Computer-aided simulation method and device for swelling of nanometer alcohol-based fracturing fluid

Abstract

The application relates to the technical field of computer-aided analysis, in particular to a computer-aided nanometer alcohol-based fracturing fluid swelling simulation method and device.The method comprises the following steps: obtaining the swelling displacement of nanometer alcohol-based fracturing fluid under different injection flow rates and injection pressures, so as to calculate the clay swelling rate and form a clay swelling matrix; obtaining the steep change degrees of each row and column vector; according to the average distribution level difference of the steep change degrees of the row and column vectors, in combination with the difference of the steep change degrees of different row vectors and the difference of the steep change degrees of different column vectors, a fitting performance orientation degree is obtained, a step analysis sequence is selected, a fitting function of the step analysis sequence at each step is obtained, a step determination coefficient of the step analysis sequence at each step fitting function is calculated, an optimal step is obtained, and a response function of a nanometer alcohol-based fracturing fluid swelling simulation response surface is obtained by using computer simulation software. The application can improve the nanometer alcohol-based fracturing fluid swelling simulation precision.

Description

Technical Field

[0001] This application relates to the field of computer-aided analysis technology, specifically to a computer-aided method and apparatus for simulating the expansion of nano-alcohol-based fracturing fluid. Background Technology

[0002] Nano-alcohol-based fracturing fluid is a type of fluid used in oil and gas well enhancement technology, which uses methanol, ethanol, and other alcohols as base fluids and adds nanomaterials. Because it is formed by mixing different materials, the performance of nano-alcohol-based fracturing fluid varies when the material composition is different. Therefore, it is necessary to evaluate the performance of nano-alcohol-based fracturing fluid to prevent the fracturing fluid from causing excessive clay expansion, clogging shale pores and fractures, reducing reservoir permeability, hindering shale oil flow, and thus seriously affecting production efficiency and recovery rate.

[0003] Traditional simulation analyses involving multiple parameters and outputs typically employ response surface methodology (RSM) to construct response surface models and analyze the optimal values ​​of each parameter for product performance. However, for the expansion simulation analysis of nano-alcohol-based fracturing fluids, the impact of different material ratios on clay expansion and permeability varies under different conditions. This reduces the accuracy of traditional fixed-order RSM simulations of nano-alcohol-based fracturing fluid expansion. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a computer-aided method and apparatus for simulating the expansion of nano-alcohol-based fracturing fluids. The specific technical solution adopted is as follows:

[0005] This application provides a computer-aided method for simulating the expansion of nano-alcohol-based fracturing fluid, including the following steps:

[0006] The expansion displacement of nano-alcohol-based fracturing fluid under different injection flow rates and injection pressures was obtained to calculate the corresponding clay expansion rate and form a clay expansion matrix.

[0007] The degree of steepness of expansion in each row and column vector of the clay expansion matrix is ​​obtained by measuring the dispersion of the clay expansion rate in each row and column vector and the number of extreme points.

[0008] Based on the differences in the average distribution level of the expansion steepness of the row and column vectors, and combined with the differences in the expansion steepness of different row vectors and the differences in the expansion steepness of different column vectors, the fitting performance orientation of the clay expansion matrix is ​​obtained.

[0009] The order analysis sequence is selected from the clay expansion matrix by using the fitting performance orientation. The fitting function of the order analysis sequence at each order is obtained by fitting. The order determination coefficient of the fitting function of the order analysis sequence at each order is obtained according to the fitting effect of the fitting function, and then the optimal order is obtained. The response function of the nano-alcohol-based fracturing fluid expansion simulation response surface is obtained by using computer simulation software.

[0010] Preferably, the construction of the clay swelling matrix includes: forming different injection flow rates and injection pressures into arrays, each array corresponding to a clay swelling rate, and forming a clay swelling matrix by forming the clay swelling rates of all arrays, wherein the horizontal axis of the clay swelling matrix is ​​the injection flow rate, the vertical axis is the injection pressure, and each element in the clay swelling matrix is ​​the clay swelling rate.

[0011] Preferably, for each row vector of the clay expansion matrix, each row vector is used as the input of the first-order absolute difference algorithm, and the output is a sequence of absolute differences of each row vector.

