Method and system for analyzing data fluctuation shale gas well production empirical decline curve

Abstract

This invention provides a method and system for analyzing the empirical decline curve of shale gas well production with data fluctuations. The method first collects dynamic production data on the well to be evaluated, including production time, daily gas production, and cumulative gas production. Low-quality data from the early production fluctuation period and abnormal data caused by testing errors or field processes are filtered out. Then, through a cumulative gas production curve fitting step, a relationship curve is plotted using the power operation data of the logarithm of cumulative gas production and production time, and the power parameter is adjusted to satisfy a linear relationship. Based on this, the coefficients of the model are calculated using the slope and intercept data of the fitted linear line, determining the empirical decline model for characterizing the production decline characteristics of fluctuating shale gas wells and predicting future development changes. This approach effectively overcomes the limitations of existing technologies for shale gas wells with data fluctuations, is unaffected by data fluctuations, and achieves reliable and accurate empirical decline curve analysis of shale gas well production based on a simple calculation process.

Description

Technical Field

[0001] This invention relates to the field of shale gas development and evaluation technology, and in particular to a method and system for analyzing empirical decline curves of shale gas well production based on data fluctuations. Background Technology

[0002] Due to the complex seepage process of shale gas, evaluating shale gas wells from the perspective of seepage mechanism requires numerous parameters and involves complex calculations. Therefore, in the field, the empirical decline method, which is simpler, requires fewer types of data, and yields relatively accurate calculation results, is often used to evaluate the production and capacity characteristics of shale gas wells.

[0003] Currently, commonly used empirical decline methods for shale gas wells include Arps decline and generalized Arps decline, power law exponent method, extended exponent method, Duong method, SEPD method, etc., all of which have achieved corresponding application results in different blocks at home and abroad. Based on this, by comparing the prediction effects of commonly used decline analysis methods at home and abroad in the Fuling shale gas field in my country, it is concluded that the Duong decline method has the best fitting effect for shale gas in my country.

[0004] However, the Duong decline method requires first plotting the logarithm of production data against the logarithm of time, calculating the fitting parameters using the slope and intercept of the fitted line, and then plotting the daily gas production against the time function to obtain the final decline curve equation using the slope of the fitted line. Therefore, the prediction accuracy is extremely sensitive to data fluctuations (e.g., Joshi K, Lee J. Comparison of various deterministic forecasting techniques in shale gas reservoirs[C]. Paper SPE 163870 presented at the SPE Hydraulic Fracturing Technology Conferenceheld in The Woodlands, Texas, USA, February 4-6, 2013). In contrast, many shale gas wells in my country experience frequent shut-in periods, resulting in drastic data fluctuations, making it difficult to apply the Duong decline method to solve the problem and failing to guarantee the accuracy and reliability of the calculation results.

[0005] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] To address the aforementioned problems, this invention provides a method for analyzing the empirical decline curve of shale gas well production data with fluctuations. This method utilizes an improved Duong decline analysis method to solve for key parameters of the decline curve based on the relationship between cumulative production and time, thus avoiding the requirements of the Duong decline method regarding the continuity and stability of daily gas production data during the analysis of the production stage. In one embodiment, the method includes:

[0007] Production data preparation steps: Collect dynamic production data for the well to be evaluated, including production time, daily gas production, and cumulative gas production.

[0008] The data processing steps involve removing data from the early production fluctuation periods in the production dynamic data and filtering out abnormal data caused by test errors or on-site processes according to the set principles to obtain the processed production dynamic data.

[0009] The cumulative gas production curve fitting steps are as follows: plot the relationship curve using the logarithm of cumulative gas production and the power operation data of production time, and adjust the power parameter of production time with the linear relationship of the curve as the fitting target.

[0010] The steps for determining the decline model include calculating the coefficients of the model based on the slope and intercept data of the fitted linear line, and determining the empirical decline model for production used to characterize the decline characteristics of fluctuating shale gas well production and predict future development changes.

