GBDT-based foam drainage injection agent type intelligent matching method, system and equipment

Abstract

The invention belongs to the field of oil and gas production, particularly relates to a GBDT-based foam scrubbing injection agent type intelligent matching method, system and equipment, and aims to solve the problem that the matching between the foam scrubbing agent type and the well production state is not accurate enough. The method comprises the following steps: acquiring target oil gas data as input data through historical data; preprocessing the input data to obtain preprocessed input data; and inputting the preprocessed input data into a trained GBDT-based foam drainage injection agent intelligent matching network, and outputting a predicted injection agent type. Through the GBDT-based foam drainage injection agent type intelligent matching network, the foam drainage injection agent types are accurately matched, the foam drainage efficiency is improved, and the effects of stable production and yield increase are achieved; based on a data-driven intelligent algorithm, the dependence of artificial experimental experience is reduced, the injection agent type can be accurately predicted according to the change of production data, and the injection agent type which is abnormally unchanged for a long time is broken.

Description

Background Art

[0002] At present, with the continuous improvement of development and reserve utilization in most gas fields, liquid accumulation and water discharge in the wellbore seriously affect gas production, and the gas production is seriously reduced. For this reason, the gas field proposes to use foam drainage technology to increase gas production, but the type of foam drainage injection is completely determined by human experience, and there is an inaccurate matching between the type of foam drainage agent and the production status of the well, resulting in slow foam breaking speed, untimely foam breaking, and poor foam drainage effect. Summary of the invention

[0003] In order to solve the above-mentioned problem in the prior art, that is, the problem that the type of foam injection agent is not accurately matched with the well production status, the present invention provides a method for intelligent matching of the type of foam injection agent based on GBDT, the method comprising:

[0004] Step S100, obtaining target oil and gas data as input data through historical data;

[0005] Step S200, preprocessing the input data to obtain preprocessed input data;

[0006] Step S300, inputting the preprocessed input data into the trained GBDT-based foam injection intelligent matching network, and outputting the predicted injection type;

[0007] The GBDT-based intelligent matching network for foam injection comprises a feature extraction module and a GBDT prediction module.

[0008] In some preferred embodiments, the oil and gas data specifically includes:

[0009] The fields include: well number, production date, production time, oil pressure, casing pressure, well inclination, choke nozzle aperture, metering differential pressure, metering static pressure, metering temperature, daily gas production, annual cumulative gas production, on-site verification production, corrected cumulative production, pressure before well opening, rod casting, water and injection type.

[0010] In some preferred embodiments, the pretreatment specifically includes:

[0011] Data cleaning and data labeling;

[0012] The data are annotated with injection type as the target variable.

[0013] In some preferred implementations, the data cleaning specifically includes:

[0014] Delete the empty input data of oil pressure, casing pressure, daily gas production, well inclination, choke nozzle aperture, metering differential pressure, metering static pressure, metering temperature and foaming dosage;

[0015] Delete the negative input data of oil pressure, casing pressure, daily gas production and foaming dosage;

[0016] Delete the input data with empty injection type.

[0017] In some preferred implementations, the step S300 specifically includes:

[0018] Step S310, based on the preprocessed input data, obtaining a first feature set and a target variable through a feature extraction module;

[0019] The first feature set includes candidate features, and the candidate features include oil pressure, casing pressure, well inclination angle, choke nozzle aperture, metering differential pressure, metering static pressure, metering temperature, daily gas production, annual cumulative gas production, on-site nuclear production, pressure before well opening, rod casting and water, and the target variable is foaming dosage;

[0020] Step S320: Calculate the first Pearson correlation coefficient between the candidate feature and the target variable based on the first feature set.

[0021] Step S330, based on a preset first Pearson correlation coefficient threshold, select all candidate features that meet the preset first Pearson correlation coefficient threshold to obtain a first candidate feature set;

[0022] Step S340, calculating the second Pearson correlation coefficients between all candidate features of the first candidate feature set in pairs, and based on a preset second threshold of the Pearson correlation coefficient, selecting candidate features that meet the preset second threshold of the Pearson correlation coefficient to obtain a second candidate feature set;

[0023] Step S350, calculate the variance of all candidate features of the second candidate feature set, select the candidate features that meet the preset variance threshold as independent variables, and the injection type as the dependent variable, input them into the GBDT prediction module, and output the predicted injection type.

