Productivity evaluation method and electronic equipment

Abstract

The embodiment of the invention discloses a productivity evaluation method and electronic equipment. The method comprises the steps that a well testing model corresponding to a target gas field is determined, a target well in the target gas field is explained according to the well testing model, and well testing explanation parameters are obtained; acquiring preset first yield change data of the target well under a preset production system, and performing simulated well testing on the target well according to the first yield change data, the well testing interpretation parameters and the well testing model to obtain first pressure change data of the target well under the preset production system; according to the first pressure change data, productivity evaluation is conducted on the target well, and a productivity evaluation result of the target well is obtained. According to the technical scheme provided by the embodiment of the invention, simple, convenient, rapid, low-cost and high-precision productivity evaluation can be realized.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of geological exploration technology, and in particular to a method for evaluating production capacity and an electronic device. Background Technology

[0002] Offshore tight gas fields possess abundant reserves and promising development prospects. However, their development is challenging, and capacity assessment is difficult, posing significant obstacles to development efforts. Capacity assessment is a crucial method for evaluating the production capacity of wells within a gas field. Based on the obtained capacity assessment results, formulating subsequent production enhancement measures for each well is of great importance for achieving efficient development of offshore tight gas fields.

[0003] However, it is currently difficult to achieve a simple, fast, low-cost, and highly accurate capacity assessment. Summary of the Invention

[0004] This invention provides a capacity evaluation method and electronic device to achieve simple, fast, low-cost and high-precision capacity evaluation.

[0005] According to one aspect of the present invention, a capacity evaluation method is provided, which may include:

[0006] Determine the well test model corresponding to the target gas field, and interpret the target well in the target gas field based on the well test model to obtain the well test interpretation parameters;

[0007] Obtain the first production change data of the target well under the preset production system, and based on the first production change data, well test interpretation parameters and well test model, conduct a simulated well test on the target well to obtain the first pressure change data of the target well under the preset production system.

[0008] Based on the first pressure change data, the production capacity of the target well is evaluated, and the production capacity evaluation results of the target well are obtained.

[0009] According to another aspect of the present invention, an electronic device is provided, which may include:

[0010] At least one processor; and

[0011] A memory that is communicatively connected to at least one processor; wherein,

[0012] The memory stores a computer program that can be executed by at least one processor, such that when the at least one processor executes the program, it implements the capacity evaluation method provided in any embodiment of the present invention.

[0013] The technical solution of this invention determines a well test model corresponding to a target gas field, interprets the target well in the target gas field based on the well test model, and obtains well test interpretation parameters; acquires the first production change data of the target well under a preset production regime, and performs a simulated well test on the target well based on the first production change data, the well test interpretation parameters, and the well test model to obtain the first pressure change data of the target well under the preset production regime. This allows the first pressure change data to be obtained without shutting down the target well or changing its operating regime. Based on the first pressure change data, the production capacity of the target well is evaluated to obtain the production capacity evaluation result, achieving high-precision production capacity evaluation. The above technical solution, by performing a simulated corrected isochronous well test on the target well based on the first production change data, the well test interpretation parameters, and the well test model to obtain the first pressure change data, can obtain the first pressure change data simply, quickly, and at low cost without shutting down the target well or changing its operating regime. Based on the first pressure change data, a high-precision production capacity evaluation is then performed, thereby achieving a simple, fast, low-cost, and high-precision production capacity evaluation.

[0014] It should be understood that the description in this section is not intended to identify key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a capacity evaluation method provided by an embodiment of the present invention;

[0017] Figure 2 This is a schematic diagram of a binomial relationship in a capacity evaluation method provided by an embodiment of the present invention;

[0018] Figure 3 This is a schematic diagram of the first pressure change data in a capacity evaluation method provided according to an embodiment of the present invention;

[0019] Figure 4 This is a flowchart of another capacity evaluation method provided by an embodiment of the present invention;

[0020] Figure 5 This is a schematic diagram of data fitting in another capacity evaluation method provided by an embodiment of the present invention;

[0021] Figure 6 This is a schematic diagram of the flow segment of the double logarithmic characteristic curve in the well test model theory of another production capacity evaluation method provided by the present invention;

[0022] Figure 7 This is a flowchart of another capacity evaluation method provided by an embodiment of the present invention;

[0023] Figure 8 This is a schematic diagram of a grid model in another capacity evaluation method provided by an embodiment of the present invention;

[0024] Figure 9 This is a schematic diagram of the fitting of production change data in another optional example of a production capacity evaluation method provided by an embodiment of the present invention;

[0025] Figure 10 This is a schematic diagram of pressure change data fitting in another optional example of a capacity evaluation method provided according to an embodiment of the present invention;

[0026] Figure 11 This is a schematic diagram of the first production change data and the corresponding first pressure change data in another optional example of a capacity evaluation method provided according to an embodiment of the present invention.

[0027] Figure 12 This is a schematic diagram of a binomial straight line in another optional example of a capacity evaluation method provided by an embodiment of the present invention;

[0028] Figure 13 This is a structural block diagram of a capacity evaluation device provided according to an embodiment of the present invention;

[0029] Figure 14 This is a schematic diagram of the structure of an electronic device that implements the capacity evaluation method of this invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The same applies to "target," "original," etc., and will not be repeated here. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Before introducing the embodiments of the present invention, we will first provide an exemplary description of the current solutions used for capacity evaluation and the reasons why they have the problem of being difficult to achieve simple, fast, low-cost and high-precision capacity evaluation, so as to better understand why the solution proposed in the embodiments of the present invention can achieve simple, fast, low-cost and high-precision capacity evaluation.

[0033] Currently, methods used for production capacity assessment include the Vogel method, the Standing modified method, the Fetkovich method, the Wiggins method, the one-point method, and the backpressure method. However, these methods suffer from significant uncertainties, resulting in low accuracy and failing to meet actual development requirements. To address this, actual testing has been proposed to improve accuracy. However, this method requires highly accurate unobstructed flow rate, which is difficult to obtain quickly and easily. Currently, obtaining the parameters needed to calculate the unobstructed flow rate relies on shutting down or changing the operating regime of the well being assessed. In actual production, shutting down or changing the operating regime after a gas well is put into production incurs high costs, and obtaining the required parameters using this method is complex and slow. Therefore, this approach has low feasibility. Consequently, a simple, rapid, low-cost, and highly accurate production capacity assessment remains elusive.

[0034] To address this, this invention provides a method for obtaining first pressure change data by performing simulated and corrected isochronous well tests on the target well based on first production change data, well test interpretation parameters, and a well test model. This method eliminates the need for shut-in operations or changes to the target well's operating regime, allowing for a simple, rapid, and low-cost acquisition of first pressure change data. Based on this first pressure change data, a high-precision production capacity evaluation is then performed, enabling a simple, rapid, low-cost, and highly accurate production capacity evaluation. This will be explained in detail below.

