Automatic matching method of oil and gas production decline curve, electronic equipment and storage medium

Abstract

The invention belongs to the technical field of electric digital data processing, and particularly relates to an automatic matching method of an oil and gas production decline curve, electronic equipment and a storage medium. According to the method, mathematical characteristics of a double logarithmic space are utilized, a complex curve matching problem is converted into a linear translation optimization problem, automatic alignment of data points is achieved in combination with an interpolation algorithm, the single well analysis time is shortened to be within 1 minute from 1-2 hours of a traditional method, and efficiency is improved by two orders of magnitude; a numerical optimization algorithm is adopted to replace manual visual matching, and by establishing a centroid initialization-based least square optimization model, an optimal translation parameter is automatically calculated, and deviation caused by human factors is eliminated, so that the parameter inversion precision is greatly improved; an overlap ratio quantitative evaluation mechanism is introduced, a matching result is objectively evaluated by setting a scientific threshold value, the problem that a traditional method lacks a quality judgment standard is solved, and high-precision and high-efficiency automatic matching of oil and gas well production data and a theoretical curve is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electronic digital data processing, and in particular relates to an automatic matching method for an oil and gas production decline curve, an electronic device and a storage medium. Background Art

[0002] In oil and gas field development, production decline analysis is a key technology for evaluating the effectiveness of individual wells and oilfield development. Currently, the mainstream analysis method is manual matching. Engineers use double-logarithmic graph paper or specialized software such as Harmony and OFM to overlay graphs, visually compare, and manually adjust the theoretical curve position to match production data. The specific process is as follows: Perform logarithmic transformation on production data. Usually, a stable production segment is selected as the reference point. Manually translate the theoretical curve transparency or software layer to match different production stages. After the matching is completed, perform a visual fit check on the entire curve and manually calculate the key point error. If the error verification passes, output the result. If not, reselect the reference point or readjust, perform a second match, and verify again until it passes.

[0003] Manual matching, the traditional standard method for production decline analysis, has significant technical flaws: its heavy reliance on engineers' subjective experience leads to widely varying matching results, with parameter deviations of 15%-30%. Analysis efficiency is low, with a single-well time consumption of 1-2 hours and a linear increase in data volume. Operations also generate cumulative errors of 2%-5% due to logarithmic transformation, visual alignment, and other steps. Furthermore, the method can only obtain locally optimal solutions, failing to achieve global optimization and quantitative assessment of matching quality. This severely restricts the reliability and timeliness of large-scale data analysis.

[0004] Currently, some methods attempt to overcome the shortcomings of manual matching or fitting. For example, Chinese patent document CN113935253A discloses a data-weighted fitting method for an empirical production decline model for shale gas wells. Based on the decline characteristics of historical production data during the decline phase, this method uses an outlier detection algorithm to identify outliers in the historical production data. It then uses exponential smoothing to correct outliers and, in combination with Euclidean distance, rationally assigns fitting weights to the historical production data. The method then employs a weighted least squares approach to solve the empirical production decline model parameters, resulting in higher fitting accuracy and more reliable prediction results. However, this method is designed specifically for shale gas wells and relies on the definition of decline phases and Euclidean distance weighting, limiting its applicability. It emphasizes prediction accuracy but does not quantitatively display the degree of curve matching, resulting in a lack of intuitive evaluation criteria. This method cannot directly measure the local matching quality of the fitted curve with actual production data, relying solely on a global error metric, which may mask local deviations. Furthermore, this method requires multiple preprocessing steps, including outlier detection, smoothing, and weight calculation, resulting in high computational complexity.

[0005] For example, Chinese patent document CN110610288A discloses an intelligent system analysis method for oil and gas well production data. The data analysis method includes a variable production pressure data interpretation method, a production decline analysis method, and a single-well water drive curve analysis method. The production data of oil and gas wells is first preprocessed to obtain more reliable production data. Because the daily production data of a single well usually has large fluctuations, the production data is subjected to a spline noise reduction interpolation to reduce the fluctuation of the production data and make its overall trend more obvious. The data is then segmented based on the noise reduction interpolation. However, although it emphasizes automation, the segmented fitting still requires a preset correlation coefficient threshold, and the processing of outliers relies on manual experience and judgment. The quadratic fitting method is computationally time-consuming due to the large parameter search space. Summary of the Invention

[0006] To address the above problems, the present invention provides an automatic matching method for oil and gas production decline curves. By organically combining the technologies of double logarithmic space transformation, least squares optimization and quantitative evaluation of coincidence, high-precision and high-efficiency automatic matching of oil and gas well production data with theoretical curves is achieved.

