Productivity well testing evaluation method and device based on unknown formation pressure
Through a method of testing well -based evaluation of unknown strata pressure, the problem of high -penetration gas -tibetan middle -class capacity testing data processing is solved, and the accuracy of air well production capacity evaluation and the reliability of production dynamics are realized.
Patent Information
- Application Number
- CN202310926854.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-07-26
- Publication Date
- 2025-01-28
AI Technical Summary
In low-permeability gas reservoirs such as shale gas reservoirs and tight sandstone gas reservoirs, there are abnormal phenomena in the processing of production capacity test well data, resulting in inaccurate formation pressure data, affecting the accuracy of gas well production capacity evaluation.
Put forward a production capacity test evaluation method based on unknown strata pressure. Through the preset processing method, obtaining the data testing data of the measured production capacity, using a correction strategy correction data, determining the air well production capacity evaluation model, and updating the strata pressure model to obtain the accurate air well two items Formulating capacity model.
Effectively restore the real testing data, improve the accuracy of gas well production dynamics, enhance the accuracy of the evaluation of Qihong production capacity and the analysis of production, and have the advantages of simple operation, high accuracy, and easy promotion.
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Figure CN119378784A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of oil and gas exploration and development, and in particular to a productivity well test evaluation method, device, storage medium and electronic equipment based on unknown formation pressure. Background Art
[0002] The purpose of the background technology description provided here is to give an overall background of the present application. The statements in this section merely provide background related to the present application and do not necessarily constitute prior art.
[0003] In the process of conventional gas reservoir development, the open flow rate is generally used to evaluate the productivity of gas wells. The determination of open flow rate is an important part of the conventional gas reservoir productivity evaluation, which is generally completed through productivity well testing. It is an important method and means to obtain formation parameters, evaluate gas well productivity and reasonably allocate production during gas field development. In order to obtain accurate productivity and formation pressure data, it is particularly important to correctly process productivity well testing data. The processing of productivity well testing data of conventional gas wells usually adopts the binomial or exponential method. The commonly used formula for processing data using the binomial productivity equation for conventional gas wells (i.e., the preset gas well binomial productivity model) is as follows:
[0004]
[0005] Among them, in this formula: P i is the original formation pressure, unit: MPa; P wf is the test bottom hole flow pressure, unit MPa; Q g is the gas well test production, unit 10 4 m 3 / d; A and B are the coefficients of the laminar and turbulent flow parts of the binomial capacity equation, respectively. After the slope B and intercept A of the straight line are determined by linear regression, the open-flow capacity of the gas well can be determined.
[0006] However, in the actual testing process, due to the combined influence of multiple complex factors such as instrument accuracy, testing methods, production conditions and geological conditions, the first-hand well test data obtained often have abnormalities, which in turn will lead to abnormalities in the curves obtained according to the conventional binomial or exponential production capacity equations, and these anomalies often cannot be eliminated by re-testing. Therefore, it is necessary to analyze the causes of various abnormal phenomena and take corresponding compensatory measures.
[0007] The conventional gas well anomaly processing method is the trial and error method, but the calculation of this method is relatively complex and the accuracy is difficult to guarantee. Zhan Zedong et al. analyzed the causes of abnormal well test data of three common gas wells, and established a unified mathematical model for abnormal data processing based on the principle of least squares method, which solved the problem of failure to obtain formation pressure or incorrect formation pressure measurement. Some scholars have also proposed corresponding correction methods for the abnormal situation of negative slope of binomial curves that often occur in productivity tests of condensate gas wells and low permeability gas reservoirs, which have certain applicability.
[0008] However, the above abnormal data processing methods are all for regular and accurate test data. When the test data is inaccurate, there is no special abnormal data processing method. Whether it is conventional gas wells or shale gas wells, due to the limitations of test conditions, more or less incorrect data will appear during multi-point testing, resulting in the test points being difficult to form a straight line when applying binomial or exponential processing. If these data are discarded, it will be a great loss.
[0009] In addition, for low permeability gas reservoirs such as shale gas reservoirs and tight sandstone gas reservoirs, there are two differences from conventional gas wells, which directly affect the processing of productivity well test data:
[0010] First, shale gas wells usually have no natural production capacity, and high production can only be achieved by using high-intensity multi-stage fracturing horizontal wells. Hydraulic fracturing reserves a large amount of fracturing fluid in the formation. During the gas well production test, a large amount of fracturing fluid will be produced. The production of fracturing fluid requires a certain additional pressure drop, which is invalid for gas production in the gas well. Therefore, directly using the measured formation pressure to process the production test data will produce wrong results.
[0011] Secondly, due to the extremely poor permeability of the shale layer, it is difficult to obtain reliable formation pressure by shutting in the well and measuring pressure recovery.
[0012] Based on the above analysis, it can be seen that there is an urgent need for an abnormal data processing method that eliminates the influence of pressure for low permeability gas reservoirs to solve the above-mentioned defects. Summary of the invention
[0013] In view of the above problems, the present application proposes a productivity well test evaluation method, device, storage medium and electronic device based on unknown formation pressure. The present application discloses a productivity well test data processing method based on unknown formation pressure, and uses the processed well test data to evaluate the productivity of gas wells, which can effectively mine productivity well test data, improve the production dynamics of gas wells, and carry out the accuracy of related analysis such as gas well productivity evaluation and production allocation, and has the advantages of simple operation, high accuracy and easy promotion.