[0035] Figure 1 This is a flowchart of a production capacity evaluation method provided in an embodiment of the present invention. This embodiment is applicable to production capacity evaluation, especially to the production capacity evaluation of wells in gas fields. The method can be executed by the production capacity evaluation device provided in this embodiment of the present invention. This device can be implemented by software and / or hardware, and can be integrated into an electronic device, which can be various user terminals or servers.

[0036] See Figure 1 The method of this invention specifically includes the following steps:

[0037] S101. Determine the well test model corresponding to the target gas field, and interpret the target well in the target gas field according to the well test model to obtain the well test interpretation parameters.

[0038] In this context, a target gas field can be understood as a gas field in which the production capacity of a target well needs to be evaluated; a target gas field can be an offshore tight gas field.

[0039] A well test model can be understood as a model used to simulate well testing of a target well to obtain the first pressure change data. The well test model can characterize the changing trend of bottom hole flow pressure and / or bottom hole formation pressure (bottom hole pressure); the well test model can be a seepage well test model.

[0040] In this embodiment of the invention, the well test model can be determined.

[0041] A target well can be understood as a well for evaluating production capacity based on demand. Horizontal wells, such as long horizontal open-hole wells, are widely used in the development of offshore tight gas fields due to their large unloading area and low seepage resistance. Therefore, a target well can be a long horizontal open-hole well or a multi-branch well, etc.

[0042] Well test interpretation parameters can be understood as parameters related to the target well obtained by interpreting the target well; well test interpretation parameters may include at least one of the key parameters of the target well, such as permeability, wellbore parameters, reservoir parameters, skin factor, skin coefficient, and effective wellbore length.

[0043] In this embodiment of the invention, well test interpretation parameters can be obtained by interpreting the target well in the target gas field based on the well test model. For example, the well test interpretation parameters can be obtained by interpreting the target well in the target gas field through pressure recovery data fitting analysis (also known as pressure instability analysis (PTA)) based on the well test model. Another example is to initialize the well test interpretation parameters and obtain the second pressure change data of the target well within a first historical time period; predict the fourth pressure change data of the target well within the first historical time period based on the well test interpretation parameters and the well test model, and fit the second and fourth pressure change data to obtain a third fitting result; if the fitting degree represented by the third fitting result is less than a preset fitting degree threshold, adjust the well test interpretation parameters and repeat the step of predicting the fourth pressure change data of the target well within the first historical time period based on the well test interpretation parameters and the well test model; if the fitting degree represented by the third fitting result is greater than or equal to the preset fitting degree threshold, the well test interpretation parameters are obtained.

[0044] S102. Obtain the first production change data of the target well under the preset production system, and conduct a simulated well test on the target well based on the first production change data, well test interpretation parameters and well test model to obtain the first pressure change data of the target well under the preset production system.

[0045] The pre-set production system can be understood as a system for the production of the target well that is set in advance.

[0046] The first pressure change data can be understood as data that characterizes the changes in bottom hole pressure of the target well under a preset production regime.

[0047] In this embodiment of the invention, first production change data can be obtained, and based on the first production change data, well test interpretation parameters, and well test model, a simulated well test is performed on the target well to obtain first pressure change data.

[0048] S103. Based on the first pressure change data, evaluate the production capacity of the target well and obtain the production capacity evaluation result of the target well.

[0049] The production capacity evaluation result can be understood as the result obtained by evaluating the production capacity of the target well.

[0050] In this embodiment of the invention, the production capacity of the target well can be evaluated based on the first pressure change data to obtain the production capacity evaluation result. For example, the production capacity of the target well can be evaluated based on the first pressure change data to obtain the unobstructed flow rate of the target well, and the production capacity evaluation result can be determined based on the unobstructed flow rate.

[0051] In this embodiment of the invention, the target well can be a typical well in the target gas field. Since the production capacity evaluation results of the typical well can characterize the production capacity of the target gas field, the production capacity evaluation results of the target gas field can also be determined based on the production capacity evaluation results of the typical well (target well).

[0052] The technical solution of this invention can achieve efficient, simple, fast, low-cost and high-precision production capacity evaluation, which is of great significance for reducing costs and increasing efficiency and efficient development of gas fields.

[0053] The technical solution of this invention determines a well test model corresponding to a target gas field, interprets the target well in the target gas field based on the well test model, and obtains well test interpretation parameters; acquires the first production change data of the target well under a preset production regime, and performs a simulated well test on the target well based on the first production change data, the well test interpretation parameters, and the well test model to obtain the first pressure change data of the target well under the preset production regime. This allows the first pressure change data to be obtained without shutting down the target well or changing its operating regime. Based on the first pressure change data, the production capacity of the target well is evaluated to obtain the production capacity evaluation result, achieving high-precision production capacity evaluation. The above technical solution, by performing a simulated corrected isochronous well test on the target well based on the first production change data, the well test interpretation parameters, and the well test model to obtain the first pressure change data, can obtain the first pressure change data simply, quickly, and at low cost without shutting down the target well or changing its operating regime. Based on the first pressure change data, a high-precision production capacity evaluation is then performed, thereby achieving a simple, fast, low-cost, and high-precision production capacity evaluation.

[0054] An optional technical solution involves determining a well test model corresponding to a target gas field, including: determining the second seepage equation, second boundary conditions, and second initial conditions corresponding to the target gas field; and solving the second seepage equation, second boundary conditions, and second initial conditions using the Laplace transform and Stefest inversion methods to obtain the well test model corresponding to the target gas field.

[0055] The second seepage equation can be understood as the seepage equation corresponding to the target gas field; for example, if the target gas field is an offshore tight gas field, the second seepage equation is a seepage equation that conforms to the characteristics of an offshore tight gas field.

[0056] The second boundary condition can be understood as a boundary condition that describes the state or constraint of the boundary of the second seepage equation and corresponds to the target gas field; for example, in the case that the target gas field is an offshore tight gas field, the second boundary condition is a boundary condition that conforms to the characteristics of an offshore tight gas field.

[0057] The second initial condition can be understood as describing the state of the second seepage equation at the initial moment, and is the initial condition corresponding to the target gas field; for example, in the case that the target gas field is an offshore tight gas field, the second boundary condition is the initial condition that conforms to the characteristics of an offshore tight gas field.

[0058] In this embodiment of the invention, a second seepage equation, a second boundary condition, and a second initial condition can be determined. For example, the second seepage equation, the second boundary condition, and the second initial condition can be determined based on the geological data of the target gas field, reservoir property data, and the method used to determine the well test model (Laplace transform and Stefair inversion method). For example, the second seepage equation can be determined as follows: The second boundary condition is The second initial condition is ,in, For dimensionless pressure in Laplace space, The distance is dimensionless. Dimensionless time, The z-coordinate is dimensionless. The length of the dimensionless wellbore. For Laplace variables, For a function of Lagrange variables, under homogeneous conditions, it can be taken as... .

[0059] In this embodiment of the invention, the second seepage equation, the second boundary condition, and the second initial condition can be solved by the Laplace transform and the Stefest inversion method to obtain the well test model.