[0011] Specifically, in one embodiment, in the production data preparation step, the production time is the cumulative production time, in order to control the impact of data fluctuation characteristics as much as possible.

[0012] Preferably, in one embodiment, after the decreasing model determination step, the method further includes:

[0013] The model optimization steps include: using the established production experience decline model to calculate the changes in daily and cumulative production data of the well to be evaluated over a set period as a function of production time; comparing this data with the actual production data in the corresponding period to determine the fit of the production experience decline model.

[0014] If the fit does not meet the set requirements, repeat the cumulative gas production curve fitting step and further adjust the power parameter until the fit of the empirical declining production model meets the set requirements.

[0015] Furthermore, in one embodiment, during the collection of production dynamic data, for wells to be evaluated for which cumulative production time has not been recorded, the cumulative production time is calculated using the following formula:

[0016]

[0017]

[0018] Where, the subscript i represents the i-th day, t represents the cumulative production time, in days; G represents the cumulative production, in 10 4 m 3 ; q represents the daily production, in 10 4 m 3 / day.

[0019] Furthermore, in one embodiment, in the data sorting step, it includes: plotting the curve of the daily gas production data changing with time, identifying the daily gas production data points whose degree of deviation from the overall curve during the production process reaches the set requirement and eliminating them;

[0020] For the production data with a change in the working system, only the data corresponding to the working system to be analyzed is retained, and other production data is eliminated.

[0021] Specifically, in one embodiment, in the step of fitting the cumulative gas production curve, the power parameter C of the production time during the adjustment process is kept within the range of -1 < C < 0.

[0022] Optionally, in one embodiment, the step of fitting the cumulative gas production curve further includes: before plotting the curve, dividing the data according to the production system so that the data in the curve fitting process belongs to the data under the same production system.

[0023] Specifically, in one embodiment, in the step of determining the decline model, the following production experience decline model is determined:

[0024]

[0025]

[0026] Where, m = 1 - C

[0027] a = BC

[0028] q1 = BCe (A+B)

[0029] In the formula, q(t) and G(t) respectively correspond to the daily production and cumulative production at time t, a, m, and q1 are fitting coefficients, A is the slope of the straight line of the data relationship between the logarithm of the cumulative gas production and the power operation of the production time after fitting, B is the intercept of the straight line of the data relationship between the logarithm of the cumulative gas production and the power operation of the production time after fitting, and C is the power parameter of the production time.

[0030] Based on other aspects of the method described in any one or more of the above embodiments, the present invention further provides a storage medium, on which program codes for implementing the method described in any one or more of the above embodiments are stored.

[0031] Based on the application aspects of the methods described in any one or more of the above embodiments, the present invention also provides a system for analyzing the empirical decline curve of shale gas well production based on data fluctuations, the system performing the methods described in any one or more of the above embodiments.

[0032] Compared with the closest prior art, the present invention also has the following beneficial effects:

[0033] This invention provides a method and system for analyzing the empirical decline curve of shale gas well production based on data fluctuations. The method first collects dynamic production data on the production time, daily gas production, and cumulative gas production of the well to be evaluated, and then filters out low-quality data from the early production fluctuation period and abnormal data caused by testing errors or field processes. The parameters used are actual measured data from development operations, which do not require intermediate theoretical calculation parameters. The data source has high reliability and avoids interference from low-quality or abnormal data on the accuracy of calculations, providing reliable support for subsequent calculations based on the data.

[0034] Furthermore, by fitting the cumulative gas production curve, a relationship curve is plotted using the logarithm of cumulative gas production and the power operation data of production time, and the power parameter is adjusted to satisfy the linear relationship. Based on this, the coefficients of the model are calculated using the slope and intercept data of the fitted linear line. An improved empirical decline analysis method for production is used to solve the key parameters of the decline curve by using the relationship between cumulative production and time. This method avoids the rigid requirements for the continuity and stability of daily gas production data in the analysis production section, overcomes the application limitations of existing technologies for shale gas wells with data fluctuations, and ensures the prediction accuracy for shale gas wells with data fluctuations due to frequent well shut-ins, etc., while obtaining accurate decline curves of daily and cumulative production.

