Carbon dioxide energy storage fracturing gas injection parameter design method based on multiple methods

Abstract

The invention relates to the technical field of oil exploitation, and provides a carbon dioxide energy storage fracturing gas injection parameter design method based on multiple methods, which comprises the following steps: collecting experimental parameters of an experimental well, and calculating a lower limit gas injection amount; in the numerical simulation process, pressure data, corresponding to different injection intensities, of all grids in the experiment well are extracted, the significance coefficient and the comprehensive influence coefficient of the grids are calculated, and the optimal gas injection intensity and the numerical simulation optimal injection amount of the experiment well are calculated according to the result that the comprehensive influence coefficient is used for denoising the pressure data; and the optimal value of the gas injection amount of the experimental well is obtained by combining the theoretical formula of the theoretical injection amount of the experimental well, the experimental parameters of the experimental well and the lower limit gas injection amount. The production increasing effect of the carbon dioxide energy storage fracturing technology can be improved.

Description

Technical Field

[0001] This application relates to the field of oil extraction technology, specifically to a multi-method-based method for designing carbon dioxide energy storage fracturing gas injection parameters. Background Technology

[0002] Carbon dioxide energy storage fracturing is a technology that utilizes the physical properties and energy storage principle of carbon dioxide to achieve fracturing and stimulation of oil and gas reservoirs. The injection parameters for carbon dioxide energy storage fracturing refer to the key technical indicators related to carbon dioxide injection during the fracturing process, including injection pressure and injection volume. Reasonable injection parameters can ensure operational safety, improve fracturing effectiveness, control operational costs, and protect the reservoir environment. Fracturing numerical simulation software can be used to simulate the fracture propagation process, carbon dioxide phase changes, and reservoir stimulation effects under different combinations of injection parameters, based on the reservoir's basic parameters, to obtain the injection parameters for carbon dioxide energy storage fracturing.

[0003] However, the heterogeneity of tight reservoirs and the complexity of artificial reservoirs created by fracturing often lead to differences between the gas injection parameters obtained from numerical simulations and the optimal gas injection parameters. This often results in unstable production improvement effects of carbon dioxide energy storage fracturing technology, making it difficult to guarantee economic benefits. Summary of the Invention

[0004] This application provides a multi-method-based design method for carbon dioxide energy storage fracturing gas injection parameters to address the problem that the heterogeneity of tight reservoirs and the complexity of fracturing-induced artificial reservoirs lead to unreasonable selection of gas injection parameters, resulting in poor production improvement effects of carbon dioxide energy storage fracturing technology. The specific technical solution adopted is as follows:

[0005] One embodiment of this application provides a multi-method-based method for designing carbon dioxide energy storage fracturing gas injection parameters, which includes the following steps:

[0006] Collect experimental parameters from the experimental well and calculate the lower limit of gas injection volume;

[0007] In the numerical simulation, pressure data of each grid in the experimental well corresponding to different injection intensities are extracted. Based on the variation trend and irregularity of all pressure data in the same grid under the same injection intensity, the first significance value of the grid is calculated. Combined with the periodic fluctuation of all pressure data in the same grid under the same injection intensity, the significance coefficient of the grid is calculated. Based on the differences in pressure data and significance coefficients between different grids of the same experimental well under the same injection intensity, the comprehensive influence coefficient of each grid is calculated. Based on the result of denoising the pressure data using the comprehensive influence coefficient, the optimal gas injection intensity is calculated. Based on the optimal gas injection intensity and the experimental parameters of the experimental well, the optimal injection volume of the experimental well in numerical simulation is calculated.

[0008] Based on the theoretical formula for the theoretical injection volume of the experimental well, the experimental parameters of the experimental well, the lower limit of gas injection volume, and the optimal injection volume of numerical simulation, the optimal value of the gas injection volume of the experimental well is obtained.

[0009] Furthermore, the experimental parameters of the experimental well include: sandstone thickness, effective thickness, porosity, well spacing, carbon dioxide volume expansion factor under reservoir conditions, and CO2 density.