[0060] In this embodiment of the invention, the determined second seepage equation, second boundary conditions, and second initial conditions are solved by Laplace transform and Stefest inversion method to obtain a well test model. This well test model is more applicable to the target gas field and thus more applicable to simulated well tests of the target well in the target gas field.

[0061] Another optional technical solution is to evaluate the production capacity of the target well based on the first pressure change data, and obtain the production capacity evaluation result of the target well, including: performing a binomial production capacity evaluation of the target well based on the first pressure change data, and obtaining the production capacity evaluation result of the target well.

[0062] Understandably, binomial productivity evaluation has advantages such as ease of operation and a simple structure of the binomial productivity equation. Therefore, based on the first pressure change data, a binomial productivity evaluation can be performed on the target well to obtain the productivity evaluation result, thus achieving rapid and convenient productivity evaluation. For example, based on the gas well binomial productivity equation... (can also be expressed as) ), determine the binomial relationship diagram (see, for example, see Figure 2 The binomial relationship diagram is (The relationship between q and q is shown in the diagram). and All figures are formation pressures in MPa. The bottom hole flowing pressure is expressed in MPa, and q is expressed in units of 10. 4 m 3 The daily production of the gas well is given by A, where A is the Darcy flow coefficient (also known as the laminar flow coefficient), and B is the non-Darcy flow coefficient (also known as the turbulent flow coefficient). A binomial relationship is fitted to the regression line equation to obtain the binomial straight line, and the slope (A) and intercept (B) of the binomial straight line are determined. Based on the slope, intercept, and the gas well binomial production capacity equation, the gas well free-flow equation is determined (for example, when the bottom-hole flow pressure is standard atmospheric pressure 0.101 MPa, the gas well free-flow equation is...). ,in, (where a is the unobstructed flow rate of the target well, and a can correspond to A, b can correspond to B); the production capacity evaluation result is determined based on the first pressure change data and the gas well unobstructed flow rate equation.

[0063] Another optional technical solution includes a preset production system comprising at least one operating system, wherein the shut-in time after production is the same as the production time; based on the first production change data, well test interpretation parameters, and well test model, a simulated well test is performed on the target well to obtain the first pressure change data of the target well under the preset production system, including: based on the first production change data, well test interpretation parameters, and well test model, a simulated corrected isochronous well test is performed on the target well to obtain the first pressure change data of the target well under at least one operating system.

[0064] The work system can be understood as the pre-set system for the production work of the target well.

[0065] The shut-in time can be understood as the duration for which the target well is shut down.

[0066] Production duration can be understood as the production duration of the target well.

[0067] It is important to note that the shut-in time after production is the same as the production time under the operating regime (since the shut-in time after production is the same as the production time, the simulated well test conducted based on this is a simulated isochronous well test), and it is not required to shut in to a stable pressure. The simulated isochronous well test conducted based on this can be called a simulated corrected isochronous well test. In this embodiment of the invention, a simulated corrected isochronous well test can be performed on the target well based on the first production change data, well test interpretation parameters, and the well test model to obtain the first pressure change data.

[0068] In embodiments of the present invention, a preset production system may include at least one work system, and for example, a preset production system may include four work systems; see [link / reference] Figure 3 The preset production system may also include an additional shut-in, which is an additional shut-in following the opening of the well in the last working cycle of at least one working cycle, to allow the first pressure change data (including...) Figure 3 The medium-stable flow pressure can include an additional shut-in pressure recovery data point. In this case, it can be based on the first production change data (including...). Figure 3 The system uses continuous production data, well test interpretation parameters, and well test models to perform simulated and corrected isochronous well tests on the target well, obtaining the first pressure change data of the target well under at least one operating regime and additional shut-in conditions.

[0069] In this embodiment of the invention, by performing a simulated and corrected isochronous well test on the target well based on the first production change data, well test interpretation parameters, and well test model, the accuracy of the first pressure change data can be improved.

[0070] Figure 4 This is a flowchart of another production capacity evaluation method provided in this embodiment of the invention. This embodiment is based on the above-described technical solutions and optimized. In this embodiment, optionally, according to the well test model, the target well in the target gas field is interpreted to obtain well test interpretation parameters, including: obtaining at least one set of first candidate interpretation parameters for the target well, and second pressure change data of the target well within a first historical time period; for each set of first candidate interpretation parameters, according to the first candidate interpretation parameters and the well test model, predicting third pressure change data of the target well within the first historical time period, and fitting the second pressure change data and the third pressure change data to obtain a first fitting result; determining the well test interpretation parameters from the at least one set of first candidate interpretation parameters according to the first fitting results corresponding to the at least one set of first candidate interpretation parameters. The explanations of terms that are the same as or corresponding to those in the above embodiments are not repeated here.

[0071] See Figure 4 The method in this embodiment may specifically include the following steps:

[0072] S201. Determine the well test model corresponding to the target gas field.

[0073] S202. Obtain at least one set of first candidate interpretation parameters for the target well, and second pressure change data of the target well within a first historical time period.

[0074] Here, the first candidate interpretation parameter can be understood as a parameter to be selected as a well test interpretation parameter.

[0075] The first historical time period can be understood as the period in history corresponding to the second pressure change data.

[0076] The second pressure change data can be understood as data that characterizes the actual changes in bottom hole pressure of the target well during the first historical time period.

[0077] In this embodiment of the invention, at least one set of first candidate interpretation parameters can be obtained. The at least one set of first candidate interpretation parameters can be pre-set to facilitate the acquisition of the at least one set of first candidate interpretation parameters.

[0078] In this embodiment of the invention, second pressure change data can be obtained.

[0079] S203. For each group of first candidate interpretation parameters, based on the first candidate interpretation parameters and the well test model, predict the third pressure change data of the target well in the first historical time period, and fit the second pressure change data and the third pressure change data to obtain the first fitting result.

[0080] Among them, the third pressure change data can be understood as predictable data characterizing the changes in bottom hole pressure of the target well within the first historical time period.

[0081] The first fitting result can be obtained by fitting the second pressure change data and the third pressure change data.

[0082] In this embodiment of the invention, third pressure change data can be predicted based on a first set of candidate interpretation parameters and a well test model. For example, third pressure change data can be predicted based on geological data, target well type data, shut-in pressure data, constant pressure production data, the first set of candidate interpretation parameters, and a well test model. Another example is that the well test model, reservoir property data, and pressure recovery data of the target well can be imported into an oil and gas management tool. In the oil and gas management tool, the well model and boundary model corresponding to the target well can be selected, and the well test parameters can be adjusted according to the first set of candidate interpretation parameters to obtain the predicted third pressure change data.

[0083] In an embodiment of the present invention, see Figure 5 The second pressure change data (actual pressure) and the third pressure change data (fitted pressure) can be fitted to obtain the first fitting result.

