Automatic calculation method for critical liquid-carrying pressure differential of shale gas well

Abstract

The present application belongs to the field of shale gas development engineering technology, and particularly relates to an automatic calculation method for critical liquid-carrying pressure difference of a shale gas well. First, daily production data of a target well in a preset time period is obtained, stable production daily data is obtained by well closure, fluctuation period and abnormal data elimination, and then stable production section data is screened out by using continuous days and a variation coefficient. Subsequently, a bucket first-order difference method is used to capture a yield inflection point, and a quadratic polynomial fitting method is used to solve a yield optimal value, so as to obtain two critical liquid-carrying pressure difference values. Finally, the maximum value of the two critical liquid-carrying pressure difference values is taken as a final critical value. The present application can solve the problem of low precision in the prior art of calculating the critical liquid-carrying pressure difference of a shale gas well.

Description

Technical Field

[0001] This invention belongs to the field of shale gas development engineering technology, and in particular relates to an automatic calculation method for the critical hydraulic pressure difference of shale gas wells. Background Technology

[0002] As an important type of unconventional natural gas, shale gas faces the critical bottleneck of wellbore fluid accumulation, which hinders efficient production. Fluid accumulation increases bottom-hole back pressure, significantly reducing gas production and, in severe cases, causing well shutdown. This directly impacts the development efficiency and recovery rate of shale gas fields. Therefore, accurate early warning of fluid accumulation is crucial for shale gas well production management. The critical pressure differential is the core threshold for fluid accumulation warning, and its calculation accuracy directly determines the effectiveness of the warning.

[0003] Current technologies for calculating critical pressure differentials and providing early warning of fluid accumulation in shale gas wells suffer from numerous shortcomings, making it difficult to meet the demands of large-scale, refined gas field development. On one hand, traditional calculations of critical pressure differentials rely on physical models such as Turner and Hagedorn-Brown. The Turner method, based on the idealized assumption of spherical droplets and uniform flow field conditions, deviates significantly from the actual non-uniform gas-liquid two-phase flow conditions within shale gas wells. Its calculation results are generally conservative, easily leading to misjudgments of fluid accumulation and unnecessary interventions such as bubble drainage and pressurization. While the Hagedorn-Brown method considers the flow pattern changes in the gas-liquid two-phase flow, its application requires a large amount of high-precision measured data, such as bottomhole flowing pressure, wellbore temperature profiles, and fluid properties. However, such data is often lacking or prohibitively expensive to obtain in shale gas well field monitoring, severely limiting the method's engineering applicability.

[0004] On the other hand, in current engineering practice, the calibration of critical hydraulic differential pressure is mostly done manually. Technicians need to screen, analyze, and determine thresholds based on historical data from gas wells based on their field production experience. This is not only time-consuming and labor-intensive with low computational efficiency, but also highly subjective, with significant differences in calibration results among different technicians. Furthermore, shale gas wells undergo various operating conditions during production, such as fracturing, foam drainage, pressurization, and intermittent resumption of production. Manually calibrated critical hydraulic differential pressure cannot adapt to dynamic adjustments under these conditions in a timely manner, easily leading to significant deviations between the calibrated values ​​and the actual production needs of the gas well. This results in a substantial decrease in the accuracy of liquid accumulation warnings, missing the optimal time for liquid accumulation control, or causing ineffective operations.

[0005] Therefore, there is an urgent need for an automated, high-precision method for calculating critical pressure differential that can match the production characteristics of a single well, so as to provide reliable technical support for automatic early warning of liquid accumulation in gas wells. Summary of the Invention

[0006] The technical problem solved by this invention is to provide an automatic calculation method for the critical hydraulic pressure difference of shale gas wells, so as to solve the problem of low accuracy in the existing calculation methods for the critical hydraulic pressure difference of shale gas wells.

