Method, device and equipment for detecting connectivity of stacked reservoirs and medium

Abstract

The embodiment of the invention discloses a connectivity detection method and device for stacked reservoirs, equipment and a medium. The method comprises the following steps: taking a reservoir connectivity coefficient of a mined well as a label of a corresponding historical attribute parameter, and generating a training sample according to the historical attribute parameter and the label; the reservoir connectivity coefficient reflects reservoir connectivity between a first historical reservoir and a second historical reservoir of the exploited well; the first historical reservoir and the second historical reservoir are in a superposed relationship in space; and training a to-be-trained connectivity detection model based on the training sample to obtain a connectivity detection model, and performing connectivity detection on the first target reservoir and the second target reservoir based on the connectivity detection model. According to the technical scheme, the model with connectivity detection capacity is obtained, the connectivity coefficient between the stacked reservoirs can be accurately determined based on the model, and the connectivity coefficient can provide theoretical guidance for dynamic analysis work, development deployment and adjustment work of the stacked reservoirs.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and in particular to a method, device, equipment and medium for detecting connectivity of stacked reservoirs. Background Art

[0002] The superimposed sandstone buried-hill reservoir is a reservoir in which sandstone directly covers the buried hill without a stable mudstone interlayer. The sandstone and the buried hill are vertically connected and interfere with each other. In the actual production process, the pressure, daily oil production and other indicators of the oil wells produced in the sandstone will be interfered by the wells produced in the buried hill reservoir. Conversely, the wells produced in the buried hill will also be interfered by the wells produced in the sandstone. How to conduct connectivity analysis for superimposed oil reservoirs is a technical problem that technicians in this field need to solve urgently.

[0003] The current technical solution only conducts dynamic analysis on oil reservoirs of a single reservoir. For superimposed reservoirs, it is impossible to avoid interference between the two types of reservoirs, and thus it is impossible to obtain accurate connectivity analysis results. Summary of the invention

[0004] The present invention provides a connectivity detection method, device, equipment and medium for superimposed reservoirs, which can accurately determine the connectivity coefficients between superimposed reservoirs, and provide theoretical guidance for dynamic analysis, development deployment and adjustment of superimposed oil reservoirs.

[0005] According to one aspect of the present invention, a method for detecting connectivity of stacked reservoirs is provided, the method comprising:

[0006] The reservoir connectivity coefficient of the produced well is used as a label of the corresponding historical attribute parameter, and a training sample is generated according to the historical attribute parameter and the label; the reservoir connectivity coefficient reflects the reservoir connectivity between the first historical reservoir and the second historical reservoir of the produced well; the first historical reservoir and the second historical reservoir are in an overlapping relationship in space;

[0007] The connectivity detection model to be trained is trained based on the training samples to obtain a connectivity detection model, so as to perform connectivity detection on the first target reservoir and the second target reservoir based on the connectivity detection model.

[0008] According to another aspect of the present invention, there is provided a device for detecting connectivity of stacked reservoirs, comprising:

[0009] A training sample generation module is used to use the reservoir connectivity coefficient of the produced well as a label of the corresponding historical attribute parameter, and generate a training sample according to the historical attribute parameter and the label; the reservoir connectivity coefficient reflects the reservoir connectivity between the first historical reservoir and the second historical reservoir of the produced well; the first historical reservoir and the second historical reservoir are in a superimposed relationship in space;

[0010] The connectivity detection model determination module is used to train the connectivity detection model to be trained based on the training samples to obtain the connectivity detection model, so as to perform connectivity detection on the first target reservoir and the second target reservoir based on the connectivity detection model.

[0011] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0012] at least one processor; and

[0013] a memory communicatively connected to the at least one processor; wherein,

[0014] 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 so that the at least one processor can perform the connectivity detection method for stacked reservoirs described in any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the connectivity detection method for stacked reservoirs described in any embodiment of the present invention when executed.

