Auxiliary screening method, system and equipment for productivity fitting calculation and medium

Abstract

The invention relates to an auxiliary screening method, system and equipment for productivity fitting calculation and a medium. The method comprises the following steps: preprocessing original production data; screening the preprocessed data based on user requirements; performing fitting calculation on the screened data; and judging whether a fitting result is available or not, if not, evaluating and calculating the fitting result to obtain a fitting effect quantitative index, and performing screening and fitting calculation on the screened data again based on the fitting effect quantitative index and user requirements until the fitting result is available. According to the method, the experience of the user is used as one of guarantees for screening the quality of the data points, multiple screening and removing means are provided, a professional algorithm tool capable of reflecting the fitting effect is provided, a visual fitting effect display service of each data point is provided for the user, a three-party algorithm can be embedded, and the method is used for displaying the fitting effect of each data point according to a certain specific fitting algorithm. And the service of processing the boundary difference abnormal data points is provided.

Description

Technical Field

[0001] The present invention belongs to the field of screening technology, and in particular relates to an auxiliary screening method, system, equipment and medium for capacity fitting calculation. Background Art

[0002] In the field of oil and gas reservoir development, production data is one of the few continuous data sources that can be directly obtained from daily production wells. This data is of great research value for understanding reservoirs and wells. Using production data from oil and gas wells to solve formation parameters and predict oil and gas well production and reserves is a method increasingly valued in the oil industry. However, since first-hand data obtained from the field is often of poor quality, direct use in production data fitting analysis is not effective. Through a review of actual data issues, the main factors affecting fitting can be divided into the following three categories:

[0003] 1. Measurement and statistical errors: Oilfield production data is generally obtained manually by professionals using specialized instruments, which can lead to errors in the measurement results. Furthermore, errors and omissions may occur during manual data entry.

[0004] 2. Data distortion caused by production splitting algorithms: Surface pipeline network designs often involve multiple wells sharing a single set of measuring equipment, which doesn't guarantee accurate measurement for each well. For example, at a gathering and transportation station, five wells share a single set of measuring equipment. The production of each well would then need to be manually split by on-site engineers, resulting in a certain degree of distortion in the production of each well.

[0005] 3. Boundary differences between actual production and fitted models: Currently commonly used fitting models often require that bottomhole pressure (RTA) or surface production (PTA) remain constant as a prerequisite for deducing reservoir productivity changes. In actual production, due to technical and cost limitations, it is impossible to directly control bottomhole pressure or surface production. Adjustments are generally made through indirect methods such as adjusting the nozzle needle valve opening and adjusting the operating system, which have limited impact on the boundary conditions of the production wells. In addition, theoretical models cannot account for production fluctuations caused by equipment failures, or even the impact of operational activities such as well shutdowns and workovers on production.

[0006] In summary, the data factors that affect the fitting effect of capacity fitting calculation can be divided into the following four types:

[0007] The errors caused by the above three reasons have different effects on the fitting calculation results:

[0008]

[0009] The fitting algorithm itself has a certain ability to offset measurement errors. Therefore, for most data anomalies caused by measurement errors, special screening is not required. Statistical outliers that exceed the reasonable range do not contain useful information in themselves, but will have a great impact on the fitting results. Therefore, such points must be screened out. When the reasonable range of the data is clear, the screening work can be directly completed by the system. The data distortion caused by the production splitting calculation will also have an adverse effect on the fitting effect. However, this part of the data does not have obvious characteristics and cannot be directly screened out using a simple algorithm. It requires the experience of developers and the help of some auxiliary screening tools to complete the screening. The direct use of data anomalies caused by boundary differences will also have an adverse effect on the fitting. However, at the same time, this part of the points also has certain reservoir information, so directly deleting them will also affect the fitting results. The best way to deal with it is to use a special processing algorithm to convert this part of the data to retain the useful information in the data to the greatest extent.