[0007] The basic solution provided by this invention is an automatic calculation method for the critical hydraulic carrying capacity difference of shale gas wells, comprising: S1: According to the 180-day time window dynamic sliding update mechanism, the daily production data of the target shale gas well within the time window is obtained, and the daily generated data is processed by well shut-in data marking, fluctuation period data marking, and abnormal data processing to obtain stable production daily data; the daily production data includes at least measured oil pressure, measured casing pressure, daily gas production, and production time; S2: Based on stable production day data, process the stable production day data according to the preset consecutive days determination rules and preset fluctuation determination rules to generate stable production segment data; S3: Based on stable production section data, a pre-defined first-order difference method for different tanks is used to capture the inflection point of the trend of output change with oil-casing pressure difference, and the critical pressure carrying difference is calculated. ; S4: Based on stable production section data, a quadratic polynomial fitting model of oil-casing pressure difference and daily gas production is constructed to calculate the critical pressure carrying difference. ; S5: Based on the obtained critical pressure carrying differential and critical carrying pressure difference The final critical pressure carrying capacity difference of the target shale gas well is obtained by adopting a fusion strategy that takes the maximum value.

[0008] Furthermore, S1 includes: S1-1: Construct a dynamically sliding and updating time window based on a 180-day time period, and obtain the daily production data of the target shale gas well within this time window; the daily production data includes at least the measured oil pressure. Actual sleeve pressure Daily gas production Production time; and calculate the oil-casing pressure difference based on the measured oil pressure and measured casing pressure. The expression is:

[0009] S1-2: Mark production time based on daily production data The operating condition data is used to obtain shut-in data and then discard it; S1-3: Based on the daily production data, mark the production data within the preset fluctuation period threshold after well opening, obtain the fluctuation period data, and remove it; S1-4: Regarding daily production data Abnormal data is removed; S1-5: Integrate the daily production data after removing S1-2 to S1-4 to obtain stable daily production data.

[0010] Furthermore, S2 includes: S2-1: Extract stable production day data segments from stable production day data where the number of consecutive stable production days is greater than or equal to the preset minimum number of consecutive stable production days; S2-2: Calculate the daily gas production in the stable production day data segment. Pressure difference with oil jacket coefficient of variation The expression is:

[0011] in, Let be the standard deviation of the data series. The arithmetic mean and standard deviation of the data series. and arithmetic mean The calculation formula is:

[0012]

[0013] in, The number of samples in the data sequence. For the i-th sample value in the data sequence, substitute it with the daily gas production and oil-jacket pressure difference data respectively; S2-3: Determine whether the coefficient of variation of daily gas production and the coefficient of variation of oil-casing pressure difference exceed the preset fluctuation threshold. If not, determine that the fluctuation of the stable production day data segment meets the requirements; otherwise, it does not meet the requirements. Integrate the stable production day data segments that meet the requirements to obtain stable production segment data.

[0014] Furthermore, S3 includes: S3-1: Calculate the stable production section data according to the oil-casing pressure difference. Sort in ascending order; S3-2: Calculate the number of bins based on the sample size of the stable production section in the stable production section data. The expression is:

[0015] in, The number of buckets is a positive integer. The function is for finding the minimum value; This is the floor function. To stabilize the total sample data volume of the production section; S3-3: Based on the calculated number of buckets The sorted oil-casing pressure differentials are divided into equal intervals (buckets), and the average oil-casing pressure differential within each bucket is calculated. Compared with average daily gas production and remove the sample number Invalid buckets, among which ; S3-4: Calculate the first-order difference of the average daily gas production between adjacent barrels, expressed as follows:

[0016] in, Let be the first-order difference between the daily gas production of the k-th barrel and the (k+1)-th barrel; Let k be the average daily gas production of the k-th barrel; This represents the average daily gas production of the (k+1)th barrel. S3-5: Determine if the first-order difference is... Transform into When this point is determined to be an inflection point in the production trend, the average oil-jacket difference corresponding to this inflection point is the critical hydraulic carrying difference calculated by the first-order difference method for each barrel. .

[0017] Furthermore, S4 includes: S4-1: Based on stable production section data, using oil-casing pressure differential as independent variable Daily gas production Dependent variable Construct a quadratic polynomial fitting model, the expression of which is:

[0018] in, Daily gas production For the oil-casing pressure difference, The coefficient of the quadratic term, ; The coefficient of the linear term, For constant terms; S4-2: Calculate the first derivative of the quadratic polynomial fitting model and set it equal to 0. Solve for the oil-casing pressure difference corresponding to the maximum daily gas production, which is characterized as the critical pressure carrying capacity difference. The expression is:

[0019] in, The first derivative of the daily gas production with respect to the oil-casing pressure difference is used to solve for X, which is the critical pressure carrying difference calculated by the quadratic polynomial fitting model. ; If the coefficient of the quadratic term in the fitting result If the fit is invalid, there is no critical pressure difference. Calculation results.

