dividing the carbon number distribution test results into multiple pseudo-components, a more refined understanding of the crude oil's compositional characteristics can be obtained. This helps to more accurately identify key factors affecting asphaltene deposition. Grey relational theory is used to calculate the grey relational degree between each pseudo-component and the asphaltene solid-phase deposition points of each crude oil sample, enabling an objective assessment of the influence of different pseudo-components on asphaltene deposition points. Based on the magnitude of the grey relational degree, two pseudo-components with high correlation are selected, providing a scientific basis for constructing the prediction formula. An empirical formula is constructed based on the wax, resin, and asphaltene content in the crude oil samples and the two selected pseudo-components with high correlation, and a prediction formula for asphaltene solid-phase deposition points is obtained through nonlinear regression fitting. A mathematical model is established between complex crude oil component information and asphaltene deposition points, making prediction more convenient and efficient. The obtained formula for predicting asphalt solid deposition points can be used to predict asphalt deposition during gas injection. By making predictions, measures can be taken in advance to prevent asphalt deposition, thereby extending equipment life and improving mining efficiency. Attached Figure Description

[0026] Figure 1 This is a flowchart of the method for predicting asphalt deposition during the gas injection process in an embodiment of the present invention; Figure 2 This is a distribution diagram of the content of wax, resin and asphalt in the sample taken in the embodiment of the present invention; Figure 3 This is a carbon number distribution diagram of all components in the sample taken in this embodiment of the invention; Figure 4 This is a columnar distribution diagram of asphalt solid phase deposition points obtained from different thermal analysis combined with capillary flow experiments in an embodiment of the present invention. Figure 5 This is a schematic diagram comparing the actual values ​​and fitting results of the asphalt solid phase deposition points of each sample in the embodiments of the present invention. Figure 6 This is a schematic diagram of the principle structure of the prediction system for asphalt deposition during the gas injection process in an embodiment of the present invention; In the diagram: 1. Crude oil sample content testing module; 2. Data partitioning module; 3. Asphaltene solid phase deposition point testing module; 4. Grey relational degree calculation module; 5. Empirical calculation module; 6. Asphaltene solid phase deposition point prediction module. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] The purpose of this invention is to provide a method and related equipment for predicting asphalt deposition during the gas injection process, so as to solve the technical problem of how to improve the prediction accuracy of asphalt deposition during the gas injection process in the prior art.

[0029] The present invention will now be described in further detail with reference to the accompanying drawings: See Figure 1 This invention provides a method for predicting asphaltene deposition during gas injection, comprising: Step 1: Obtain crude oil samples after degassing at different times, and test the obtained crude oil samples to obtain the contents of wax, gum and asphaltene in the crude oil samples respectively; Step 2: Perform a full group analysis on the components of the crude oil sample to obtain the carbon number distribution test results of the crude oil sample, and divide the carbon number distribution test results into multiple pseudo-components; Step 3: Test the asphaltene solid phase deposition points of the crude oil samples to obtain the asphaltene solid phase deposition points of each crude oil sample. Step 4: The grey relational theory is used to calculate the grey relational degree between each pseudo-component and the asphaltene solid phase deposition point of each crude oil sample, and the two pseudo-components with the larger grey relational degree are selected according to the magnitude of the grey relational degree. Specifically, the process is as follows: S1, construct a reference sequence from the asphaltene solid phase deposition points of each crude oil sample, and construct a comparison sequence from the predefined pseudo-components; combine the reference sequence and the comparison sequence into a data matrix; Specifically, the expression for the reference sequence is as follows: (1) The expression for the comparison sequence is as follows: (2) in, Representing the first of the matrix OK; Representing the first of the matrix List.

[0030] S2, perform infinitesimal hardening on the data matrix, calculate the correlation coefficient, and calculate the grey relational degree based on the correlation coefficient; Specifically, the expression for quantizing the data matrix is ​​as follows: (3) in, It is an indicator The The attribute values ​​of each sample; , Distribution as an indicator The mean and standard deviation in the data.

