A reservoir type determination method, apparatus, device and medium

Abstract

Embodiments of the present application disclose a reservoir type determination method and device, electronic equipment and medium. The method comprises: obtaining the fracturing pressure of a horizontal well fracturing section, and statistically classifying the fracturing pressure to determine the reservoir type corresponding to each fracturing section; performing cluster analysis on the drilling parameters of the fracturing sections of different reservoir types based on a clustering algorithm to determine the correspondence between different reservoir types and drilling parameters; determining a sample set for model training according to the correspondence between different reservoir types and drilling parameters to obtain a reservoir type prediction model, and predicting the reservoir type based on the reservoir type prediction model and target drilling parameters that have not been fractured. Through the technical solution of the embodiments of the present application, the differentiated features of the drilling parameters corresponding to different reservoir types can be accurately mined through cluster analysis, the correspondence between the two is determined, the reservoir type of the target fracturing section is accurately predicted through the reservoir type prediction model, and the operation cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oil and gas field development engineering, and particularly relates to a reservoir type determination method and device, electronic equipment and medium. BACKGROUND

[0002] With the expansion of the scale of unconventional oil and gas exploration and development and the expansion of the operation field, the shale gas horizontal well drilling and staged fracturing technology is becoming more and more difficult, and the quality and safety risks and construction costs are also becoming higher and higher. The problem of difficulty in predicting the reservoir type in the operation is increasingly prominent. Therefore, a method for accurately predicting the reservoir type is urgently needed. SUMMARY

[0003] The present application provides a reservoir type determination method and device, electronic equipment and medium, to accurately mine the differentiated characteristics of the drilling parameters corresponding to different reservoir types through cluster analysis, determine the corresponding relationship between the two, realize the accurate prediction of the reservoir type of the target fracturing section through the reservoir type prediction model, and reduce the construction operation cost.

[0004] According to an aspect of the present application, a reservoir type determination method is provided, which comprises:

[0005] obtaining the fracturing pressure of the horizontal well fracturing section, and statistically classifying the fracturing pressure to determine the reservoir type corresponding to each fracturing section;

[0006] based on the cluster algorithm, performing cluster analysis on the drilling parameters of the fracturing sections of different reservoir types to determine the corresponding relationship between the different reservoir types and the drilling parameters;

[0007] determining the sample set based on the corresponding relationship between the different reservoir types and the drilling parameters to train the model, obtaining the reservoir type prediction model, and predicting the reservoir type based on the reservoir type prediction model and the unfractured target drilling data.

[0008] According to another aspect of the present application, a reservoir type determination device is provided, which comprises:

[0009] a reservoir type determination module for obtaining the fracturing pressure of the horizontal well fracturing section, and statistically classifying the fracturing pressure to determine the reservoir type corresponding to each fracturing section;

[0010] a corresponding relationship determination module for performing cluster analysis on the drilling parameters of the fracturing sections of different reservoir types based on the cluster algorithm to determine the corresponding relationship between the different reservoir types and the drilling parameters;

[0011] The reservoir type prediction module is configured to determine a sample set for model training according to the correspondence between different reservoir types and drilling parameters, to obtain a reservoir type prediction model, and to perform reservoir type prediction based on the reservoir type prediction model and unfractured target drilling data.

[0012] According to another aspect of the present application, an electronic device is provided, the device comprising:

[0013] at least one processor; and

[0014] a memory in communication with the at least one processor; wherein

[0015] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the reservoir type determination method of any of the embodiments of the present application.

[0016] According to another aspect of the present application, a computer readable storage medium is provided, the computer readable storage medium storing computer instructions for enabling a processor to implement the reservoir type determination method of any of the embodiments of the present application when executed by the processor.

[0017] The technical solution of the embodiments of the present application obtains the fracture pressure of the fracturing section of the horizontal well, and performs statistics and classification on the fracture pressure to determine the reservoir type corresponding to each fracturing section; performs clustering analysis on the drilling parameters of the fracturing sections of different reservoir types based on a clustering algorithm to determine the correspondence between different reservoir types and drilling parameters; determines a sample set for model training according to the correspondence between different reservoir types and drilling parameters, to obtain a reservoir type prediction model, and performs reservoir type prediction based on the reservoir type prediction model and unfractured target drilling parameters. Through the technical solution of the embodiments of the present application, the differentiating features of the drilling parameters corresponding to different reservoir types can be accurately mined through clustering analysis, the correspondence between the two is determined, the reservoir type of the target fracturing section is accurately predicted through the reservoir type prediction model, and the operation cost is reduced.