[0010] Professional computing platforms and software that provide capacity fitting calculations need to consider providing users with tools to assist in data screening during fitting. From the above analysis, we can see that a good auxiliary screening tool needs to have the following four characteristics:

[0011] 1. It needs to be designed to interact with users, taking user experience as one of the guarantees for the quality of screening data points;

[0012] 2. A variety of screening and elimination methods are required;

[0013] 3. It is necessary to provide professional algorithm tools that can reflect the fitting effect and provide users with intuitive fitting effect display services for each data point;

[0014] 4. A design that can embed a third-party algorithm module is required to provide services for processing abnormal data points with boundary differences for a specific fitting algorithm.

[0015] The most common processing method currently used in various publicly available software platforms and related professional software in the petroleum industry is to directly perform the screening work of removing zero and negative values ​​in the background, while allowing users to manually screen in the interface. This design relies on users to pre-enter the data using experience or other tools to obtain a good fitting result. No auxiliary screening tool or method has yet been developed that meets the above four requirements. Summary of the Invention

[0016] The purpose of the present invention is to provide an auxiliary screening method, system, device and medium for capacity fitting calculation in order to solve the above problems.

[0017] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0018] An auxiliary screening method for capacity fitting calculation includes the following steps:

[0019] Preprocess the original production data to obtain preprocessed data;

[0020] Filtering the pre-processed data based on user needs to obtain filtered data;

[0021] Performing fitting calculation on the screened data to obtain a fitting result;

[0022] Determine whether the fitting result is usable. If not, evaluate and calculate the fitting result to obtain a quantitative index of the fitting effect. Based on the quantitative index of the fitting effect and user needs, re-screen and re-fit the filtered data until the fitting result is usable.

[0023] As a further optimization solution of the present invention, the specific process of preprocessing the original production data to obtain the preprocessed data is as follows:

[0024] The original production data is recorded as data set G0;

[0025] A data set G1 obtained by processing the original production data based on a preset screening condition;

[0026] Determine whether to perform boundary difference error processing and set the visual display data set G2; if not, set G2=G1; if so, perform boundary difference error processing to obtain data set G3, set G2=G3, and the data set G3 is the processed data.

[0027] As a further optimization solution of the present invention, the preset screening condition is determined according to a fitting calculation method, which includes an unstable well test method set. Based on the unstable well test method set, the data set G0 can be expressed by the following formula:

[0028] G0={q i , t i , Δt i , i∈[0,N]};

[0029] Where q i , t i , Δt i are the i-th output, time and production time in the original production data respectively, and N represents the number of original production data;

[0030] The unstable well testing method set includes a modified extended exponential decline method, a modified hyperbolic decline method-decline rate fitting method, a modified hyperbolic decline method-cumulative production fitting method and an empirical analysis method for production decline of fractured gas reservoirs;

[0031] When the fitting calculation method is the modified extended exponential decrement method, the preset screening condition is q i ∈(0,+∞),Δt i ∈(0,+∞),i∈[0,N];

[0032] When the fitting calculation method is the modified hyperbolic decline method-decline rate fitting method, the preset screening condition is q i ∈(0,+∞),Δt i ∈ (0, +∞), i∈[0, N]; q i ≠q i-1 , i∈[1,N];

[0033] When the fitting calculation method is the modified decreasing method-cumulative yield fitting method, the preset screening condition is q i ∈(0,+∞),Δt i ∈(0,+∞),i∈[0,N];

[0034] When the fitting calculation method is the production decline empirical analysis method of fracture gas reservoir, the preset screening condition is q i ∈[0,+∞),Δt i ∈(0, +∞), i∈[0, N]; q0≥q i , i∈[0,N].

[0035] As a further optimization solution of the present invention, the basis for determining whether to perform boundary difference error processing is whether the fitting calculation method to be used and the data set G1 are complete;

[0036] When the fitting calculation method is the modified extended exponential decline method, the modified hyperbolic decline method-decline rate fitting method, the modified hyperbolic decline method-cumulative production fitting method, or the production decline empirical analysis method for fractured gas reservoirs, if complete bottomhole flowing pressure or wellhead oil pressure data exist in data set G1, the judgment is yes and the original production data is corrected. If complete bottomhole flowing pressure or wellhead oil pressure data do not exist in data set G1, the judgment is no.

[0037] As a further optimization scheme of the present invention, the basis for judging whether the fitting result is usable includes: judging based on the maximum reasonable range of the fitting result given by the user, or judging based on whether the calculation parameters in the fitting calculation method are within a preset range, or allowing the user to judge whether to re-fit based on the fitting result.