[0010] Furthermore, the specific method for obtaining the first saliency value of the grid is as follows:

[0011] The peak undulation of the grid is calculated based on the changing trend of all pressure data in the same grid under the same injection intensity.

[0012] Calculate the fractal dimension of the same grid based on all pressure data from the same grid.

[0013] The result of the positive correlation between the peak undulation of the grid and the fractal dimension is denoted as the first significant value of the grid.

[0014] Furthermore, the specific method for obtaining the crest undulation of the grid is as follows:

[0015] Curve fitting is performed on all pressure data of the same grid under the same injection intensity to obtain the pressure fitting curve of the same grid. The difference between the peak value on the pressure fitting curve and the minimum value within the peak width range of the peak value is recorded as the adjacent difference of the peak value.

[0016] The minimum value of the fitted pressure data during the pressure data acquisition period is recorded as the minimum pressure data. The average of the ratios of the adjacent differences of all peaks to the minimum pressure data is recorded as the peak undulation of the same grid.

[0017] Furthermore, the specific method for determining the significance coefficients of the grid is as follows:

[0018] Establish a stress sequence of the grid, calculate the periodic term of the stress sequence, and calculate the second significant value of the grid based on the time interval between adjacent maxima and adjacent minima in the periodic term.

[0019] The ratio of the first significance value to the second significance value of the grid is denoted as the significance coefficient of the grid.

[0020] Furthermore, the specific method for obtaining the second saliency value of the grid is as follows:

[0021] The coefficient of variation of the time interval between adjacent maxima in the periodic term is denoted as the first coefficient of variation.

[0022] The coefficient of variation of the time interval between adjacent minimum values ​​in the periodic term is denoted as the second coefficient of variation.

[0023] The positive correlation between the first and second coefficients of variation is recorded as the second significant value of the grid.

[0024] Furthermore, the specific method for determining the comprehensive influence coefficient of the grid is as follows:

[0025] Based on the differences in pressure data and significance coefficients between different grids of the same injection intensity and the same experimental well, calculate the pressure differences and significance coefficient differences between the grids.

[0026] The weighted sum of the pressure differences and significance coefficient differences of the grid is denoted as the comprehensive influence coefficient of the grid.

[0027] Furthermore, the method for obtaining the pressure difference and significance coefficient difference of the grid is as follows:

[0028] Let any one grid be designated as the target grid, and let the average DTW distance between the target grid and other grids of the same injection intensity and the same experimental well be designated as the pressure difference of the target grid.

[0029] The mean of the absolute values ​​of the differences in significance coefficients between the target grid and other grids of the same injection intensity and the same experimental well is denoted as the significance coefficient difference of the target grid.

[0030] Furthermore, the specific calculation method for the optimal gas injection intensity is as follows:

[0031] The maximum value of the product of the normalized value of the comprehensive influence coefficient of the grid and the first preset coefficient, and the preset step size lower limit, is denoted as the adjustment step size of the grid.

[0032] Denoise all pressure data collected by the grid based on the grid adjustment step size;

[0033] Based on the denoised pressure data of all grids in all experimental wells under all injection intensities, the miscibility pressure of the target layer is obtained; the average pressure of the injection intensity is recorded as the average pressure of all grids at the bottom of all experimental wells within the influence range of this single well, including the well spacing and half row spacing, under the same injection intensity; the injection intensity with the minimum average pressure that is greater than or equal to the miscibility pressure of the target layer is recorded as the optimal gas injection intensity.

[0034] Furthermore, the specific method for obtaining the optimal value of the gas injection volume of the experimental well based on the theoretical formula of the theoretical injection volume, the experimental parameters of the experimental well, the lower limit of the gas injection volume, and the optimal injection volume of the numerical simulation includes:

[0035] The theoretical injection volume of the experimental well was calculated based on the theoretical formula;

[0036] The average of the numerical simulation optimal injection rate and the theoretical injection rate of the experimental well is denoted as the optimal injection rate of the experimental well. When the optimal injection rate of the experimental well is less than the lower limit gas injection rate, the optimal injection rate of the experimental well is assigned as the lower limit gas injection rate. The optimal injection rate is the optimal value of the gas injection rate of the experimental well.