[0031] Specifically, the formula for calculating the correlation coefficient is as follows: (4) in, It is the correlation coefficient; The resolution coefficient is typically between 0 and 1.

[0032] Specifically, the grey relational degree is calculated based on the correlation coefficient, and the calculation expression is as follows: (5) in: The calculated correlation value is where correlation value The closer a value is to 1, the stronger the correlation between the sequences; correlation score The closer a value is to 0, the weaker the correlation between the sequences.

[0033] S3. Based on the obtained grey relational degree, select the two pseudo-components with the larger grey relational degree.

[0034] Step 5: Based on the contents of wax, gum and asphaltenes in the crude oil sample and the two pseudo-components with high correlation, construct an empirical formula, and perform nonlinear regression fitting on the empirical formula to obtain the prediction formula for asphaltenes solid phase deposition points. Specifically, the expression of the empirical formula is as follows: (6) in, These are asphaltene solid deposition points. o C; , and These represent wax content, resin content, and asphaltene content, respectively, in wt%; , They are respectively and The total mass percentage of all components, % , , , , and These are the coefficients to be fitted.

[0035] In this process, a nonlinear regression fitting is performed on the empirical formula, and the fitting coefficients are obtained. , , , , and The formula for calculating the nonlinear regression fit is as follows: (7) in, For the coefficients to be solved, For input matrices or vectors, To output a matrix or vector, Is with Functions that are matrix or vector values ​​of the same size.

[0036] Substituting the obtained fitting coefficients into formula (6), we obtain the formula for predicting the asphalt solid phase deposition point.

[0037] Step 6: Based on the obtained prediction formula for asphalt solid deposition points, predict the asphalt deposition during the gas injection process.

[0038] In summary, this invention provides a method for predicting asphaltene deposition during gas injection. First, crude oil samples after degassing at different times were obtained, and detailed component analysis was performed on these samples, including the determination of wax, resin, and asphaltene content. This ensures the accuracy and reliability of subsequent analyses. By performing a full component analysis of the crude oil samples and dividing the carbon number distribution test results into multiple pseudo-components, a more refined understanding of the crude oil's compositional characteristics can be obtained. This helps to more accurately identify key factors affecting asphaltene deposition. Grey relational theory is used to calculate the grey relational degree between each pseudo-component and the asphaltene solid-phase deposition points of each crude oil sample, enabling an objective assessment of the influence of different pseudo-components on the asphaltene deposition points. Based on the magnitude of the grey relational degree, two pseudo-components with high correlation are selected, providing a scientific basis for constructing the prediction formula. An empirical formula is constructed based on the wax, resin, and asphaltene content in the crude oil samples and the two selected pseudo-components with high correlation, and a prediction formula for asphaltene solid-phase deposition points is obtained through nonlinear regression fitting. A mathematical model is established between complex crude oil component information and asphaltene deposition points, making prediction more convenient and efficient. The obtained formula for predicting asphalt solid deposition points can be used to predict asphalt deposition during gas injection. By making predictions, measures can be taken in advance to prevent asphalt deposition, thereby extending equipment life and improving mining efficiency.

[0039] Example 1 This embodiment 1 provides a prediction system for asphaltene deposition during the gas injection process, the specific process of which is as follows: (1) Referring to standard SY / T 7550-2012 "Determination of Wax, Gum and Asphaltene Content in Crude Oil", samples produced from 11 example wells in the example gas injection reservoir were tested. The test results are shown in Table 1 and Figure 2 As shown in the test results, the composition of the sample varies greatly with time, production rate, and different time periods after gas injection breakthrough, making it difficult to quantify its characterization of the deposition mechanism.