[0018] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0020] Figure 1 is a flow chart of a reservoir type determination method according to an embodiment of the present application;

[0021] Figure 2 is a flow chart of a reservoir type determination method according to an embodiment of the present application;

[0022] Figure 3 is an engineering reservoir type classification chart of a Lorenz cumulative probability curve method according to an embodiment of the present application;

[0023] Figure 4 is a partial drilling parameter feature analysis chart of each engineering reservoir type according to an embodiment of the present application;

[0024] Figure 5 is a drilling parameter overlap matrix chart of different reservoir types based on a grid density clustering algorithm according to an embodiment of the present application;

[0025] Figure 6 is a structural schematic diagram of a reservoir type determination apparatus according to an embodiment of the present application;

[0026] Figure 7 is a structural schematic diagram of an electronic device implementing a reservoir type determination method according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0028] It should be noted that the terms "first", "second", "third", "fourth", "actual", "preset" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily have to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, method, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment one

[0030] Figure 1 A flowchart of a reservoir type determination method provided by Embodiment One of the present application, the embodiments of the present application can be applicable to the case of determining the reservoir type of oil and gas. The method can be executed by a reservoir type determination device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device. As shown in the figure, the method comprises: Figure 1

[0031] S110, obtaining the fracture pressure of the horizontal well fracturing section, and statistically classifying the fracture pressure to determine the corresponding reservoir type of each fracturing section.

[0032] The horizontal well fracturing section refers to the area in which the horizontal well is drilled and the fracturing operation is performed in the horizontal section during the oil and gas exploration and development process. The horizontal well is a special type of well, whose drilling direction is horizontal or close to horizontal within a certain stratum range. In order to increase the productivity of the horizontal well, it is usually necessary to perform fracturing operation on the horizontal section. Fracturing refers to the injection of high-pressure liquid to fracture and expand the reservoir, so as to increase the fracture network and effective permeability, and promote the flow of oil and gas from the reservoir. The fracture pressure refers to the minimum pressure applied to the reservoir to cause it to fracture, which is an important parameter for measuring the anti-fracture strength of the reservoir, and is of great significance to oil and gas exploration and development.

[0033] It can be understood that different reservoir types correspond to different anti-fracture strengths, i.e. the minimum pressure applied to different reservoirs to cause them to fracture is different. Therefore, by obtaining the fracture pressure of the horizontal well fracturing section and statistically classifying it, the corresponding reservoir type of each fracturing section can be determined. In field operation, downhole measurement technology is usually used to obtain the fracture pressure, such as installing pressure sensors in the injection pipeline or wellbore, arranging strain gauges around the well wall or wellbore equipment, etc.

[0034] S120, clustering analysis of the drilling parameters of the fracturing sections of different reservoir types based on a clustering algorithm to determine the corresponding relationship between different reservoir types and drilling parameters.

[0035] The clustering algorithm is an unsupervised learning algorithm for dividing the objects in the data set into groups or clusters with similar characteristics. Common clustering algorithms include K-means clustering, hierarchical clustering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and the like.

[0036] ​After determining the reservoir types corresponding to each fracturing section according to the breakdown pressure of the fracturing section, the drilling parameters of different reservoir types can be analyzed by a clustering algorithm, and the drilling parameters are divided into different groups according to the reservoir types, that is, the corresponding relationship between different reservoir types and drilling parameters is determined.

[0037] In S130, a reservoir type prediction model is obtained by determining a sample set according to the corresponding relationship between different reservoir types and drilling parameters, and the reservoir type prediction model is used for reservoir type prediction based on the reservoir type prediction model and target drilling parameters of a fracturing section that has not been fractured.

[0038] After determining the corresponding relationship between different reservoir types and drilling parameters, the corresponding relationship between the reservoir type of a fracturing section and the drilling parameters of the fracturing section can be used as a sample set for model training to obtain a reservoir type prediction model. After obtaining the reservoir type prediction model, the reservoir type of a target fracturing section that has not been fractured can be accurately predicted according to the target drilling parameters of the target fracturing section, thereby reducing the construction cost.