[0037] The beneficial effects of this application are:

[0038] This application considers that during CO2 injection at the same injection intensity, the instantaneous jump in injection rate, the storage effect of the wellbore, and the randomness of rock fracture development can affect the trend of pressure data changes, leading to sudden spikes in pressure data. This application evaluates the degree to which the pressure data of the grid is affected by changes in injection rate and rock fractures, obtains the second significance value of the grid, and combines this with the significance of the periodic oscillation characteristics exhibited by the grid's pressure data to obtain the significance coefficient of the grid. The larger the significance coefficient, the more significant the sudden spikes and periodic oscillations of the grid, and the more significant the abnormal fluctuations within the grid during CO2 injection at the corresponding injection intensity. Furthermore, the application further evaluates the grid... The impact of abnormal fluctuations on the pressure data acquired at the location is evaluated, and the comprehensive influence coefficient of the grid is obtained. The comprehensive influence coefficient is used to denoise the pressure data, eliminating the interference of the above-mentioned factors on the pressure data, and then determining the optimal injection volume for numerical simulation. This avoids the influence of the heterogeneity of tight reservoirs and the complexity of artificial reservoirs induced by fracturing on the numerical simulation process, and improves the accuracy of the optimal injection volume obtained by numerical simulation. Finally, combined with the theoretical formula of the theoretical injection volume of the experimental well, the optimal value of the gas injection volume of the experimental well is obtained, which solves the problem that the heterogeneity of tight reservoirs and the complexity of artificial reservoirs induced by fracturing lead to unreasonable selection of gas injection parameters, resulting in poor production improvement effect of carbon dioxide energy storage fracturing technology. Attached Figure Description

[0039] To more clearly illustrate the technical solutions 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.

[0040] Figure 1 This is a schematic flowchart of a multi-method carbon dioxide energy storage fracturing gas injection parameter design method provided in one embodiment of this application. Detailed Implementation

[0041] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0042] Please see Figure 1 The diagram illustrates a flowchart of a multi-method-based carbon dioxide energy storage fracturing gas injection parameter design method provided in one embodiment of this application. The method includes the following steps:

[0043] Step S001: Collect experimental parameters from the experimental well and calculate the lower limit of gas injection.

[0044] In this embodiment, the gas injection rate is selected as the gas injection parameter for carbon dioxide energy storage fracturing. Taking experimental well A and experimental well B as examples, the gas injection rate for carbon dioxide energy storage fracturing is calculated.

[0045] First, the experimental parameters of the experimental wells were extracted. These parameters included: sandstone thickness, effective thickness, porosity, well spacing, carbon dioxide volume expansion factor under reservoir conditions, and CO2 density.

[0046] In this embodiment, the experimental parameters for test well A are as follows: actual sandstone thickness of 12.0 m, effective thickness of 10.8 m, porosity of 14.7%, well spacing of 150 m, carbon dioxide volume expansion factor under reservoir conditions of 1.87, and CO2 density of 0.44 t / m³. 3 .

[0047] In this embodiment, the experimental parameters for test well B are as follows: actual sandstone thickness of 24.6m, effective thickness of 12.6m, porosity of 10.9%, well spacing of 150m, carbon dioxide volume expansion factor under reservoir conditions of 1.87, and CO2 density of 0.44t / m³. 3 .

[0048] Using the initial annual oil production target of the oil well as the benchmark value, the lower limit of gas injection volume is determined based on the carbon dioxide huff-and-puff oil exchange rate of the same formation or block. The lower limit of gas injection volume is the product of the initial annual oil production target and the carbon dioxide huff-and-puff oil exchange rate of the same formation.