[0040] Table 1. Results of Asphaltene, Wax and Resin Content Determination in Samples

[0041] (2) Referring to standard SH / T 0400-92 "Determination of Carbon Number Distribution of Paraffin by Gas Chromatography", the components of these 11 groups of samples were analyzed using an Agilent 8890 gas chromatograph (GC) system. The analytical results are as follows: Figure 3 As shown. According to Figure 3 The statistical results of the carbon number distribution in the sample divide it into three pseudo-components: ≤C 14 C 15 ~C 33 ≥C 34 The division results are shown in Table 2.

[0042] Table 2. Classification and Statistical Results of the Proposed Component Mass Proportions

[0043] (3) Referring to SY / T 0545-2012 "Determination of Thermal Characteristics Parameters of Crude Oil Wax Separation - Differential Scanning Calorimetry", the asphaltene solid phase deposition point of the sample was tested using a differential scanning calorimeter (DSC). The test results are as follows: Figure 4 As shown.

[0044] (4) Analysis of the test data revealed that the temperature of most deposition points was around 60°C. o Approximately C. Then, based on the grey relational analysis method, the correlation degree between each pseudo-component and the asphaltene solid phase deposition point was calculated, and the calculation results are shown in Table 3. The order of their influence on the deposition point is as follows: M ≤C14 > M ≥C34 > M C15~C33 Analysis revealed that ≤C 14 ≥C 34 The correlation with the deposition points is high, and all are greater than 0.65, indicating that they have a significant impact on the deposition of asphalt.

[0045] Table 3. Grey relational degree corresponding to each pseudo-component

[0046] (5) Based on the established empirical formula model, the above data are fitted using the least squares nonlinear fitting method. The empirical prediction formula for asphalt solid deposition points is obtained through fitting: (8) The fitted value can be calculated using the empirical formula for fitting, and then compared with the measured value obtained by differential thermal analysis to find its coefficient of determination. R 2 The value is as high as 0.8465. This indicates that the empirical formula has a good fitting effect, and the fitting comparison results are as follows: Figure 5 As shown.

[0047] (6) Crude oil samples were selected from a total of 3 wells in different blocks of the oilfield. The wax content, gum content, asphaltene content, and ≤C content were analyzed. 14 ≥C 34 The required parameters were obtained through analysis and testing of asphalt solid phase deposition points. The corresponding data were then substituted into the fitted empirical formula to obtain the predicted values ​​of the deposition points. The comparison results are shown in Table 4.

[0048] Table 4 Comparison of experimental and predicted values ​​of asphalt solid deposition points

[0049] By calculating the average absolute percentage error between the experimental and predicted values, it was found that the method provided by this invention has a calculation error of only 5.10%. This indicates that the prediction method achieves good prediction results and has good field practicality, and can be used to understand the asphaltene deposition during reservoir gas injection.

[0050] In summary, the method for predicting asphaltene deposition during gas injection provided in this embodiment firstly clarifies the component composition (C1~C55+) distribution characteristics of oil wells with different gas-oil ratios at the gas injection site by testing the paraffin-colloid-asphaltene content and conducting full component analysis. Secondly, due to the complexity of asphaltene deposition and the numerous components in its system, grey relational theory is used to analyze the grey relational degree between different pseudo-components and the experimental test values ​​of wax precipitation points. The pseudo-component characteristics with higher correlation are the main influencing factors. Based on the wax content, colloidal content, asphaltene content, highly correlated pseudo-component characteristics, and deposition point data measured by differential thermal analysis in the crude oil sample, an empirical formula is established and regression fitting is performed to obtain a prediction formula with a fitting effect R2 of over 0.8. Case analysis shows that the calculation error of this asphaltene solid phase deposition point calculation formula is only 5.10%, demonstrating strong generalization ability in practical applications.