[0024] In some preferred embodiments, the method for calculating the first Pearson correlation coefficient includes:

[0025]

[0026] Among them, x is the alternative feature; y is the target variable; cov(x,y) is the covariance between the alternative feature and the target variable; σx is the standard deviation of the alternative feature; σy is the standard deviation of the target variable (injection dose).

[0027] In some preferred embodiments, the training method of the GBDT-based foam injection intelligent matching network specifically includes:

[0028] Step A100, using a training data set as training input data; the training data set includes target oil and gas data;

[0029] Step A200, preprocessing the training input data to obtain preprocessed training input data;

[0030] Step A300, splitting the preprocessed training input data into training set data and test set data;

[0031] Step A400, input the training set data into the GBDT-based foam injection intelligent matching network, output the injection type predicted by the training set, and obtain the training set prediction loss value;

[0032] Step A500, using the test set data and the injection type predicted by the output training set to verify, and comparing the model indicators of the training set and the test set;

[0033] Step A600, adjust the network parameters through the stochastic gradient descent algorithm, repeat the method of steps A300-A500 until the test set model index reaches the preset test set judgment threshold and the training set prediction loss value is lower than the set training set judgment threshold, and obtain the trained GBDT-based foam injection intelligent matching network.

[0034] Another aspect of the present invention provides a system for intelligent matching of blister injection agent types based on GBDT, characterized in that the system comprises:

[0035] An input data acquisition module, configured to acquire target oil and gas data as input data through historical data;

[0036] A preprocessed input data generating module, configured to preprocess the input data to obtain preprocessed input data;

[0037] The injection type prediction module is configured to input the preprocessed input data into a trained GBDT-based foam injection intelligent matching network and output a predicted injection type.

[0038] According to a third aspect of the present invention, an electronic device is provided, comprising:

[0039] at least one processor; and

[0040] a memory communicatively connected to at least one of the processors; wherein,

[0041] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned method for intelligent matching of bubble injection agent types based on GBDT.

[0042] In a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-mentioned method for intelligent matching of bubble injection agent types based on GBDT.

[0043] Beneficial effects of the present invention:

[0044] (1) The present invention accurately matches the types of foam injection agents through an intelligent matching network of foam injection agent types based on GBDT, thereby improving the foam injection efficiency and achieving the effects of stable and increased production.

[0045] (2) The present invention is based on a data-driven intelligent algorithm, which reduces the reliance on human experimental experience and can accurately predict the injection type according to the changes in production data, breaking the long-term abnormal and unchanged injection type;

[0046] (3) The method of the present invention is also applicable to scenarios such as accurate injection type prediction at each well site. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0048] Figure 1 It is a flow chart of a method for intelligent matching of blister injection types based on GBDT;

[0049] Figure 2 It is a technical solution block diagram of a method for intelligent matching of foam injection agent types based on GBDT;

[0050] Figure 3 It is an algorithm execution flow chart of a method for intelligent matching of foam injection agent types based on GBDT;

[0051] Figure 4 It is a structural diagram of a computer system of a server for implementing the method, system, and device embodiments of the present application. DETAILED DESCRIPTION

[0052] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0053] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0054] The present invention provides a method for intelligent matching of foam injection agent types based on GBDT, comprising:

[0055] Step S100, obtaining target oil and gas data as input data through historical data;

[0056] Step S200, preprocessing the input data to obtain preprocessed input data;

[0057] Step S300, inputting the preprocessed input data into the trained GBDT-based foam injection intelligent matching network, and outputting the predicted injection type;

[0058] The GBDT-based intelligent matching network for foam injection comprises a feature extraction module and a GBDT prediction module.

[0059] In order to more clearly explain the method for intelligent matching of foam injection agent types based on GBDT in the present invention, the following is combined with Figure 1 Each step in the embodiment of the present invention is described in detail.

[0060] In the first embodiment of the present invention, a method for intelligent matching of foam injection agent types based on GBDT is proposed, such as Figure 1 As shown, each step is described in detail as follows:

[0061] Step S100, obtaining target oil and gas data as input data through historical data;

[0062] In this embodiment, the oil and gas data specifically includes:

[0063] The fields include: well number, production date, production time, oil pressure, casing pressure, well inclination, choke nozzle aperture, metering differential pressure, metering static pressure, metering temperature, daily gas production, annual cumulative gas production, on-site verification production, corrected cumulative production, pressure before well opening, rod casting, water and injection type.