[0049] Specifically, in this embodiment, the initial annual oil production target is set at 4,500 tons, and the same-layer carbon dioxide huff and puff oil exchange rate is set at 1.

[0050] At this point, the experimental parameters and lower limit of gas injection volume of the experimental well have been obtained.

[0051] Step S002: During the numerical simulation, extract the pressure data of each grid in the experimental well corresponding to different injection intensities. Based on the changing trend and irregularity of all pressure data in the same grid under the same injection intensity, calculate the first significance value of the grid. Combined with the periodic fluctuation of all pressure data in the same grid under the same injection intensity, calculate the significance coefficient of the grid. Based on the differences in pressure data and significance coefficients between different grids of the same experimental well under the same injection intensity, calculate the comprehensive influence coefficient of each grid. Based on the result of denoising the pressure data using the comprehensive influence coefficient, calculate the optimal gas injection intensity. Based on the optimal gas injection intensity and the experimental parameters of the experimental well, calculate the optimal injection volume of the experimental well in the numerical simulation.

[0052] This embodiment selects the Eclipse E300 simulator as the fracturing numerical simulation software. A random disturbance model is introduced into the simulation to simulate real working conditions and determine the gas injection volume in the gas injection parameters. A multi-well parallel injection method is adopted, and the pressure data of each grid in the experimental well corresponding to different injection intensities is obtained through the simulator during the numerical simulation process.

[0053] In order to more realistically reflect the geological heterogeneity and random disturbances during the fracturing process, Gaussian white noise or random disturbance terms were superimposed on the pressure field of the numerical simulation model to simulate pressure fluctuations under actual working conditions, thus achieving the purpose of introducing a random disturbance model to simulate real working conditions.

[0054] In this embodiment, the data acquisition time interval is set to 2 seconds, and pressure data within 5 minutes are collected. The maximum-minimum normalization method is used to remove dimensions from all pressure data. In practical applications, as other implementation methods, implementers can decide the sampling frequency and sampling duration according to the actual situation. This application does not impose any special restrictions.

[0055] During the injection of CO2 at the same injection intensity, the instantaneous jump in injection rate will generate shock waves in the wellbore. The storage effect of the wellbore will cause a sharp increase in pressure in a short period of time. The development of rock fractures is random, and the instantaneous increase in pressure will also trigger the sudden opening of fractures and the redistribution of proppant, resulting in sudden spikes in pressure data.

[0056] Curve fitting is performed on all pressure data of the same grid under the same injection intensity to obtain the pressure fitting curve of the same grid. All maximum points of the pressure fitting curve during the pressure data acquisition period are identified. The maximum points are the peak points of the pressure fitting curve. The difference between the peak and the minimum value within the peak width range of the peak is recorded as the adjacent difference of the peak. The minimum value of the fitted value of the pressure data during the pressure data acquisition period is identified and recorded as the minimum pressure data. The mean of the ratios of the adjacent differences of all peaks to the minimum pressure data is recorded as the peak undulation of the same grid. The fractal dimension of the same grid is calculated based on all pressure data of the same grid. The positive correlation between the peak undulation of the grid and the fractal dimension is recorded as the first significant value of the grid.

[0057] In the process of calculating the ratio, in order to avoid the denominator being zero, a preset value needs to be added to the denominator. In this example, the preset value is 0.01.

[0058] It is understood that a positive correlation is applied to the crest undulation and fractal dimension of the grid, ensuring that both the crest undulation and fractal dimension are positively correlated with the first significant value of the grid. It is also understood that the positive correlation in this application refers to the relationship between the independent and dependent variables, where the independent variables are the crest undulation and fractal dimension of the grid, and the dependent variable is the first significant value of the grid. The positive correlation means that the dependent variable increases (decreases) as the independent variables increase (decreases), and can be an additive, multiplicative, or other relationship.