[0039] The technical scheme of the embodiment of the present application obtains the breakdown pressure of a fracturing section of a horizontal well, and performs statistics and classification on the breakdown pressure to determine the reservoir type corresponding to each fracturing section. The drilling parameters of fracturing sections of different reservoir types are analyzed by a clustering algorithm to determine the corresponding relationship between different reservoir types and drilling parameters. A sample set is determined according to the corresponding relationship between different reservoir types and drilling parameters for model training to obtain a reservoir type prediction model, and the reservoir type prediction model is used for reservoir type prediction based on the reservoir type prediction model and target drilling parameters of a fracturing section that has not been fractured. Through the technical scheme of the embodiment of the present application, the differentiating features of the drilling parameters corresponding to different reservoir types can be accurately mined by clustering analysis, the corresponding relationship between the two is determined, the reservoir type of a target fracturing section is accurately predicted by the reservoir type prediction model, and the construction operation cost is reduced.

[0040] Embodiment Two

[0041] Figure 2 A flowchart of a reservoir type determination method provided in Embodiment Two of the present application is shown in FIG. 2. The method of Embodiment Two of the present application is based on the above-described embodiments and is optimized. The solutions not described in detail in Embodiment Two of the present application are described in the above-described embodiments. As shown in FIG. 2, the method of Embodiment Two of the present application specifically includes the following steps: Figure 2

[0042] In S210, the breakdown pressure of a fracturing section of a horizontal well is obtained, and statistics and classification are performed on the breakdown pressure to determine the reservoir type corresponding to each fracturing section.

[0043] ​Different reservoir types correspond to different fracture resistance strengths, meaning the minimum pressure required to cause fracture varies depending on the reservoir. Therefore, by obtaining the fracture pressure of the fractured section in a horizontal well and statistically analyzing and classifying it, the reservoir type corresponding to each fractured section can be determined.

[0044] Specifically, the fracturing pressures are statistically analyzed and categorized to determine the reservoir type corresponding to each fracturing stage, including:

[0045] Plot the relationship between rupture pressure and cumulative probability of rupture pressure; where the cumulative probability of rupture pressure is the ratio of the number of identical rupture pressure values ​​to the total number of rupture pressure values.

[0046] Based on the relationship diagram, the fracturing pressures are categorized to determine the reservoir type corresponding to each fracturing segment.

[0047] In this embodiment of the application, after obtaining the fracture pressure of the horizontal well fracturing section, the ratio of the number of the same fracture pressure value to the total number of fracture pressure values ​​is calculated, i.e., the cumulative probability of fracture pressure, and a relationship diagram between fracture pressure and cumulative probability of fracture pressure is drawn. The fracture pressure is classified according to the relationship diagram to determine the reservoir type corresponding to each fracturing section.

[0048] Specifically, the fracturing pressures are categorized based on the relationship diagram to determine the reservoir type corresponding to each fracturing stage, including:

[0049] The relationship diagrams are classified based on the Lorenz cumulative probability curve method to determine the reservoir type corresponding to the fracture pressure range;

[0050] The reservoir type corresponding to each fracturing section is determined based on the fracturing pressure corresponding to each fracturing section and the reservoir type corresponding to the range of fracturing pressure.

[0051] The Lorenz curve is a method for measuring unequal distributions, while the Lorenz cumulative probability curve characterizes the fracture pressure and its distribution range. In this embodiment, the relationship diagram can be categorized based on the Lorenz cumulative probability curve method to determine the reservoir type corresponding to the fracture pressure range. After determining the reservoir type corresponding to the fracture pressure range, the reservoir type corresponding to each fractured segment can be determined based on the fracture pressure range of each fractured segment. This embodiment applies the Lorenz cumulative probability curve classification method to calculate the cumulative probability curve using the basic parameters of fracture pressure, thus upgrading the single-dimensional fracture pressure to two-dimensional feature data. Quantitative classification and evaluation of reservoir types are then performed based on the data distribution line graph.

[0052] For example, Figure 3A Lorenz cumulative probability curve method engineering reservoir type classification chart is shown, as shown in the figure, the X axis represents the fracture pressure, and the Y axis represents the fracture pressure cumulative probability. The broken line in the figure represents a class of engineering reservoirs, a class of engineering reservoirs, a class of engineering reservoirs, and a class of engineering reservoirs from left to right, and different engineering reservoirs correspond to different fracture pressure ranges.

[0053] S220, using a grid density-based clustering algorithm to cluster analyze the drilling parameters of the fracturing sections of different reservoir types, and determining the overlap degree of the drilling parameters of different reservoir types.