[0007] The invention also discloses an electronic device for implementing the method.

[0008] The present invention also discloses a machine-readable storage medium for implementing the above method.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions: An automatic matching method for oil and gas production decline curves comprises the following steps: S101, data preprocessing: performing the same logarithmic transformation on the actual production data and the time-yield data of all curves in the theoretical curve library, to obtain the logarithmic time-logarithmic yield data of the actual production data, the logarithmic time-logarithmic yield data of the theoretical curve, the logarithmic transformed actual production data curve, and the logarithmic transformed theoretical curve; S102, selecting the logarithmic time-logarithmic output data of a theoretical curve in step S101, calculating the center of mass offset (dx, dy) between the logarithmic time-logarithmic output data of the theoretical curve and the actual production data, and using the center of mass offset (dx, dy) as the initial translation vector; S103. Based on the initial translation vector obtained in step S102, the actual production data curve after logarithmic transformation is translated and the residual of the logarithmic yield between the actual production data curve after logarithmic transformation and the theoretical curve selected in step S102 is calculated. The residual sum of squares is minimized based on the least squares method to obtain the optimal translation vector. S104, after translating the logarithmically transformed actual production data curve obtained in step S101 according to the optimal translation vector obtained in step S103, counting the number of coincidence points between the actual production data curve and the logarithmically transformed theoretical curve selected in step S102, and calculating the degree of coincidence; S105. Traverse all the logarithmically transformed theoretical curves in the theoretical curve library, repeat steps S102 to S104, calculate the overlap between the logarithmically transformed actual production data curve and each logarithmically transformed theoretical curve, select the logarithmically transformed theoretical curve with the highest overlap as the optimal matching curve, and output the optimal matching curve and key parameters: oil leakage radius r eD , decreasing index b; S106 , plotting the optimal matching curve outputted in step S105 in a double logarithmic coordinate system.

[0010] Preferably, in step S102, the theoretical curve selected for the first time is r eD The smallest theoretical curve, from r eD The minimum theoretical curve begins to traverse.

[0011] Preferably, the centroid offset (dx, dy) in step S102 is the difference between the centroid of the logarithmic time-logarithmic yield data of the theoretical curve and the centroid of the logarithmic time-logarithmic yield data of the actual production data.

[0012] Further preferably, the centroid described in step S102 is calculated according to formulas (1) and (2), which are as follows: , (1) , (2) Among them, formula (1) 、 is the theoretical curve after logarithmic transformation l1 The centroid coordinates of the points on the double logarithmic coordinate system, N is the number of time points of the theoretical curve, is the dimensionless time, is the dimensionless yield; in formula (2), l2 is the actual production data curve after logarithmic transformation, 、 The actual production data curve after logarithmic transformation l2 The centroid coordinates of the points on the double logarithmic coordinate system, n is the number of time points of actual production data, is the number of production days at the i-th time point, is the daily output at the i-th time point. In formula (1) and formula (2), i represents the i-th time point.

[0013] Preferably, the calculation of the residual of the logarithmic yield of the logarithmically transformed theoretical curve selected in step S102 is specifically as follows: the theoretical yield value corresponding to the time point of the logarithmically transformed actual production data curve after translation is calculated by linear interpolation on the logarithmically transformed theoretical curve, and then the difference between the logarithmic yield of the logarithmically transformed actual production data curve after translation and the theoretical yield value of the logarithmically transformed theoretical curve is calculated as the residual.

[0014] Preferably, the step S103 described in minimizing the residual sum of squares based on the least squares method to obtain the optimal translation vector is specifically: using the optimization function in the SciPy library for solving nonlinear least squares problems to minimize the residual sum of squares to obtain the optimal translation vector.