[0084] In this embodiment of the invention, before fitting the second pressure change data and the third pressure change data to obtain the first fitting result, typical well test curves, such as the theoretical double logarithmic characteristic curve of the well test model, can be generated based on the well test model, and the flow section of the typical well test curves can be divided, for example, see [link to relevant documentation]. Figure 6The flow segment of the double logarithmic characteristic curve in the well test model theory can be divided into five stages. The first stage shows a wellbore reservoir effect with a slope of 1; the second stage shows a skin effect with a hump feature; the third stage shows an early radial flow stage with horizontal lines; the fourth stage shows a matrix linear flow segment with a slope of 0.5; and the fifth stage is a system radial flow segment with horizontal lines. The second and third pressure change data are fitted to obtain the first fitting result, which can improve the accuracy of the fitting.

[0085] S204. Based on the first fitting results corresponding to at least one set of first candidate interpretation parameters, determine the well test interpretation parameters from at least one set of first candidate interpretation parameters.

[0086] In this embodiment of the invention, well test interpretation parameters can be determined from at least one set of first candidate interpretation parameters based on the first fitting results corresponding to at least one set of first candidate interpretation parameters. For example, the first candidate interpretation parameter corresponding to the first fitting result that represents the highest overlap between the second pressure change data and the third pressure change data from at least one set of first candidate interpretation parameters can be used as the well test interpretation parameter.

[0087] S205. Obtain the first production change data of the target well under the preset production system, and conduct a simulated well test on the target well based on the first production change data, well test interpretation parameters and well test model to obtain the first pressure change data of the target well under the preset production system.

[0088] S206. Based on the first pressure change data, evaluate the production capacity of the target well and obtain the production capacity evaluation result of the target well.

[0089] The technical solution of this invention involves acquiring at least one set of first candidate interpretation parameters for a target well, and second pressure change data of the target well within a first historical time period. For each set of first candidate interpretation parameters, based on the first candidate interpretation parameters and a well test model, third pressure change data of the target well within the first historical time period is predicted. The second and third pressure change data are then fitted to obtain a first fitting result. Based on the first fitting results corresponding to the at least one set of first candidate interpretation parameters, well test interpretation parameters are determined from the at least one set of first candidate interpretation parameters. This technical solution can improve the accuracy of the determined well test interpretation parameters.

[0090] An optional technical solution, before determining well test interpretation parameters from at least one set of first candidate interpretation parameters based on the first fitting results corresponding to at least one set of first candidate interpretation parameters, further includes: determining a second adaptability of the well test model based on the first fitting results corresponding to at least one set of first candidate interpretation parameters; determining well test interpretation parameters from at least one set of first candidate interpretation parameters based on the first fitting results corresponding to at least one set of first candidate interpretation parameters, including: when the second adaptability satisfies a second preset adaptability condition, determining well test interpretation parameters from at least one set of first candidate interpretation parameters based on the first fitting results corresponding to at least one set of first candidate interpretation parameters.

[0091] The second adaptability can be understood as the adaptability of the well test model to simulate well testing of the target well, which can characterize whether the well test model is suitable for simulating well testing of the target well.

[0092] In this embodiment of the invention, a second fitness can be determined based on the first fitting results corresponding to at least one set of first candidate interpretation parameters. For example, it can be determined whether there is a first fitting result with a fitness value greater than a preset fitness value among the first fitting results corresponding to at least one set of first candidate interpretation parameters. If such a result exists, the second fitness is determined to be fitness; otherwise, the second fitness is determined to be poor fitness.

[0093] The second preset adaptation condition can be understood as the condition under which well test interpretation parameters can be determined after the preset second adaptability is met.

[0094] In this embodiment of the invention, if the second adaptability satisfies the second preset adaptability condition, indicating whether the well test model is suitable for simulating well testing on the target well, a subsequent step can be performed to determine the well test interpretation parameters from the at least one set of first candidate interpretation parameters based on the first fitting results corresponding to each of the at least one set of first candidate interpretation parameters determined by the well test model. For example, if the second preset adaptability condition is that the second adaptability is adaptive, the well test interpretation parameters can be determined from the at least one set of first candidate interpretation parameters based on the first fitting results corresponding to each of the at least one set of first candidate interpretation parameters when the second adaptability is adaptive.

[0095] In this embodiment of the invention, when the determined second adaptability meets the second preset adaptability condition, well test interpretation parameters are determined from at least one set of first candidate interpretation parameters based on the first fitting results corresponding to at least one set of first candidate interpretation parameters. This avoids determining inaccurate well test interpretation parameters that are not suitable for simulating well tests on the target well based on the first fitting results determined by a well test model that is not suitable for simulating well tests on the target well.

[0096] Figure 7This is a flowchart of another production capacity evaluation method provided in this embodiment of the invention. This embodiment is based on the above-mentioned technical solutions and optimized. In this embodiment, optionally, the production capacity evaluation method further includes: determining a production capacity determination model corresponding to the target gas field; obtaining at least one set of second candidate interpretation parameters for the target well, and second production capacity change data of the target well in a second historical time period; for each set of second candidate interpretation parameters, predicting third production capacity change data of the target well in the second historical time period according to the second candidate interpretation parameters and the production capacity determination model, and fitting the second production capacity change data and the third production capacity change data to obtain a second fitting result; determining a target interpretation parameter from at least one set of second candidate interpretation parameters according to the second fitting results corresponding to the at least one set of second candidate interpretation parameters; comparing the well test interpretation parameters and the target interpretation parameters, and updating the well test interpretation parameters according to the comparison results.

[0097] The explanations of terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0098] See Figure 7 The method in this embodiment may specifically include the following steps:

[0099] S301. Determine the well test model corresponding to the target gas field.

[0100] S302. Determine the production determination model corresponding to the target gas field.

[0101] The production determination model can be understood as a model used to determine the second production change data of the target well; the production determination model can also be called the production interpretation model.

[0102] In this embodiment of the invention, a production determination model can be determined. For example, a production determination model can be determined using numerical simulation.

[0103] S303. Obtain at least one set of second alternative interpretation parameters for the target well, and second production change data of the target well within a second historical time period.

[0104] The second candidate interpretation parameter can be understood as a parameter to be selected as the target interpretation parameter.

[0105] The second historical time period can be understood as the period in history with corresponding data on the second change in output.

[0106] The second production change data can be understood as data that characterizes the actual production output changes of the target well during the second historical time period.

[0107] In this embodiment of the invention, at least one set of second candidate interpretation parameters can be obtained. The at least one set of second candidate interpretation parameters may be pre-set to facilitate the acquisition of at least one set of second candidate interpretation parameters.

[0108] In this embodiment of the invention, second production change data can be obtained.

[0109] S304. For each group of second candidate interpretation parameters, based on the second candidate interpretation parameters and the production determination model, predict the third production change data of the target well in the second historical time period, and fit the second production change data and the third production change data to obtain the second fitting result.

[0110] Among them, the third production change data can be understood as predictable data characterizing the changes in the production output of the target well during the second historical period.

[0111] The second fitting result can be obtained by fitting the second and third production change data.