[0059] Preferably, as an embodiment of this application, the product of the crest undulation of the grid and the fractal dimension is denoted as the first salient value of the grid.

[0060] In this embodiment, the least squares method is used to achieve curve fitting, and the Higuchi algorithm is used to calculate the fractal dimension.

[0061] The crest undulation of the grid is used to evaluate the degree of undulation of the peaks in the pressure fitting curve of the grid. The greater the crest undulation, the greater the undulation of the peaks in the pressure fitting curve. The fractal dimension of the grid is used to evaluate the significance of the randomness of sudden spikes in the pressure fitting curve. The greater the fractal dimension, the more significant the randomness of sudden spikes in the pressure fitting curve. The larger the first significance value of the grid, the more significantly the pressure data of the grid is affected by the injection rate and changes in rock fractures.

[0062] After the injected gas permeates into the reservoir, the seepage resistance gradually changes over time, causing periodic fluctuations in permeability and flow resistance. Simultaneously, factors such as pressure gradient regulation between the wellbore and reservoir, temperature changes, and microseismic activity generate coupled oscillations over time, resulting in regular, periodic fluctuations in bottom hole pressure. Therefore, it is necessary to extract and analyze the potential periodic oscillation characteristics in the pressure data.

[0063] All pressure data from the same grid under the same injection intensity are arranged sequentially according to the time of acquisition to obtain the pressure sequence of the same grid under the same injection intensity. The periodic term of the pressure sequence is calculated using the STL sequence decomposition algorithm. The coefficient of variation of the time interval between adjacent maxima in the periodic term is recorded as the first coefficient of variation, and the coefficient of variation of the time interval between adjacent minima in the periodic term is recorded as the second coefficient of variation. The positive correlation result between the first coefficient of variation and the second coefficient of variation is recorded as the second significant value of the grid.

[0064] Preferably, as an embodiment of this application, the mean of the first coefficient of variation and the second coefficient of variation is denoted as the second significant value of the grid.

[0065] The smaller the second significance value of the grid, the smaller the degree of fluctuation of the periodic oscillation of the grid's pressure data, and the more obvious the periodic oscillation characteristics of the grid's pressure data.

[0066] The ratio of the first significance value to the second significance value of the grid is denoted as the significance coefficient of the grid.

[0067] In the process of calculating the ratio, in order to avoid the denominator being zero, a preset value needs to be added to the denominator. In this example, the preset value is 0.01.

[0068] The larger the significance coefficient of the grid, the more significant the sudden spikes and periodic oscillations of the grid become. During the injection of CO2 at the corresponding injection intensity, the abnormal fluctuations within the grid become more pronounced.

[0069] When multiple wells are injected in parallel, the bottom hole pressures of different experimental wells may couple. For example, when the injection sweep ranges of adjacent experimental wells overlap, the bottom hole pressures in the intersection area will have a superposition effect, causing local overpressure phenomena. This leads to significant differences in the pressure field of the entire injection network, interfering with the accurate identification of the optimal injection intensity. Therefore, based on the evaluation of the significance of the abnormal fluctuation characteristics exhibited within the grid, the impact of abnormal fluctuations on the pressure data collected at the grid location is further evaluated.

[0070] Any grid is designated as the target grid. The average DTW distance between the target grid and other grids with the same injection intensity and the same experimental well is designated as the pressure difference of the target grid. The average absolute value of the difference between the significance coefficients of the target grid and other grids with the same injection intensity and the same experimental well is designated as the significance coefficient difference of the target grid. The weighted sum of the pressure difference and the significance coefficient difference of the target grid is designated as the comprehensive influence coefficient of the target grid.

[0071] Preferably, as an embodiment of this application, when weighted summing of the pressure difference and significance coefficient difference of the target grid, the weights of the pressure difference and significance coefficient difference of the target grid are both 0.5.

[0072] Because of the hysteresis effect in the pressure response of different regions, directly aligning the time axis to calculate the error cannot accurately reflect the waveform similarity. Therefore, DTW (Time-Difference Waveform) must be used to measure shape similarity rather than time synchronization. The hysteresis effect is the phase difference in time.