[0015] Preferably, step S104 is specifically as follows: within the intersection range of the horizontal coordinates of the logarithmically transformed theoretical curve and the translated logarithmically transformed actual production data curve, the vertical coordinate value corresponding to the logarithmically transformed theoretical curve under the horizontal coordinate of the translated logarithmically transformed actual production data curve is calculated by linear interpolation, and the absolute value of the vertical coordinate difference between the translated logarithmically transformed actual production data curve and the logarithmically transformed theoretical curve after interpolation is calculated. If the absolute value of the difference is less than a threshold value, it is considered that the point is overlapped, and the overlapping points that meet the conditions are counted to calculate the degree of overlap.

[0016] More preferably, the threshold is 0.01~0.1.

[0017] Further preferably, the overlap is the percentage of points that meet the difference between actual output and theoretical output, and the overlap = the number of overlap points that meet the conditions / the number of time points of the actual production data after logarithmic conversion.

[0018] Preferably, step S106 specifically comprises: drawing a double logarithmic curve using matplotlib in a double logarithmic coordinate system, superimposing actual production data points in the form of scattered points and the optimal matching curve displayed in the form of a continuous curve, and marking the degree of fit.

[0019] The present invention also provides an application of the automatic matching method for oil and gas well production dynamic analysis, productivity prediction and recoverable reserves assessment, and realizes rapid and accurate inversion of reservoir parameters by automatically matching actual production data with theoretical curves.

[0020] In another aspect of the present invention, an electronic device is provided, comprising: at least one processor; and A memory storing instructions, which, when executed by the at least one processor, causes the at least one processor to execute the automatic matching method for the oil and gas production decline curve as described above.

[0021] In another aspect of the present invention, a machine-readable storage medium is provided, which stores executable instructions. When the instructions are executed, the machine executes the automatic matching method of oil and gas production decline curves as described above.

[0022] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention addresses the core problems of the existing technology, such as strong experience dependence, low efficiency, error accumulation and lack of objective standards, and constructs an automated solution: using the mathematical properties of the double logarithmic space, the complex curve matching problem is transformed into a linear translation optimization problem, and the interpolation algorithm is combined to realize automatic alignment of data points, which shortens the single well analysis time from 1-2 hours in the traditional method to less than 1 minute, and improves the efficiency by two orders of magnitude; a numerical optimization algorithm is used to replace manual visual matching, and a least squares optimization model based on centroid initialization is established to automatically calculate the optimal translation parameters, eliminate the deviation caused by human factors, and greatly improve the parameter inversion accuracy; a quantitative evaluation mechanism of coincidence is introduced, and the matching results are objectively evaluated by setting scientific thresholds, which solves the problem that the traditional method lacks quality judgment standards.

[0023] (2) The present invention transforms complex nonlinear decreasing relationships into simple linear translation optimization problems through innovative double logarithmic transformation linearization processing, greatly reducing the difficulty of solving. This method uses centroid initialization to accelerate convergence and uses the centroid offset of the two curves as the initial guess, which reduces the number of iterations by more than 50% compared with random initialization; it aligns data points in real time through a dynamic interpolation objective function to solve the problem of time point mismatch; it combines threshold coincidence evaluation and intelligent search of the entire curve library to automatically select the optimal matching result, improves the parameter inversion accuracy to within ±3%, and eliminates the subjective bias of manual selection. Compared with traditional manual matching, the present invention realizes end-to-end automated analysis, shortens the single well analysis time from 1-2 hours to less than 1 minute, and improves efficiency by 120 times. Standardized result output ensures data comparability, and visual diagnosis intuitively displays the matching effect. This method is not only easy to operate, non-professionals can also complete the analysis with one click, but also significantly reduces resource consumption: it can reduce the use of 5 tons of paper each year and reduce storage space requirements by 99%. At the same time, it improves the working environment of engineers and reduces 90% of physical labor and occupational disease risks, achieving a dual breakthrough in environmental protection and efficiency.