[0012] Preferably, the process for obtaining the steepness of change of expansion in each row vector of the clay expansion matrix is ​​as follows: In the formula, This represents the steepness of the expansion gradient in the i-th row vector of the clay expansion matrix. Let represent the standard deviation of all elements in the sequence of absolute differences of the i-th row vector in the clay expansion matrix. This represents the number of extreme points in the i-th row vector of the clay expansion matrix.

[0013] Preferably, the process for obtaining the fitting performance orientation of the clay expansion matrix is ​​as follows: In the formula, The degree of orientation represents the fitting performance of the clay swelling matrix. , Let represent the mean of the steepness of expansion variation of all row vectors and the mean of the steepness of expansion variation of all column vectors in the clay expansion matrix, respectively. , represents the range of the steepness of expansion variation of all row vectors and the range of the steepness of expansion variation of all column vectors in the clay expansion matrix, respectively.

[0014] Preferably, the selection process of the order analysis sequence includes: when the fitting performance guidance degree is positive, selecting the column vector with the largest change in expansion steepness among the column vectors in the clay expansion matrix as the order analysis sequence; when the fitting performance guidance degree is non-positive, selecting the row vector with the largest change in expansion steepness among the row vectors in the clay expansion matrix as the order analysis sequence.

[0015] Preferably, the fitting function for the order analysis sequence at each order is: In the formula, This represents the fitting function of the sequence at order t in order analysis. , , Let represent the coefficients of the 0th, 1st, and kth degree terms of the independent variable, respectively, and let x represent the independent variable. This represents the preset maximum order of the function, where the injection flow rate and injection pressure are independent variables, and the clay expansion rate is the dependent variable.

[0016] Preferably, the process of obtaining the order determination coefficients of the order analysis sequence for each order fitting function is as follows: In the formula, The coefficients represent the order determination coefficients of the fitting function of the t-th order analysis sequence. , represents the goodness of fit of the sequence in the t-order fitting function and the p-value of the significance F-test, respectively.

[0017] Preferably, the order corresponding to the function with the largest order determination coefficient is taken as the optimal order.

[0018] This application also provides a computer-aided nano-alcohol-based fracturing fluid expansion simulation device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described computer-aided nano-alcohol-based fracturing fluid expansion simulation methods.

[0019] As can be seen from the above, the computer-aided method and apparatus for simulating the expansion of nano-alcohol-based fracturing fluid provided in this application have at least the following beneficial effects:

[0020] This application analyzes simulated expansion data of nano-alcohol-based fracturing fluid to examine the tortuosity of clay swelling rate changes under varying variables, and calculates the steepness of expansion changes in each row or column vector of the clay swelling matrix. This effectively characterizes the smoothness of changes in different parameters of clay swelling rate.

[0021] Furthermore, this application evaluates the fitting performance orientation of the clay expansion matrix by comparing the degree of change of expansion steepness under different parameter states, determines the data fitting direction, which can reduce the order in the fitting process, reduce the computational load of computer-aided engineering, and improve the efficiency of computer-aided engineering.

[0022] This application analyzes simulated expansion data of nano-alcohol-based fracturing fluid and performs response surface fitting on clay expansion rate data using an adaptive order. Compared to the traditional default second-order response surface fitting, the fitted response surface function is closer to the actual data, and the fitted value is more accurate. Attached Figure Description

[0023] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating the steps of a computer-aided method for simulating the expansion of nano-alcohol-based fracturing fluid provided in this application. Detailed Implementation

[0025] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a computer-aided method and apparatus for simulating the expansion of nano-alcohol-based fracturing fluid according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0026] Unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0027] The following description, in conjunction with the accompanying drawings, details the specific scheme of the computer-aided nano-alcohol-based fracturing fluid expansion simulation method and apparatus provided in this application.

[0028] Please see Figure 1 The document illustrates a flowchart of a computer-aided method for simulating the expansion of nano-alcohol-based fracturing fluid according to an embodiment of this application, including the following steps:

[0029] Step 1: Obtain the expansion displacement of the nano-alcohol-based fracturing fluid under different injection flow rates and injection pressures to calculate the corresponding clay expansion rate and form a clay expansion matrix.