[0073] The larger the comprehensive influence coefficient of the target grid, the greater the degree of abnormal changes in the pressure data collected at the target grid location due to the influence of multiple factors, and the more necessary it is to reduce the interference of abnormal fluctuations on the pressure data collected at the target grid location.

[0074] The same method can be used to obtain the comprehensive influence coefficient of each grid in each experimental well under each injection intensity.

[0075] The product of the normalized value of the comprehensive influence coefficient of the grid and the first preset coefficient is recorded as the adjustment step size of the grid. When the adjustment step size of the grid is less than the preset lower limit, the adjustment step size of the grid is assigned to the preset lower limit. The adjustment step size of the grid is used as the step size factor of LMS filtering. LMS filtering is used to denoise all the pressure data collected by the grid to obtain the denoised pressure data.

[0076] The first preset coefficient should be greater than or equal to 0.01 and less than or equal to 0.1. In this embodiment, the first preset coefficient is set to 0.1, and the preset step size lower limit is set to 0.01. The purpose of the preset step size lower limit is to avoid the convergence speed of LMS filtering being too slow. In the process of using LMS filtering to denoise all pressure data collected by the grid, it is necessary to generate a pseudo-expectation signal. The generation of pseudo-expectation signal is a well-known technique and will not be described in detail here. In this embodiment, the arithmetic mean of the pressure data of the previous 100 time steps is used as the expected signal of LMS filter. In this embodiment, the tanh function is used to calculate the normalized value. The tanh function is a well-known technique and will not be described in detail here. As other implementation methods, implementers can use other methods of existing technology to calculate the normalized value, such as the sigmoid function.

[0077] Using the equation-of-state method, the miscibility pressure of the target layer is obtained from the denoised pressure data of all grids in all experimental wells under all injection intensities. The average pressure of the injection intensity is recorded as the average pressure of all grids at the bottom of all experimental wells within the influence range of this single well, at the same injection intensity, with the sum of well spacing and half row spacing. The injection intensity with the minimum average pressure greater than or equal to the miscibility pressure of the target layer is recorded as the optimal injection intensity.

[0078] Specifically, if the average pressure at all injection intensities is less than the miscibility pressure of the target layer, the maximum injection intensity in the simulation is selected as the optimal injection intensity; if the average pressure at all injection intensities is greater than the miscibility pressure of the target layer, the minimum injection intensity is selected.

[0079] The use of the equation of state method to obtain the miscibility pressure of the target layer is a well-known technique and will not be described in detail here. In this embodiment, the miscibility pressure of the target layer is measured by a thin tube experiment.

[0080] It is understandable that the heterogeneity of tight reservoirs and the complexity of fracturing-induced artificial reservoirs can lead to abnormal fluctuations in pressure data. The denoised pressure data from the grid is pressure data after eliminating the interference of these factors. Therefore, the optimal gas injection intensity is determined based on pressure data that has eliminated abnormal influences. This can avoid the impact of the heterogeneity of tight reservoirs and the complexity of fracturing-induced artificial reservoirs on the numerical simulation process and improve the accuracy of the optimal injection volume obtained through numerical simulation.

[0081] Based on the optimal gas injection intensity and the experimental parameters of the experimental well, the optimal injection rate for numerical simulation of the experimental well is calculated. The formula for calculating the optimal injection rate for numerical simulation is:

[0082]

[0083] In the formula, This indicates the optimal injection amount in the numerical simulation. Indicates the effective thickness of the experimental well; This indicates the optimal gas injection intensity of the experimental well; Indicates the sandstone thickness of the experimental well; This indicates the CO2 density of the experimental well.

[0084] Thus, the optimal injection rate for the experimental well was obtained through numerical simulation.