[0035] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0036] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0037] Figure 1 This is a flowchart illustrating a method for analyzing the empirical decline curve of shale gas well production based on data fluctuations, according to an embodiment of the present invention.

[0038] Figure 2 This is a detailed flowchart of the method for analyzing the empirical decline curve of shale gas well production based on data fluctuations, provided in another embodiment of the present invention.

[0039] Figure 3 This is an example diagram of production dynamic data provided by the method for analyzing the empirical decline curve of shale gas well production based on data fluctuations in the embodiments of the present invention;

[0040] Figure 4 This is an example diagram of the conventional analysis method results of the method for analyzing the empirical decline curve of shale gas well production based on data fluctuations provided in another embodiment of the present invention;

[0041] Figure 5 This is a schematic diagram illustrating the relationship between cumulative production time and daily gas production and cumulative gas production in the method for analyzing the empirical decline curve of shale gas well production based on data fluctuations provided in this embodiment of the invention.

[0042] Figure 6 This is an embodiment of the present invention providing a method for analyzing the empirical decline curve of shale gas well production based on data fluctuations, specifically LnG and t. C Linear fitting chart;

[0043] Figure 7 This is a comparison chart of predicted cumulative production and actual cumulative production provided by the method for analyzing the empirical decline curve of shale gas well production based on data fluctuations in another embodiment of the present invention.

[0044] Figure 8 This is a schematic diagram of the system for analyzing the empirical decline curve of shale gas well production based on fluctuation data, provided in an embodiment of the present invention. Detailed Implementation

[0045] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples. Those skilled in the art will then fully understand how the present invention uses technical means to solve technical problems and achieve technical effects, and will be able to implement the present invention specifically based on the above-described implementation process. It should be noted that, as long as there is no conflict, the various embodiments and features of the present invention can be combined with each other, and the resulting technical solutions are all within the protection scope of the present invention.

[0046] Although the flowchart describes the operations as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. The order of the operations can be rearranged. A process can terminate when its operation is complete, but it may also have additional steps not included in the diagram. A process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0047] The term “and / or” as used herein includes any and all combinations of one or more of the associated items listed. When a unit is referred to as “connected” or “coupled” to another unit, it may be directly connected to or coupled to said other unit, or there may be an intermediate unit present.

[0048] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms “a” and “an” as used herein are also intended to include the plural. It should also be understood that the terms “comprising” and / or “including” as used herein specify the presence of the stated features, integers, steps, operations, units, and / or components, without excluding the presence or addition of one or more other features, integers, steps, operations, units, components, and / or combinations thereof.

[0049] Due to the complex seepage process of shale gas, evaluating shale gas wells from the perspective of seepage mechanism requires numerous parameters and involves complex calculations. Therefore, in the field, the empirical decline method, which is simpler, requires fewer types of data, and yields relatively accurate calculation results, is often used to evaluate the production and capacity characteristics of shale gas wells.