[0030] Computer-aided engineering mainly includes preprocessing, solving, and postprocessing. Preprocessing is used to import and process data collected by the simulation device and determine the boundaries of various parameters. Solving is used to simulate and analyze the expansion data of nano-alcohol-based fracturing fluid using the finite element method, and to construct and solve the expansion model equation. Postprocessing is used to visualize the solved response surface and generate an evaluation report.

[0031] In this embodiment, firstly, a representative shale core is selected and cut into standard sizes. In this embodiment, the shale core is cylindrical with a diameter of 25 mm and a length of 50 mm. The shale core is then cleaned, dried, and subjected to vacuum saturation treatment. A pure clay sample is prepared and made into a sample of a specific specification. The temperature of the constant temperature reaction chamber is set as the target temperature, which is 80°C in this embodiment.

[0032] Furthermore, the saturated shale core was placed into the core holder, and clay samples were placed around the core. Nano-alcohol-based fracturing fluid was injected into the core and clay samples. During the injection process, a displacement measurement sensor was used to monitor the expansion displacement of the clay samples, that is, to obtain the expansion displacement of the clay samples under different injection flow rates and injection pressures. Data acquisition was synchronized during this process.

[0033] In this experiment, the fracturing fluid injection was stopped after 2 hours. The clay swelling rate was calculated based on the displacement measurement data. The calculation formula is the existing technology. In this embodiment, the calculation formula is: Clay swelling rate (%) = (displacement after expansion - initial displacement) / initial displacement × 100%.

[0034] It should be noted that in this embodiment, the clay swelling rate was obtained when the injection flow rate was 1, 2, 3...10 L / min and the injection pressure was 5, 10, 15...50 MPa. Different injection flow rates and injection pressures were grouped into arrays, specifically (1,5), (2,10),..., (10,50) in this embodiment. Each array corresponds to a clay swelling rate, and the clay swelling rates corresponding to all arrays form a clay swelling matrix, denoted as A. In this matrix, the horizontal axis represents the injection flow rate, the vertical axis represents the injection pressure, and each element represents the clay swelling rate.

[0035] Step 2: By measuring the dispersion of the clay swelling rate in each row and column vector of the clay swelling matrix and the number of extreme points, the steepness of the expansion variation in each row and column vector of the clay swelling matrix is ​​obtained.

[0036] In the construction and solution of the expansion simulation equation of nano-alcohol-based fracturing fluid using computer-aided engineering finite element analysis, the clay swelling rate of shale cores varies with different injection pressures and flow rates. Therefore, when one parameter changes while another remains constant, the steeper the change in clay swelling rate, the higher the order required for computer-aided engineering to fit the clay swelling rate and improve fitting accuracy when simulating expansion using non-measurable parameters. However, as the fitting order increases, the computational workload increases exponentially, resulting in a large data volume and long construction time during equation construction. Therefore, when the clay swelling rate changes relatively smoothly, a lower order is used, resulting in less computational workload compared to higher orders, and higher fitting accuracy.

[0037] Therefore, for the row vectors of the clay expansion matrix, each row vector is used as input to the first-order absolute difference algorithm, and the output is a sequence of absolute differences for each row. It should be noted that the first-order absolute difference algorithm involves first-order difference and then taking the absolute value of the difference. The specific calculation is existing technology and is not specifically limited in this embodiment, so it will not be elaborated further. The sequence of absolute differences is used to characterize the change in clay expansion rate under a parameter variation of the nano-alcohol-based fracturing fluid. The smaller the difference between the values ​​in the absolute difference sequence, the smaller the rate of change of clay expansion rate, the closer the clay expansion rate is to a straight line, and the higher the smoothness of the data. Furthermore, each row vector is used as input to the extreme point algorithm, and the output is all the extreme points in each row vector. It should be noted that in this embodiment, the first and last elements of the row vector are also considered as extreme points. The more extreme points there are, the higher the tortuosity and steepness of the change in clay expansion rate. The calculation of the first-order absolute difference algorithm and the extreme point detection algorithm are known technologies, and the specific calculation steps will not be elaborated here.