[0112] In this embodiment of the invention, a third production change data can be predicted based on a second candidate interpretation parameter and a production determination model. For example, the third production change data can be predicted based on geological data, well type data, the second candidate interpretation parameter, and the production determination model. Another example is that the production determination model, reservoir property data, and production data of the target well can be imported into an oil and gas management tool. In the oil and gas management tool, the well model, boundary model, and analytical model corresponding to the target well can be selected, and the production determination parameters can be adjusted according to the second candidate interpretation parameter to obtain the predicted third production change data.

[0113] In this embodiment of the invention, historical fitting of the production dynamic characteristics of the target well can be performed, i.e., see, for example, [reference needed]. Figure 5 The second set of production change data (actual production) and the third set of production change data (fitted production) are fitted together to obtain the second fitting result.

[0114] S305. Based on the second fitting results corresponding to at least one set of second candidate explanatory parameters, determine the target explanatory parameter from at least one set of second candidate explanatory parameters.

[0115] The target interpretation parameters can be understood as parameters related to the target well obtained by interpreting the target well based on the production determination model. The target interpretation parameters may include at least one of the key parameters of the target well, such as permeability, wellbore parameters, reservoir parameters, skin factor, skin coefficient, and effective wellbore length. For example, see Table 1 below. The target interpretation parameters corresponding to target well 1 include skin coefficient and effective wellbore length.

[0116] Table 1 Target Explanation Parameters

[0117]

[0118] In this embodiment of the invention, a target explanatory parameter can be determined from at least one set of second candidate explanatory parameters based on the second fitting results corresponding to at least one set of second candidate explanatory parameters. For example, the second candidate explanatory parameter corresponding to the second fitting result that represents the highest overlap between the second and third production change data among the at least one set of second candidate explanatory parameters can be used as the target explanatory parameter.

[0119] In this embodiment of the invention, the process of determining the target interpretation parameters described above can be regarded as the process of interpreting the real-time yield instability analysis (RTA).

[0120] In this embodiment of the invention, after determining the production determination model corresponding to the target gas field, the target interpretation parameters can be determined in the following manner: initializing the target interpretation parameters and obtaining the second production change data of the target well in the second historical time period; predicting the fourth production change data of the target well in the second historical time period according to the target interpretation parameters and the production determination model, and fitting the second production change data and the fourth production change data to obtain the fourth fitting result; if the fitting degree represented by the fourth fitting result is less than the preset fitting degree threshold, adjusting the target interpretation parameters and repeating the step of predicting the fourth production change data of the target well in the second historical time period according to the target interpretation parameters and the production determination model; if the fitting degree represented by the fourth fitting result is greater than or equal to the preset fitting degree threshold, the target interpretation parameters are obtained.

[0121] S306. Obtain at least one set of first candidate interpretation parameters for the target well, and second pressure change data of the target well within a first historical time period.

[0122] S307. For each group of first candidate interpretation parameters, based on the first candidate interpretation parameters and the well test model, predict the third pressure change data of the target well in the first historical time period, and fit the second pressure change data and the third pressure change data to obtain the first fitting result.

[0123] In this embodiment of the invention, the third pressure change data of the target well within a first historical time period can be predicted based on the first candidate interpretation parameters and the well test model. For example, the third pressure change data of the target well within a first historical time period can be predicted based on the third production change data corresponding to the target interpretation parameters, the first candidate interpretation parameters, and the well test model.

[0124] S308. Based on the first fitting results corresponding to at least one set of first candidate interpretation parameters, determine the well test interpretation parameters from at least one set of first candidate interpretation parameters.

[0125] S309. Compare the well test interpretation parameters with the target interpretation parameters, and update the well test interpretation parameters based on the comparison results.

[0126] The comparison results can be understood as the results obtained by comparing the well test interpretation parameters with the target interpretation parameters.

[0127] In this embodiment of the invention, well test interpretation parameters and target interpretation parameters can be compared, and the well test interpretation parameters can be updated based on the comparison results. For example, the well test interpretation parameters and target interpretation parameters can be compared. If the comparison results indicate that the similarity between the well test interpretation parameters and target interpretation parameters is greater than a preset similarity, it means that the target interpretation parameters determined based on the production determination model are usable, and the well test interpretation parameters can be updated according to the well test interpretation parameters and target interpretation parameters (e.g., by averaging, taking the maximum or minimum value, etc.). Another example is that the well test interpretation parameters and target interpretation parameters can be compared, and if the comparison results indicate that the similarity between the well test interpretation parameters and target interpretation parameters is greater than a preset similarity, the well test interpretation parameters are not updated; otherwise, the target interpretation parameters are updated to the well test interpretation parameters.

[0128] S310. Obtain the first production change data of the target well under the preset production system, and perform a simulated well test on the target well based on the first production change data, well test interpretation parameters and well test model to obtain the first pressure change data of the target well under the preset production system.

[0129] S311. Based on the first pressure change data, evaluate the production capacity of the target well and obtain the production capacity evaluation result of the target well.

[0130] The technical solution of this invention involves: determining a production determination model corresponding to a target gas field; acquiring at least one set of second candidate interpretation parameters for the target well, and second production change data of the target well within a second historical time period; for each set of second candidate interpretation parameters, predicting third production change data of the target well within the second historical time period based on the second candidate interpretation parameters and the production determination model, and fitting the second production change data and the third production change data to obtain a second fitting result; determining a target interpretation parameter from the at least one set of second candidate interpretation parameters based on the second fitting results corresponding to the at least one set of second candidate interpretation parameters; comparing the well test interpretation parameters and the target interpretation parameters, and updating the well test interpretation parameters based on the comparison results. This technical solution, by comparing the well test interpretation parameters and the target interpretation parameters and updating the well test interpretation parameters based on the comparison results, can improve the accuracy of the well test interpretation parameters, thereby ensuring the reliability of the well test interpretation parameters.

[0131] An optional technical solution involves determining a production determination model corresponding to a target gas field, including: acquiring a gas field model of the target gas field and dividing the gas field model into a grid to obtain a grid model; determining a first seepage equation, a first boundary condition, and a first initial condition corresponding to the target gas field, and discretizing the first seepage equation in the grid model based on the first boundary condition and the first initial condition; and determining the production determination model corresponding to the target gas field based on the obtained discretization result.

[0132] Here, a gas field model can be understood as a model obtained by modeling a target gas field; for example, a gas field model can be a model of the gas reservoir of the target gas field.

[0133] In this embodiment of the invention, a gas field model can be obtained by pre-modeling the target gas field to achieve the acquisition of the gas field model.

[0134] A grid model can be understood as a model obtained by dividing a gas field model into grids.

[0135] In this embodiment of the invention, the gas field model can be meshed to obtain a mesh model. For example, the unstructured perpendicular bisection grid (PEBI) meshing method can be used to mesh the gas field model to obtain a mesh model (see, for example, [link to relevant documentation]). Figure 8 , Figure 8 (This refers to the region in the mesh model that corresponds to the target well of the long open-hole horizontal well).