[0064] Step S200, preprocessing the input data to obtain preprocessed input data;

[0065] In this embodiment, the preprocessing specifically includes:

[0066] Data cleaning and data labeling;

[0067] The data are annotated with injection type as the target variable.

[0068] In this embodiment, the data cleaning specifically includes:

[0069] Delete the empty input data of oil pressure, casing pressure, daily gas production, well inclination, choke nozzle aperture, metering differential pressure, metering static pressure, metering temperature and foaming dosage;

[0070] Delete the negative input data of oil pressure, casing pressure, daily gas production and foaming dosage;

[0071] Delete the input data with empty injection type.

[0072] Step S300, inputting the preprocessed input data into the trained GBDT-based foam injection intelligent matching network, and outputting the predicted injection type;

[0073] The GBDT-based intelligent matching network for foam injection comprises a feature extraction module and a GBDT prediction module.

[0074] In this embodiment, the GBDT-based bubble injection intelligent matching network, its training method is as follows Figure 3 As shown, specifically including:

[0075] Step A100, using a training data set as training input data; the training data set includes target oil and gas data;

[0076] Step A200, preprocessing the training input data to obtain preprocessed training input data;

[0077] Step A300, splitting the preprocessed training input data into training set data and test set data in a ratio of 8:2;

[0078] Step A400, input the training set data into the GBDT-based foam injection intelligent matching network, output the injection type predicted by the training set, and obtain the training set prediction loss value;

[0079] Step A500, using the test set data and the injection type predicted by the output training set to verify, and comparing the model indicators of the training set and the test set;

[0080] Step A600, adjust the network parameters through the stochastic gradient descent algorithm, repeat the method of steps A300-A500 until the test set model index, i.e., the accuracy, reaches 90% and the training set prediction loss value is lower than the set training set judgment threshold, to obtain a trained GBDT-based intelligent matching network for foam injection.

[0081] In this embodiment, the step S300 specifically includes:

[0082] Step S310, based on the preprocessed input data, obtaining a first feature set and a target variable through a feature extraction module;

[0083] The first feature set includes candidate features, and the candidate features include oil pressure, casing pressure, well inclination angle, choke nozzle aperture, metering differential pressure, metering static pressure, metering temperature, daily gas production, annual cumulative gas production, on-site nuclear production, pressure before well opening, rod casting and water, and the target variable is foaming dosage;

[0084] Step S320, based on the first feature set, calculating the first Pearson correlation coefficient between the candidate feature and the target variable;

[0085] Step S330, based on a preset first Pearson correlation coefficient threshold, select all candidate features that meet the preset first Pearson correlation coefficient threshold to obtain a first candidate feature set;

[0086] The first threshold of the first Pearson correlation coefficient is 0.6, and the candidate features whose first Pearson correlation coefficient is greater than 0.6 are selected;

[0087] Step S340, calculating the second Pearson correlation coefficients between all candidate features of the first candidate feature set in pairs, and based on a preset second threshold of the Pearson correlation coefficient, selecting candidate features that meet the preset second threshold of the Pearson correlation coefficient to obtain a second candidate feature set;

[0088] The second threshold of the second Pearson correlation coefficient is 0.6, and the candidate features whose second Pearson correlation coefficient is greater than 0.6 are selected;

[0089] Because if the Pearson correlation coefficient is greater than 0.6, it means that there is a strong correlation between the features, so just keep one of the candidate features;

[0090] Step S350, calculate the variance of all candidate features of the second candidate feature set, select the candidate features that meet the preset variance threshold as independent variables, and the injection type as the dependent variable, input them into the GBDT prediction module, and output the predicted injection type.

[0091] The preset variance threshold is 8, and the candidate features with variance greater than the threshold are selected;

[0092] In this embodiment, the candidate features that meet the preset variance threshold are, for example, oil pressure, casing pressure, well inclination, choke nozzle aperture, metering differential pressure, metering static pressure, and metering temperature;

[0093] In this embodiment, the calculation method of the first Pearson correlation coefficient includes:

[0094]

[0095] Among them, x is the alternative feature; y is the target variable; cov(x,y) is the covariance between the alternative feature and the target variable; σx is the standard deviation of the alternative feature; σy is the standard deviation of the target variable (injection dose).

