Method, device and equipment for predicting yield of tight sandstone oil and gas reservoir and storage medium

Abstract

The embodiment of the invention provides a tight sandstone oil and gas reservoir yield prediction method, device and equipment and a storage medium, and the method comprises the steps: collecting single well data of a target oil and gas field, and obtaining a first data set, the single well data comprising geological parameters, engineering parameters and yield data of a single well; determining geological master control parameters and engineering master control parameters which influence the yield data; carrying out normalization processing on the geological master control parameters and the engineering master control parameters in the first data set; using a k-nearest neighbor algorithm to calculate the yield data of the well with the attribute closest to that of each well in the training set; an XGBoost algorithm is used, data in the training set are used, and a yield prediction model is established; and inputting the related parameters of the new well to be predicted into the yield prediction model to obtain a yield prediction result of the new well to be predicted. According to the embodiment of the invention, the prediction precision of the model can be improved, thereby achieving the precise prediction of the new well yield, providing a reliable basis for the planning and deployment of the new well yield of an oil field, and assisting the efficient development of oil and gas reservoirs.

Description

Technical Field

[0001] The present application relates to the field of oil and gas exploration technology, and specifically to a method, device, equipment and storage medium for predicting the production of tight sandstone oil and gas reservoirs. Background Art

[0002] Recoverable reserves are the material basis for the development of oil and gas fields and one of the main indicators for evaluating the development effect of oil and gas fields. In the prior art, the methods for predicting recoverable reserves mainly include reservoir numerical simulation methods, typical curve methods and machine learning methods. Among them, the reservoir numerical simulation method studies the reservoir law by establishing a mathematical model describing the fluid seepage law in the oil and gas reservoir according to the geology of the oil and gas reservoir and the actual development situation, and using a computer to obtain a numerical solution; the essence of the reservoir numerical simulation method is the replication of the actual reservoir by mathematical, geological, physical, computer and other methods, and its basic theory is Darcy's seepage law. The typical curve method is to predict the production of new wells by assuming that the wells in the block or oil field have similar production laws, so as to predict the production of new wells with the help of the production laws of old wells. Machine learning methods include: fully connected neural network (FCNN), random forest (RF), support vector machine (SVM), etc. The machine learning method automatically learns the production laws of a large number of single wells based on dynamic and static data, thereby obtaining a single well production prediction model.

[0003] In the process of implementing this application, the applicant found that the prior art has at least the following problems:

[0004] (1) The production prediction method based on numerical simulation method has a limited range of parameters, too many theoretical assumptions, and cannot describe the high-dimensional and nonlinear relationship between geological and engineering parameters and production, resulting in low prediction accuracy. The seepage mechanism of tight sandstone gas reservoirs is complex, and numerical simulation methods are difficult to accurately simulate the laws of gas reservoirs;

[0005] (2) The production prediction method based on the typical curve method cannot take into account the impact of complex geological and engineering conditions on production. It can only make a rough estimate of the production of new wells based on the production patterns of old wells in the block or oil field.

[0006] (3) Most of the current yield prediction methods based on machine learning only consider the impact of geological or engineering parameters on yield. However, in actual production, the impact of the coupling effect of geological and engineering parameters on yield must be considered. It is difficult to accurately predict yield by only considering simple geological or engineering factors.

[0007] (4) The current yield prediction methods based on machine learning, such as linear regression, random forest, SVR and other methods, have a single algorithm model structure and limited fitting ability, and the prediction effect is poor when used alone. It is necessary to establish a composite machine learning model to improve the prediction accuracy;

[0008] (5) Today’s yield prediction methods based on machine learning contain many hyperparameters. The adjustment of model hyperparameters is time-consuming and labor-intensive, and has a certain impact on the performance of the final model.

[0009] It should be pointed out that the information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or suggestion in any form that the information constitutes prior art already known to those skilled in the art. Summary of the invention

[0010] In view of this, the present application provides a method, device, equipment and storage medium for predicting the production of tight sandstone oil and gas reservoirs, so as to solve the problems existing in the prior art.