[0014] In a first aspect of the present application, a productivity well test evaluation method based on unknown formation pressure is provided, the method comprising:
[0015] Conducting a gas well productivity test on the target productivity test well by a preset processing method to obtain measured productivity test well data; wherein the measured productivity test well data includes bottom hole flow pressure data and test production data;
[0016] Correcting the measured productivity well test data according to a preset correction strategy to obtain corrected productivity well test data;
[0017] Determine a gas well productivity evaluation model according to the revised productivity well test data and a preset gas well binomial productivity model, and determine the intercept and slope of the gas well productivity evaluation model; and determine the original formation pressure through the preset gas well binomial productivity model according to the revised productivity well test data, the intercept and the slope;
[0018] The preset gas well binomial productivity model is updated according to the original formation pressure to obtain an updated gas well binomial productivity model, and the target productivity well test is evaluated by using the updated gas well binomial productivity model.
[0019] Furthermore, the preset processing method includes:
[0020] One or more of the following: multi-point backpressure well testing method, isochronous well testing method, modified isochronous well testing method and one-point well testing method.
[0021] Furthermore, the measured productivity well test data includes:
[0022] Bottom hole pressure data and test production data obtained according to various working systems.
[0023] Further, when the test point in the measured productivity well test data is greater than a preset threshold, determining the test point with the largest error among all the test points;
[0024] A deletion operation is performed on the test point with the largest error, or the error test point is fine-tuned by trial and error method until the error test point meets the preset condition.
[0025] Furthermore, the method of determining a gas well productivity evaluation model based on the corrected productivity well test data and a preset gas well binomial productivity model includes:
[0026] Acquire data of two test points from the corrected productivity well test data, namely first test data and second test data;
[0027] Determine a first expression according to the first test data and the preset gas well binomial productivity model, and determine a second expression according to the second test data and the preset gas well binomial productivity model;
[0028] The gas well productivity evaluation model is determined according to the difference between the first expression and the second expression.
[0029] Furthermore, the gas well productivity evaluation model includes:
[0030]
[0031] Where j is the jth test point in the corrected productivity well test data; P wfj is the bottom hole pressure at the jth test point; P wfj-1 is the bottom hole flow pressure at the j-1th test point; Q gj is the test yield at the jth test point; Q gj-1 is the test output at the j-1th test point; A and B are the intercept and slope respectively.
[0032] Further, the determining the original formation pressure by using a preset gas well binomial productivity model according to the corrected productivity well test data, the intercept and the slope includes:
[0033] Determine a plurality of formation pressure values through the preset gas well binomial productivity model according to the intercept and the slope and the data of each test point in the corrected productivity well test data;
[0034] An average value of the multiple formation pressure values is used as the original formation pressure.
[0035] The second aspect of the present application provides a productivity well test evaluation device based on unknown formation pressure, the device comprising:
[0036] An acquisition module is used to perform a gas well productivity test on a target productivity test well by a preset processing method to obtain measured productivity test well data; wherein the measured productivity test well data includes bottom hole flow pressure data and test production data;
[0037] A correction module, used to correct the measured productivity well test data according to a preset correction strategy to obtain corrected productivity well test data;
[0038] A determination module is used to determine a gas well productivity evaluation model according to the revised productivity well test data and a preset gas well binomial productivity model, and determine the intercept and slope of the gas well productivity evaluation model; and determine the original formation pressure through the preset gas well binomial productivity model according to the revised productivity well test data, the intercept and the slope;
[0039] An updating module is used to update the preset gas well binomial productivity model according to the original formation pressure, obtain an updated gas well binomial productivity model, and evaluate the target productivity well test through the updated gas well binomial productivity model.
[0040] According to a third aspect of the present application, a computer-readable storage medium is provided, wherein a computer program stored in the computer-readable storage medium can be executed by one or more processors to implement the steps of the method described above.
[0041] The fourth aspect of the present application provides an electronic device, including a memory and one or more processors, wherein the memory stores a computer program, the memory and the one or more processors are communicatively connected to each other, and when the computer program is executed by the one or more processors, the steps of the method described above are implemented.
[0042] Compared with the prior art, the advantages or beneficial effects of the technical solution of the present application include:
[0043] This application obtains the bottom hole flow pressure and test production from the gas well productivity test to obtain the measured productivity test data; and based on certain principles, the "elimination method" is used to correct the errors of the measured productivity test data points to obtain the corrected standardized productivity test data; the standardized productivity test data is substituted into the gas well productivity evaluation model to determine the original formation pressure, and then the gas well binomial productivity model is updated according to the original formation pressure to obtain an accurate gas well binomial productivity model. It can effectively restore the real test data, lay a certain foundation for accurately grasping the production dynamics of gas wells, conducting relevant analyses such as gas well productivity evaluation and production allocation, and has high practical value and application value. BRIEF DESCRIPTION OF THE DRAWINGS
[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in the relevant field, other drawings can be obtained based on the provided drawings without paying any creative work.
[0045] It should also be noted that, for the convenience of description, only the parts related to the present disclosure are shown in the drawings. The drawings constituting a part of the specification of this application are used to provide a further understanding of the present application. The illustrative embodiments and their descriptions in this application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:
[0046] Figure 1 A flow chart of a productivity well test evaluation method based on unknown formation pressure provided in an embodiment of the present application;
[0047] Figure 2 A flow chart of another productivity well testing evaluation method based on unknown formation pressure provided in an embodiment of the present application;
[0048] Figure 3 A straight line representation of a shale gas well binomial provided in an embodiment of the present application;
[0049] Figure 4 The processing result of the productivity test data of a shale gas well provided in the embodiment of the present application;
[0050] Figure 5 The formation pressure and test points provided in the embodiment of the present application are the results of binomial processing after correction;
[0051] Figure 6 A schematic diagram of the structure of a device provided in an embodiment of the present application;
[0052] Figure 7 A connection block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0053] The following will describe the implementation methods of the present application in detail with reference to the accompanying drawings and embodiments, so that the implementation process of how the present application applies technical means to solve technical problems and achieve corresponding technical effects can be fully understood and implemented accordingly. The embodiments of the present application and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the protection scope of the present application.