[0024] (3) Compared with manual matching, the method of the present invention has the advantages of significantly improved analysis accuracy, significantly improved analysis efficiency, simple and standardized operation, and energy and resource savings: Manual matching relies on experience, and the analysis results of different engineers for the same well can vary by more than ±20%. Through automatic optimization algorithms and quantification of coincidence, the parameter inversion accuracy is improved to within ±3%. Traditional manual matching of a single well takes 1-2 hours, including data sorting, curve superposition, parameter adjustment, etc., while automated analysis takes <60 seconds per well on an Intel i7 processor and Python environment, with an efficiency improvement of 120 times. Manual matching requires professional engineers to operate, with a training period of ≥6 months, and the results are greatly affected by human factors. The present invention uses one-click analysis to automatically output results by inputting raw data, and can be operated by non-professionals. Manual analysis requires printing of a large number of curve drawings, an average of 10 per well, and requires the assistance of a high-performance workstation. The present invention is paperless and reduces paper consumption by about 5 tons per year based on 1,000 wells. It also improves environmental protection and labor intensity. Manual matching causes engineers to work at desks for long periods of time, with a high risk of occupational diseases. Paper report storage takes up approximately 1m³ / 100 wells. This system can reduce manual labor by 90%, eliminating repetitive operations such as curve overlays and manual adjustments. Fully electronic storage achieves a data compression ratio of 1:100, reducing storage space requirements by 99%. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic flow diagram of the method of the present invention; Figure 2 This is a graph showing actual production data over time for Example 1 of the present invention; Figure 3 This is the automatic matching result diagram of Example 1 of the present invention; Figure 4 This is a time chart of the automatic matching method according to Example 1 of the present invention; Figure 5 This is the theoretical curve chart used in the manual matching method of Example 1 of the present invention; Figure 6 This is the theoretical curve chart used in the automatic matching method of Example 1 of the present invention. DETAILED DESCRIPTION

[0026] Example 1 The present invention provides an automatic matching method for oil and gas production decline curves. Figure 1 It is a flow chart of the method of the present invention, referring to Figure 1 It can be seen that the method includes the following steps: S101: Data preprocessing: Perform the same logarithmic transformation on the actual production data and the time-yield data of all curves in the theoretical curve library, and obtain the logarithmic time-logarithmic yield data of the actual production data, the logarithmic time-logarithmic yield data of the theoretical curve, the logarithmic transformed actual production data curve, and the logarithmic transformed theoretical curve respectively; Specifically, the production history data table of Well A was read from an Excel file, and the time (d)-production (m³ / d) data was extracted and converted into logarithmic coordinates. The same logarithmic transformation was performed on all curves in the theoretical curve library to facilitate translation matching in double logarithmic coordinates, thus converting the curve matching problem into a linear translation optimization problem.

[0027] Figure 2 The actual output of actual production data changes over time.

[0028] S102: Initial translation vector: Select the logarithmic time-logarithmic yield data of a theoretical curve in step S101, calculate the centroid offset (dx, dy) between the logarithmic time-logarithmic yield data of the theoretical curve and the actual production data, and use the centroid offset (dx, dy) as the initial translation vector, where dx and dy represent the translation required to move the centroid of the actual production data to the centroid of the theoretical curve data; Specifically, the theoretical curve selected for the first time is r eD The smallest theoretical curve, from r eD The minimum theoretical curve begins to traverse.

[0029] Specifically, the center of mass (mean coordinate) of the logarithmically transformed actual production data and the logarithmically transformed theoretical curve is calculated. The center of mass represents the "average position" of the curve data points in double logarithmic coordinates. The center of mass offset (dx, dy) is used as the initial translation vector, which initially aligns the center of mass of the logarithmically transformed actual production data with the center of mass of the logarithmically transformed theoretical curve. Specifically, the logarithmically transformed actual production data curve is defined as l2, and the logarithmically transformed theoretical curve is defined as l1. The center of mass of the logarithmically transformed actual production data curve l2 and the logarithmically transformed theoretical curve l1 are calculated using the following formula: the initial translation vector is the center of mass of the logarithmic time-logarithmic output data of the theoretical curve minus the center of mass of the logarithmic time-logarithmic output data of the actual production data. If the two curves match perfectly, centerxl1 = centerxl2 and centeryl1 = centeryl2, that is, dx = dy = 0. If there is an offset, dx and dy represent the translation required to move the center of mass of the logarithmically transformed actual production data to the center of mass of the logarithmically transformed theoretical curve. Using the center of mass offset (dx, dy) as the initial translation vector reduces the number of iterations by 60% compared to random initialization, and the probability of avoiding falling into a local optimal solution increases to over 95%.