[0085] Step S003: Based on the theoretical formula for the theoretical injection volume of the experimental well, the experimental parameters of the experimental well, the lower limit of gas injection volume, and the optimal injection volume of numerical simulation, obtain the optimal value of the gas injection volume of the experimental well.

[0086] Based on the theoretical formula for the theoretical injection rate of the experimental well and the experimental parameters of the experimental well, the theoretical injection rate of the experimental well is calculated. The formula for calculating the theoretical injection rate is as follows:

[0087]

[0088] In the formula, This indicates the theoretical injection volume of the experimental well; This represents pi (π). In this embodiment, pi is calculated to be 3.14. This indicates half the spacing between the rows of experimental wells; Indicates the effective thickness of the experimental well; Indicates the porosity of the experimental well; This indicates the CO2 density of the experimental well; This indicates the volume expansion factor of carbon dioxide under reservoir conditions in the experimental well.

[0089] The average of the numerical simulation optimal injection rate and the theoretical injection rate of the experimental well is recorded as the optimal injection rate of the experimental well. When the optimal injection rate of the experimental well is less than the lower limit gas injection rate, the optimal injection rate of the experimental well is assigned as the lower limit gas injection rate to ensure the stability of the production improvement effect of carbon dioxide storage fracturing.

[0090] It is understandable that the optimal injection rate of the experimental well is the optimal value of the gas injection rate of the experimental well.

[0091] The experimental results for experimental wells A and B in this embodiment are shown in Table 1.

[0092] Table 1. Calculation Results of Gas Injection Volume in Tight Oil Carbon Dioxide Storage Fracturing Test Wells

[0093]

[0094] Thus, the optimal value of the gas injection rate for carbon dioxide storage fracturing has been obtained.

[0095] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A multi-method-based design method for carbon dioxide energy storage fracturing gas injection parameters, characterized in that, The method includes the following steps: Collect experimental parameters from the experimental well and calculate the lower limit of gas injection volume; In the numerical simulation, pressure data of each grid in the experimental well corresponding to different injection intensities are extracted. Based on the variation trend and irregularity of all pressure data in the same grid under the same injection intensity, the first significance value of the grid is calculated. Combined with the periodic fluctuation of all pressure data in the same grid under the same injection intensity, the significance coefficient of the grid is calculated. Based on the differences in pressure data and significance coefficients between different grids of the same experimental well under the same injection intensity, the comprehensive influence coefficient of each grid is calculated. Based on the result of denoising the pressure data using the comprehensive influence coefficient, the optimal gas injection intensity is calculated. Based on the optimal gas injection intensity and the experimental parameters of the experimental well, the optimal injection volume of the experimental well in numerical simulation is calculated. Based on the theoretical formula for the theoretical injection volume of the experimental well, the experimental parameters of the experimental well, the lower limit of gas injection volume, and the optimal injection volume of numerical simulation, the optimal value of the gas injection volume of the experimental well is obtained.

2. The method for designing carbon dioxide energy storage fracturing gas injection parameters based on multiple methods according to claim 1, characterized in that, The experimental parameters of the test well include: sandstone thickness, effective thickness, porosity, well spacing, carbon dioxide volume expansion factor under reservoir conditions, and CO2 density.

3. The method for designing carbon dioxide energy storage fracturing gas injection parameters based on multiple methods according to claim 1, characterized in that, The specific method for obtaining the first significant value of the grid is as follows: The peak undulation of the grid is calculated based on the changing trend of all pressure data in the same grid under the same injection intensity. Calculate the fractal dimension of the same grid based on all pressure data from the same grid. The result of the positive correlation between the peak undulation of the grid and the fractal dimension is denoted as the first significant value of the grid.

4. The method for designing carbon dioxide energy storage fracturing gas injection parameters based on multiple methods according to claim 3, characterized in that, The specific method for obtaining the crest undulation of the grid is as follows: Curve fitting is performed on all pressure data of the same grid under the same injection intensity to obtain the pressure fitting curve of the same grid. The difference between the peak value on the pressure fitting curve and the minimum value within the peak width range of the peak value is recorded as the adjacent difference of the peak value. The minimum value of the fitted pressure data during the pressure data acquisition period is recorded as the minimum pressure data. The average of the ratios of the adjacent differences of all peaks to the minimum pressure data is recorded as the peak undulation of the same grid.