[0038] As a further optimization solution of the present invention, the specific process of evaluating and calculating the fitting results to obtain the quantitative index of the fitting effect is as follows:

[0039] The evaluation calculation is based on the square value of the distance between the data point and the fitting effect curve in the y-axis direction as a quantitative indicator reflecting the fitting effect of the point. The specific algorithm is as follows:

[0040] The filtered data is set as data set G4, which is expressed as:

[0041] G4={x j ,y j , j∈[1,N4]};

[0042] Where x j is the independent variable of the jth data point in G4 during fitting, y j is the dependent variable of the jth data point in G4 during fitting, and N4 is the number of data points in G4;

[0043] Substitute each data point in the data set G4 into the fitting formula one by one and calculate x j The corresponding fitting calculation value The formula is as follows:

[0044]

[0045] Where f is the fitting calculation method selected by the user, C1, C2... are fitting parameters;

[0046] The square value E of the distance between data point j and the fitting effect curve in the y-axis direction 2 j Use the following formula to calculate:

[0047]

[0048] E 2 j It is the quantitative index of fitting effect, E 2 j The larger the value is, the worse the fitting effect of data point j is. 2 j The correlation coefficient R that can characterize the fitting effect of the entire data set 2 There is a direct relationship, the correlation coefficient R 2 Use the following formula to calculate:

[0049]

[0050] in, is the average y value of all elements in data set G4, calculated using the following formula:

[0051]

[0052] Correlation coefficient R 2 With E 2 jThe relationship is expressed by the following formula:

[0053] R 2 =1-cE 2 j ;

[0054] Where c is the coefficient related to the entire data set and is calculated using the following formula:

[0055]

[0056] As a further optimization solution of the present invention, calculate the E of each data point in G4 2 After the value, press E to 2 The values ​​are sorted from large to small to form queue A. When the user chooses to filter out the s% of data points with the worst fitting effect, where 100≥s≥0, the first s% of the data points in queue A are directly removed. Remove data points.

[0057] An auxiliary screening system for capacity fitting calculation, comprising:

[0058] Data preprocessing module, used to preprocess the original production data to obtain preprocessed data;

[0059] An autonomous screening module, configured to screen the pre-processed data based on user needs to obtain screened data;

[0060] A fitting calculation module is used to perform fitting calculation on the screened data to obtain a fitting result;

[0061] The fitting result judgment module is used to judge whether the fitting result is usable;

[0062] The auxiliary evaluation module is used to evaluate and calculate the fitting results when it is judged that the fitting results are unavailable, obtain quantitative indicators of the fitting effect, and then transmit the filtered data to the autonomous screening module to re-screen and fit the filtered data until the fitting results are available.

[0063] An electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0064] Memory for storing computer programs;

[0065] The processor is used to implement an auxiliary screening method for capacity fitting calculation when executing the program stored in the memory.

[0066] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements an auxiliary screening method for capacity fitting calculation.

[0067] The beneficial effects of the present invention are:

[0068] The present invention takes user experience as one of the guarantees for the quality of screened data points, has a variety of screening and elimination methods, provides professional algorithm tools that can reflect the fitting effect, and provides users with an intuitive fitting effect display service for each data point. It has an embeddable three-party algorithm, which is used to provide services for processing abnormal data points with boundary differences for a specific fitting algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is a flow chart of the method of the present invention;

[0070] Figure 2 This is a system workflow diagram in an embodiment of the present invention;

[0071] Figure 3 This is a system structure diagram in an embodiment of the present invention;

[0072] Figure 4 It is a block diagram of the device structure in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The present application is described in further detail below in conjunction with the accompanying drawings. It is necessary to point out that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technicians in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0074] like Figure 1 As shown, an auxiliary screening method for capacity fitting calculation includes the following steps:

[0075] Preprocess the original production data to obtain preprocessed data;

[0076] Filtering the pre-processed data based on user needs to obtain filtered data;

[0077] Performing fitting calculation on the screened data to obtain a fitting result;

[0078] Determine whether the fitting result is usable. If not, evaluate and calculate the fitting result to obtain a quantitative index of the fitting effect. Based on the quantitative index of the fitting effect and user needs, re-screen and re-fit the filtered data until the fitting result is usable.