[0054] Among them, common grid density-based clustering algorithms include DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and DENCLUE (DENsity based CLUstEring). Generally speaking, the workflow of the grid density-based clustering algorithm is as follows: 1, grid division: divide the data space into regular grid cells, each grid cell can be square, rectangular or other shape, depending on the implementation of the algorithm and the type of input data. 2, density calculation: traverse each data point in the data set, count the number of data points contained in each grid cell, and calculate the density value of each grid cell. 3, cluster generation: according to the set threshold or density threshold, determine which grid cells have high enough density to form a cluster. 4, cluster expansion: for the results of initial cluster generation, consider merging low-density grids with their adjacent density grids into the corresponding clusters to expand the coverage of the cluster. 5, cluster labeling: for the generated clusters, you can identify them by assigning each data point to the cluster ID or label it belongs to.

[0055] It can be understood that the characteristics of the drilling parameters of the fracturing sections of different reservoir types are different as a whole, but there will be overlapping parts between the drilling parameters corresponding to different reservoir types. Therefore, it is necessary to use a grid density-based clustering algorithm to cluster analyze the characteristics of the drilling parameters of the fracturing sections of different reservoir types, and determine the overlap degree of the drilling parameters of different reservoir types.

[0056] Exemplarily, Figure 4 Part of the drilling parameter characteristic analysis chart of each engineering reservoir type is shown, as Figure 4As shown, from top to bottom, they are the characteristic analysis diagrams of the drilling parameters corresponding to the first-class engineering reservoirs, the second-class engineering reservoirs, the third-class engineering reservoirs and the fourth-class engineering reservoirs. The horizontal direction is the comparison between different drilling parameters corresponding to the same engineering reservoir, and the vertical direction is the comparison between the same drilling parameters corresponding to different engineering reservoirs. Among them, MSE refers to the mean square error in the drilling process, Zuansu refers to the drilling speed in the drilling process, DGFH is the hook load, and Frequency is the number. Figure 4 It can be known that the drilling parameters corresponding to different reservoir types are different as a whole, but there will be some overlapping parts, so it is still necessary to perform cluster analysis on the characteristics of the drilling parameters of the fracturing sections of different reservoir types. Figure 5 The drilling parameter overlap matrix diagram of different reservoir types based on the grid density clustering algorithm is shown in FIG. 6, Figure 5 wherein ERT1 represents the first-class engineering reservoir, ERT2 represents the second-class engineering reservoir, ERT3 represents the third-class engineering reservoir, and ERT4 represents the fourth-class engineering reservoir. It can be understood that the overlap of the drilling parameters of two same reservoir types is 1, and the overlap of the drilling parameters corresponding to other reservoir types is a value from 0 to 1.

[0057] S230, determining the corresponding relationship between different reservoir types and drilling parameters according to the overlap.

[0058] It can be understood that the overlap can be used to represent the similarity between the drilling parameters corresponding to different reservoir types. The greater the overlap, the higher the similarity, and vice versa. Therefore, by selecting a specific drilling parameter, the drilling parameter can be used to represent a specific reservoir type, thereby distinguishing different reservoir types, that is, determining the corresponding relationship between different reservoir types and drilling parameters according to the overlap.

[0059] Specifically, determining the corresponding relationship between different reservoir types and drilling parameters according to the overlap includes:

[0060] Determining the drilling parameter with the smallest overlap corresponding to different reservoir types, and establishing the corresponding relationship between the drilling parameter and different reservoir types.

[0061] The smaller the overlap between the drilling parameters corresponding to different reservoir types, the lower the similarity between the drilling parameters corresponding to different reservoir types, and the more accurate the distinction between different reservoir types by the drilling parameter. In the embodiment of the application, the drilling parameter with the smallest overlap corresponding to different reservoir types is determined, and the corresponding relationship between the drilling parameter and different reservoir types is established, which can maximize the accuracy of the corresponding relationship between the drilling parameter and different reservoir types.

[0062] Exemplarily, as shown in Figure 5 If the overlap degree between the drilling parameter corresponding to the second type of engineering reservoir and the drilling parameter corresponding to the fourth type of engineering reservoir is 0, it indicates that the drilling parameter corresponding to the second type of engineering reservoir is irrelevant to the drilling parameter corresponding to the fourth type of engineering reservoir. At this time, the corresponding relationship between the drilling parameter and the second type of engineering reservoir and the fourth type of engineering reservoir can be established, and the second type of engineering reservoir and the fourth type of engineering reservoir can be distinguished through the drilling parameter.