[0011] In a first aspect, an embodiment of the present application provides a method for predicting the production of a tight sandstone oil and gas reservoir, comprising:

[0012] Collecting single well data of the target oil and gas field to obtain a first data set, wherein the single well data includes geological parameters, engineering parameters and production data of the single well;

[0013] Determining geological main control parameters and engineering main control parameters that affect the production data from among the geological parameters and the engineering parameters;

[0014] Normalizing the geological main control parameters and the engineering main control parameters in the first data set to obtain a second data set, wherein the single well data in the second data set includes normalized geological main control parameters, normalized engineering main control parameters and production data;

[0015] Calculating the production data of the well closest to the attribute of each well in the training set using a k-nearest neighbor algorithm, wherein the training set belongs to the second data set;

[0016] Using the XGBoost algorithm, the normalized geological main control parameters, the normalized engineering main control parameters, the production data of the well with the closest attributes, and the production data in the training set are used to establish a production prediction model;

[0017] The geological main control parameters, engineering main control parameters and production data of the well with the closest attributes of the new well to be predicted are input into the production prediction model to obtain the production prediction result of the new well to be predicted.

[0018] In a possible implementation, the method further includes:

[0019] Using a k-nearest neighbor algorithm, calculating the production data of the well closest to the attribute of each well in the test set, the test set belonging to the second data set;

[0020] The production prediction model is tested using the normalized geological main control parameters, normalized engineering main control parameters, production data of the well with the closest attributes, and production data in the test set.

[0021] In a possible implementation, the method further includes:

[0022] Using a Bayesian optimization algorithm to optimize hyperparameters of the yield prediction model;

[0023] The Bayesian optimization algorithm is used to optimize and adjust the tree depth, learning rate, number of sub-models, solution method and / or regularization term weight of the k-nearest neighbor algorithm.

[0024] In a possible implementation, the geological parameter includes: at least one of water saturation, porosity, brittleness index and average fracture pressure;

[0025] The engineering parameters include: at least one of: fracturing operation displacement, average section length, number of sections, horizontal section length, proppant dosage per section, minimum pump stop pressure, maximum pump stop pressure, nitrogen volume pumped into each section, average operation pressure and azimuth;

[0026] The production data include: the ultimate recoverable reserves of a single well (EUR).

[0027] In a possible implementation, the single well data further includes location parameters, and the location parameters include at least one of the bottom hole longitude, bottom hole latitude, and average depth of the development layer.

[0028] In a possible implementation, the determining of the geological main control parameters and the engineering main control parameters that affect the production data from the geological parameters and the engineering parameters respectively includes:

[0029] The XGBoost algorithm is applied to calculate the influence of the geological parameters and the engineering parameters on the production data, and the geological main control parameters and the engineering main control parameters that affect the production data are determined from the geological parameters and the engineering parameters according to the influence degrees.

[0030] In a possible implementation, determining the geological main control parameters and the engineering main control parameters that affect the production data from among the geological parameters and the engineering parameters according to the degree of influence includes:

[0031] The geological parameters and engineering parameters ranked in the top 50% of the influence degree are taken as the geological main control parameters and engineering main control parameters affecting the production data.

[0032] In a second aspect, an embodiment of the present application provides a tight sandstone oil and gas reservoir production prediction device, comprising:

[0033] A data collection module, used to collect single well data of the target oil and gas field to obtain a first data set, wherein the single well data includes geological parameters, engineering parameters and production data of the single well;

[0034] A main control parameter determination module, used to determine the geological main control parameters and engineering main control parameters that affect the production data from the geological parameters and the engineering parameters;

[0035] A normalization processing module, used for normalizing the geological main control parameters and the engineering main control parameters in the first data set to obtain a second data set, wherein the single well data in the second data set includes normalized geological main control parameters, normalized engineering main control parameters and production data;

[0036] a production data calculation module of the well with the closest attribute, used to calculate the production data of the well with the closest attribute for each well in the training set using a k-nearest neighbor algorithm, wherein the training set belongs to the second data set;

[0037] A production prediction model building module is used to build a production prediction model using the normalized geological main control parameters, normalized engineering main control parameters, production data of the well with the closest attributes, and production data in the training set by using the XGBoost algorithm;

[0038] The new well production prediction module is used to input the geological main control parameters, engineering main control parameters and production data of the well with the closest attributes of the new well to be predicted into the production prediction model to obtain the production prediction result of the new well to be predicted.

[0039] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0040] processor;

[0041] Memory;

[0042] And a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, and when the instructions are executed by the processor, the electronic device executes any one of the methods described in the first aspect.

[0043] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described in the first aspect.