[0079] like Figure 2 As shown, in this embodiment, it specifically includes:

[0080] Step 1: Data loading, enter the original production data into the system, and record the current data set as G0;

[0081] Step 2: System screening: The system screens the original production data and performs related data processing according to the pre-set screening conditions. The final data set is recorded as G1;

[0082] Step 3: Determine whether to process the boundary difference error and set the visualization data set G2. If the judgment result is yes, proceed to step 4; if not, go directly to step 5 and set G2 = G1;

[0083] Step 4: Call the tripartite algorithm to process the data. The processed data set is recorded as G3. Let G2 = G3;

[0084] Step 5: Visualization. The system can display all the data in G2 in the form of graphs and tables. If it is not the first fitting, it is also necessary to use graphs and tables to display the quantitative indicators of the fitting effect of each point.

[0085] Step 6: User screening, users screen data by interacting with the system, and the screened data set is recorded as G4;

[0086] Step 7: Fitting calculation, the system uses all the data in G4 for fitting calculation;

[0087] Step 8: Determine whether the fitting result is available. If so, proceed directly to step 10; if not, proceed to step 9;

[0088] Step 9: Call the auxiliary evaluation module to calculate the quantitative index of the fitting effect of each point and return to step 5;

[0089] Step 10: Save the calculation results and the calculation ends.

[0090] The following are additional details for each step:

[0091] 1. In step 2, the judgment criteria used by the system must be simple and clear, and the screening conditions must be designed accordingly based on the different fitting methods used. This is illustrated by using the unsteady well test (RTA) method to fit the productivity of a gas well. First, for the unsteady well test method, the data set G0 can be expressed as follows:

[0092] G0={q i , t i , Δt i , i∈[0,N]};

[0093] Where q i , t i , Δt i They are the i-th output, time and production time in the original data respectively. The data in the dataset are used according to t i Arrange from small to large.i is the time of the i-th group of data in this data set, which is the independent variable involved in fitting, and Δt i is the production time, which is a parameter required for fitting calculation. When the system does not perform data screening, it can be considered that data acquisition is continuous. At this time, Δt i =t i -t i-1 , i∈[1, N], so there is no need to consider Δt specifically when establishing a data set. However, in this system, data screening will destroy the adjacent relationship between data in the original data, so a special parameter needs to be set to protect data information.

[0094] For different fitting methods, the screening conditions set by the system are different:

[0095]

[0096]

[0097] The "Screening Conditions" column in the table above shows the reasonable value range of the parameters in the fitting method, that is, data that meets the conditions in this column is selected, and those that do not meet the conditions are screened out. Among them, the SEPD method's yield cannot be 0, and there cannot be repeated yield points; the Arps method's decline rate fitting method's yield cannot be 0, and there cannot be repeated yield points. At the same time, if the yield of two points is the same, these two points need to be merged; the Duong method's yield cannot be 0, and there cannot be repeated yield points. At the same time, this method can only select data after the maximum yield for fitting. During actual fitting, the system will select the corresponding screening conditions according to the fitting method selected by the user, and select those that meet the conditions and screen out those that do not meet the conditions.

[0098] 2. In step 3, the judgment conditions need to be determined based on whether the method to be used and the auxiliary data are complete. Taking the use of the unsteady well test method (RTA) to fit the production capacity of a gas well as an example, when the user selects the modified SEPD algorithm, the modified Arps algorithm, and the Duong algorithm for fitting, if complete bottomhole pressure or wellhead oil pressure data exists, the system will judge it as yes and can choose to call the material balance algorithm or other methods to correct the original production data; if these basic data are not available, or the user specifies not to process errors caused by boundary differences, or the user does not have an available processing module, the system will judge it as no.

[0099] 3. The judgment criteria in step 8 can be based on the maximum reasonable range of the fitting results provided by the user, or on whether the calculation parameters in the fitting method are within a reasonable range to determine whether the fitting calculation results are valid, or directly display the fitting results, allowing the user to decide whether to refit. This system uses the last method by default, providing the user with a detailed, visual display of the fitting results, mainly in the form of graphs and tables, and allowing the user to use their own experience and professional knowledge to decide whether to refit.