[0020] Furthermore, in step S5, the expression for the fusion strategy that takes the maximum value is:

[0021] in, The final critical pressure carrying capacity differential for the target shale gas well. This is the function for finding the maximum value.

[0022] The principle and advantages of this invention are as follows: Based on feature extraction and dual-algorithm fusion threshold calibration of actual production data of a single well, this invention achieves accurate data screening through a dual data purification mechanism of "stable production day - stable production period" and combined with core calculation formulas such as coefficient of variation and oil casing pressure difference, eliminating various kinds of interference data and ensuring the authenticity and effectiveness of the calculation basis data. The first-order difference method for dividing the barrels uses formulas such as the number of barrels and the first-order difference to capture the inflection point of the production trend from the perspective of actual engineering, which is close to the working conditions of actual shale gas well production; the quadratic polynomial fitting method uses fitting and differentiation formulas to accurately solve the optimal production threshold from a mathematical perspective, ensuring the rigor of the calculation. Ultimately, by using a fusion formula that takes the larger of the two results, both engineering trends and mathematical accuracy are balanced, avoiding the calculation bias of a single method, and achieving automated and high-precision calibration of critical hydraulic differential.

[0023] The advantages are as follows: This application departs from the idealized assumptions of traditional physical models, does not require a large number of high-precision measured parameters, completes the calculation based on the daily production data of the field, and can dynamically adjust the critical value according to the update of gas well production data, adapting to changes in working conditions such as fracturing and foam drainage operations. It solves the problems of low accuracy, low efficiency of manual calibration and large subjective error of traditional methods, and provides a reliable judgment threshold for automatic early warning of liquid accumulation in shale gas wells. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of an embodiment of the present invention. Detailed Implementation

[0025] The following detailed description illustrates the specific implementation method: The basic implementation examples are as follows: Figure 1 As shown: An automatic calculation method for the critical pressure carrying capacity difference of shale gas wells, comprising: S1: Following a 180-day time window dynamic sliding update mechanism, daily production data of the target shale gas well within the time window is acquired. This data is then processed by marking shut-in data, fluctuation period data, and handling abnormal data to obtain stable daily production data. In this embodiment, a production well from a shale gas field is used to feed the target well. The time window is approximately 180 days, the fluctuation period is 3 days, the minimum consecutive days are 7 days, and the fluctuation threshold is... Furthermore, the time window is updated using a dynamic sliding update method, and then the critical hydraulic pressure difference of the well is automatically calculated.

[0026] The acquired daily production data includes at least oil pressure, casing pressure, daily gas production, and production time, and may also include date data; specifically, S1 includes: S1-1: Construct a dynamically sliding and updating time window based on a 180-day time period, and obtain the daily production data of the target shale gas well within this time window; the daily production data includes at least the measured oil pressure. Actual sleeve pressure Daily gas production Production time; and calculate the oil-casing pressure difference based on the measured oil pressure and measured casing pressure. The expression is:

[0027] S1-2: Mark production time based on daily production data The operating condition data is used to obtain shut-in data and then discard it; S1-3: Based on the daily production data, mark the production data within the preset fluctuation period threshold after well opening, obtain the fluctuation period data, and remove it; S1-4: Regarding daily production data Abnormal data is removed; S1-5: Integrate the daily production data after removing S1-2 to S1-4 to obtain stable production day data; the obtained stable production day data can reflect the normal production of gas wells, and the data dimension includes the oil-casing pressure difference on stable production days. Daily gas production .

[0028] S2: Based on stable production day data, process the stable production day data according to preset consecutive days determination rules and preset fluctuation determination rules to generate stable production segment data; wherein S2 includes: S2-1: Extract stable production day data segments from stable production day data where the number of consecutive stable production days is greater than or equal to the preset minimum number of consecutive days; in the shale gas well of the aforementioned example, the preset minimum number of consecutive days is 7 days; by filtering stable production day data segments where the number of consecutive days is greater than or equal to the preset minimum number of consecutive days, it can be ensured that the data can reflect the long-term steady-state production characteristics of the gas well.