[0044] The technical solution provided by the embodiments of the present application has at least the following technical effects:

[0045] (1) By adding the production data of the wells with the closest attributes to the data set, the correlation between the input parameter group and the output parameter in the production prediction process is improved, thereby improving the prediction accuracy of the model;

[0046] (2) By combining supervised and unsupervised learning, the relationship between tight sandstone production data and parameter groups is learned, and a nonlinear mapping relationship between production and its parameter groups is established;

[0047] (3) The Bayesian hyperparameter optimization method is used to optimize the model structure and comprehensively improve the prediction accuracy of the model, thereby achieving accurate prediction of the production of new wells, providing a reliable basis for the planning and deployment of the production of new wells in oil fields, and facilitating the efficient development of oil and gas reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0049] Figure 1 A schematic diagram of a flow chart of a method for predicting the production of tight sandstone oil and gas reservoirs provided in an embodiment of the present application;

[0050] Figure 2 A technical framework diagram of a method for predicting the production of tight sandstone oil and gas reservoirs provided in an embodiment of the present application;

[0051] Figure 3 A schematic diagram of a data set provided in an embodiment of the present application;

[0052] Figure 4 A schematic diagram of parameter influence ranking provided in an embodiment of the present application;

[0053] Figure 5 A single well production prediction plan diagram provided in an embodiment of the present application;

[0054] Figure 6 A structural block diagram of a tight sandstone oil and gas reservoir production prediction device provided in this application;

[0055] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0057] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0058] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0059] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0060] In view of the technical problems existing in the prior art, the embodiment of the present application provides a method for predicting the production of tight sandstone oil and gas reservoirs. By combining supervised and unsupervised learning, the relationship between tight sandstone production data and parameter groups is learned, and a nonlinear mapping relationship between production and its parameter groups is established; and the Bayesian hyperparameter optimization method is used to optimize the model structure, and the prediction accuracy of the model is comprehensively improved, thereby achieving accurate prediction of new well production, providing a reliable basis for the planning and deployment of new well production in oil fields, and facilitating the efficient development of oil and gas reservoirs. The specific implementation method is described in detail below.

[0061] See also Figure 1 , which is a flow chart of a method for predicting the production of tight sandstone oil and gas reservoirs provided in an embodiment of the present application; see Figure 2 , is a technical framework diagram of a method for predicting the production of tight sandstone oil and gas reservoirs provided in an embodiment of the present application. Figure 1 Combined with Figure 2 As shown, the tight sandstone oil and gas reservoir production prediction method mainly includes the following steps.

[0062] Step S101: Collect single well data of a target oil and gas field to obtain a first data set, where the single well data includes geological parameters, engineering parameters and production data of the single well.

[0063] In one possible implementation, geological parameters include: at least one of water saturation, porosity, brittleness index and average fracture pressure; engineering parameters include: fracturing operation displacement, average segment length, number of segments, horizontal segment length, proppant dosage per segment, minimum pump stop pressure, maximum pump stop pressure, nitrogen volume pumped into each segment, average operation pressure and at least one of azimuth; production data include: ultimate recoverable reserves EUR of a single well. In addition, in addition to the above data, single well data may also include location parameters, wherein the location parameters include at least one of bottom hole longitude, bottom hole latitude and average depth of the development layer. Exemplarily, the first data set is such as Figure 3 shown.

[0064] In a possible implementation, after obtaining the first data set, the data in the first data set can be divided into a training set, a validation set, and a test set according to a certain ratio. Of course, the division of the training set, the validation set, and the test set can also be performed in subsequent steps, and the embodiment of the present application does not specifically limit this.

[0065] Step S102: Determine geological master control parameters and engineering master control parameters that affect production data from among geological parameters and engineering parameters.

[0066] In a possible implementation, the XGBoost algorithm is applied to calculate the influence of geological parameters and engineering parameters on production data, and the geological main control parameters and engineering main control parameters that affect the production data are determined among the geological parameters and engineering parameters according to the influence. In a specific implementation, the geological parameters and engineering parameters ranked in the top 50% of the influence can be used as the geological main control parameters and engineering main control parameters that affect the production data.

[0067] For example, the influence degree of geological parameters and engineering parameters is ranked as follows: Figure 4 As shown, the first 50% (9) of the parameters are taken as the main control parameters. Specifically, the geological main control parameters include water saturation, porosity, brittleness index and average fracture pressure; the engineering main control parameters include fracturing operation displacement, segment length, number of segments, horizontal segment length and proppant dosage per segment.