[0100] 4. The auxiliary evaluation module in step 9 is used to calculate the quantitative value of the fitting effect for each data point. This part requires a fitting effect evaluation algorithm. This system uses the square of the distance between the data point and the fitting effect curve in the y-axis direction as a quantitative indicator reflecting the fitting effect of the point. The specific algorithm of this method is as follows:

[0101] 1) The data set G4 used in the fitting calculation is expressed as follows:

[0102] G4={x j ,y j , ,j∈[1,N4]};

[0103] Where x j is the independent variable of the jth data point in G4 during fitting, y j is the dependent variable of the jth data point in G4 during fitting. For the unstable well test method in 1, x corresponds to q gsysslt , y corresponds to t sysslt ; N4 is the number of data points in G4.

[0104] 2) Substitute each point in the data set G4 into the fitting formula one by one and calculate x j The corresponding fitting calculation value

[0105]

[0106] Where f is the fitting method selected by the user, C1, C2... are the fitting parameters of the method. The square value E of the distance between the data point j and the fitting effect curve in the y-axis direction is 2 j Use the following formula to calculate:

[0107]

[0108] E 2 j This is the quantitative index used by this auxiliary evaluation module to characterize the fitting effect, E 2 jThe larger the value is, the worse the fitting effect of data point j is. This value can effectively represent the fitting effect because the correlation coefficient R between this indicator and the fitting effect of the entire data set is 2 There is a direct relationship, the correlation coefficient R 2 Use the following formula to calculate:

[0109]

[0110] in is the average y value of all elements in G4, calculated using the following formula:

[0111]

[0112] Considering E 2 j The method for calculating the correlation coefficient R 2 With E 2 j The relationship can be expressed by the following formula:

[0113] R 2 =1-cE 2 j ;

[0114] Where c is the coefficient related to the entire data set and is calculated using the following formula:

[0115]

[0116] According to the correlation coefficient R 2 From the properties of R 2 The closer the value is to 1, the better the fitting effect is. 2 j The larger the value, the greater the R 2 The smaller the value, the smaller the value. 2 It can effectively measure the impact of each data point on the fitting effect. 2 In addition, use E 2 The index obtained by further processing the value by means of square root, average value, etc. is similar to the principle of E 2 Similarly, it can also be used as a quantitative indicator to characterize the fitting effect.

[0117] The auxiliary evaluation module calculates the E of each point in G4 2 After the value is set, all points can be pressed E 2 The values ​​are sorted from large to small, and the results are sent back to the front desk for visualization together with the original data, which is used as a quantitative display indicator to assist users in screening; then all the points are pressed E 2The values ​​are sorted from large to small to form a queue A, which is used to help users quickly filter by proportion. When the user chooses to filter out the s% of data points with the worst fitting effect (100≥s≥0), the system directly adds the first s% of the data points in A to the list. (Use truncation or rounding The maximum number of data points is not more than N4) and removed, thereby achieving rapid screening.

[0118] 5. Auxiliary screening system is provided by Figure 2 The second, third, fourth, sixth, eighth and ninth parts of the system are composed of automatic screening module, boundary difference error judgment module, data processing module, autonomous screening module, fitting result judgment module and auxiliary evaluation module. The system uses comprehensive means to help users achieve fast and accurate screening. It does not perform fitting calculations itself, so it can be used in different production capacity fitting systems for unconventional oil and gas reservoirs such as oil reservoirs, gas reservoirs, tight gas reservoirs, shale gas reservoirs, coalbed methane reservoirs, and shale oil reservoirs.