[0063] S240, determining a sample set according to the corresponding relationship between different reservoir types and drilling parameters to obtain a reservoir type prediction model, so as to predict the reservoir type based on the reservoir type prediction model and the target drilling parameter of the unfractured target well.

[0064] In the embodiment of the present application, after the corresponding relationship between different reservoir types and drilling parameters is established, the sample set can be determined according to the corresponding relationship between different reservoir types and drilling parameters, the model is trained, the training model for reservoir type prediction is obtained, and the reservoir type of the target fracturing section is predicted through the target drilling parameter of the unfractured target fracturing section.

[0065] Specifically, the sample set is determined according to the corresponding relationship between different reservoir types and drilling parameters to train the model and obtain the reservoir type prediction model, which includes:

[0066] According to the corresponding relationship between different reservoir types and drilling parameters, the drilling parameter is taken as the feature data, the reservoir type corresponding to the drilling parameter is taken as the label data, and the sample set is constructed.

[0067] The model is trained and iteratively optimized according to the sample set until the reservoir type prediction model is obtained.

[0068] In the embodiment of the present application, the drilling parameter can be taken as the feature data, and the reservoir type corresponding to the drilling parameter can be taken as the label data to construct the sample set. The model is trained and iteratively optimized according to the sample set until the reservoir type prediction model is obtained. Optionally, when the sample set is constructed, the cross-validation method can be used, 60% of the data in the sample set is taken as the training set, 30% of the data is taken as the test set, and the remaining 10% of the data is taken as the test set. When the model is trained, the training set data is used for model training, the minimum prediction residual of the test set is taken as the iteration optimization parameter of the parameter, and the minimum prediction residual of the test set is taken as the iteration optimization algorithm of the algorithm. Finally, the reservoir type prediction model with the lowest prediction error and the highest accuracy is obtained.

[0069] S250, determining each target fracturing section divided for the unfractured target well.

[0070] It should be noted that the division of the fracturing section of the horizontal well is a complex engineering problem that needs to consider multiple factors and professional knowledge. In actual operation, it is usually necessary to use geological exploration and simulation technology, horizontal well test data, and on-site comprehensive analysis and judgment to determine the optimal fracturing section division scheme.

[0071] S260, input the target drilling parameters of each target fracturing section into the reservoir type prediction model to determine the target reservoir type of each target fracturing section.

[0072] After determining each target fracturing section obtained by division for the target well that has not been fractured, the target drilling parameters of each target fracturing section are input into the reservoir type prediction model, and the target reservoir type of each target fracturing section is accurately determined through the reservoir type prediction model, thereby reducing the production operation cost.

[0073] The reservoir type determination method provided in the embodiments of the present application obtains the breakdown pressure of the fracturing section of the horizontal well, and statistically classifies the breakdown pressure to determine the reservoir type corresponding to each fracturing section; a clustering algorithm based on grid density is used to perform clustering analysis on the drilling parameters of the fracturing sections of different reservoir types, to determine the overlap degree of the drilling parameters of different reservoir types; the correspondence between different reservoir types and drilling parameters is determined according to the overlap degree; a sample set is determined for model training according to the correspondence between different reservoir types and drilling parameters, to obtain a reservoir type prediction model, so as to perform reservoir type prediction based on the reservoir type prediction model and the target well data that has not been fractured; each target fracturing section obtained by division is determined for the target well that has not been fractured; and the target drilling parameters of each target fracturing section are input into the reservoir type prediction model to determine the target reservoir type of each target fracturing section. Through the technical solution of the embodiments of the present application, the differentiating features of the drilling parameters corresponding to different reservoir types can be accurately mined through clustering analysis, the correspondence between the two is determined, the reservoir type of the target fracturing section is accurately predicted through the reservoir type prediction model, and the operation cost is reduced.

[0074] Embodiment Three

[0075] Figure 6 A structural schematic diagram of a reservoir type determination device provided in Embodiment Three of the present application is shown in FIG. 3. The device can execute the reservoir type determination method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method. As shown in FIG. 3, the device includes: Figure 6

[0076] The reservoir type determination module 310 is configured to obtain the breakdown pressure of the fracturing section of the horizontal well, and statistically classify the breakdown pressure to determine the reservoir type corresponding to each fracturing section.