[0029] S2-2: Calculate the daily gas production in the stable production day data segment. Pressure difference with oil jacket coefficient of variation The expression is:

[0030] in, Let be the standard deviation of the data series. The arithmetic mean and standard deviation of the data series. and arithmetic mean The calculation formula is:

[0031]

[0032] in, The number of samples in the data sequence. For the i-th sample value in the data sequence, substitute it with the daily gas production and oil-jacket pressure difference data respectively; S2-3: Determine whether the coefficient of variation of daily gas production and the coefficient of variation of oil-casing pressure difference exceed the preset fluctuation threshold. If not, determine that the fluctuation of the stable production day data segment meets the requirements; otherwise, it does not meet the requirements. Integrate the stable production day data segments that meet the requirements to obtain stable production segment data. Among them, when the coefficient of variation of daily gas production is less than the preset threshold of 15... The fluctuation threshold, and the coefficient of variation of the oil-casing pressure differential is less than the aforementioned preset 15. When the fluctuation threshold is reached, the fluctuation of the data segment is determined to meet the requirements. Therefore, the dataset that simultaneously meets the requirements of the number of consecutive days and the degree of fluctuation is the stable production segment data.

[0033] S3: Based on stable production section data, a pre-defined first-order difference method for different tanks is used to capture the inflection point of the trend of output change with oil-casing pressure difference, and the critical pressure carrying difference is calculated. S3 includes: S3-1: Calculate the stable production section data according to the oil-casing pressure difference. Sort in ascending order; S3-2: Calculate the number of bins based on the sample size of the stable production section in the stable production section data. The expression is:

[0034] in, The number of buckets is a positive integer. The function is for finding the minimum value; This is the floor function. To stabilize the total sample data volume of the production section; S3-3: Based on the calculated number of buckets The sorted oil-casing pressure differentials are divided into equal intervals (buckets), and the average oil-casing pressure differential within each bucket is calculated. Compared with average daily gas production and remove the sample number Invalid buckets, among which ; S3-4: Calculate the first-order difference of the average daily gas production between adjacent barrels, expressed as follows:

[0035] in, Let be the first-order difference between the daily gas production of the k-th barrel and the (k+1)-th barrel; Let k be the average daily gas production of the k-th barrel; This represents the average daily gas production of the (k+1)th barrel. S3-5: Determine if the first-order difference is... Transform into When this point is determined to be an inflection point in the production trend, the average oil-jacket difference corresponding to this inflection point is the critical hydraulic carrying difference calculated by the first-order difference method for each barrel. .

[0036] To better understand the meaning of each step in S3 above, an example is given below: Based on steps S3-1 to S3-5 above, taking a total of 21 stable production segment data as an example, the determined number of bins is 4, which is divided into 4 bins and the sample number is removed. For ineffective barrels, calculate the average oil-jacket pressure difference and average daily gas production for each barrel, through... Calculating the first-order difference reveals that the difference between the second and third barrels changes from positive to negative. This inflection point corresponds to the critical pressure carrying capacity of 1.2 MPa, representing the average oil-sleeve pressure difference in the second barrel. .

[0037] S4: Based on stable production section data, a quadratic polynomial fitting model of oil-casing pressure difference and daily gas production is constructed to calculate the critical pressure carrying difference. S4 includes: S4-1: Based on stable production section data, using oil-casing pressure differential as independent variable Daily gas production Dependent variable Construct a quadratic polynomial fitting model, the expression of which is:

[0038] in, Daily gas production For the oil-casing pressure difference, The coefficient of the quadratic term, ; The coefficient of the linear term, For constant terms; S4-2: Calculate the first derivative of the quadratic polynomial fitting model and set it equal to 0. Solve for the oil-casing pressure difference corresponding to the maximum daily gas production, which is characterized as the critical pressure carrying capacity difference. The expression is:

[0039] in, The first derivative of the daily gas production with respect to the oil-casing pressure difference is used to solve for X, which is the critical pressure carrying difference calculated by the quadratic polynomial fitting model. ; If the coefficient of the quadratic term in the fitting result If the fit is invalid, there is no critical pressure difference. Calculation results.

[0040] To better understand the meaning of each step in S4 above, an example is given below: using the coefficient of the quadratic term , , For example, through Solve, and obtain That is, the critical pressure difference .