[0119] like Figure 3 As shown, an embodiment of the present disclosure provides an auxiliary screening system for capacity fitting calculation, including:

[0120] The data preprocessing module is used to preprocess the original production data to obtain preprocessed data; the data processing module includes an automatic screening module, a boundary difference error judgment module, and a data processing module;

[0121] Visual display module, used to display data and quantitative indicators of fitting effects of data points in the form of graphs and tables;

[0122] An autonomous screening module, configured to screen the pre-processed data based on user needs to obtain screened data;

[0123] A fitting calculation module is used to perform fitting calculation on the screened data to obtain a fitting result;

[0124] The fitting result judgment module is used to judge whether the fitting result is usable;

[0125] The auxiliary evaluation module is used to evaluate and calculate the fitting results when it is judged that the fitting results are unavailable, obtain quantitative indicators of the fitting effect, and then transmit the filtered data to the autonomous screening module to re-screen and fit the filtered data until the fitting results are available.

[0126] The implementation process of the functions and effects of each module in the above system is specifically described in the implementation process of the corresponding steps in the above method, which will not be repeated here.

[0127] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described above is only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the present invention. Those of ordinary skill in the art can understand and implement it without paying any creative work.

[0128] In the above embodiment, any number of all modules can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. At least one of all modules can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware and firmware or in a suitable combination of any of them. Alternatively, at least one of all modules can be at least partially implemented as a computer program module, which can perform the corresponding function when the computer program module is run.

[0129] See also Figure 4 The electronic device provided by an embodiment of the present disclosure includes a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140;

[0130] Memory 1130, for storing computer programs;

[0131] The processor 1110 is configured to implement the auxiliary screening method for capacity fitting calculation as shown below when executing the program stored in the memory 1130 .

[0132] The communication bus 1140 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the figure shows only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0133] The communication interface 1120 is used for communication between the electronic device and other devices.

[0134] The memory 1130 may include a random access memory (RAM) or a non-volatile memory, such as at least one disk storage. Alternatively, the memory 1130 may be at least one storage device located away from the processor 1110.

[0135] The above-mentioned processor 1110 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0136] The embodiments of the present disclosure further provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the auxiliary screening method for capacity fitting calculation as described above.

[0137] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments, or may exist independently and not incorporated into the device / apparatus. The computer-readable storage medium carries one or more programs that, when executed, implement the auxiliary screening method for capacity fitting calculation according to the embodiments of the present disclosure.

[0138] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0139] The above-described embodiments merely illustrate several implementations of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.

Claims

1. An auxiliary screening method for capacity fitting calculation, characterized in that: The following steps are involved: Preprocess the original production data to obtain preprocessed data; Filtering the pre-processed data based on user needs to obtain filtered data; Performing fitting calculation on the screened data to obtain a fitting result; Determine whether the fitting result is usable. If not, evaluate and calculate the fitting result to obtain a quantitative index of the fitting effect. Based on the quantitative index of the fitting effect and user needs, re-screen and re-fit the filtered data until the fitting result is usable.

2. The auxiliary screening method for capacity fitting calculation according to claim 1, characterized in that: The specific process of preprocessing the original production data to obtain the preprocessed data is as follows: The original production data is recorded as data set G0; A data set G1 obtained by processing the original production data based on a preset screening condition; Determine whether to perform boundary difference error processing and set the visual display data set G2; if not, set G2=G1; if so, perform boundary difference error processing to obtain data set G3, set G2=G3, and the data set G3 is the processed data.

3. The auxiliary screening method for capacity fitting calculation according to claim 2, characterized in that: The preset screening condition is determined according to a fitting calculation method, which includes an unstable well test method set. Based on the unstable well test method set, the data set G0 can be expressed by the following formula: G0={q i ,t i ,Δt i ,i∈[0,N]}; Where q i ,t i ,Δt i are the i-th output, time and production time in the original production data respectively, and N represents the number of original production data; The unstable well testing method set includes a modified extended exponential decline method, a modified hyperbolic decline method-decline rate fitting method, a modified hyperbolic decline method-cumulative production fitting method and an empirical analysis method for production decline of fractured gas reservoirs; When the fitting calculation method is the modified extended exponential decrement method, the preset screening condition is q i ∈(0,+∞),Δt i ∈(0,+∞),i∈[0,N]; When the fitting calculation method is the modified hyperbolic decline method-decline rate fitting method, the preset screening condition is q i ∈(0,+∞),Δt i ∈(0,+∞),i∈[0,N];q i ≠q i-1 ,i∈[1,N]; When the fitting calculation method is the modified decreasing method-cumulative yield fitting method, the preset screening condition is q i ∈(0,+∞),Δt i ∈(0,+∞),i∈[0,N]; When the fitting calculation method is the production decline empirical analysis method of fracture gas reservoir, the preset screening condition is q i ∈[0,+∞),Δt i ∈(0,+∞),i∈[0,N]; q0≥q i ,i∈[0,N].