[0030] , (1) , (2) Among them, formula (1) 、 is the centroid coordinate of the point on the logarithmic transformed theoretical curve l1 in the double logarithmic coordinate system, N is the number of time points of the theoretical curve, is the dimensionless time, is the dimensionless output; in formula (2), l2 is the actual production data curve after logarithmic transformation, 、 is the centroid coordinate of the point on the logarithmic transformed actual production data curve l2 in the double logarithmic coordinate system, n is the number of time points of the actual production data, is the number of production days at the i-th time point, is the daily output at the i-th time point. In formula (1) and formula (2), i represents the i-th time point.

[0031] S103, optimizing translation parameters using the least squares method: The initial translation vector is obtained according to step S102, the actual production data curve after logarithmic transformation is translated, and the residual difference between the actual production data curve after logarithmic transformation and the logarithmic yield of the theoretical curve selected in step S102 is calculated. The residual sum of squares is minimized based on the least squares method to obtain the optimal translation vector; The least-squares method is used to optimize the translation vector (dx, dy) to minimize the matching error between the actual production data curve and the theoretical curve after translation in logarithmic coordinates. The translation vector is applied to the logarithmically transformed actual production data curve to obtain the translated curve. The theoretical yield value corresponding to the time point of the translated curve is calculated using linear interpolation on the logarithmically transformed theoretical curve. The difference between the interpolated theoretical value and the actual yield after translation is calculated as the logarithmic yield difference. Using the optimization function in the SciPy library for solving nonlinear least-squares problems, the sum of squared residuals is minimized to obtain the optimal translation vector.

[0032] Specifically, the matching error between the shifted, logarithmically transformed actual production data curve and the theoretical curve is calculated. After the logarithmically transformed actual production data curve is shifted, linear interpolation is performed on the logarithmically transformed theoretical curve l1 to calculate the theoretical production value corresponding to the time point of the shifted, logarithmically transformed l2 curve. The difference between the logarithmic production of the shifted, logarithmically transformed actual production data curve and the production at the interpolated point is calculated as the residual.

[0033] Specifically, the current translation amount is applied to the logarithmically transformed actual production data curve l2, and the translation is applied to each point of the logarithmically transformed actual production data curve l2, so as to move the logarithmically transformed actual production data curve l2 to the currently guessed optimal position.

[0034] (3) in, is the actual production data curve l2 after logarithmic transformation, is the nth time point of actual production data, is the nth output value of the actual production data, dx is the translation in the time direction, and dy is the translation in the output direction.

[0035] For each time point of the actual production data curve after translation and logarithmic transformation , find two adjacent time points in the theoretical curve and , then linearly interpolate to calculate the corresponding theoretical yield value. Calculate the residual: the logarithmically transformed actual production yield after translation minus the interpolated yield of the logarithmically transformed theoretical curve. Minimize the sum of squared residuals to ensure that the logarithmically transformed actual production data best matches the logarithmically transformed theoretical curve.

[0036] S104, calculating the matching coincidence degree: after translating the logarithmically transformed actual production data curve obtained in step S101 according to the optimal translation vector obtained in step S103, counting the number of coincidence points between the actual production data curve and the logarithmically transformed theoretical curve selected in step S102, and calculating the coincidence degree; After translation using the optimized optimal translation vector (dx, dy), within the intersection of the abscissas of the logarithmically transformed theoretical curve l1 and the translated logarithmically transformed actual production data curve l2, linear interpolation is used to calculate the corresponding ordinate value of the logarithmically transformed theoretical curve under the abscissa of the translated logarithmically transformed actual production data curve, i.e., the theoretical output. The absolute value of the ordinate difference between the interpolated logarithmically transformed theoretical curve l1 and the translated logarithmically transformed actual production data curve l2 is compared. If the absolute value of the difference is less than a threshold, the point is considered to be overlapped. The number of overlapping points that meet the conditions is counted, and the degree of overlap is calculated.

[0037] The threshold is 0.1, and the overlap is the percentage of points that meet the difference between actual output and theoretical output. The overlap = the number of overlapping points that meet the conditions / the number of time points of the actual production data after logarithmic conversion.