[0054] It should be clear that the embodiments described below are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by technicians in the relevant field without making creative work are within the scope of protection of the present application.
[0055] As can be seen from the background technology, in the process of conventional gas reservoir development, the open flow rate is generally used to evaluate the productivity of gas wells. The determination of open flow rate is an important part of the conventional gas reservoir productivity evaluation, which is generally completed through productivity well testing. It is an important method and means to obtain formation parameters, evaluate gas well productivity and reasonably allocate production during gas field development. In order to obtain accurate productivity and formation pressure data, it is particularly important to correctly process productivity well testing data. The processing of productivity well testing data of conventional gas wells usually adopts the binomial or exponential method. The commonly used formula for processing data using the binomial productivity equation for conventional gas wells (i.e., the preset gas well binomial productivity model) is as follows:
[0056]
[0057] Wherein, in formula (1): P i is the original formation pressure, unit: MPa; P wf is the test bottom hole flow pressure, unit MPa; Q g is the gas well test production, unit 10 4 m3 / d; A and B are the coefficients of the laminar and turbulent flow parts of the binomial capacity equation, respectively. After the slope B and intercept A of the straight line are determined by linear regression, the open-flow capacity of the gas well can be determined.
[0058] However, in the actual testing process, due to the combined influence of multiple complex factors such as instrument accuracy, testing methods, production conditions and geological conditions, the first-hand well test data obtained often have abnormalities, which in turn will lead to abnormalities in the curves obtained according to the conventional binomial or exponential production capacity equations, and these anomalies often cannot be eliminated by re-testing. Therefore, it is necessary to analyze the causes of various abnormal phenomena and take corresponding compensatory measures.
[0059] The conventional gas well anomaly processing method is the trial and error method, but the calculation of this method is relatively complex and the accuracy is difficult to guarantee. Zhan Zedong et al. analyzed the causes of abnormal well test data of three common gas wells, and established a unified mathematical model for abnormal data processing based on the principle of least squares method, which solved the problem of failure to obtain formation pressure or incorrect formation pressure measurement. Some scholars have also proposed corresponding correction methods for the abnormal situation of negative slope of binomial curves that often occur in productivity tests of condensate gas wells and low permeability gas reservoirs, which have certain applicability.
[0060] However, the above abnormal data processing methods are all for regular and accurate test data. When the test data is inaccurate, there is no special abnormal data processing method. Whether it is conventional gas wells or shale gas wells, due to the limitations of test conditions, more or less incorrect data will appear during multi-point testing, resulting in the test points being difficult to form a straight line when applying binomial or exponential processing. If these data are discarded, it will be a great loss.
[0061] In addition, for low permeability gas reservoirs such as shale gas reservoirs and tight sandstone gas reservoirs, there are two differences from conventional gas wells, which directly affect the processing of productivity well test data:
[0062] First, shale gas wells usually have no natural production capacity, and high production can only be achieved by using high-intensity multi-stage fracturing horizontal wells. Hydraulic fracturing reserves a large amount of fracturing fluid in the formation. During the gas well production test, a large amount of fracturing fluid will be produced. The production of fracturing fluid requires a certain additional pressure drop, which is invalid for gas production in the gas well. Therefore, directly using the measured formation pressure to process the production test data will produce wrong results.
[0063] Secondly, due to the extremely poor permeability of the shale layer, it is difficult to obtain reliable formation pressure by shutting in the well and measuring pressure recovery.
[0064] In view of this, the present application proposes a productivity well test evaluation method based on unknown formation pressure, which solves the defects mentioned above.
[0065] Embodiment 1
[0066] This embodiment provides a productivity well test evaluation method based on unknown formation pressure. Figure 1 A flow chart of a productivity well test evaluation method based on unknown formation pressure provided in an embodiment of the present application is as follows: Figure 1 As shown, the method disclosed in this embodiment includes the following steps:
[0067] Step 110: Conduct a gas well productivity test on the target productivity test well by a preset processing method to obtain measured productivity test well data; wherein the measured productivity test well data includes bottom hole flow pressure data and test production data.
[0068] In some embodiments, the preset processing method includes:
[0069] One or more of the following: multi-point backpressure well testing method, isochronous well testing method, modified isochronous well testing method and one-point well testing method.
[0070] In some embodiments, the measured productivity well test data includes:
[0071] Bottom hole pressure data and test production data obtained according to various working systems.
[0072] As an example, measured production capacity well test data are first-hand data obtained from multi-point backpressure well test method, isochronous well test method, modified isochronous well test method and "one-point method" well test method; among them, measured production capacity well test data include bottom hole flow pressure and test production data obtained using different working systems.
[0073] Step 120: Correct the measured productivity well test data according to a preset correction strategy to obtain corrected productivity well test data.
[0074] In some embodiments, when the test point in the measured productivity well test data is greater than a preset threshold, determining the test point with the largest error among all the test points;
[0075] A deletion operation is performed on the test point with the largest error, or the error test point is fine-tuned by trial and error method until the error test point meets the preset condition.