[0096] In this embodiment, the calculation method of the second Pearson correlation coefficient is consistent with the calculation method of the first Pearson correlation coefficient.

[0097] In this embodiment, the GBDT prediction module specifically includes:

[0098] All trees in the GBDT prediction module are regression trees, which classify and regress data through linear combinations of basis functions and continuously reduce the residuals generated during training.

[0099] The model parameters are as follows:

[0100] -n_estimators: specifies the number of iterations, that is, the number of decision trees. The default value is 100.

[0101] -learning_rate: specifies the learning rate, which is used to adjust the weight of each decision tree. The smaller the learning rate, the more stable the model is, but more iterations are required to achieve better results. The default is 0.1.

[0102] -max_depth: specifies the maximum depth of each decision tree. The default is 3.

[0103] -min_samples_split: Specifies the minimum number of samples a node requires before it is split. Defaults to 2.

[0104] -min_samples_leaf: Specifies the minimum number of samples required for a leaf node. The default value is 1.

[0105] -max_features: specifies the number of features to consider for each node when splitting. The default is None, which means that all features are considered.

[0106] The training process is as follows:

[0107] GBDT goes through multiple rounds of iterations, and each round of iteration generates a weak classifier. Each classifier is trained based on the residual of the previous round of classifiers. The requirements for weak classifiers are generally simple enough, low variance and high bias. The weak classifier is generally selected as CART TREE (that is, classification and regression tree). The final total classifier is the weighted sum of the weak classifiers obtained in each round of training (that is, the additive model).

[0108] Although the various steps in the above embodiment are described in the above-mentioned order, those skilled in the art can understand that in order to achieve the effect of this embodiment, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple changes are within the scope of protection of the present invention.

[0109] A system for intelligent matching of foam injection agent types based on GBDT according to a second embodiment of the present invention is as follows Figure 2 As shown, including:

[0110] An input data acquisition module, configured to acquire target oil and gas data as input data through historical data;

[0111] A preprocessed input data generating module, configured to preprocess the input data to obtain preprocessed input data;

[0112] The injection type prediction module is configured to input the preprocessed input data into a trained GBDT-based foam injection intelligent matching network and output a predicted injection type.

[0113] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0114] It should be noted that the system for intelligent matching of the type of foam injection agent based on GBDT provided in the above embodiment is only illustrated by the division of the above functional modules. In practical applications, the above functional distribution can be completed by different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be decomposed or combined. For example, the modules in the above embodiment can be combined into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not regarded as improper limitations of the present invention.

[0115] An electronic device according to a third embodiment of the present invention includes:

[0116] at least one processor; and

[0117] a memory communicatively connected to at least one of the processors; wherein,

[0118] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned method for intelligent matching of bubble injection agent types based on GBDT.

[0119] A fourth embodiment of the present invention is a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-mentioned method for intelligent matching of blister injection agent types based on GBDT.

[0120] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the storage device and processing device described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0121] Those skilled in the art should be able to appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software or a combination of the two, and the programs corresponding to the software modules and method steps can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the technical field. In order to clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0122] Reference below Figure 4 , which shows a schematic diagram of the structure of a computer system of a server for implementing the method, system, and device embodiments of the present application. Figure 4 The server shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0123] like Figure 4 As shown, the computer system includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage part 608 to the random access memory (RAM) 603. Various programs and data required for system operation are also stored in the RAM 603. The CPU 601, ROM 602 and RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0124] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed so that a computer program read therefrom is installed into the storage section 608 as needed.

[0125] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above-mentioned functions defined in the method of the present application are executed. It should be noted that the above-mentioned computer-readable medium of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more conductors, 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), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, an apparatus or a device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, an apparatus or a device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0126] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0127] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0128] The terms "first", "second", etc. are used to distinguish similar objects rather than to describe or indicate a particular order or sequence.

[0129] The term "comprise" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus / device.

[0130] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for intelligent matching of foam injection types based on GBDT, characterized in that: The method comprises: Step S100, obtaining target oil and gas data as input data through historical data; Step S200, preprocessing the input data to obtain preprocessed input data; Step S300, inputting the preprocessed input data into the trained GBDT-based foam injection intelligent matching network, and outputting the predicted injection type; The GBDT-based intelligent matching network for foam injection comprises a feature extraction module and a GBDT prediction module.