[0068] In one possible implementation, after determining the geological main control parameters and the engineering main control parameters, only the geological main control parameters in the geological parameters and the engineering main control parameters in the engineering parameters can be retained in the first data set, and other geological parameters and engineering parameters can be deleted to achieve the update of the data in the first data set.

[0069] Step S103: normalizing the geological main control parameters and engineering main control parameters in the first data set to obtain a second data set, wherein the single well data in the second data set includes normalized geological main control parameters, normalized engineering main control parameters and production data.

[0070] In a possible implementation, the maximum and minimum values ​​of each attribute data in the geological master control parameters and engineering master control parameters in the training set can be used to normalize the data of each attribute respectively, so as to obtain the normalized geological master control parameters and normalized engineering master control parameters in the training set; then, the maximum and minimum values ​​of each attribute data in the training set are used to perform the same processing on the data in the validation set and the test set, so as to obtain the corresponding normalized geological master control parameters and normalized engineering master control parameters. In the specific implementation, the normalization formula is shown in Formula 1.

[0071] Formula 1:

[0072]

[0073] Among them, X is the value of a certain attribute data, X min is the minimum value of a certain attribute data, X max is the maximum value of a certain attribute data, X norm It is the normalized value of a certain attribute data.

[0074] It can be understood that after the normalized value is obtained, the normalized value is used for corresponding calculations in subsequent steps. Therefore, the normalized geological master control parameters and the normalized engineering master control parameters can be used to update the geological master control parameters and the engineering master control parameters in the first data set respectively. For the sake of distinction, the data set containing the normalized geological master control parameters, the normalized engineering master control parameters and the production data is referred to as the second data set.

[0075] Step S104: Using the k-nearest neighbor algorithm, calculate the production data of the well whose attributes are closest to each well in the training set.

[0076] That is, a new attribute is added to each well in the training set: the production data of the well whose attribute is closest to that of the well. For example, if the attribute of well A in the training set is closest to that of well B, then the production data of well B is the production data of the well whose attribute is closest to that of well A.

[0077] In the specific implementation, the k-nearest neighbor algorithm (KNN) can be used to set the method (distance function) for calculating the closeness of attributes between wells to the Euclidean distance. Since the KNN algorithm calculates the average production data of the K wells that are closest to the attributes of the well to be found, the K value is set to 1, that is, the production data of the well that is closest to the attributes of the well to be found is calculated.

[0078] Step S105: Using the XGBoost algorithm, the normalized geological main control parameters, the normalized engineering main control parameters, the production data of the well with the closest attributes, and the production data in the training set are used to establish a production prediction model.

[0079] In a possible implementation, the k-nearest neighbor algorithm can also be used to calculate the production data of the well whose attributes are closest to each well in the test set; then, the production prediction model is tested using the normalized geological main control parameters, normalized engineering main control parameters, the production data of the well whose attributes are closest to the well, and the production data in the test set, and based on the test results, it is determined whether the production prediction model needs to be further trained to achieve an ideal prediction effect.

[0080] Step S106: Input the geological main control parameters, engineering main control parameters and production data of the well with the closest attributes of the new well to be predicted into the production prediction model to obtain the production prediction result of the new well to be predicted.

[0081] In a specific implementation, the geological main control parameters and engineering main control parameters of the new well to be predicted can be the geological main control parameters and engineering main control parameters normalized by the above normalization method. In addition, since only old wells have production data, the well with the closest attribute to the new well to be predicted should be the old well.

[0082] It can be understood that after the geological main control parameters, engineering main control parameters, and production data of the well with the closest attributes of the new well to be predicted are input into the production prediction model, the production prediction model can output the production data of the new well to be predicted, that is, obtain the production prediction result. After obtaining the production prediction result, a single well production prediction plane map can also be drawn based on the production prediction result, such as Figure 5 shown.

[0083] In a possible implementation, in order to obtain better prediction results, the Bayesian optimization algorithm can also be used to optimize the hyperparameters of the yield prediction model; the Bayesian optimization algorithm can be used to optimize the tree depth, learning rate, number of sub-models, solution method and / or regularization term weight of the k-nearest neighbor algorithm. Among them, during Bayesian optimization, several sets of hyperparameters will be randomly used for trial calculations. The number of trial calculations can be set manually, for example, the number of trial calculations is set to 10; then, the real Bayesian optimization is performed. After the trial calculation, each Bayesian optimization will refer to the results of the previous calculation, that is, the performance of the model on the validation set, so as to select the hyperparameters, batch training number and iterative training number that should be used for the next calculation.