5. The method for designing carbon dioxide energy storage fracturing gas injection parameters based on multiple methods according to claim 1, characterized in that, The specific method for determining the significance coefficient of the grid is as follows: Establish a stress sequence of the grid, calculate the periodic term of the stress sequence, and calculate the second significant value of the grid based on the time interval between adjacent maxima and adjacent minima in the periodic term. The ratio of the first significance value to the second significance value of the grid is denoted as the significance coefficient of the grid.

6. The method for designing carbon dioxide storage fracturing gas injection parameters based on multiple methods according to claim 5, characterized in that, The specific method for obtaining the second significant value of the grid is as follows: The coefficient of variation of the time interval between adjacent maxima in the periodic term is denoted as the first coefficient of variation. The coefficient of variation of the time interval between adjacent minimum values ​​in the periodic term is denoted as the second coefficient of variation. The positive correlation between the first and second coefficients of variation is recorded as the second significant value of the grid.

7. The method for designing carbon dioxide energy storage fracturing gas injection parameters based on multiple methods according to claim 1, characterized in that, The specific method for determining the comprehensive influence coefficient of the grid is as follows: Based on the differences in pressure data and significance coefficients between different grids of the same injection intensity and the same experimental well, calculate the pressure differences and significance coefficient differences between the grids. The weighted sum of the pressure differences and significance coefficient differences of the grid is denoted as the comprehensive influence coefficient of the grid.

8. The method for designing carbon dioxide energy storage fracturing gas injection parameters based on multiple methods according to claim 7, characterized in that, The method for obtaining the pressure difference and significance coefficient difference of the grid is as follows: Let any one grid be designated as the target grid, and let the average DTW distance between the target grid and other grids of the same injection intensity and the same experimental well be designated as the pressure difference of the target grid. The mean of the absolute values ​​of the differences in significance coefficients between the target grid and other grids of the same injection intensity and the same experimental well is denoted as the significance coefficient difference of the target grid.

9. The method for designing carbon dioxide storage fracturing gas injection parameters based on multiple methods according to claim 1, characterized in that, The specific calculation method for the optimal gas injection intensity is as follows: The maximum value of the product of the normalized value of the comprehensive influence coefficient of the grid and the first preset coefficient, and the preset step size lower limit, is denoted as the adjustment step size of the grid. Denoise all pressure data collected by the grid based on the grid adjustment step size; Based on the denoised pressure data of all grids in all experimental wells under all injection intensities, the miscibility pressure of the target layer is obtained; the average pressure of the injection intensity is recorded as the average pressure of all grids at the bottom of all experimental wells within the influence range of this single well, including the well spacing and half row spacing, under the same injection intensity; the injection intensity with the minimum average pressure that is greater than or equal to the miscibility pressure of the target layer is recorded as the optimal gas injection intensity.

10. The method for designing carbon dioxide energy storage fracturing gas injection parameters based on multiple methods according to claim 1, characterized in that, The method for obtaining the optimal value of the gas injection volume of the experimental well based on the theoretical formula of the theoretical injection volume, the experimental parameters of the experimental well, the lower limit of gas injection volume, and the optimal injection volume of numerical simulation includes the following specific methods: The theoretical injection volume of the experimental well was calculated based on the theoretical formula; The average of the numerical simulation optimal injection rate and the theoretical injection rate of the experimental well is denoted as the optimal injection rate of the experimental well. When the optimal injection rate of the experimental well is less than the lower limit gas injection rate, the optimal injection rate of the experimental well is assigned as the lower limit gas injection rate. The optimal injection rate is the optimal value of the gas injection rate of the experimental well.

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