[0038] Therefore, the steepness of the expansion gradient in each row vector of the clay expansion matrix is ​​calculated: In the formula, This represents the steepness of the expansion gradient in the i-th row vector of the clay expansion matrix. Let represent the standard deviation of all elements in the sequence of absolute differences of the i-th row vector in the clay expansion matrix. This represents the number of extreme points in the i-th row vector of the clay expansion matrix.

[0039] As the flow rate of nano-alcohol-based fracturing fluid injected into shale cores increases, the clay swelling rate also increases. However, this increased swelling can lead to excessive clay expansion, clogging shale pores. Consequently, the clay swelling rate decreases with increasing nano-alcohol-based fracturing fluid injection flow rate. Therefore, the greater the variation in the data within the row vectors of the clay swelling matrix, the more tortuous the data becomes, resulting in a greater number of extreme points and a larger standard deviation of all elements in the absolute difference sequence of the row vectors. This leads to a steeper variation in the swelling rate of the row vectors. The higher the complexity of the variation in clay swelling rate with the nano-alcohol-based fracturing fluid injection flow rate, the higher the order of curve fitting required in computer-aided engineering to ensure the accuracy of the fitting.

[0040] Accordingly, for each column vector in the clay expansion matrix, this embodiment uses the calculation steps of the expansion steepness variation degree of each row vector to obtain the expansion steepness variation degree of each column vector.

[0041] Step 3: Based on the differences in the average distribution level of the expansion steepness of the row and column vectors, combined with the differences in the expansion steepness of different row vectors and the differences in the expansion steepness of different column vectors, the fitting performance orientation of the clay expansion matrix is ​​obtained.

[0042] In performing surface fitting of collected clay swelling rates using response surface methodology, different orders of fitting are required for different rows or columns of data. During the process, directions with higher smoothness should be selected to reduce the fitting order, thereby decreasing the computational load in equation construction for computer-aided engineering and improving the accuracy of surface fitting of clay swelling rates. Therefore, the smaller the steepness of the swelling variation in the row vectors, the lower the fitting order should be selected during the fitting process to reduce the computational load of computer-aided engineering.

[0043] For a clay expansion matrix A, the distribution of its row vectors may differ from that of its column vectors. Therefore, the best-fit order for the row vectors will differ from that for the column vectors. Consequently, during the selection process, it is necessary to first fit the direction with the smaller best-fit order to improve the speed of computer-aided engineering simulation analysis of nano-alcohol-based fracturing fluid expansion.

[0044] Therefore, the fitting performance orientation of the clay swelling matrix is ​​calculated to characterize the selection of the fitting direction. The formula for calculating the fitting performance orientation of the clay swelling matrix is ​​as follows:

[0045] In the formula, This represents the fitting performance orientation of the clay expansion matrix. , Let represent the mean of the steepness of expansion of all row vectors and the mean of the steepness of expansion of all column vectors in the clay expansion matrix, respectively. , represents the range of the steepness of expansion of all row vectors and the range of the steepness of expansion of all column vectors in the clay expansion matrix, respectively.

[0046] It is understandable that when the tortuosity of the data in the row vectors of the clay expansion matrix is ​​greater than that in the column vectors, the mean of the steepness of expansion change in all row vectors is greater than the mean of the steepness of expansion change in all column vectors. Furthermore, the smaller the difference in the steepness of expansion change between different row vectors, the more uniform the data change in the same direction. After fitting, the data will fit better in another direction. Therefore, when the fitting performance orientation of the clay expansion matrix is ​​positive, the column direction is chosen as the fitting direction; when the value is negative or 0, the row direction is chosen. This allows for higher accuracy in computer-aided engineering analysis of the expansion model of nano-alcohol-based fracturing fluid.

[0047] Step 4: Select the order analysis sequence from the clay expansion matrix using the fitting performance guidance degree, and obtain the fitting function of the order analysis sequence at each order through fitting. Obtain the order determination coefficient of the fitting function of the order analysis sequence at each order based on the fitting effect of the fitting function, and then obtain the optimal order. Use computer simulation software to obtain the response function of the nano-alcohol-based fracturing fluid expansion simulation response surface.