[0084] In the specific implementation, based on the same training set, three methods, namely KNN, XGBoost and typical curve, were used to establish the single well production prediction model of the reservoir. The prediction accuracy of each optimized model was evaluated using the same untrained test set data. The evaluation results are shown in Table 1.

[0085] Table 1:

[0086] Model R2 Prediction Accuracy This application method 0.82 81.8％ KNN 0.63 69％ XGBoost 0.59 64％ Typical curve 0.43 49％

[0087] The above results show that the prediction effect of the prediction method provided by the embodiment of the present application is much better than the traditional method.

[0088] In summary, the technical solution provided by the embodiments of the present application has at least the following technical effects:

[0089] (1) By adding the production data of the wells with the closest attributes to the data set, the correlation between the input parameter group and the output parameter in the production prediction process is improved, thereby improving the prediction accuracy of the model;

[0090] (2) By combining supervised and unsupervised learning, the relationship between tight sandstone production data and parameter groups is learned, and a nonlinear mapping relationship between production and its parameter groups is established;

[0091] (3) The Bayesian hyperparameter optimization method is used to optimize the model structure and comprehensively improve the prediction accuracy of the model, thereby achieving accurate prediction of the production of new wells, providing a reliable basis for the planning and deployment of the production of new wells in oil fields, and facilitating the efficient development of oil and gas reservoirs.

[0092] Corresponding to the above-mentioned embodiment, the present application also provides a tight sandstone oil and gas reservoir production prediction device.

[0093] See also Figure 6 , is a structural block diagram of a tight sandstone oil and gas reservoir production prediction device provided in this application. Figure 6 As shown, it mainly includes the following modules.

[0094] The data collection module 601 is used to collect single well data of the target oil and gas field to obtain a first data set, wherein the single well data includes geological parameters, engineering parameters and production data of the single well;

[0095] A main control parameter determination module 602 is used to determine the geological main control parameters and engineering main control parameters that affect the production data from the geological parameters and the engineering parameters;

[0096] A normalization processing module 603 is used to perform normalization processing on the geological main control parameters and the engineering main control parameters in the first data set to obtain a second data set, wherein the single well data in the second data set includes normalized geological main control parameters, normalized engineering main control parameters and production data;

[0097] The production data calculation module 604 of the well with the closest attribute is used to calculate the production data of the well with the closest attribute for each well in the training set using the k-nearest neighbor algorithm, and the training set belongs to the second data set;

[0098] The production prediction model building module 605 is used to build a production prediction model by using the normalized geological main control parameters, normalized engineering main control parameters, production data of the well with the closest attribute and production data in the training set using the XGBoost algorithm;

[0099] The new well production prediction module 606 is used to input the geological main control parameters, engineering main control parameters and production data of the well with the closest attributes of the new well to be predicted into the production prediction model to obtain the production prediction result of the new well to be predicted.

[0100] It should be pointed out that the specific contents involved in the embodiments of the present application can be found in the description of the above method embodiments, and for the sake of brevity, they will not be repeated here.

[0101] Corresponding to the above embodiment, an embodiment of the present application further provides an electronic device.

[0102] See also Figure 7 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 7 As shown, the electronic device 700 may include: a processor 701, a memory 702 and a communication unit 703. These components communicate via one or more buses. Those skilled in the art may understand that the electronic device structure shown in the figure does not constitute a limitation on the embodiments of the present application. It may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently.

[0103] The communication unit 703 is used to establish a communication channel so that the electronic device can communicate with other devices.

[0104] The processor 701 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It runs or executes software programs and / or modules stored in the memory 702, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 701 can include only a central processing unit (CPU). In the embodiment of the present application, the CPU can be a single computing core or multiple computing cores.

[0105] The memory 702 is used to store the execution instructions of the processor 701. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0106] When the execution instructions in the memory 702 are executed by the processor 701 , the electronic device 700 is enabled to execute part or all of the steps in the above method embodiments.

[0107] Corresponding to the above embodiment, the embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium may store a program, wherein when the program is running, the device where the computer-readable storage medium is located may be controlled to perform some or all of the steps in the above method embodiment. In a specific implementation, the computer-readable storage medium may be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0108] Corresponding to the above-mentioned embodiment, the embodiment of the present application further provides a computer program product, which includes executable instructions. When the executable instructions are executed on a computer, the computer executes some or all of the steps in the above-mentioned method embodiment.