[0077] ​The correspondence determining module 320 is configured to perform clustering analysis on the drilling parameters of the fracturing sections of different reservoir types based on a clustering algorithm, and determine the correspondence between the different reservoir types and the drilling parameters.

[0078] The reservoir type prediction module 330 is configured to perform model training on the sample set according to the correspondence between the different reservoir types and the drilling parameters, obtain a reservoir type prediction model, and perform reservoir type prediction based on the reservoir type prediction model and the target drilling data that is not fractured.

[0079] In the embodiment of the present application, the reservoir type determining module 310 comprises:

[0080] The relationship diagram drawing unit is configured to draw a relationship diagram of the fracture pressure and the fracture pressure cumulative probability, wherein the fracture pressure cumulative probability is a ratio of the number of the same fracture pressure value to the total number of the fracture pressure values.

[0081] The reservoir type determining unit is configured to classify the fracture pressure according to the relationship diagram, and determine the reservoir type corresponding to each fracturing section.

[0082] Optionally, the reservoir type determining unit comprises:

[0083] The relationship diagram classification subunit is configured to classify the relationship diagram based on the Lorenz cumulative probability curve method, and determine the reservoir type corresponding to the fracture pressure range.

[0084] The reservoir type determining subunit is configured to determine the reservoir type corresponding to each fracturing section according to the fracture pressure corresponding to each fracturing section and the reservoir type corresponding to the fracture pressure range.

[0085] In the embodiment of the present application, the correspondence determining module 320 comprises:

[0086] The overlap degree determining unit is configured to perform clustering analysis on the drilling parameters of the fracturing sections of different reservoir types by using a grid density-based clustering algorithm, and determine the overlap degree of the drilling parameters of different reservoir types.

[0087] The correspondence determining unit is configured to determine the correspondence between the different reservoir types and the drilling parameters according to the overlap degree.

[0088] Optionally, the correspondence determining unit comprises:

[0089] The correspondence establishing subunit is configured to determine the drilling parameter corresponding to the minimum overlap degree of different reservoir types, and establish the correspondence between the drilling parameter and the different reservoir types.

[0090] In the embodiment of the present application, the reservoir type prediction module 330 comprises:

[0091] The sample set construction unit is configured to construct a sample set by taking the drilling parameters as feature data and taking the reservoir types corresponding to the drilling parameters as label data according to the correspondence between different reservoir types and the drilling parameters.

[0092] The model training unit is configured to perform model training and iterative optimization according to the sample set until a reservoir type prediction model is obtained by meeting an iteration condition.

[0093] Optionally, the apparatus further comprises:

[0094] The target fracturing section determination module is configured to determine each target fracturing section obtained by the division for the target wellbore that has not been fractured.

[0095] The target fracturing section reservoir prediction module is configured to input the target drilling parameters of each target fracturing section into the reservoir type prediction model to determine the target reservoir type of each target fracturing section.

[0096] The reservoir type determination apparatus provided in the embodiments of the present application can perform the reservoir type determination method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of performing the method.

[0097] Embodiment Four

[0098] Figure 7 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0099] As Figure 7As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., communicatively connected to the at least one processor 11, where the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0100] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a speaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0101] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the reservoir type determination method.

[0102] In some embodiments, the reservoir type determination method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the reservoir type determination method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the reservoir type determination method by any other appropriate means, such as by means of firmware.

[0103] Various implementations of the methods and techniques described above can be realized in digital electronic circuit methods, integrated circuit methods, a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), an application-specific standard products (ASSP), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable processing device, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage device, at least one input device, and at least one output device.

[0104] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable processing device, such that the computer programs, when executed, enable the functions / acts specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine or entirely on the remote machine or server.

[0105] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor methods, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0106] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0107] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0108] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0109] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in a different order, as long as the desired information of the technical solutions of the present disclosure can be achieved, which is not limited herein.

[0110] The specific embodiments described hereinabove are not intended to be limiting, and persons skilled in the art will appreciate that various modifications, combinations, sub-combinations and alternatives can be made to the specific embodiments without departing from the spirit and scope of the disclosure. Any further modifications, equivalents and / or alternatives thereof are also included within the scope of the present disclosure.