[0076] As an example, when the gas well productivity test data cannot be processed normally due to various reasons, it cannot be assumed that all data are wrong. Here, it is assumed that in the multi-point productivity test, at most only one test point has a large error (when there are more than two large error points, the productivity test is considered to have failed); the data processing of the error data can be solved by the elimination method. When the error point is found, the point can be deleted directly, or the trial and error method can be used to fine-tune the data point until all test points are on the same straight line.
[0077] The correction of data cannot be arbitrary. Specifically, the correction of error points must meet the following principles:
[0078] (1) For data with 4 or more test points (i.e., the preset threshold), the point with the largest error can be selected;
[0079] (2) The error in data adjustment should not be too large, and is generally not allowed to exceed the commonly accepted 10%;
[0080] (3) If the adjustment of the maximum error point cannot obtain the best result, the adjustment is declared a failure;
[0081] (4) When there are two points with similar errors in the data, no correction is allowed.
[0082] Step 130: determine a gas well productivity evaluation model based on the corrected productivity well test data and a preset gas well binomial productivity model, and determine the intercept and slope of the gas well productivity evaluation model; and determine the original formation pressure through the preset gas well binomial productivity model based on the corrected productivity well test data, the intercept and the slope.
[0083] In some embodiments, determining the gas well productivity evaluation model according to the corrected productivity well test data and a preset gas well binomial productivity model includes:
[0084] Acquire data of two test points from the corrected productivity well test data, namely first test data and second test data;
[0085] Determine a first expression according to the first test data and the preset gas well binomial productivity model, and determine a second expression according to the second test data and the preset gas well binomial productivity model;
[0086] The gas well productivity evaluation model is determined according to the difference between the first expression and the second expression.
[0087] As an example, the preset gas well binomial productivity model can be expressed as:
[0088] p i2 -p wf 2 =AQ g +BQ g 2 (2)
[0089] First, select any two points j and j-1 from the corrected productivity test data, substitute these two points into formula (2), and obtain the following two expressions respectively:
[0090] p i 2 -p wfj 2 =AQ gj +BQ gj 2 (3)
[0091] p i 2 -p wfj-1 2 =AQ gj-1 +BQ gj-1 2 (4)
[0092] Then, subtract equation (3) from equation (4) and divide both sides of the difference by (Q gj-1 -Q gj ), we get the following formula:
[0093]
[0094] Formula (5) is the gas well productivity evaluation model based on unknown formation pressure, where j is the jth test point in the corrected productivity well test data; P wfj is the bottom hole pressure at the jth test point; P wfj-1 is the bottom hole flow pressure at the j-1th test point; Q gj is the test yield at the jth test point; Q gj-1 is the test output at the j-1th test point; A and B are the intercept and slope respectively.
[0095] Furthermore, based on the corrected productivity test data, the intercept A and slope B of the straight line in equation (5) are determined by linear regression.
[0096] In some embodiments, determining the original formation pressure by using a preset gas well binomial productivity model according to the corrected productivity well test data, the intercept and the slope includes:
[0097] Determine a plurality of formation pressure values through the preset gas well binomial productivity model according to the intercept and the slope and the data of each test point in the corrected productivity well test data;
[0098] An average value of the multiple formation pressure values is used as the original formation pressure.
[0099] As an example, any set of test data (P wf , Q g ) into formula (6) to obtain the original formation pressure of the gas well:
[0100]
[0101] Those skilled in the art can understand that formula (6) can be derived from formula (2).
[0102] Furthermore, in order to eliminate or reduce the influence of the error of the test data itself on the formation pressure calculation result, each set of test data can be substituted into formula (6), the formation pressure can be calculated respectively, and then according to the following formula (7):
[0103]
[0104] To determine the arithmetic mean of the formation pressure P i and take the average value as the original formation pressure.
[0105] Step 140: Update the preset gas well binomial productivity model according to the original formation pressure to obtain an updated gas well binomial productivity model, and evaluate the target productivity well test by using the updated gas well binomial productivity model.
[0106] As an example, the original formation pressure P i Substituting constants A and B into formula (2) can yield P wf and Q g The binomial equation is used to obtain an updated gas well binomial productivity model. Further, the target productivity well test is evaluated by the updated gas well binomial productivity model.
[0107] This embodiment obtains bottom hole flow pressure and test production from the gas well productivity test to obtain measured productivity well test data; and based on certain principles, applies the "elimination method" to perform error correction on the measured productivity well test data points to obtain corrected standardized productivity well test data; substitutes the standardized productivity well test data into the gas well productivity evaluation model to determine the original formation pressure, and then updates the gas well binomial productivity model according to the original formation pressure to obtain an accurate gas well binomial productivity model. Specifically: a gas well productivity test is conducted on a target productivity well test by a preset processing method to obtain measured productivity well test data; wherein the measured productivity well test data includes bottom hole flow pressure data and test production data; the measured productivity well test data is corrected according to a preset correction strategy to obtain corrected productivity well test data; a gas well productivity evaluation model is determined according to the corrected productivity well test data and a preset gas well binomial productivity model, and the intercept and slope of the gas well productivity evaluation model are determined; and the original formation pressure is determined through a preset gas well binomial productivity model according to the corrected productivity well test data, the intercept and the slope; the preset gas well binomial productivity model is updated according to the original formation pressure to obtain an updated gas well binomial productivity model, and the target productivity well test is evaluated through the updated gas well binomial productivity model.
[0108] The productivity well test evaluation method based on unknown formation pressure disclosed in this embodiment can effectively restore real test data, lay a certain foundation for accurately grasping the production dynamics of gas wells, conducting relevant analyses such as gas well productivity evaluation and production allocation, and has high practical value and application value.