[0136] The first seepage equation can be understood as the seepage equation corresponding to the target gas field; for example, if the target gas field is an offshore tight gas field, the first seepage equation is a seepage equation that conforms to the characteristics of an offshore tight gas field.

[0137] The first boundary condition can be understood as a boundary condition that describes the state or constraint of the first seepage equation and corresponds to the target gas field; for example, in the case that the target gas field is an offshore tight gas field, the first boundary condition is a boundary condition that conforms to the characteristics of an offshore tight gas field.

[0138] The first initial condition can be understood as describing the state of the first seepage equation at the initial moment, and is the initial condition corresponding to the target gas field; for example, in the case that the target gas field is an offshore tight gas field, the first boundary condition is the initial condition that conforms to the characteristics of an offshore tight gas field.

[0139] In this embodiment of the invention, a first seepage equation, a first boundary condition, and a first initial condition corresponding to the target gas field can be determined. For example, the first seepage equation, the first boundary condition, and the first initial condition can be determined based on the geological data of the target gas field, reservoir property data, and the method used to determine the production determination model (e.g., a method for discretizing the first seepage equation in a grid model). For example, the first seepage equation can be determined as follows: The first boundary condition is and the first initial condition is ,in, The unit is MPa 2 The pseudo-pressure of the gas reservoir is / (mPa.s). The fluid viscosity is expressed in mPa·s. The unit is m 3 / d / m 3 The source and sink items, The unit is MPa -1 The overall compression coefficient, Porosity The unit is MPa 2 The initial formation pseudopressure is given by / (mPa·s), where t is the production time in hours. , and These represent the permeability in the x, y, and z directions, respectively, in mD.

[0140] In this embodiment of the invention, the first seepage equation can be discretized in the mesh model based on the first boundary conditions and the first initial conditions. For example, the finite volume method can be used to discretize the first seepage equation in the mesh model based on the first boundary conditions and the first initial conditions.

[0141] The discretization result can be understood as the result obtained by discretizing the first seepage equation in the grid model.

[0142] In this embodiment of the invention, a production determination model can be determined based on the obtained discrete processing results. For example, a discrete equation can be determined based on the obtained discrete processing results. ,in, and These represent the pressure difference between grid i and its adjacent grid j in the mesh model. Q is the flow coefficient, which is the product of the flow coefficient λ between any two adjacent grid center points in the grid model and its geometric factor G. Q is expressed in meters. 3 / d of underground production; based on the discrete equation, determine the numerically discrete coefficient matrix, and solve the coefficient matrix to obtain the production change trend; based on the production change trend, determine the production determination model.

[0143] In this embodiment of the invention, by discretizing the first seepage equation in the grid model according to the first boundary conditions and the first initial conditions, and then determining the production determination model corresponding to the target gas field based on the obtained discretization results, the obtained production determination model can be more applicable to the target gas field, and thus more applicable to participating in updating the well test interpretation parameters corresponding to the target well in the target gas field.

[0144] Another optional technical solution, before determining the target explanatory parameter from at least one set of second candidate explanatory parameters based on the second fitting results corresponding to at least one set of second candidate explanatory parameters, further includes: determining a first fitness of the yield determination model based on the second fitting results corresponding to at least one set of second candidate explanatory parameters; determining the target explanatory parameter from at least one set of second candidate explanatory parameters based on the second fitting results corresponding to at least one set of second candidate explanatory parameters, including: when the first fitness satisfies a first preset fitness condition, determining the target explanatory parameter from at least one set of second candidate explanatory parameters based on the second fitting results corresponding to at least one set of second candidate explanatory parameters.

[0145] The first adaptability can be understood as the adaptability of the production determination model to the simulated well test of the target well, which can characterize whether the production determination model is suitable for participating in the updating of well test interpretation parameters.

[0146] In this embodiment of the invention, the first fitness can be determined based on the second fitting results corresponding to at least one set of second candidate explanatory parameters. For example, it can be determined whether there is a second fitting result among the second fitting results corresponding to at least one set of second candidate explanatory parameters whose degree of fit is greater than a preset degree of fit. If there is, the first fitness is determined to be fit; if not, the first fitness is determined to be unfit.

[0147] The first preset adaptation condition can be understood as the condition under which the target interpretation parameters can be determined after the preset first adaptation is met.

[0148] In this embodiment of the invention, the first adaptability satisfies the first preset adaptability condition, which means that the production determination model can characterize the production status of the target well. The target interpretation parameters obtained based on the production determination model can be applied. Therefore, when the first adaptability satisfies the first preset adaptability condition, the target interpretation parameter can be determined from at least one set of second candidate interpretation parameters based on the second fitting results corresponding to at least one set of second candidate interpretation parameters. For example, if the first preset adaptability condition is that the first adaptability is adaptive, the target interpretation parameter can be determined from at least one set of second candidate interpretation parameters based on the second fitting results corresponding to at least one set of second candidate interpretation parameters when the first adaptability is adaptive.

[0149] In this embodiment of the invention, when the first adaptability meets the first preset adaptability condition, the target interpretation parameter is determined from the at least one set of second candidate interpretation parameters according to the second fitting results corresponding to the at least one set of second candidate interpretation parameters. This avoids determining a target interpretation parameter that is not suitable for updating the well test interpretation parameters, thereby avoiding inaccurate updates to the well test interpretation parameters.

[0150] To better understand the technical solutions of the above embodiments of the present invention, an optional example is provided here. For example, data collection can systematically collect geological data, reservoir property data, well test data, and production data of the target gas field; model determination can be based on geological data and well type data to determine a suitable well test model and production determination model for the target gas field; production instability analysis can be performed on the target well using RTA interpretation based on geological data, production data, well test data, and production determination model to obtain target interpretation parameters; pressure instability analysis can be performed on the target well using PTA interpretation based on geological data, production data, well test data, and well test model to obtain well test interpretation parameters, and the reliability of the target interpretation parameters is verified based on the well test interpretation parameters. If the reliability is greater than or equal to the preset reliability, the target interpretation parameters are updated to the well test interpretation parameters; corrected isochronous well test simulation can be performed on the target well using the first production change data, well test interpretation parameters, and well test model to obtain the first pressure change data; and production capacity evaluation can be performed on the target well using binomial production capacity evaluation based on the first pressure change data to calculate the unobstructed flow rate of the target well, and the production capacity evaluation result is obtained based on the unobstructed flow rate.