Claims

1. A method of reservoir type determination, characterized by, The method comprises: acquiring the fracturing pressure of the fracturing section of the horizontal well, and statistically classifying the fracturing pressure to determine the reservoir type corresponding to each fracturing section; performing clustering analysis on the drilling parameters of the fracturing sections of different reservoir types based on a clustering algorithm to determine the correspondence between different reservoir types and the drilling parameters; training a model based on the correspondence between different reservoir types and the drilling parameters to obtain a reservoir type prediction model, and predicting the reservoir type based on the reservoir type prediction model and the target drilling parameters of the unfractured target drilling well; statistically classifying the fracturing pressure to determine the reservoir type corresponding to each fracturing section, comprising: drawing a graph of the relationship between the fracturing pressure and the cumulative probability of the fracturing pressure, wherein the cumulative probability of the fracturing pressure is the ratio of the number of the same fracturing pressure value to the total number of the fracturing pressure values; classifying the fracturing pressure based on the graph to determine the reservoir type corresponding to each fracturing section; classifying the fracturing pressure based on the graph to determine the reservoir type corresponding to each fracturing section, comprising: classifying the graph based on the Lorenz cumulative probability curve method to determine the reservoir type corresponding to the fracturing pressure range; determining the reservoir type corresponding to each fracturing section based on the fracturing pressure corresponding to each fracturing section and the reservoir type corresponding to the fracturing pressure range.

2. The method of claim 1, wherein, performing clustering analysis on the drilling parameters of the fracturing sections of different reservoir types based on a clustering algorithm to determine the correspondence between different reservoir types and the drilling parameters, comprising: performing clustering analysis on the drilling parameters of the fracturing sections of different reservoir types based on a grid density-based clustering algorithm to determine the overlap degree of the drilling parameters of different reservoir types; determining the correspondence between different reservoir types and the drilling parameters based on the overlap degree.

3. The method of claim 2, wherein, determining the correspondence between different reservoir types and the drilling parameters based on the overlap degree, comprising: determining the drilling parameter corresponding to the minimum overlap degree of different reservoir types, and establishing the correspondence between the drilling parameter and different reservoir types.

4. The method of claim 1, wherein, training a model based on the correspondence between different reservoir types and the drilling parameters to obtain a reservoir type prediction model, comprising: based on the correspondence between different reservoir types and the drilling parameters, taking the drilling parameters as feature data and taking the reservoir type corresponding to the drilling parameters as label data to construct a sample set; training and iteratively optimizing the model based on the sample set until the reservoir type prediction model is obtained.

5. The method of claim 1, wherein, The method further comprises: determining each target fracturing section obtained by division for the unfractured target drilling well; inputting the target drilling parameters of each target fracturing section into the reservoir type prediction model to determine the target reservoir type of each target fracturing section.

6. A reservoir type determination apparatus characterized by comprising: The device comprises: a reservoir type determination module configured to acquire the fracturing pressure of the fracturing section of the horizontal well, and statistically classify the fracturing pressure to determine the reservoir type corresponding to each fracturing section; a correspondence determination module configured to perform clustering analysis on the drilling parameters of the fracturing sections of different reservoir types based on a clustering algorithm to determine the correspondence between different reservoir types and the drilling parameters; The reservoir type prediction module is configured to determine model training of the sample set according to a corresponding relationship between different reservoir types and drilling parameters, to obtain a reservoir type prediction model, and to perform reservoir type prediction based on the reservoir type prediction model and unfractured target drilling data. The reservoir type determination module includes: A relationship graph drawing unit configured to draw a relationship graph of the fracture pressure and the fracture pressure cumulative probability, wherein the fracture pressure cumulative probability is a ratio of a number of the same fracture pressure value to a total number of the fracture pressure values; A reservoir type determination unit configured to classify the fracture pressure according to the relationship graph, and to determine the reservoir type corresponding to each fracturing section; The reservoir type determination unit includes: A relationship graph classification subunit configured to classify the relationship graph based on a Lorentz cumulative probability curve method, and to determine the reservoir type corresponding to the fracture pressure range; A reservoir type determination subunit configured to determine the reservoir type corresponding to each fracturing section according to the fracture pressure corresponding to each fracturing section and the reservoir type corresponding to the fracture pressure range.

7. An electronic device, comprising: The device includes: At least one processor; and A memory connected in communication with the at least one processor; wherein The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the reservoir type determination method of any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing the processor to perform the reservoir type determination method of any one of claims 1-5 when executed.

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