[0109] Embodiment 2
[0110] This embodiment provides a specific example, and in this embodiment, the productivity well test evaluation method based on unknown formation pressure disclosed in this application is further described in detail.
[0111] The existing conventional gas well productivity test data processing usually adopts binomial or exponential methods, but for low permeability gas reservoirs, there are still two major defects that have not been solved, which affects the accuracy of gas well productivity test data processing. In view of this, this application proposes a productivity test data processing method based on unknown formation pressure.
[0112] As an example, Figure 2 As shown, the method disclosed in this embodiment includes the following steps:
[0113] Step 1: Obtain bottom hole flow pressure and test production from gas well productivity test to obtain measured productivity well test data.
[0114] In this embodiment, it is known that the original formation pressure of a shale gas well before the productivity test is 71 MPa. Four working systems are used to conduct productivity tests respectively, and four sets of measured productivity test data are obtained from the gas well productivity test. The measured productivity test data include the bottom hole flow pressure P wf and test yield Q g .
[0115] Optionally, the four groups of measured data points are shown in the first and second rows of data in Table 1 below:
[0116] Table 1 Productivity test results of a shale gas well
[0117]
[0118] Step 2: Correct the error points from the measured productivity well test data to obtain standardized productivity well test data.
[0119] In this embodiment, when the conventional binomial (1) is applied to process the data, the results are shown in Table 1. Then, a scatter plot is drawn with the data in the 2nd and 3rd rows in Table 1 as the horizontal and vertical coordinates (refer to Figure 3 ), a straight line with a negative slope was obtained. Obviously, this result cannot be used to evaluate production capacity, indicating that the test data obtained is abnormal and needs further correction.
[0120] As an example, an optional correction process step includes:
[0121] (1) In this embodiment, taking a multi-point productivity well test as an example, it is assumed that at most one test point has a large error (when there are more than two test points with large errors, the productivity well test is considered to have failed);
[0122] (2) The data correction process for the data with error can be solved by the elimination method. Specifically, in the four groups of test data provided in this embodiment, the first test point is deleted first, and the data is processed by formula (5) to determine whether the remaining test points can form a good straight line. If a good straight line is formed, it indicates that the test point currently deleted is a test point with a large error; if the remaining test points are still in a disorderly state, the test point is retained, and another test point is deleted, and the above process is performed again until a good straight line is obtained; by eliminating all test points one by one, the point with a large error can be found.
[0123] (3) Data correction cannot be arbitrary. Specifically, the correction of error points must meet the following principles:
[0124] ① For data with 4 or more test points, the point with the largest error can be selected;
[0125] ② The error of data adjustment cannot be too large, and is generally not allowed to exceed the commonly accepted 10%;
[0126] ③ If the adjustment of the maximum error point cannot obtain the best result, the adjustment is declared a failure;
[0127] ④ When there are two points with similar errors in the data, no correction is required.
[0128] In this embodiment, the second group of test points are abnormal points, and the corresponding bottom hole pressure of 50.58MPa is corrected to 53.08MPa (adjustment range is 4.9%). By correcting the error points, we get Figure 4 The results shown, R 2 =0.999 (R represents the correlation coefficient), achieving the goal of better data correction, thereby obtaining the corrected standardized production capacity well test data, such as the data in the fourth row of Table 1.
[0129] Step 3: Substitute the standardized productivity test data into the productivity model based on unknown formation pressure to obtain the original formation pressure.
[0130] As an example, the following steps are included:
[0131] (1) Establish a productivity evaluation model based on unknown formation pressure, such as formula (5). Specifically, the results of processing the productivity test data of unknown formation pressure using formula (5) are listed in Table 2. The four groups of test data are combined in pairs to calculate the (P wf j 2 -P wf j-1 2 ) / (Q g j -Q g j-1 ); Draw a scatter plot with the data in the 3rd and 4th columns as the horizontal and vertical coordinates. The result is visible Figure 4 , and we get a pretty good straight line.
[0132] Table 2 Treatment results of type (5) in a shale gas well
[0133]
[0134] (2) Determine the intercept A and slope B of the straight line in equation (5) by linear regression. Specifically, Figure 5 Linear regression analysis of the straight line in the figure yields the following results: intercept A = 70.5858, slope B = 2.9682, and correlation coefficient is 0.999.
[0135] (3) Any set of test data (P wf , Q g ) into formula (6), the original formation pressure of the gas well can be obtained.
[0136] Furthermore, the calculation formula for determining the original formation pressure of the gas well is shown in formula (8):
[0137]
[0138] (4) In order to eliminate or reduce the influence of the error of the test data itself on the formation pressure calculation result, each set of test data can be substituted into formula (6) to obtain multiple formation pressure data and determine the arithmetic mean value P of the multiple formation pressure data. i The arithmetic mean of the multiple formation pressure data is taken as the original formation pressure. Specifically, the arithmetic mean of the original formation pressure of a gas well is 62.464 MPa.
[0139] Step 4: Based on the determined original formation pressure, obtain the accurate gas well productivity equation.
[0140] As an example, the original formation pressure P i Substituting A, B into formula (2) respectively, we can get P wf and Q g The binomial equation of , that is, the accurate production capacity equation of a gas well is:
[0141] P wf 2 =3901.75-70.5858Q g -2.9682Q g 2 (9)
[0142] After the productivity equation of a gas well is obtained by the above method in this embodiment, the IPR curve of a shale gas well can be drawn, and then the corresponding open-flow rate of the gas well can be obtained. In this embodiment, the purpose of processing abnormal data of productivity well test based on unknown formation pressure is verified, and the operation is simple, replicable and applicable. It can effectively restore the real test data, lay a certain foundation for accurately grasping the production dynamics of gas wells, conducting relevant analyses such as gas well productivity evaluation and production allocation, and has high practical value and application value.