[0048] In the computer-aided engineering simulation analysis of the expansion of nano-alcohol-based fracturing fluids, a higher order leads to greater complexity and computational burden when solving the equations and fitting the response surface. Therefore, while ensuring the accuracy of fitting the expansion data of nano-alcohol-based fracturing fluids, it is crucial to minimize the order in the fitting process and reduce computational complexity. Thus, directions with higher smoothness in the clay expansion matrix are selected to lower the order during fitting. Simultaneously, to enhance fitting accuracy, data with higher complexity within these smoother directions are chosen to cover the accuracy of other data.

[0049] Therefore, when the fitting performance guidance is positive, the column vector with the largest steepness of expansion change in the clay expansion matrix is ​​selected as the order analysis sequence; when the fitting performance guidance is non-positive, the row vector with the largest steepness of expansion change in the clay expansion matrix is ​​selected as the order analysis sequence. In selecting the optimal order, polynomial fitting of the data is required. The polynomial fitting function and process are existing technologies, and this embodiment does not impose any special restrictions on them. The fitting function in this embodiment is: In the formula, This represents the fitting function of the sequence at order t in order analysis. , , These represent the coefficients of the 0th, 1st, and kth degree terms of the independent variable, respectively. x represents the independent variable, where the array consisting of injection flow rate and injection pressure is the independent variable, and the clay swelling rate is the dependent variable. This represents the preset maximum order of the function, with a value range of [2, 10]. In this implementation, t is set to 10.

[0050] Furthermore, the goodness of fit of the sequence in each order of the analysis was calculated. The p-value of the F-test is used to measure the goodness of fit of the fitted function to the sequence in the order analysis. A higher goodness of fit indicates a better fit, while a lower p-value indicates a more significant coefficient. The goodness of fit is... The calculation of the significance F-test is a well-known technique, and the specific calculation steps will not be described in detail here.

[0051] Therefore, the order determination coefficients of each order of fitting function are calculated; In the formula, The coefficients represent the order determination coefficients of the fitting function of the order analysis sequence at order t. , represents the goodness of fit of the sequence in the t-order fitting function and the p-value of the significance F-test, respectively.

[0052] The higher the accuracy of the fitting function for the order analysis sequence, the better the fit between the fitting function and the order analysis sequence. This indicates that the fitting function calculated by computer-aided engineering can better capture the trend and details of the expansion data of nano-alcohol-based fracturing fluid. Therefore, the better the fitting effect, the greater the goodness of fit of the fitting function, and the smaller the p-value of the significance F test, the larger the order determination coefficient of the fitting function. When using computer-aided engineering to solve the equations, using this order for response surface fitting will yield better results and higher accuracy.

[0053] Furthermore, considering that a larger order determination coefficient of the order fitting function indicates a better fitting effect, the order corresponding to the fitting function with the largest order determination coefficient is extracted and denoted as the optimal order. Next, the optimal order is used as input to the PolynomialFeatures function in the sklearn library to generate a polynomial model. Then, the injection velocity, injection pressure, and clay expansion matrix are used as input to the LinearRegression function in the sklearn library, with the array of injection velocity and injection pressure as independent variables and the corresponding clay expansion rate as the dependent variable. Coefficients for each term are generated and substituted into the polynomial model to obtain the response function of the nano-alcohol-based fracturing fluid expansion simulation response surface. This achieves computer-aided engineering solution, realizing the construction and solution of the nano-alcohol-based fracturing fluid expansion simulation equation. The use and calculation of the PolynomialFeatures and LinearRegression functions in the sklearn library are formulaic techniques; the specific calculation steps will not be elaborated here.

[0054] Based on the same inventive concept as the above method, this application embodiment also provides a computer-aided nano-alcohol-based fracturing fluid expansion simulation device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described computer-aided nano-alcohol-based fracturing fluid expansion simulation methods.

[0055] It is understood that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0056] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0057] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.