[0151] To better understand the technical solutions of the above embodiments of the present invention, another optional example is provided here. For example, Well A is completed and put into production, with a designed drilling depth of 6105m, vertical depth of 4604.7m, elevation of -4555.0m, maximum horizontal displacement of 3539m, maximum well inclination angle of 54.42°, and bottom hole inclination angle of 30.18°. Based on geological data and well type data, a well testing model and a production determination model suitable for the target gas field are determined; see [link to relevant documentation]. Figure 9 Using the production dynamics interpretation method, RTA interpretation (determination, fitting, and interpretation of production change data) was carried out on well A to obtain the target interpretation parameters; see [link to relevant documentation]. Figure 10 Using well testing methods, based on the third pressure change data (fitted pressure), PTA interpretation (determination, fitting, and interpretation of pressure change data) is performed on well A to obtain well test interpretation parameters. For example, refer to Table 2 below to compare the well test interpretation parameters with the target interpretation parameters, and update the well test interpretation parameters based on the comparison results. The first production change data, well test interpretation parameters, and well test model obtained from simulating the preset production system of well A are imported into the commercial numerical software KAPPA. In KAPPA, the well model and boundary model corresponding to well A are selected to simulate the first pressure change data of well A (see...). Figure 11 Five work cycles were preset, the first four being 24-hour cycles and the last being 120-hour cycles, with simulated outputs of 5, 10, 15, 20, and 12.5 (10) respectively. 4 m 3 / d) Production lasts 24 hours. The bottom hole pressure gradually decreases as production progresses. After each work cycle, the well is shut in, and the bottom hole pressure gradually recovers. This process is repeated for 5 work cycles, with no shut-in for the last work cycle. (See Table 3 below.) The bottom hole pressure at the beginning and end of each work cycle is obtained as the first pressure change data, including the simulated pressure data. Figure 12 The binomial straight line was determined. Based on the binomial straight line and the first pressure change data, the production capacity of the target well was evaluated, and the unobstructed flow rate was found to be 95.23 × 10⁻⁶. 4 m 3 / d, based on the unobstructed flow rate, determine the capacity evaluation result.

[0152] Table 2 Comparison of Well Test Interpretation Parameters and Target Interpretation Parameters

[0153]

[0154] Table 3. Data on First Pressure Changes

[0155]

[0156] Figure 13This is a structural block diagram of a capacity evaluation device provided in an embodiment of the present invention. This device is used to execute the capacity evaluation method provided in any of the above embodiments. This device and the capacity evaluation methods of the above embodiments belong to the same inventive concept. Details not described in detail in the embodiments of the capacity evaluation device can be found in the embodiments of the above capacity evaluation methods. See also... Figure 13 The device may specifically include: a well test interpretation parameter acquisition module 410, a first pressure change data acquisition module 420, and a production capacity evaluation result acquisition module 430.

[0157] Among them, the well test interpretation parameter acquisition module 410 is used to determine the well test model corresponding to the target gas field, and interpret the target well in the target gas field according to the well test model to obtain the well test interpretation parameters;

[0158] The first pressure change data acquisition module 420 is used to acquire the first production change data of the target well under the preset production system, and to conduct a simulated well test on the target well based on the first production change data, well test interpretation parameters and well test model to obtain the first pressure change data of the target well under the preset production system.

[0159] The production capacity evaluation result module 430 is used to evaluate the production capacity of the target well based on the first pressure change data, and obtain the production capacity evaluation result of the target well.

[0160] Optionally, the well test interpretation parameter acquisition module 410 may include:

[0161] The second pressure change data acquisition submodule is used to acquire at least one set of first candidate interpretation parameters for the target well, as well as the second pressure change data of the target well within a first historical time period.

[0162] The first fitting result submodule is used to predict the third pressure change data of the target well in the first historical time period for each group of first candidate interpretation parameters, based on the first candidate interpretation parameters and the well test model, and to fit the second pressure change data and the third pressure change data to obtain the first fitting result.

[0163] The well test interpretation parameter determination submodule is used to determine well test interpretation parameters from at least one set of first candidate interpretation parameters based on the first fitting results corresponding to at least one set of first candidate interpretation parameters.

[0164] Optionally, based on the above-described apparatus, the apparatus may further include:

[0165] The production determination model determination module is used to determine the production determination model corresponding to the target gas field;

[0166] The second production change data acquisition module is used to acquire at least one set of second candidate interpretation parameters for the target well, and second production change data of the target well within a second historical time period.

[0167] The second fitting result module is used to predict the third production change data of the target well in the second historical time period for each group of second candidate interpretation parameters, based on the second candidate interpretation parameters and the production determination model, and to fit the second production change data and the third production change data to obtain the second fitting result.

[0168] The target explanatory parameter determination module is used to determine the target explanatory parameter from at least one set of second candidate explanatory parameters based on the second fitting results corresponding to at least one set of second candidate explanatory parameters.

[0169] The well test interpretation parameter update module is used to compare the well test interpretation parameters with the target interpretation parameters and update the well test interpretation parameters based on the comparison results.

[0170] Optionally, based on the above-described apparatus, the output determination model determination module may include:

[0171] The grid model obtains a sub-module, which is used to acquire the gas field model of the target gas field and divide the gas field model into grids to obtain the grid model;

[0172] The grid model discretization submodule is used to determine the first seepage equation, the first boundary condition, and the first initial condition corresponding to the target gas field, and to discretize the first seepage equation in the grid model based on the first boundary condition and the first initial condition.

[0173] The production determination model determination submodule is used to determine the production determination model corresponding to the target gas field based on the obtained discrete processing results.

[0174] Optionally, based on the above-described apparatus, the apparatus may further include:

[0175] The first fitness determination module is used to determine the first fitness of the yield determination model based on the second fitting results corresponding to the at least one set of second candidate explanatory parameters before determining the target explanatory parameter from the at least one set of second candidate explanatory parameters based on the second fitting results corresponding to the at least one set of second candidate explanatory parameters respectively.

[0176] The target interpretation parameter determination module may include:

[0177] The target interpretation parameter determination submodule is used to determine the target interpretation parameter from at least one set of second candidate interpretation parameters based on the second fitting results corresponding to at least one set of second candidate interpretation parameters, provided that the first adaptability meets the first preset adaptability condition.

[0178] Optionally, based on the above-described apparatus, the apparatus may further include:

[0179] The second adaptability determination module is used to determine the second adaptability of the well test model based on the first fitting results corresponding to the first set of first candidate interpretation parameters before determining the well test interpretation parameters from the at least one set of first candidate interpretation parameters based on the first fitting results corresponding to the first set of first candidate interpretation parameters respectively.

[0180] The well test interpretation parameter determination submodule may include:

[0181] The well test interpretation parameter determination unit is used to determine well test interpretation parameters from at least one set of first candidate interpretation parameters based on the first fitting results corresponding to at least one set of first candidate interpretation parameters, when the second adaptability meets the second preset adaptability conditions.

[0182] Optionally, the well test interpretation parameter acquisition module 410 may include:

[0183] The second initial condition determination submodule is used to determine the second seepage equation, second boundary conditions, and second initial conditions corresponding to the target gas field.

[0184] The well test model yields a submodule, which is used to solve the second seepage equation, the second boundary condition, and the second initial condition using the Laplace transform and Stefest inversion method, to obtain the well test model corresponding to the target gas field.

[0185] Optionally, module 430, which obtains the capacity evaluation results, may include:

[0186] The production capacity evaluation result acquisition submodule is used to perform a binomial production capacity evaluation on the target well based on the first pressure change data, and obtain the production capacity evaluation result of the target well.