[0038] S105, automatic search for the best match in the entire curve library: traverse all the theoretical curves in the theoretical curve library that have been logarithmically transformed, that is, different r eDCombine a and b, repeat steps S102 to S104, calculate the overlap between the logarithmically transformed actual production data curve and each logarithmically transformed theoretical curve, select the curve with the highest overlap as the optimal matching curve, and output the optimal matching curve and key parameters: oil leakage radius r eD , decreasing index b; Specifically, loop through and repeat steps S102 to S104: calculate the translation vector that makes the logarithmically transformed actual production data curve l2 have the highest degree of overlap with the logarithmically transformed l1 after translation through the optimization algorithm. Then, interpolate and calculate the corresponding vertical coordinate value of the logarithmically transformed theoretical curve l1 under the horizontal coordinate value of the logarithmically transformed actual production data curve l2, and calculate the proportion of points whose vertical coordinate difference of the two curves is less than the threshold value as the current overlap of l1. Update the result. If the overlap of the current curve is higher than the historical best value, update the best matching curve, translation vector and overlap. Return the result. After the traversal is completed, return the theoretical curve with the highest overlap. Output the optimal theoretical matching curve and key parameters: r eD Oil leakage radius, b decreasing index.

[0039] S106 , visualization and result output: plotting the optimal matching curve output in step S105 in a double logarithmic coordinate system.

[0040] In the double logarithmic coordinate system, use matplotlib to draw the double logarithmic curve, superimpose the actual production data points in the form of scattered points and the optimal matching curve displayed in the form of a continuous curve, and mark the degree of fit, such as Figure 3 shown.

[0041] Using the method of the present invention, the analysis time for a single well in Example 1 is 11.4s. The specific running time results are shown in Figure 4 According to the same scenario as Example 1, a single well is analyzed using the traditional method. Based on experience, an approximate time range, operation steps, and time spent are given in Table 1.

[0042] Table 1 Operation steps and time spent

[0043] Manual matching method eD The values ​​of and b have strict requirements and must be consistent with the discrete values ​​preset in the theoretical plate. The theoretical curve plate used is shown in Figure 5 , the number of theoretical curves is relatively small, and the matching error is large; the method of the present invention can break through the limitation of the plate and output continuous values. The theoretical curve plate used is shown in Figure 6 , r eD The number of curve combinations of a and b is greater, and the number of theoretical curves is much greater than that of the manual matching method, which effectively reduces errors and improves accuracy. Example 2 This embodiment further provides an electronic device, including: at least one processor; and A memory storing instructions, which, when executed by the at least one processor, causes the at least one processor to execute the automatic matching method for the oil and gas production decline curve as described above.

[0044] In this embodiment, electronic devices may include, but are not limited to: personal computers, server computers, workstations, desktop computers, laptop computers, notebook computers, mobile computing devices, smart phones, tablet computers, cellular phones, personal digital assistants (PDAs), handheld devices, messaging devices, wearable computing devices, consumer electronic devices, and the like.

[0045] Example 3 This embodiment further provides a machine-readable storage medium storing executable instructions, which, when executed, enable the machine to perform the above-mentioned automatic matching method for oil and gas production decline curves.

[0046] Specifically, a system or device equipped with a readable storage medium can be provided, on which software program codes that implement the functions of any of the above-mentioned embodiments are stored, and a computer or processor of the system or device can read and execute instructions stored in the readable storage medium.

[0047] In this case, the program code itself read from the machine-readable medium can implement the functions of any one of the above embodiments, and thus the machine-readable code and the machine-readable storage medium storing the machine-readable code constitute part of this specification.

[0048] Examples of readable storage media include floppy disks, hard disks, magneto-optical disks, optical disks (e.g., CD-ROMs, CD-Rs, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs, DVD-RWs), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer or a cloud via a communication network.