Claims

1. A computer-aided method for simulating the expansion of nano-alcohol-based fracturing fluid, characterized in that, Includes the following steps: The expansion displacement of nano-alcohol-based fracturing fluid under different injection flow rates and injection pressures was obtained to calculate the corresponding clay expansion rate and form a clay expansion matrix. The degree of steepness of expansion in each row and column vector of the clay expansion matrix is ​​obtained by measuring the dispersion of the clay expansion rate in each row and column vector and the number of extreme points. Based on the differences in the average distribution level of the expansion steepness of the row and column vectors, and combined with the differences in the expansion steepness of different row vectors and the differences in the expansion steepness of different column vectors, the fitting performance orientation of the clay expansion matrix is ​​obtained. The order analysis sequence is selected from the clay expansion matrix by using the fitting performance orientation. The fitting function of the order analysis sequence at each order is obtained by fitting. The order determination coefficient of the fitting function of the order analysis sequence at each order is obtained according to the fitting effect of the fitting function, and then the optimal order is obtained. The response function of the nano alcohol-based fracturing fluid expansion simulation response surface is obtained by using computer simulation software. The process of obtaining the fitting performance orientation of the clay expansion matrix is ​​as follows: In the formula, The degree of orientation represents the fitting performance of the clay swelling matrix. , Let represent the mean of the steepness of expansion variation of all row vectors and the mean of the steepness of expansion variation of all column vectors in the clay expansion matrix, respectively. , represents the range of the steepness of expansion variation of all row vectors and the range of the steepness of expansion variation of all column vectors in the clay expansion matrix, respectively.

2. The computer-aided method for simulating the expansion of nano-alcohol-based fracturing fluid as described in claim 1, characterized in that, The construction of the clay swelling matrix includes: forming different injection flow rates and injection pressures into arrays, each array corresponding to a clay swelling rate, and forming a clay swelling matrix by combining the clay swelling rates of all arrays. The horizontal axis of the clay swelling matrix represents the injection flow rate, the vertical axis represents the injection pressure, and each element in the clay swelling matrix represents the clay swelling rate.

3. The computer-aided method for simulating the expansion of nano-alcohol-based fracturing fluid as described in claim 1, characterized in that, For each row vector of the clay expansion matrix, each row vector is used as input to the first-order absolute difference algorithm, and the output is a sequence of absolute differences of each row vector.

4. The computer-aided method for simulating the expansion of nano-alcohol-based fracturing fluid as described in claim 3, characterized in that, The process of obtaining the degree of change in the steepness of expansion of each row vector in the clay expansion matrix is ​​as follows: In the formula, This represents the steepness of the expansion gradient in the i-th row vector of the clay expansion matrix. Let represent the standard deviation of all elements in the sequence of absolute differences of the i-th row vector in the clay expansion matrix. This represents the number of extreme points in the i-th row vector of the clay expansion matrix.

5. The computer-aided method for simulating the expansion of nano-alcohol-based fracturing fluid as described in claim 1, characterized in that, The selection process of the order analysis sequence includes: when the fitting performance guidance degree is positive, selecting the column vector with the largest change in expansion steepness among the column vectors in the clay expansion matrix as the order analysis sequence; when the fitting performance guidance degree is non-positive, selecting the row vector with the largest change in expansion steepness among the row vectors in the clay expansion matrix as the order analysis sequence.

6. The computer-aided method for simulating the expansion of nano-alcohol-based fracturing fluid as described in claim 1, characterized in that, The fitting functions for the sequence at each order in the order analysis are: In the formula, This represents the fitting function of the sequence at order t in order analysis. , , Let represent the coefficients of the 0th, 1st, and kth degree terms of the independent variable, respectively, and let x represent the independent variable. This represents the preset maximum order of the function, where the injection flow rate and injection pressure are independent variables, and the clay expansion rate is the dependent variable.

7. The computer-aided method for simulating the expansion of nano-alcohol-based fracturing fluid as described in claim 1, characterized in that, The process of obtaining the order determination coefficients of the order analysis sequence for each order fitting function is as follows: In the formula, The coefficients represent the order determination coefficients of the fitting function of the t-th order analysis sequence. , represents the goodness of fit of the sequence in the t-order fitting function and the p-value of the significance F-test, respectively.

8. The computer-aided method for simulating the expansion of nano-alcohol-based fracturing fluid as described in claim 1, characterized in that, The order corresponding to the function with the largest fitting coefficient of the order determination coefficient is taken as the optimal order.

9. A computer-aided nano-alcohol-based fracturing fluid expansion simulation device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the computer-aided nano-alcohol-based fracturing fluid expansion simulation method as described in any one of claims 1-8.

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