[0051] Example 2 according to Figure 6 As shown, this embodiment 2 provides a prediction system for asphaltene deposition during the gas injection process, including a crude oil sample content testing module 1, a data partitioning module 2, an asphaltene solid phase deposition point testing module 3, a grey relational degree calculation module 4, an empirical calculation module 5, and an asphaltene solid phase deposition point prediction module 6. Crude oil sample content testing module 1 is used to obtain crude oil samples after degassing at different times, and to test the contents of wax, gum and asphalt in the crude oil samples respectively. Data partitioning module 2 is used to perform a full group analysis of the components of crude oil samples to obtain the carbon number distribution test results of crude oil samples, and to divide the carbon number distribution test results into multiple pseudo-components; The asphaltene solid deposition point test module 3 is used to test the asphaltene solid deposition point of crude oil samples to obtain the asphaltene solid deposition point of each crude oil sample. The grey relational degree calculation module 4 is used to calculate the grey relational degree between each pseudo-component and the asphaltene solid phase deposition point of each crude oil sample using grey relational theory, and select the two pseudo-components with the larger grey relational degree based on the magnitude of the grey relational degree. Empirical calculation module 5 is used to construct empirical formulas based on the contents of wax, gum and asphaltenes in crude oil samples and two selected pseudo-components with high correlation, and to obtain the prediction formula for asphaltenes solid phase deposition points by nonlinear regression fitting of the empirical formulas. The asphalt solids deposition point prediction module 6 is used to predict asphalt deposition during the gas injection process based on the obtained asphalt solids deposition point prediction formula.

[0052] Example 3 The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, such as a program for predicting asphalt deposition during gas injection.

[0053] When the processor executes the computer program, it implements the steps of the above-described method for predicting asphaltene deposition during the gas injection process, for example: Crude oil samples were obtained after degassing at different times, and the contents of wax, gum and asphaltenes in the crude oil samples were tested to obtain the contents of wax, gum and asphaltenes in the crude oil samples. A complete analysis of the components of crude oil samples was performed to obtain the carbon number distribution test results of the crude oil samples, and the carbon number distribution test results were divided into multiple pseudo-components; The asphaltene solid phase deposition points of each crude oil sample were obtained by testing the asphaltene solid phase deposition points of each crude oil sample. Grey relational theory was used to calculate the grey relational degree between each pseudo-component and the asphaltene solid phase deposition point of each crude oil sample, and the two pseudo-components with the larger grey relational degree were selected based on the magnitude of the grey relational degree. An empirical formula was constructed based on the contents of wax, gum, and asphaltenes in crude oil samples and two pseudo-components with high correlation. The empirical formula was then fitted with nonlinear regression to obtain a formula for predicting asphaltenes solid deposition points. The prediction of asphalt deposition during the gas injection process is completed based on the obtained formula for predicting asphalt solid deposition points.

[0054] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system, for example: Crude oil sample content testing module 1 is used to obtain crude oil samples after degassing at different times, and to test the contents of wax, gum and asphalt in the crude oil samples respectively. Data partitioning module 2 is used to perform a full group analysis of the components of crude oil samples to obtain the carbon number distribution test results of crude oil samples, and to divide the carbon number distribution test results into multiple pseudo-components; The asphaltene solid deposition point test module 3 is used to test the asphaltene solid deposition point of crude oil samples to obtain the asphaltene solid deposition point of each crude oil sample. The grey relational degree calculation module 4 is used to calculate the grey relational degree between each pseudo-component and the asphaltene solid phase deposition point of each crude oil sample using grey relational theory, and select the two pseudo-components with the larger grey relational degree based on the magnitude of the grey relational degree. Empirical calculation module 5 is used to construct empirical formulas based on the contents of wax, gum and asphaltenes in crude oil samples and two selected pseudo-components with high correlation, and to obtain the prediction formula for asphaltenes solid phase deposition points by nonlinear regression fitting of the empirical formulas. The asphalt solids deposition point prediction module 6 is used to predict asphalt deposition during the gas injection process based on the obtained asphalt solids deposition point prediction formula.

[0055] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the mobile terminal.