[0187] Optionally, the preset production system includes at least one working system, wherein the shut-in time after production is the same as the production time.

[0188] The first pressure change data acquisition module 420 may include:

[0189] The first pressure change data acquisition submodule is used to perform simulated correction isochronous well testing on the target well based on the first production change data, well test interpretation parameters, and well test model, to obtain the first pressure change data of the target well under at least one working regime.

[0190] The production capacity evaluation device provided in this embodiment of the invention determines the well test model corresponding to the target gas field through a well test interpretation parameter acquisition module, and interprets the target well in the target gas field according to the well test model to obtain well test interpretation parameters; it acquires the first production change data of the target well under a preset production system through a first pressure change data acquisition module, and performs a simulated well test on the target well according to the first production change data, well test interpretation parameters, and well test model to obtain the first pressure change data of the target well under the preset production system, so as to obtain the first pressure change data without shutting down the target well or changing the working system of the target well; and it performs production capacity evaluation on the target well according to the first pressure change data through a production capacity evaluation result acquisition module to obtain the production capacity evaluation result of the target well, thereby achieving high-precision production capacity evaluation. The aforementioned device obtains first pressure change data by performing simulated and corrected isochronous well testing on the target well based on first production change data, well test interpretation parameters, and well test model. This allows for the simple, rapid, and low-cost acquisition of first pressure change data without the need for shut-in operations or changes to the target well's operating regime. Based on this first pressure change data, high-precision production capacity evaluation is then performed, thus enabling a simple, rapid, low-cost, and high-precision production capacity evaluation.

[0191] The capacity evaluation device provided in this embodiment of the invention can execute the capacity evaluation method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0192] It is worth noting that in the embodiments of the above-mentioned capacity evaluation device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of the present invention.

[0193] Figure 14 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0194] like Figure 14As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer programs stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0195] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0196] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as capacity evaluation methods.

[0197] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0198] In some embodiments, the capacity evaluation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the capacity evaluation method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the capacity evaluation method by any other suitable means (e.g., by means of firmware).

[0199] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0200] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0201] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0202] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0203] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0204] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0205] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0206] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A capacity evaluation method, characterized in that, include: Determine the well test model corresponding to the target gas field, and interpret the target well in the target gas field according to the well test model to obtain the well test interpretation parameters; Obtain the first production change data of the target well under the preset production system, and perform a simulated well test on the target well based on the first production change data, the well test interpretation parameters and the well test model to obtain the first pressure change data of the target well under the preset production system. Based on the first pressure change data, the production capacity of the target well is evaluated to obtain the production capacity evaluation result of the target well.

2. The method according to claim 1, characterized in that, The step involves interpreting the target wells in the target gas field based on the well test model to obtain well test interpretation parameters, including: Obtain at least one set of first candidate interpretation parameters for the target well, and second pressure change data of the target well within a first historical time period; For each set of first candidate interpretation parameters, based on the first candidate interpretation parameters and the well test model, the third pressure change data of the target well in the first historical time period is predicted, and the second pressure change data and the third pressure change data are fitted to obtain the first fitting result; Based on the first fitting results corresponding to at least one set of the first candidate interpretation parameters, well test interpretation parameters are determined from at least one set of the first candidate interpretation parameters.

3. The method according to claim 2, characterized in that, Also includes: Determine the production determination model corresponding to the target gas field; Obtain at least one set of second candidate interpretation parameters for the target well, and second production change data of the target well within a second historical time period; For each set of second candidate interpretation parameters, based on the second candidate interpretation parameters and the production determination model, the third production change data of the target well in the second historical time period is predicted, and the second production change data and the third production change data are fitted to obtain a second fitting result; Based on the second fitting results corresponding to at least one set of the second candidate explanatory parameters, the target explanatory parameter is determined from at least one set of the second candidate explanatory parameters; The well test interpretation parameters and the target interpretation parameters are compared, and the well test interpretation parameters are updated based on the comparison results.

4. The method according to claim 3, characterized in that, The production determination model corresponding to the target gas field includes: Obtain the gas field model of the target gas field, and divide the gas field model into a grid to obtain a grid model; Determine the first seepage equation, first boundary condition, and first initial condition corresponding to the target gas field, and discretize the first seepage equation in the grid model according to the first boundary condition and the first initial condition; Based on the obtained discrete processing results, a production determination model corresponding to the target gas field is determined.

5. The method according to claim 3, characterized in that, Before determining the target explanatory parameter from at least one set of second candidate explanatory parameters based on the second fitting results corresponding to at least one set of second candidate explanatory parameters, the method further includes: The first fitness of the yield determination model is determined based on the second fitting results corresponding to at least one set of the second candidate interpretation parameters; The step of determining the target explanatory parameter from at least one set of second candidate explanatory parameters based on the second fitting results corresponding to at least one set of second candidate explanatory parameters includes: When the first adaptability meets the first preset adaptability condition, the target explanatory parameter is determined from the at least one set of the second candidate explanatory parameters based on the second fitting results corresponding to at least one set of the second candidate explanatory parameters.

6. The method according to claim 2, characterized in that, Before determining the well test interpretation parameters from at least one set of first candidate interpretation parameters based on the first fitting results corresponding to at least one set of first candidate interpretation parameters, the method further includes: The second fitness of the well test model is determined based on the first fitting results corresponding to at least one set of the first candidate interpretation parameters; The step of determining well test interpretation parameters from at least one set of first candidate interpretation parameters based on the first fitting results corresponding to at least one set of first candidate interpretation parameters includes: When the second adaptability meets the second preset adaptability condition, well test interpretation parameters are determined from at least one set of the first candidate interpretation parameters based on the first fitting results corresponding to at least one set of the first candidate interpretation parameters.

7. The method according to claim 1, characterized in that, The determination of the well test model corresponding to the target gas field includes: Determine the second seepage equation, second boundary conditions, and second initial conditions corresponding to the target gas field; By using the Laplace transform and Stefest inversion method, the second seepage equation, the second boundary condition, and the second initial condition are solved to obtain the well test model corresponding to the target gas field.

8. The method according to claim 1, characterized in that, The step of evaluating the production capacity of the target well based on the first pressure change data to obtain the production capacity evaluation result of the target well includes: Based on the first pressure change data, a binomial productivity evaluation is performed on the target well to obtain the productivity evaluation result of the target well.

9. The method according to claim 1, characterized in that, The preset production system includes at least one working system, wherein the well shut-in time after production is the same as the production time. The step of conducting a simulated well test on the target well based on the first production change data, the well test interpretation parameters, and the well test model to obtain the first pressure change data of the target well under the preset production regime includes: Based on the first production change data, the well test interpretation parameters, and the well test model, a simulated corrected isochronous well test is performed on the target well to obtain the first pressure change data of the target well under at least one of the operating conditions.

10. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to cause the at least one processor to perform the capacity evaluation method as described in any one of claims 1-9.

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