[0143] Embodiment 3
[0144] This embodiment provides a productivity well test evaluation device based on unknown formation pressure. This device embodiment can be used to execute the method embodiment of this application. For details not disclosed in this device embodiment, please refer to the method embodiment of this application. Figure 6 A schematic diagram of a well test evaluation device for productivity based on unknown formation pressure is provided in an embodiment of the present application, as shown in FIG. Figure 6 As shown, the device 600 disclosed in this embodiment includes:
[0145] The acquisition module 601 is used to perform a gas well productivity test on a target productivity test well by a preset processing method to obtain measured productivity test well data; wherein the measured productivity test well data includes bottom hole flow pressure data and test production data;
[0146] A correction module 602 is used to correct the measured productivity well test data according to a preset correction strategy to obtain corrected productivity well test data;
[0147] The determination module 603 is used to determine the gas well productivity evaluation model according to the modified productivity well test data and the preset gas well binomial productivity model, and determine the intercept and slope of the gas well productivity evaluation model; and determine the original formation pressure through the preset gas well binomial productivity model according to the modified productivity well test data, the intercept and the slope;
[0148] The updating module 604 is used to update the preset gas well binomial productivity model according to the original formation pressure, obtain the updated gas well binomial productivity model, and evaluate the target productivity well test through the updated gas well binomial productivity model.
[0149] In some embodiments, the preset processing method includes:
[0150] One or more of the following: multi-point backpressure well testing method, isochronous well testing method, modified isochronous well testing method and one-point well testing method.
[0151] In some embodiments, the measured productivity well test data includes:
[0152] Bottom hole pressure data and test production data obtained according to various working systems.
[0153] In some embodiments, when the test point in the measured productivity well test data is greater than a preset threshold, determining the test point with the largest error among all the test points;
[0154] A deletion operation is performed on the test point with the largest error, or the error test point is fine-tuned by trial and error method until the error test point meets the preset condition.
[0155] In some embodiments, determining the gas well productivity evaluation model according to the corrected productivity well test data and a preset gas well binomial productivity model includes:
[0156] Acquire data of two test points from the corrected productivity well test data, namely first test data and second test data;
[0157] Determine a first expression according to the first test data and the preset gas well binomial productivity model, and determine a second expression according to the second test data and the preset gas well binomial productivity model;
[0158] The gas well productivity evaluation model is determined according to the difference between the first expression and the second expression.
[0159] In some embodiments, the gas well productivity evaluation model includes:
[0160]
[0161] Where j is the jth test point in the corrected productivity well test data; P wfj is the bottom hole pressure at the jth test point; P wfj-1 is the bottom hole flow pressure at the j-1th test point; Q gj is the test yield at the jth test point; Q gj-1 is the test output at the j-1th test point; A and B are the intercept and slope respectively.
[0162] In some embodiments, determining the original formation pressure by using a preset gas well binomial productivity model according to the corrected productivity well test data, the intercept and the slope includes:
[0163] Determine a plurality of formation pressure values through the preset gas well binomial productivity model according to the intercept and the slope and the data of each test point in the corrected productivity well test data;
[0164] An average value of the multiple formation pressure values is used as the original formation pressure.
[0165] It can be understood by those skilled in the art that Figure 6 The structure shown in the figure does not constitute a limitation on the device of the embodiment of the present application, and may include more or fewer modules / units than shown in the figure, or a combination of certain modules / units, or different module / unit arrangements.
[0166] It should be understood by those skilled in the art that the above modules or steps of the present application can be implemented by a general-purpose computing device, and they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation.
[0167] Technicians in the relevant field can clearly understand that, for the convenience and simplicity of description, the specific working process of the productivity well testing evaluation device based on unknown formation pressure can refer to the corresponding process in the aforementioned method embodiment, and this embodiment will not be repeated here.
[0168] The device provided in this embodiment includes: an acquisition module 601, which is used to perform a gas well productivity experiment on a target productivity well test through a preset processing method to obtain measured productivity well test data; wherein the measured productivity well test data includes bottom hole flow pressure data and test production data; a correction module 602, which is used to correct the measured productivity well test data according to a preset correction strategy to obtain corrected productivity well test data; a determination module 603, which is used to determine a gas well productivity evaluation model according to the corrected productivity well test data and a preset gas well binomial productivity model, and determine the intercept and slope of the gas well productivity evaluation model; and determine the original formation pressure through the preset gas well binomial productivity model according to the corrected productivity well test data, the intercept and the slope; an update module 604, which is used to update the preset gas well binomial productivity model according to the original formation pressure, obtain an updated gas well binomial productivity model, and evaluate the target productivity well test through the updated gas well binomial productivity model. It can effectively restore real test data, lay a certain foundation for accurately grasping the production dynamics of gas wells, conducting relevant analyses such as gas well capacity evaluation and production allocation, and has high practical and application value.
[0169] Embodiment 4
[0170] This embodiment provides a computer-readable storage medium.