[0056] For example, the computer program can be divided into a crude oil sample content testing module 1, a data partitioning module 2, an asphaltene solid phase deposition point testing module 3, a grey relational degree calculation module 4, an empirical calculation module 5, and an asphaltene solid phase deposition point prediction module 6. The specific functions of each module are as follows: Crude oil sample content testing module 1 is used to obtain crude oil samples after degassing at different times, and to test the contents of wax, gum and asphalt in the crude oil samples respectively. Data partitioning module 2 is used to perform a full group analysis of the components of crude oil samples to obtain the carbon number distribution test results of crude oil samples, and to divide the carbon number distribution test results into multiple pseudo-components; The asphaltene solid deposition point test module 3 is used to test the asphaltene solid deposition point of crude oil samples to obtain the asphaltene solid deposition point of each crude oil sample. The grey relational degree calculation module 4 is used to calculate the grey relational degree between each pseudo-component and the asphaltene solid phase deposition point of each crude oil sample using grey relational theory, and select the two pseudo-components with the larger grey relational degree based on the magnitude of the grey relational degree. Empirical calculation module 5 is used to construct empirical formulas based on the contents of wax, gum and asphaltenes in crude oil samples and two selected pseudo-components with high correlation, and to obtain the prediction formula for asphaltenes solid phase deposition points by nonlinear regression fitting of the empirical formulas. The asphalt solids deposition point prediction module 6 is used to predict asphalt deposition during the gas injection process based on the obtained asphalt solids deposition point prediction formula.

[0057] The mobile terminal can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The mobile terminal may include, but is not limited to, a processor and memory.

[0058] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the mobile terminal, connecting various parts of the mobile terminal via various interfaces and lines.

[0059] The memory can be used to store the computer program and / or module. The processor implements various functions of the mobile terminal by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0060] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMediaCards (SMC), Secure Digital (SD) cards, FlashCards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0061] Example 4 The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for predicting asphalt deposition during gas injection.

[0062] If the modules / units integrated in the mobile terminal are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0063] Based on this understanding, all or part of the processes in the above method can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-described aggregated reinforcement learning resource scheduling method. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form.

[0064] The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0065] It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0066] Example 5 A computer program product includes computer instructions that instruct a computing device to perform operations corresponding to the prediction method for asphaltene deposition during the gas injection process as described above.

[0067] 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 it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting asphaltene deposition during gas injection, characterized in that, include: Crude oil samples were obtained after degassing at different times, and the contents of wax, gum and asphaltenes in the crude oil samples were tested to obtain the contents of wax, gum and asphaltenes in the crude oil samples. A complete analysis of the components of crude oil samples was performed to obtain the carbon number distribution test results of the crude oil samples, and the carbon number distribution test results were divided into multiple pseudo-components; The asphaltene solid phase deposition points of each crude oil sample were obtained by testing the asphaltene solid phase deposition points of each crude oil sample. Grey relational theory was used to calculate the grey relational degree between each pseudo-component and the asphaltene solid phase deposition point of each crude oil sample, and the two pseudo-components with the larger grey relational degree were selected based on the magnitude of the grey relational degree. An empirical formula was constructed based on the contents of wax, gum, and asphaltenes in crude oil samples and two pseudo-components with high correlation. The empirical formula was then fitted with nonlinear regression to obtain a formula for predicting asphaltenes solid deposition points. The prediction of asphalt deposition during the gas injection process is completed based on the obtained formula for predicting asphalt solid deposition points.

2. The method for predicting asphaltene deposition during gas injection according to claim 1, characterized in that, The specific process of calculating the correlation between each pseudo-component and the asphaltene solid phase deposition points of each crude oil sample using grey relational theory, and selecting the two pseudo-components with the highest correlation based on the magnitude of the correlation, is as follows: A reference sequence was constructed from the asphaltene solid phase deposition points of each crude oil sample, and a comparison sequence was constructed from the predefined pseudo-components; the reference sequence and the comparison sequence were combined to form a data matrix. The data matrix is ​​subjected to infinitesimal hardening, the correlation coefficient is calculated, and the grey relational degree is calculated based on the correlation coefficient. Based on the obtained grey relational degree, select the two pseudo-components with the larger grey relational degree.