[0109] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and subsequent associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0110] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0111] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0112] In several embodiments provided in the present application, any function can be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., and other media that can store program codes.

[0113] The above is only a specific implementation of the present application. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. The protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for predicting the production of tight sandstone oil and gas reservoirs, characterized in that: include: Collecting single well data of the target oil and gas field to obtain a first data set, wherein the single well data includes geological parameters, engineering parameters and production data of the single well; Determining geological main control parameters and engineering main control parameters that affect the production data from among the geological parameters and the engineering parameters; Normalizing the geological main control parameters and the engineering main control parameters in the first data set to obtain a second data set, wherein the single well data in the second data set includes normalized geological main control parameters, normalized engineering main control parameters and production data; Using a k-nearest neighbor algorithm, calculating the production data of the well closest to the attribute of each well in the training set, the training set belonging to the second data set; Using the XGBoost algorithm, the normalized geological main control parameters, the normalized engineering main control parameters, the production data of the well with the closest attributes, and the production data in the training set are used to establish a production prediction model; The geological main control parameters, engineering main control parameters and production data of the well with the closest attributes of the new well to be predicted are input into the production prediction model to obtain the production prediction result of the new well to be predicted.

2. The method according to claim 1, characterized in that The method further comprises: Using a k-nearest neighbor algorithm, calculating the production data of the well closest to the attribute of each well in the test set, the test set belonging to the second data set; The production prediction model is tested using the normalized geological main control parameters, normalized engineering main control parameters, production data of the well with the closest attributes, and production data in the test set.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: Using a Bayesian optimization algorithm to optimize hyperparameters of the yield prediction model; The Bayesian optimization algorithm is used to optimize and adjust the tree depth, learning rate, number of sub-models, solution method and / or regularization term weight of the k-nearest neighbor algorithm.

4. The method according to claim 1, characterized in that The geological parameters include: at least one of water saturation, porosity, brittleness index and average fracture pressure; The engineering parameters include: at least one of: fracturing operation displacement, average section length, number of sections, horizontal section length, proppant dosage per section, minimum pump stop pressure, maximum pump stop pressure, nitrogen volume pumped into each section, average operation pressure and azimuth; The production data include: the ultimate recoverable reserves of a single well (EUR).

5. The method according to claim 1, characterized in that The single well data also include location parameters, which include at least one of the bottom hole longitude, bottom hole latitude and the average depth of the development layer.

6. The method according to claim 1, characterized in that The determining of the geological main control parameters and the engineering main control parameters affecting the production data from the geological parameters and the engineering parameters respectively includes: The XGBoost algorithm is applied to calculate the influence of the geological parameters and the engineering parameters on the production data, and the geological main control parameters and the engineering main control parameters that affect the production data are determined from the geological parameters and the engineering parameters according to the influence degrees.

7. The method according to claim 6, characterized in that Determining the geological main control parameters and the engineering main control parameters that affect the production data from the geological parameters and the engineering parameters according to the degree of influence includes: The geological parameters and engineering parameters ranked in the top 50% of the influence degree are taken as the geological main control parameters and engineering main control parameters affecting the production data.

8. A device for predicting the production of tight sandstone oil and gas reservoirs, characterized in that: include: A data collection module, used to collect single well data of the target oil and gas field to obtain a first data set, wherein the single well data includes geological parameters, engineering parameters and production data of the single well; A main control parameter determination module, used to determine the geological main control parameters and engineering main control parameters that affect the production data from the geological parameters and the engineering parameters; A normalization processing module, used for normalizing the geological main control parameters and the engineering main control parameters in the first data set to obtain a second data set, wherein the single well data in the second data set includes normalized geological main control parameters, normalized engineering main control parameters and production data; a production data calculation module of the well with the closest attribute, used to calculate the production data of the well with the closest attribute for each well in the training set using a k-nearest neighbor algorithm, wherein the training set belongs to the second data set; A production prediction model building module is used to build a production prediction model using the normalized geological main control parameters, normalized engineering main control parameters, production data of the well with the closest attributes, and production data in the training set by using the XGBoost algorithm; The new well production prediction module is used to input the geological main control parameters, engineering main control parameters and production data of the well with the closest attributes of the new well to be predicted into the production prediction model to obtain the production prediction result of the new well to be predicted.

9. An electronic device, characterized in that: include: processor; Memory; And a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, and when the instructions are executed by the processor, the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

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