[0049] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the technical solutions of the present invention, and are not intended to limit the specific implementation methods of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for automatically matching oil and gas production decline curves, characterized in that: The following steps are involved: S101, data preprocessing: performing the same logarithmic transformation on the actual production data and the time-yield data of all curves in the theoretical curve library, to obtain the logarithmic time-logarithmic yield data of the actual production data, the logarithmic time-logarithmic yield data of the theoretical curve, the logarithmic transformed actual production data curve, and the logarithmic transformed theoretical curve; S102, selecting the logarithmic time-logarithmic output data of a theoretical curve in step S101, calculating the center of mass offset (dx, dy) between the logarithmic time-logarithmic output data of the theoretical curve and the actual production data, and using the center of mass offset (dx, dy) as the initial translation vector; S103. Based on the initial translation vector obtained in step S102, the actual production data curve after logarithmic transformation is translated and the residual of the logarithmic yield between the actual production data curve after logarithmic transformation and the theoretical curve selected in step S102 is calculated. The residual sum of squares is minimized based on the least squares method to obtain the optimal translation vector. S104, after translating the logarithmically transformed actual production data curve obtained in step S101 according to the optimal translation vector obtained in step S103, counting the number of coincidence points between the actual production data curve and the logarithmically transformed theoretical curve selected in step S102, and calculating the degree of coincidence; S105. Traverse all the logarithmically transformed theoretical curves in the theoretical curve library, repeat steps S102 to S104, calculate the overlap between the logarithmically transformed actual production data curve and each logarithmically transformed theoretical curve, select the logarithmically transformed theoretical curve with the highest overlap as the optimal matching curve, and output the optimal matching curve and key parameters: oil leakage radius r eD , decreasing index b; S106 , plotting the optimal matching curve outputted in step S105 in a double logarithmic coordinate system.

2. The automatic matching method according to claim 1, characterized in that: The centroid offset (dx, dy) in step S102 is the difference between the centroid of the logarithmic time-logarithmic yield data of the theoretical curve and the centroid of the logarithmic time-logarithmic yield data of the actual production data.

3. The automatic matching method according to claim 2, characterized in that: The centroid described in step S102 is calculated according to formulas (1) and (2), which are as follows: ， （1） ， （2） Among them, formula (1) 、 is the theoretical curve after logarithmic transformation l1 The centroid coordinates of the points on the double logarithmic coordinate system, N is the number of time points of the theoretical curve, is the dimensionless time, is the dimensionless yield; in formula (2), l2 is the actual production data curve after logarithmic transformation, 、 The actual production data curve after logarithmic transformation l2 The centroid coordinates of the points on the double logarithmic coordinate system, n is the number of time points of actual production data, is the number of production days at the i-th time point, is the daily output at the i-th time point. In formula (1) and formula (2), i represents the i-th time point.

4. The automatic matching method according to claim 1, characterized in that: The calculation of the residual between the logarithmic yield of the logarithmically transformed theoretical curve selected in step S102 and step S103 is specifically as follows: the theoretical yield value corresponding to the time point of the logarithmically transformed actual production data curve after translation is calculated by linear interpolation on the logarithmically transformed theoretical curve, and then the difference between the logarithmic yield of the logarithmically transformed actual production data curve after translation and the theoretical yield value of the logarithmically transformed theoretical curve is calculated as the residual.

5. The automatic matching method according to claim 1, characterized in that: Step S104 is specifically as follows: within the intersection range of the horizontal coordinates of the logarithmically transformed theoretical curve and the translated logarithmically transformed actual production data curve, the vertical coordinate value corresponding to the logarithmically transformed theoretical curve under the horizontal coordinate of the translated logarithmically transformed actual production data curve is calculated by linear interpolation, and the absolute value of the vertical coordinate difference between the translated logarithmically transformed actual production data curve and the logarithmically transformed theoretical curve after interpolation is calculated. If the absolute value of the difference is less than a threshold value, it is considered that the point is overlapped, and the overlapping points that meet the conditions are counted to calculate the degree of overlap.

6. The automatic matching method according to claim 5, characterized in that: The threshold value is 0.01~0.

1.

7. The automatic matching method according to claim 5, characterized in that: The overlap is the percentage of points that meet the difference between actual output and theoretical output, and the overlap = overlap points that meet the conditions / number of time points of actual production data after logarithmic transformation.

8. The automatic matching method according to any one of claims 1 to 7, characterized in that: The automatic matching method is used for oil and gas well production performance analysis, productivity prediction and recoverable reserves assessment.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and A memory storing instructions, which, when executed by the at least one processor, causes the at least one processor to execute the automatic matching method for oil and gas production decline curves according to any one of claims 1 to 7.

10. A machine-readable storage medium storing executable instructions, characterized in that: When the instructions are executed, the machine executes the automatic matching method for oil and gas production decline curves as described in any one of claims 1 to 7.

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