2. The method for intelligent matching of foam injection agent types based on GBDT according to claim 1, characterized in that: The oil and gas data specifically include: The fields include: well number, production date, production time, oil pressure, casing pressure, well inclination, choke nozzle aperture, metering differential pressure, metering static pressure, metering temperature, daily gas production, annual cumulative gas production, on-site verification production, corrected cumulative production, pressure before well opening, rod casting, water and injection type.

3. The method for intelligent matching of foam injection agent types based on GBDT according to claim 1, characterized in that: The preprocessing specifically includes: Data cleaning and data labeling; The data are annotated with injection type as the target variable.

4. The method for intelligent matching of foam injection agent types based on GBDT according to claim 3, characterized in that: The data cleaning specifically includes: Delete the empty input data of oil pressure, casing pressure, daily gas production, well inclination, choke nozzle aperture, metering differential pressure, metering static pressure, metering temperature and foaming dosage; Delete the negative input data of oil pressure, casing pressure, daily gas production and foaming dosage; Delete the input data with empty injection type.

5. The method for intelligent matching of foam injection agent types based on GBDT according to claim 1, characterized in that: The step S300 specifically includes: Step S310, based on the preprocessed input data, obtaining a first feature set and a target variable through a feature extraction module; The first feature set includes candidate features, and the candidate features include oil pressure, casing pressure, well inclination angle, choke nozzle aperture, metering differential pressure, metering static pressure, metering temperature, daily gas production, annual cumulative gas production, on-site nuclear production, pressure before well opening, rod casting and water, and the target variable is foaming dosage; Step S320, based on the first feature set, calculating the first Pearson correlation coefficient between the candidate feature and the target variable; Step S330, based on a preset first Pearson correlation coefficient threshold, select all candidate features that meet the preset first Pearson correlation coefficient threshold to obtain a first candidate feature set; Step S340, calculating the second Pearson correlation coefficients between all candidate features of the first candidate feature set in pairs, and based on a preset second threshold of the Pearson correlation coefficient, selecting candidate features that meet the preset second threshold of the Pearson correlation coefficient to obtain a second candidate feature set; Step S350, calculate the variance of all candidate features of the second candidate feature set, select the candidate features that meet the preset variance threshold as independent variables, and the injection type as the dependent variable, input them into the GBDT prediction module, and output the predicted injection type.

6. The method for intelligent matching of blister injection agent types based on GBDT according to claim 5, characterized in that: The calculation method of the first Pearson correlation coefficient includes: Among them, x is the candidate feature; y is the target variable; cov(x,y) is the covariance between the candidate feature and the target variable; σ x is the standard deviation of the candidate features; σ y is the standard deviation of the target variable.

7. The method for intelligent matching of blister injection agent types based on GBDT according to claim 5, characterized in that: The training method of the GBDT-based foam injection intelligent matching network specifically includes: Step A100, using a training data set as training input data; the training data set includes target oil and gas data; Step A200, preprocessing the training input data to obtain preprocessed training input data; Step A300, splitting the preprocessed training input data into training set data and test set data; Step A400, input the training set data into the GBDT-based foam injection intelligent matching network, output the injection type predicted by the training set, and obtain the training set prediction loss value; Step A500, using the test set data and the injection type predicted by the output training set to verify, and comparing the model indicators of the training set and the test set; Step A600, adjust the parameters through the stochastic gradient descent algorithm, and repeat the method of steps A300-A500 until the test set model index reaches the preset test set judgment threshold and the training set prediction loss value is lower than the set training set judgment threshold, so as to obtain the trained GBDT-based intelligent matching network for foam injection.

8. A system for intelligent matching of foam injection types based on GBDT, characterized in that: The system comprises: An input data acquisition module, configured to acquire target oil and gas data as input data through historical data; A preprocessed input data generating module, configured to preprocess the input data to obtain preprocessed input data; The injection type prediction module is configured to input the preprocessed input data into a trained GBDT-based foam injection intelligent matching network and output a predicted injection type.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the method for intelligent matching of blister injection agent types based on GBDT as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the method for intelligent matching of blister injection agent types based on GBDT according to any one of claims 1 to 7.

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