[0171] A computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the method in the aforementioned method embodiment can be implemented:
[0172] A gas well productivity test is conducted on a target productivity well test by a preset processing method to obtain measured productivity well test data; wherein the measured productivity well test data includes bottom hole flow pressure data and test production data; the measured productivity well test data is corrected according to a preset correction strategy to obtain corrected productivity well test data; a gas well productivity evaluation model is determined according to the corrected productivity well test data and a preset gas well binomial productivity model, and the intercept and slope of the gas well productivity evaluation model are determined; and the original formation pressure is determined through a preset gas well binomial productivity model according to the corrected productivity well test data, the intercept and the slope; the preset gas well binomial productivity model is updated according to the original formation pressure to obtain an updated gas well binomial productivity model, and the target productivity well test is evaluated through the updated gas well binomial productivity model.
[0173] Among them, the computer-readable storage medium may also include computer programs, data files, data structures, etc. alone, or include a combination thereof. The computer-readable storage medium or computer program may be specifically designed and understood by a technician in the field of computer software, or the computer-readable storage medium may be known and available to a technician in the field of computer software. Examples of computer-readable storage media include: magnetic media, such as hard disks, floppy disks, and tapes; optical media, such as CDROM disks and DVDs; magneto-optical media, such as optical disks; and hardware devices, specifically configured to store and execute computer programs, such as read-only memory (ROM), random access memory (RAM), flash memory; or servers, app application stores, etc. Examples of computer programs include machine code (e.g., code generated by a compiler) and files containing high-level code, which can be executed by a computer using an interpreter. The described hardware device may be configured to be used as one or more software modules to perform the operations and methods described above, and vice versa. In addition, the computer-readable storage medium may be distributed in a networked computer system, and program code or computer programs may be stored and executed in a decentralized manner.
[0174] Embodiment 5
[0175] This embodiment provides a computer program product.
[0176] The computer program product includes a computer program or instructions. When the computer program or instructions are executed by a processor, all or part of the steps of the method in the aforementioned method embodiment are implemented, and this embodiment will not be repeated here.
[0177] Further, the computer program product may include one or more computer executable components configured to perform the embodiments when the program is run; the computer program product may also include a computer program tangibly contained on a readable medium, the computer program including program code for performing any method in the embodiments of the present disclosure. In such an embodiment, the computer program may be downloaded and installed from a network through a communication portion, and / or installed from a removable medium.
[0178] Embodiment 6
[0179] This embodiment provides an electronic device.
[0180] As an example, Figure 7 A connection block diagram of an electronic device provided in an embodiment of the present application, such as Figure 7 As shown, the electronic device 700 may include: one or more processors 701 , a memory 702 , a multimedia component 703 , an input / output (I / O) interface 704 , and a communication component 705 .
[0181] The one or more processors 701 are used to execute all or part of the steps in the above method embodiments. The memory 702 is used to store various types of data, which may include instructions of any application or method in the electronic device, and data related to the application.
[0182] The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.
[0183] The one or more processors 701 may be implemented by an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and are used to execute all or part of the steps in the aforementioned method embodiment:
[0184] A gas well productivity test is conducted on a target productivity well test by a preset processing method to obtain measured productivity well test data; wherein the measured productivity well test data includes bottom hole flow pressure data and test production data; the measured productivity well test data is corrected according to a preset correction strategy to obtain corrected productivity well test data; a gas well productivity evaluation model is determined according to the corrected productivity well test data and a preset gas well binomial productivity model, and the intercept and slope of the gas well productivity evaluation model are determined; and the original formation pressure is determined through a preset gas well binomial productivity model according to the corrected productivity well test data, the intercept and the slope; the preset gas well binomial productivity model is updated according to the original formation pressure to obtain an updated gas well binomial productivity model, and the target productivity well test is evaluated through the updated gas well binomial productivity model.
[0185] The multimedia component 703 may include a screen and an audio component, the screen may be a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, and the microphone is used to receive external audio signals. The received audio signal may be further stored in a memory or sent via a communication component. The audio component also includes at least one speaker for outputting audio signals.
[0186] The I / O interface 704 provides an interface between one or more processors 701 and other interface modules, which may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons.
[0187] The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wired communication includes communication through a network port, a serial port, etc.; wireless communication includes: Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, 5G, or a combination of one or more of them.
[0188] In summary, the present application provides a productivity well test evaluation method, device, computer-readable storage medium and electronic device based on unknown formation pressure. The method obtains bottom hole flow pressure and test production from gas well productivity test to obtain measured productivity well test data; and based on certain principles, applies the "elimination method" to perform error correction on measured productivity well test data points to obtain corrected standardized productivity well test data; substitutes the standardized productivity well test data into the gas well productivity evaluation model to determine the original formation pressure, and then updates the gas well binomial productivity model according to the original formation pressure to obtain an accurate gas well binomial productivity model. Specifically: a gas well productivity test is conducted on the target productivity test well by a preset processing method to obtain measured productivity test well data; wherein the measured productivity test well data includes bottom hole flow pressure data and test production data; the measured productivity test well data is corrected according to a preset correction strategy to obtain corrected productivity test well data; a gas well productivity evaluation model is determined according to the corrected productivity test well data and a preset gas well binomial productivity model, and the intercept and slope of the gas well productivity evaluation model are determined; and the original formation pressure is determined by a preset gas well binomial productivity model according to the corrected productivity test well data, the intercept and the slope; the preset gas well binomial productivity model is updated according to the original formation pressure to obtain an updated gas well binomial productivity model, and the target productivity test well is evaluated by the updated gas well binomial productivity model. It can effectively restore the real test data, lay a certain foundation for accurately grasping the production dynamics of gas wells, conducting relevant analyses such as gas well productivity evaluation and production allocation, and has high practical value and application value.