3. The method for predicting asphaltene deposition during gas injection according to claim 2, characterized in that, The expression for the reference sequence is as follows: ； The expression for the comparison sequence is as follows: ； in, Representing the first of the matrix OK; Representing the first of the matrix List.

4. The method for predicting asphaltene deposition during gas injection according to claim 2, characterized in that, The expression for quantization of the data matrix is ​​as follows: in, It is an indicator The The attribute values ​​of each sample; , Distribution as an indicator The mean and standard deviation in the data.

5. The method for predicting asphaltene deposition during gas injection according to claim 2, characterized in that, The formula for calculating the correlation coefficient is as follows: in, It is the correlation coefficient; The resolution coefficient is typically between 0 and 1.

6. The method for predicting asphaltene deposition during gas injection according to claim 2, characterized in that, The grey relational degree is calculated based on the correlation coefficient, and the calculation expression is as follows: in: The calculated correlation value is where correlation value The closer a value is to 1, the stronger the correlation between the sequences; correlation score The closer a value is to 0, the weaker the correlation between the sequences.

7. The method for predicting asphaltene deposition during gas injection according to claim 1, characterized in that, In the step of constructing an empirical formula based on the contents of wax, resin, and asphaltenes in the crude oil sample and two selected pseudo-components with high correlation, and then performing nonlinear regression fitting on the empirical formula to obtain the prediction formula for asphaltenes solid phase deposition points, the expression of the empirical formula is as follows: in, These are asphaltene solid deposition points. o C; , and These represent the wax content, resin content, and asphaltene content, respectively, in wt%; , They are respectively and The total mass percentage of all components, % , , , , and These are the coefficients to be fitted.

8. The method for predicting asphaltene deposition during gas injection according to claim 7, characterized in that, The empirical formula is fitted using nonlinear regression, and the fitting coefficients are obtained. , , , , and The formula for calculating the nonlinear regression fit is as follows: in, For the coefficients to be solved, For input matrices or vectors, To output a matrix or vector, Is with Functions that are matrix or vector values ​​of the same size.

9. A prediction system for asphaltene deposition during gas injection, characterized in that, include: The crude oil sample content testing module is used to obtain crude oil samples after degassing at different times, and to test the contents of wax, gum and asphaltenes in the crude oil samples. The data partitioning module is used to perform a full group analysis of the components of crude oil samples to obtain the carbon number distribution test results of crude oil samples, and to divide the carbon number distribution test results into multiple pseudo-components; The asphaltene solid deposition point test module is used to test the asphaltene solid deposition point of crude oil samples to obtain the asphaltene solid deposition point of each crude oil sample. The grey relational degree calculation module is used to calculate the grey relational degree between each pseudo-component and the asphaltene solid phase deposition point of each crude oil sample using grey relational theory, and select the two pseudo-components with the larger grey relational degree based on the magnitude of the grey relational degree. The empirical calculation module is used to construct empirical formulas based on the contents of wax, gum and asphaltenes in crude oil samples and two selected pseudo-components with high correlation, and to obtain the prediction formula for asphaltenes solid phase deposition points by performing nonlinear regression fitting on the empirical formulas. The asphalt solids deposition point prediction module is used to predict asphalt deposition during the gas injection process based on the obtained asphalt solids deposition point prediction formula.

10. A mobile terminal, 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 predicting asphalt deposition during the gas injection process as described in any one of claims 1-8.

11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for predicting asphalt deposition during the gas injection process as described in any one of claims 1-8.

12. A computer program product comprising computer instructions, characterized in that, The computer instructions instruct the computing device to perform the operation corresponding to the prediction method for asphalt deposition during the gas injection process as described in any one of claims 1-8.

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