4. The auxiliary screening method for capacity fitting calculation according to claim 3, characterized in that: The basis for determining whether to perform boundary difference error processing is whether the fitting calculation method to be used and the data set G1 are complete; When the fitting calculation method is the modified extended exponential decline method, the modified hyperbolic decline method-decline rate fitting method, the modified hyperbolic decline method-cumulative production fitting method, or the production decline empirical analysis method for fractured gas reservoirs, if complete bottomhole flowing pressure or wellhead oil pressure data exist in data set G1, the judgment is yes and the original production data is corrected. If complete bottomhole flowing pressure or wellhead oil pressure data do not exist in data set G1, the judgment is no.

5. The auxiliary screening method for capacity fitting calculation according to claim 4, characterized in that: The basis for judging whether the fitting result is usable includes: judging based on the maximum reasonable range of the fitting result given by the user, judging based on whether the calculation parameters in the fitting calculation method are within the preset range, or allowing the user to judge whether to re-fit based on the fitting result.

6. The auxiliary screening method for capacity fitting calculation according to claim 5, characterized in that: The specific process of evaluating and calculating the fitting results to obtain the quantitative indicators of the fitting effect is as follows: The evaluation calculation is based on the square value of the distance between the data point and the fitting effect curve in the y-axis direction as a quantitative indicator reflecting the fitting effect of the point. The specific algorithm is as follows: The filtered data is set as data set G4, which is expressed as: G4={x j ,y j ,j∈[1,N4]}; Where x j is the independent variable of the jth data point in G4 during fitting, y j is the dependent variable of the jth data point in G4 during fitting, and N4 is the number of data points in G4; Substitute each data point in the data set G4 into the fitting formula one by one and calculate x j The corresponding fitting calculation value The formula is as follows: Where f is the fitting calculation method selected by the user, C1, C2... are fitting parameters; The square value E of the distance between data point j and the fitting effect curve in the y-axis direction 2 j Use the following formula to calculate: E 2 j It is the quantitative index of fitting effect, E 2 j The larger the value is, the worse the fitting effect of data point j is. 2 j The correlation coefficient R that can characterize the fitting effect of the entire data set 2 There is a direct relationship, the correlation coefficient R 2 Use the following formula to calculate: in, is the average y value of all elements in data set G4, calculated using the following formula: Correlation coefficient R 2 With E 2 j The relationship is expressed by the following formula: Where c is the coefficient related to the entire data set and is calculated using the following formula:

7. The auxiliary screening method for capacity fitting calculation according to claim 6, characterized in that: Calculate E for each data point in G4 2 After the value, press E 2 The values ​​are sorted from large to small to form queue A. When the user chooses to filter out the s% of data points with the worst fitting effect, where 100≥s≥0, the first s% of the data points in queue A are directly removed. data points removed.

8. An auxiliary screening system for capacity fitting calculation, characterized in that: include: Data preprocessing module, used to preprocess the original production data to obtain preprocessed data; An autonomous screening module, configured to screen the pre-processed data based on user needs to obtain screened data; A fitting calculation module is used to perform fitting calculation on the screened data to obtain a fitting result; The fitting result judgment module is used to judge whether the fitting result is usable; The auxiliary evaluation module is used to evaluate and calculate the fitting results when it is judged that the fitting results are unavailable, obtain quantitative indicators of the fitting effect, and then transmit the filtered data to the autonomous screening module to re-screen and fit the filtered data until the fitting results are available.

9. An electronic device, characterized in that: The processor, the communication interface, the memory and the communication bus are connected to each other via the communication bus. Memory for storing computer programs; The processor is configured to implement the auxiliary screening method for capacity fitting calculation according to any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the auxiliary screening method for capacity fitting calculation according to any one of claims 1 to 7 is implemented.

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