[0189] In addition, it should be understood that the method or device disclosed in the embodiments provided in the present application can also be implemented in other ways. The method or device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the methods and devices according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a computer program segment or a part of a computer program, and the module, computer program segment or a part of a computer program contains one or more computer programs for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings, and can actually be executed in parallel. They can also be executed in the opposite order sometimes, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer programs.
[0190] In this application, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements that are inherent to such process, method, article, or apparatus. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, device or equipment including the elements; if there is a description of "first", "second", etc., it is only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features; in the description of this application, unless otherwise specified, the terms "multiple" and "multiple" mean at least two; if there is a description of a server, it should be noted that the server can be an independent physical server or terminal, or a server cluster composed of multiple physical servers, or a cloud server that can provide basic cloud computing services such as cloud servers, cloud databases, cloud storage and CDN; if there is a description of a smart terminal or mobile device in this application, it should be noted that the smart terminal or mobile device can be a mobile phone, a tablet computer, a smart watch, a netbook, a wearable electronic device, a personal digital assistant (PDA), an augmented reality technology device (AR), a virtual reality device (VR), a smart TV, a smart speaker, a personal computer (PC), a tablet computer, a smart phone, a smart TV, a smart speaker, a smart phone ... Computer, referred to as PC), etc., but not limited to this, the present application does not specifically limit the specific form of the smart terminal or mobile device.
[0191] Finally, it should be noted that, in the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "an example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.
[0192] Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary, and the contents described are only embodiments adopted for the convenience of understanding the present application, and are not intended to limit the present application. Any technician in the technical field to which the present application belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present application, but the scope of protection of the present application shall still be based on the scope defined in the attached claims.
Claims
1. A productivity well test evaluation method based on unknown formation pressure, characterized in that: The method comprises: Conducting a gas well productivity test on the target productivity test well by a preset processing method to obtain measured productivity test well data; wherein the measured productivity test well data includes bottom hole flow pressure data and test production data; Correcting the measured productivity well test data according to a preset correction strategy to obtain corrected productivity well test data; Determine a gas well productivity evaluation model according to the revised productivity well test data and a preset gas well binomial productivity model, and determine the intercept and slope of the gas well productivity evaluation model; and determine the original formation pressure through the preset gas well binomial productivity model according to the revised productivity well test data, the intercept and the slope; The preset gas well binomial productivity model is updated according to the original formation pressure to obtain an updated gas well binomial productivity model, and the target productivity well test is evaluated by using the updated gas well binomial productivity model.
2. The productivity well test evaluation method based on unknown formation pressure according to claim 1, characterized in that: The preset processing method includes: One or more of the following: multi-point backpressure well testing method, isochronous well testing method, modified isochronous well testing method and one-point well testing method.
3. The productivity well test evaluation method based on unknown formation pressure according to claim 1, characterized in that: The measured productivity well test data include: Bottom hole pressure data and test production data obtained according to various working systems.
4. The productivity well test evaluation method based on unknown formation pressure according to claim 1, characterized in that: When the test point in the measured productivity well test data is greater than a preset threshold, determining the test point with the largest error among all the test points; A deletion operation is performed on the test point with the largest error, or the error test point is fine-tuned by trial and error method until the error test point meets the preset condition.
5. The productivity well test evaluation method based on unknown formation pressure according to claim 1, characterized in that: The method of determining the gas well productivity evaluation model according to the corrected productivity well test data and the preset gas well binomial productivity model includes: Acquire data of two test points from the corrected productivity well test data, namely first test data and second test data; Determine a first expression according to the first test data and the preset gas well binomial productivity model, and determine a second expression according to the second test data and the preset gas well binomial productivity model; The gas well productivity evaluation model is determined according to the difference between the first expression and the second expression.
6. The productivity well test evaluation method based on unknown formation pressure according to claim 1, characterized in that: The gas well productivity evaluation model includes: Where j is the jth test point in the corrected productivity well test data; P wfj is the bottom hole pressure at the jth test point; P wfj-1 is the bottom hole flow pressure at the j-1th test point; Q gj is the test yield at the jth test point; Q gj-1 is the test output at the j-1th test point; A and B are the intercept and slope respectively.
7. The productivity well test evaluation method based on unknown formation pressure according to claim 1, characterized in that: The determining of the original formation pressure by using a preset gas well binomial productivity model according to the corrected productivity well test data, the intercept and the slope includes: Determine a plurality of formation pressure values through the preset gas well binomial productivity model according to the intercept and the slope and the data of each test point in the corrected productivity well test data; An average value of the multiple formation pressure values is used as the original formation pressure.
8. A productivity well test evaluation device based on unknown formation pressure, characterized in that: include: An acquisition module is used to perform a gas well productivity test on a target productivity test well by a preset processing method to obtain measured productivity test well data; wherein the measured productivity test well data includes bottom hole flow pressure data and test production data; A correction module, used to correct the measured productivity well test data according to a preset correction strategy to obtain corrected productivity well test data; A determination module is used to determine a gas well productivity evaluation model according to the revised productivity well test data and a preset gas well binomial productivity model, and determine the intercept and slope of the gas well productivity evaluation model; and determine the original formation pressure through the preset gas well binomial productivity model according to the revised productivity well test data, the intercept and the slope; An updating module is used to update the preset gas well binomial productivity model according to the original formation pressure, obtain an updated gas well binomial productivity model, and evaluate the target productivity well test through the updated gas well binomial productivity model.
9. A computer-readable storage medium, characterized in that: The computer program stored in the computer-readable storage medium, when executed by one or more processors, implements the method according to any one of claims 1 to 7.
10. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.