[0050] Currently, commonly used empirical decline methods for shale gas wells include Arps decline and generalized Arps decline, power law exponent method, extended exponent method, Duong method, and SEPD method. These methods have achieved good application results in different blocks at home and abroad. Wang Ke et al. (Wang Ke, Li Haitao, Li Liujie, et al. Three commonly used empirical decline methods for shale gas wells - taking the Weiyuan block of Sichuan Basin as an example [J]. Natural Gas Geoscience, 2019, 30(7):946-954.) compared the prediction effects of commonly used decline analysis methods at home and abroad in the Fuling shale gas field in my country and found that the Duong decline method has the best fitting effect for shale gas in my country. In conclusion, this is because the Duong decline method, unlike other empirical decline methods, is not derived from summarizing the production patterns of a large number of gas wells, but rather from the actual seepage patterns of fracture flow. Specifically, since the Duong decline method is based on a large amount of actual shale gas production data, it found that the ratio of production to cumulative production and production time have a linear relationship in a double logarithmic coordinate system. The analysis suggests that the reason why shale gas wells exhibit fracture-like linear flow is mainly because shale gas fractures are well-developed and the permeability of the matrix rock is low. Production mainly comes from fractures, so it is difficult for gas wells to exhibit pseudo-radial flow and pseudo-steady-state flow during historical production.

[0051] However, the Duong decline method requires first plotting the logarithm of production data Ln(q / Gp) and the logarithm of time Lnt, then calculating the fitting parameters a and m using the slope and intercept of the fitted line, and finally plotting the daily gas production q and the time function t(a,m) using the slope of the fitted line to obtain the final decline curve equation. Therefore, the prediction accuracy is extremely sensitive to data fluctuations (Joshi K, Lee J. Comparison of various deterministic forecasting techniques in shale gas reservoirs[C]. Paper SPE 163870 presented at the SPE Hydraulic Fracturing Technology Conference held in The Woodlands, Texas, USA, February 4-6, 2013). In my country, many shale gas wells are frequently shut down, resulting in drastic data fluctuations, making it difficult to apply the Duong decline method to solve the problem.

[0052] To address the problem that the Duong decline method is extremely sensitive to data fluctuations and is difficult to apply to shale gas wells in my country with drastic data fluctuations, this invention provides a method for analyzing the empirical decline curve of shale gas well production due to data fluctuations. This method utilizes the relationship between cumulative production and time to obtain decline parameters, and is an improved Duong decline analysis method. This method not only has the advantages of the Duong method, such as simple calculation steps, readily available parameters, and reliable calculation results, but also ensures the prediction accuracy for shale gas wells with data fluctuations caused by frequent well shutdowns and other reasons.

[0053] The following describes the detailed flow of the method according to an embodiment of the present invention with reference to the accompanying drawings, the steps of which can be executed in a computer system containing, for example, a set of computer-executable instructions. Although the logical order of the steps is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0054] Example 1

[0055] Figure 1 This diagram illustrates a flowchart of a method for analyzing the empirical decline curve of shale gas well production based on data fluctuations, as provided in Embodiment 1 of the present invention. Figure 1 As can be seen, the method includes the following steps.

[0056] Production data preparation steps: Collect dynamic production data for the well to be evaluated, including production time, daily gas production, and cumulative gas production.

[0057] The data processing steps involve removing data from the early production fluctuation periods in the production dynamic data and filtering out abnormal data caused by test errors or on-site processes according to the set principles to obtain the processed production dynamic data.

[0058] The cumulative gas production curve fitting steps are as follows: plot the relationship curve using the logarithm of cumulative gas production and the power operation data of production time, and adjust the power parameter of production time with the linear relationship of the curve as the fitting target.

[0059] The steps for determining the decline model include calculating the coefficients of the model based on the slope and intercept data of the fitted linear line, and determining the empirical decline model for production used to characterize the decline characteristics of fluctuating shale gas well production and predict future development changes.

[0060] Based on the implementation logic in the above embodiments, the key parameters of the decline model are obtained by utilizing the relationship between cumulative production and time, thereby realizing improved empirical decline analysis of production. This avoids the impact of data fluctuations on calculation accuracy and better supports the characterization of the decline characteristics of fluctuating shale gas well production and the prediction of future development changes in actual engineering.

[0061] In practical applications, in a preferred embodiment, after the decreasing model determination step, the following steps are also included:

[0062] The model optimization steps include: using the established production experience decline model to calculate the changes in daily and cumulative production data of the well to be evaluated over a set period as a function of production time; comparing this data with the actual production data in the corresponding period to determine the fit of the production experience decline model.