[0041] S5: Based on the obtained critical pressure carrying differential and critical carrying pressure difference The final critical pressure carrying capacity differential of the target shale gas well is obtained by using a fusion strategy that takes the maximum value; where S5 includes: S5-1: Critical pressure carrying differential and critical carrying pressure difference The relative deviation is expressed as:

[0042] in, The relative deviation is set to a preset deviation threshold. In this embodiment, the preset deviation threshold is 20%, which can be adjusted according to the overall development accuracy requirements of the gas field; S5-2: If the relative deviation Determine the critical pressure difference. and critical carrying pressure difference To meet engineering requirements, the final critical hydraulic carrying differential is obtained using a fusion strategy that takes the maximum value. The expression is:

[0043] in, The final critical pressure carrying capacity differential for the target shale gas well. This is a function to find the maximum value. Based on the examples above, relative deviation If the preset deviation threshold of less than 20% meets the engineering requirements, then the final critical pressure difference of the target well is 1.3 MPa.

[0044] S5-3: If the relative deviation If the result exceeds the reasonable range, an abnormal result alarm will be automatically triggered. The alarm information will be pushed to the gas field production control platform at the same time, indicating that the deviation of the critical value of the dual algorithm in the current calculation cycle of the well is too large, and that the technical personnel need to manually review the data quality, changes in operating conditions and well status.

[0045] After an alarm is triggered, the final critical pressure difference of the previous calculation cycle is temporarily used as the temporary judgment threshold for the current cycle. The critical value will be replaced after manual review and correction or after the next sliding window calculation update, so as to avoid false alarms or missed alarms caused by abnormal calculation results.

[0046] Therefore, based on the technical solution of this application described above, the core technical highlight is as follows: Innovative stable production data screening logic: Through a dual mechanism of "stable production day calibration + stable production segment screening", it accurately eliminates interference data such as well shut-in and well opening fluctuation periods and outliers, and calculates thresholds based solely on the normal stable production data of a single well, which closely matches the actual production characteristics of gas wells.

[0047] The threshold calibration method integrating dual algorithms combines first-order difference binning with quadratic polynomial fitting. It captures the trend characteristics of output inflection points through binning and ensures the mathematical rigor of the threshold through fitting. Finally, the maximum value is taken as the critical value, balancing reliability and accuracy.

[0048] Full-process automated adaptation: The entire process of data preprocessing, stable section identification, and threshold calculation can be completed without manual intervention. It is automatically recalibrated every quarter and can dynamically adapt to changes in working conditions such as gas well fracturing, bubble drainage, and pressurization, solving the problem of lag in manual calibration.

[0049] The advantages are: More accurate threshold calibration: It avoids the idealized assumptions of traditional physical models (such as Turner), and back-calculates the threshold based on the actual production data of a single well. The deviation from the manually calibrated value is within an acceptable range, and it can correct the subjective error and adaptation lag of the manual calibration.

[0050] Balancing efficiency and adaptability: The entire process of single-well threshold calculation is automated, and it is also compatible with different types of shale gas wells (new pumping wells, intermittent wells, resuming production wells, etc.).

[0051] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. An automatic calculation method for the critical pressure carrying capacity difference of shale gas wells, characterized in that: include: S1: According to the 180-day time window dynamic sliding update mechanism, the daily production data of the target shale gas well within the time window is obtained, and the daily generated data is processed by well shut-in data marking, fluctuation period data marking, and abnormal data processing to obtain stable production daily data; the daily production data includes at least measured oil pressure, measured casing pressure, daily gas production, and production time; S2: Based on stable production day data, process the stable production day data according to the preset consecutive days determination rules and preset fluctuation determination rules to generate stable production segment data; S3: Based on stable production section data, a pre-defined first-order difference method for different tanks is used to capture the inflection point of the trend of output change with oil-casing pressure difference, and the critical pressure carrying difference is calculated. ; S4: Based on stable production section data, a quadratic polynomial fitting model of oil-casing pressure difference and daily gas production is constructed to calculate the critical pressure carrying difference. ; S5: Based on the obtained critical pressure carrying differential and critical carrying pressure difference The final critical pressure carrying capacity difference of the target shale gas well is obtained by adopting a fusion strategy that takes the maximum value.