[0063] If the fit does not meet the set requirements, repeat the cumulative gas production curve fitting step and further adjust the power parameter until the fit of the empirical declining production model meets the set requirements.

[0064] Figure 2 The diagram shows a detailed execution flow of the method for analyzing the empirical decline curve of shale gas well production based on data fluctuations, as provided in this embodiment of the invention. Figure 2 As shown, further, for the collected production time, due to the special characteristics of data fluctuation gas wells, the cumulative production time is used as much as possible; therefore, in one embodiment, the production time in the production data preparation step is the cumulative production time, in order to control the impact of data fluctuation characteristics as much as possible.

[0065] Specifically, in one embodiment, during the collection of production dynamic data, for wells to be evaluated for which cumulative production time has not been recorded, the cumulative production time is calculated using the following formula:

[0066]

[0067]

[0068] In the formula, the subscript i represents the i-th day, t represents the cumulative production time, in days; G represents the cumulative production, in 10 4 m 3 ; q represents the daily production, in 10 4 m 3 / d.

[0069] To ensure the validity of the collected production dynamic data, the researchers of the present invention designed to screen the production data before formal input calculation, eliminate the early production fluctuation section and data abnormal points caused by test errors, on-site processes, etc. The present invention eliminates the data points where the daily gas production deviates too much from the overall curve during the production process by plotting the daily gas production change data. Therefore, in one embodiment, in the data sorting step, it includes: plotting the curve of the daily gas production data changing with time, identifying the daily gas production data points whose degree of deviation from the overall curve during the production process reaches the set requirement and eliminating them;

[0070] In addition, it also includes: for the production data with a change in the working system, only retaining the data corresponding to the working system to be analyzed and eliminating other production data.

[0071] Furthermore, through the cumulative production curve fitting step, using the power operation data of the logarithm of the cumulative production and the production time to plot the relationship curve, and adjusting the power parameter of the production time with the fitting target that the curve relationship satisfies the linear relationship. In actual application, using the logarithm of the cumulative production LnG and the C-th power t of the production time C to plot the relationship curve, and adjusting the value of the coefficient C to make the fitting relationship become a linear relationship; initially, the technical worker manually sets the value of C, and then adjusts it step by step;

[0072] Specifically, generally, the power parameter takes values that are less than 0 but close to 0 (such as -0.03, -0.2, etc.). The greater the deviation of the power parameter value from 0, the greater the calculation result error. Therefore, in a preferred embodiment, in the cumulative production curve fitting step, during the adjustment process, the power parameter C of the production time is kept within the range of -1 < C < 0, and when adjusting, avoid the coefficient C being greater than zero or having too large a gap from zero.

[0073] Furthermore, considering that when there are multiple production systems in the fitting data, there will be multiple straight lines corresponding to the final fitting image. Therefore, in one embodiment, it is designed that in the cumulative production curve fitting step, it also includes: before plotting the curve, dividing the data according to the production system so that the data in the curve fitting process belongs to the data under the same production system to ensure the unity of the working system of the production section before fitting.

[0074] After fitting the cumulative gas production curve to satisfy the linear relationship, the decline model determination step is initiated. Based on the slope and intercept data of the fitted linear line, the coefficients of the model are calculated to determine the production empirical decline model used to characterize the decline characteristics of fluctuating shale gas well production and predict future development changes.

[0075] Specifically, in one embodiment, the final yield empirical decline model can be represented by the following formula:

[0076]

[0077]

[0078] The fitting coefficients in the decreasing model can be determined by the following formula:

[0079] m = 1 - C

[0080] a = BC

[0081] q1=BCe (A+B)

[0082] In the formula, q(t) and G(t) correspond to the daily output and cumulative output at time t, respectively; a, m and q1 are fitting coefficients; A is the slope of the straight line relating the logarithm of cumulative gas production to the power of production time; B is the intercept of the straight line relating the logarithm of cumulative gas production to the power of production time; and C is the power parameter of production time.

[0083] Furthermore, considering that the production experience decline model determined by the above logic does not match the actual production data 100% perfectly, this invention designs a model optimization step to use the constructed production experience decline model to calculate the changes in daily and cumulative production data of the well to be evaluated within a set period as a function of production time, and compares it with the actual production data within the corresponding period to determine the degree of fit of the production experience decline model, so as to verify the reliability of the model curve's fit to the actual production data.

[0084] Furthermore, if the calculation results have a large error with the existing data, the coefficient C needs to be readjusted to obtain a fitting curve with a higher degree of linear fit (usually represented by R2). Therefore, if the degree of fit does not meet the set requirements, the cumulative gas production curve fitting step is restarted, and the power parameter is further adjusted until the degree of fit of the production experience decline model meets the set requirements. The final production decline model is then used in subsequent shale gas well development work to analyze the decline characteristics and predict future development index changes.

[0085] Compared with existing technologies, this invention provides an empirical decline curve analysis method for shale gas well production with fluctuating data. This method utilizes the relationship between cumulative production and time, obtaining key parameters of the decline curve through a single linear fitting, thus improving the Duong decline analysis method and avoiding the impact of data fluctuations on calculation accuracy. This invention eliminates the limitations of the Duong decline analysis method for shale gas wells and effectively solves the problems of production data analysis and production indicator prediction for shale gas wells with fluctuating data due to frequent well shutdowns and other reasons.

[0086] Implementation Case:

[0087] The invention will now be further described with reference to the accompanying drawings.

[0088] The production data of well A in a shale gas block in the Sichuan Basin, which experienced drastic data fluctuations due to frequent well shutdowns, was selected from the previous year's production data, as shown in the attached figure. Figure 3 As shown. Due to frequent well shut-ins, well A cannot be analyzed using the Duong method for decline. Its daily gas production q is no longer a straight line with the time function t(a,m), but rather exhibits an exponentially rapid increase, making linear fitting impossible. Figure 4 As shown. The decreasing analysis is performed using the method of this invention:

[0089] (1) Collect daily gas production, cumulative gas production, and cumulative production time data. This example has cumulative production time data, which can be used directly. The relationship between cumulative production time and daily gas production and cumulative gas production is shown in the appendix. Figure 5 .

[0090] (2) Screening production data: Since there was no change in the production system of well A, and the production fluctuations were all caused by well shut-in, there was no sudden change in production data without cause, so no data was deleted.

[0091] (3) Using the logarithm of cumulative gas production LnG and the C power of production time t C When the relationship curve is plotted and the coefficient C is adjusted to -0.2, the fitted relationship of the data points becomes linear, as shown below. Figure 6 As shown. At this point, the intercept of the fitted line is A = 15.969 and the slope is B = -8.066.

[0092] (4) The fitting coefficients m = 1.2; a = 1.6132; q1 = 4364.33 were calculated, and the empirical decline model for the production and cumulative production of well A was obtained as follows:

[0093]

[0094]

[0095] (5) The cumulative production was recalculated using the model and compared with the actual cumulative production. The production data of the previous year were completely matched, and the declining model was obtained, which is the final declining model of well A.

[0096] The predicted daily and cumulative production of Well A for the next three years were calculated and compared with the actual production characteristics of Well A. The results show that the predicted daily production after three years is 14,630 cubic meters per day, and the cumulative production is 10.43 million cubic meters per day. The actual cumulative production of Well A is 10.2 million cubic meters per day. The fitting effect is good, proving that the results of this invention are reliable. (See attached figure.) Figure 7 As shown.

[0097] For the foregoing method embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0098] It should be noted that, in other embodiments of the present invention, the method can also combine one or more of the above embodiments to obtain a new empirical decline curve analysis method for production, so as to optimize the development of shale gas wells.