2. The automatic calculation method for critical hydraulic carrying differential of shale gas wells according to claim 1, characterized in that: S1 includes: S1-1: Construct a dynamically sliding and updating time window based on a 180-day time period, and obtain the daily production data of the target shale gas well within this time window; the daily production data includes at least the measured oil pressure. Measured sleeve pressure Daily gas production Production time; and calculate the oil-casing pressure difference based on the measured oil pressure and measured casing pressure. The expression is: S1-2: Mark production time based on daily production data The operating condition data is used to obtain shut-in data and then discard it; S1-3: Based on the daily production data, mark the production data within the preset fluctuation period threshold after well opening, obtain the fluctuation period data, and remove it; S1-4: Regarding daily production data Abnormal data is removed; S1-5: Integrate the daily production data after removing S1-2 to S1-4 to obtain stable daily production data.

3. The automatic calculation method for critical hydraulic carrying differential of shale gas wells according to claim 2, characterized in that: S2 includes: S2-1: Extract stable production day data segments from stable production day data where the number of consecutive stable production days is greater than or equal to the preset minimum number of consecutive stable production days; S2-2: Calculate the daily gas production in the stable production day data segment. Pressure difference with oil jacket coefficient of variation The expression is: in, The standard deviation of the data series, The arithmetic mean and standard deviation of the data series. and arithmetic mean The calculation formula is: in, The number of samples in the data sequence. For the i-th sample value in the data sequence, substitute it with the daily gas production and oil-jacket pressure difference data respectively; S2-3: Determine whether the coefficient of variation of daily gas production and the coefficient of variation of oil-jacket pressure difference exceed the preset fluctuation threshold. If not, determine that the fluctuation of the stable production day data segment meets the requirements; otherwise, it does not meet the requirements. Integrate the stable production day data segments that meet the requirements to obtain stable production segment data.

4. The automatic calculation method for critical hydraulic carrying differential of shale gas wells according to claim 3, characterized in that: S3 includes: S3-1: Calculate the stable production section data according to the oil-casing pressure difference. Sort in ascending order; S3-2: Calculate the number of bins based on the sample size of the stable production section in the stable production section data. The expression is: in, The number of buckets is a positive integer. The function is for finding the minimum value; This is the floor function. To stabilize the total sample data volume of the production section; S3-3: Based on the calculated number of buckets The sorted oil-casing pressure differentials are divided into equal intervals (buckets), and the average oil-casing pressure differential within each bucket is calculated. Compared with average daily gas production And remove the sample number Invalid buckets, among which ; S3-4: Calculate the first-order difference of the average daily gas production between adjacent barrels, expressed as follows: in, Let be the first-order difference between the daily gas production of the k-th barrel and the (k+1)-th barrel; Let k be the average daily gas production of the k-th barrel; This represents the average daily gas production of the (k+1)th barrel. S3-5: Determine if the first-order difference is... Transform into When this point is determined to be an inflection point in the production trend, the average oil-jacket difference corresponding to this inflection point is the critical hydraulic carrying difference calculated by the first-order difference method for each barrel. .

5. The automatic calculation method for critical hydraulic carrying differential of shale gas wells according to claim 4, characterized in that: S4 includes: S4-1: Based on stable production section data, using oil-casing pressure differential as independent variable Daily gas production Dependent variable Construct a quadratic polynomial fitting model, the expression of which is: in, Daily gas production For the oil-casing pressure difference, The coefficient of the quadratic term, ; The coefficient of the linear term, For constant terms; S4-2: Calculate the first derivative of the quadratic polynomial fitting model and set it equal to 0. Solve for the oil-casing pressure difference corresponding to the maximum daily gas production, which is characterized as the critical pressure carrying capacity difference. The expression is: in, The first derivative of the daily gas production with respect to the oil-casing pressure difference is used to solve for X, which is the critical pressure carrying difference calculated by the quadratic polynomial fitting model. ; If the coefficient of the quadratic term in the fitting result If the fit is invalid, there is no critical pressure difference. Calculation results.

6. The automatic calculation method for critical hydraulic carrying differential of shale gas wells according to claim 5, characterized in that: In S5, the expression for the fusion strategy that takes the maximum value is: in, The final critical pressure carrying capacity differential for the target shale gas well. This is the function for finding the maximum value.

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