[0099] It should be noted that, based on the methods in any one or more embodiments of the present invention described above, the present invention also provides a storage medium storing program code that can implement the methods described in any one or more embodiments. When the program code is executed by the operating system, it can implement the method described above for analyzing data fluctuations and the empirical decline curve of shale gas well production.

[0100] Example 2

[0101] The methods described in the above-disclosed embodiments of the present invention are detailed. These methods can be implemented using various forms of apparatus or systems. Therefore, based on other aspects of the methods described in any one or more of the above embodiments, the present invention also provides a system for analyzing the empirical decline curve of shale gas well production based on data fluctuations. This system is used to execute the method described in any one or more of the above embodiments for analyzing the empirical decline curve of shale gas well production based on data fluctuations. Specific embodiments are given below for detailed description.

[0102] Specifically, Figure 8 The diagram shows a schematic representation of the system for analyzing fluctuations in the empirical decline curve of shale gas well production provided in an embodiment of the present invention. Figure 8 As shown, the system includes:

[0103] The production data preparation module 81 is configured to collect dynamic production data for the well to be evaluated, including production time, daily gas production, and cumulative gas production.

[0104] The data processing module 83 is configured to remove data from the early production fluctuation period in the production dynamic data and filter out abnormal data caused by test errors or on-site processes according to the set principles to obtain the processed production dynamic data.

[0105] The cumulative gas production curve fitting module 85 is configured to draw a relationship curve using the logarithm of cumulative gas production and the power operation data of production time, and adjust the power parameter of production time with the linear relationship of the curve as the fitting target.

[0106] The decline model determination module 87 is configured to calculate the coefficients of the model based on the slope and intercept data of the fitted linear line, and determine the empirical decline model for characterizing the decline characteristics of fluctuating shale gas well production and predicting future development changes.

[0107] In one embodiment, the production data preparation module is configured to collect accumulated production time for production time input calculation, so as to control the impact of data fluctuation characteristics as much as possible.

[0108] Furthermore, in a preferred embodiment, the system further includes:

[0109] The curve optimization module 89 is configured to use the constructed production experience decline model to calculate the changes in daily and cumulative production data of the well to be evaluated within a set period as a function of production time, compare it with the actual production data within the corresponding period, and determine the degree of fit of the production experience decline model.

[0110] If the fit does not meet the set requirements, repeat the cumulative gas production curve fitting step and further adjust the power parameter until the fit of the empirical declining production model meets the set requirements.

[0111] Specifically, in one embodiment, during the collection of production dynamic data, for wells to be evaluated for which cumulative production time has not been recorded, the cumulative production time is calculated using the following formula:

[0112]

[0113]

[0114] In the formula, i represents day i, t represents cumulative production time d, and G represents cumulative output. 4 m 3 ; q represents daily output, 10 4 m 3 / d.

[0115] Preferably, in one embodiment, the data processing module is specifically configured to perform the following operations:

[0116] Plot the curve of daily gas production data over time, identify and remove daily gas production data points that deviate from the overall curve to the set requirements during the production process;

[0117] For production data where work systems have changed, only the data corresponding to the work systems that need to be analyzed should be retained, and other production data should be removed.

[0118] Furthermore, in one embodiment, during the process of adjusting the power parameter by the cumulative gas production curve fitting module, the power parameter C of the production time is set to remain within the range of less than 0 and greater than -1.

[0119] In an optional embodiment, the cumulative gas production curve fitting module is further configured to: divide the data according to the production system before drawing the curve, so that the data in the curve fitting process belong to the same production system.

[0120] Preferably, in one embodiment, the declining model determination module specifically determines the production empirical declining model as shown in the following formula:

[0121]

[0122]

[0123] Where m = 1 - C

[0124] a = BC

[0125] q1=BCe (A+B)

[0126] In the formula, q(t) and G(t) correspond to the daily output and cumulative output at time t, respectively; a, m and q1 are fitting coefficients; A is the slope of the straight line relating the logarithm of cumulative gas production to the power of production time; B is the intercept of the straight line relating the logarithm of cumulative gas production to the power of production time; and C is the power parameter of production time.

[0127] In the system for analyzing the empirical decline curve of shale gas well production based on fluctuation data provided in this embodiment of the invention, each module or unit structure can operate independently or in combination according to actual analysis and calculation needs to achieve the corresponding technical effects.

[0128] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should be extended to equivalent substitutions of these features as understood by those skilled in the art. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0129] The phrase "an embodiment" in the specification means that a specific feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "an embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0130] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for analyzing empirical decline curves of shale gas well production based on data fluctuations, characterized in that, The method includes: A production data preparation step of collecting production performance data for the well to be evaluated, including production time, daily gas production, and cumulative gas production; A data sorting step of removing data in the early production fluctuation section from the production performance data, and screening out abnormal data caused by test errors or on-site processes according to set principles to obtain sorted production performance data; A cumulative gas production curve fitting step of plotting a relationship curve using the power operation data of the logarithm of cumulative gas production and production time, and adjusting the power parameter of production time with the fitting target that the curve relationship satisfies a linear relationship; A decline model determination step of calculating the coefficients of the model based on the slope and intercept data of the linear straight line after fitting, and determining a production empirical decline model for characterizing the production decline characteristics of fluctuating shale gas wells and predicting future development changes; In the decline model determination step, determine a production empirical decline model as shown in the following formula: in, ; ; ; In the formula, and q1 and q2 represent the daily output and cumulative output at time t, respectively. a, m, and q1 are the fitting coefficients. A is the slope of the straight line relating the logarithm of cumulative gas production to the power of production time. B is the intercept of the straight line relating the logarithm of cumulative gas production to the power of production time. C is the power parameter of production time.

2. The method according to claim 1, characterized in that, In the production data preparation step, the production time uses the cumulative production time to control the influence of data fluctuation characteristics as much as possible.

3. The method according to claim 1, characterized in that, After the decline model determination step, it further includes: A model optimization step of calculating the changes in the daily production and cumulative production data of the well to be evaluated over the production time within a set period using the established production empirical decline model, comparing with the actual production data within the corresponding period, and judging the fitting degree of the production empirical decline model; If the fitting degree does not meet the set requirements, repeat the cumulative gas production curve fitting step to further adjust the power parameter until the fitting degree of the production empirical decline model meets the set requirements.

4. The method according to claim 1, characterized in that, During the process of collecting production performance data, for the well to be evaluated without recorded cumulative production time, calculate the cumulative production time through the following formula: In the formula, i represents day i, t represents cumulative production time d, and G represents cumulative output. 4 m 3 ; q represents daily output, 10 4 m 3 / d.

5. The method according to claim 1, characterized in that, In the data sorting step, it includes: Plotting a curve of daily gas production data over time, identifying and removing the daily gas production data points whose degree of deviation from the overall curve during the production process reaches the set requirements; For production data with a change in the working system, only retain the data corresponding to the working system to be analyzed and remove other production data.

6. The method according to claim 1, characterized in that, In the cumulative gas production curve fitting step, during the adjustment process, keep the power parameter C of the production time within the range of -1 < C < 0.

7. The method according to claim 1 or 6, characterized in that, The cumulative gas production curve fitting step further includes: before plotting the curve, dividing the production performance data according to the production system so that the data in the curve fitting process belongs to the data under the same production system.

8. A storage medium, characterized in that, The storage medium stores program codes that can implement the method described in any one of claims 1 to 7.

9. A system for analyzing empirical decline curves of shale gas well production based on data fluctuations, characterized in that, The system executes the method described in any one of claims 1 to 7.

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