[0016] The technical solution of the embodiment of the present application includes: taking the reservoir connectivity coefficient of the produced well as the label of the corresponding historical attribute parameter, and generating training samples according to the historical attribute parameter and the label; the reservoir connectivity coefficient reflects the reservoir connectivity between the first historical reservoir and the second historical reservoir of the produced well; the first historical reservoir and the second historical reservoir are in a superimposed relationship in space; based on the training samples, the connectivity detection model to be trained is trained to obtain a connectivity detection model, so as to perform connectivity detection on the first target reservoir and the second target reservoir based on the connectivity detection model. The technical solution takes the reservoir connectivity coefficient as the label of the corresponding historical attribute parameter, generates training samples, and obtains a model with connectivity detection capability. Based on the model, the connectivity coefficient between superimposed reservoirs can be accurately determined, and the connectivity coefficient can provide theoretical guidance for dynamic analysis, development deployment and adjustment of superimposed oil reservoirs.

[0017] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 is a flow chart of a method for detecting connectivity of stacked reservoirs provided according to Embodiment 1 of the present application;

[0020] Figure 2 is a flow chart of a method for detecting connectivity of stacked reservoirs provided according to Embodiment 2 of the present application;

[0021] FIG3( a ) is a cross-plot of predicted values ​​and true values ​​corresponding to the multivariate linear regression algorithm provided in Example 2 of the present application;

[0022] FIG3( b ) is a cross-plot of predicted values ​​and true values ​​corresponding to the random forest algorithm provided in Example 2 of the present application;

[0023] FIG3( c ) is a cross-plot of the predicted value and the true value corresponding to the BP neural network algorithm provided in Example 2 of the present application;

[0024] FIG3( d ) is a cross-plot of the predicted value and the true value corresponding to XGBoost provided in Example 2 of the present application;

[0025] FIG4( a ) is a cross-plot of predicted values ​​and true values ​​corresponding to the multivariate linear regression algorithm provided in Example 2 of the present application;

[0026] FIG4( b ) is a cross-plot of predicted values ​​and true values ​​corresponding to the random forest algorithm provided in Example 2 of the present application;

[0027] FIG4( c ) is a cross-plot of the predicted value and the true value corresponding to the BP neural network algorithm provided in Example 2 of the present application;

[0028] FIG4( d ) is a cross-plot of the predicted value and the true value corresponding to XGBoost provided in Example 2 of the present application;

[0029] Figure 5 is a structural schematic diagram of a connectivity detection device for stacked reservoirs provided according to Embodiment 3 of the present application;

[0030] Figure 6 It is a structural schematic diagram of an electronic device for implementing a method for detecting connectivity of stacked reservoirs according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only embodiments of a part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", "target", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Embodiment 1

[0034] Figure 1 A flowchart of a method for detecting connectivity of stacked reservoirs is provided for the first embodiment of the present application. The embodiment of the present application is applicable to the case of analyzing the connectivity of stacked reservoirs. The method can be performed by a device for detecting connectivity of stacked reservoirs. The device for detecting connectivity of stacked reservoirs can be implemented in the form of hardware and / or software. The device for detecting connectivity of stacked reservoirs can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0035] S110, taking the reservoir connectivity coefficient of the produced well as the label of the corresponding historical attribute parameter, and generating a training sample according to the historical attribute parameter and the label; the reservoir connectivity coefficient reflects the reservoir connectivity between the first historical reservoir and the second historical reservoir of the produced well; the first historical reservoir and the second historical reservoir are in an overlapping relationship in space.

[0036] In the embodiment of the present application, the reservoir connectivity coefficient reflects the connectivity parameter between the first historical reservoir and the second historical reservoir. The number of reservoir connectivity coefficients can be two, for example, reflecting the connectivity from the first historical reservoir to the second historical reservoir, and the connectivity from the second historical reservoir to the first historical reservoir. The historical attribute parameter refers to the attribute parameter of the first historical reservoir and the second historical reservoir. The historical attribute parameter is used to train the connectivity detection model. Exemplarily, the historical attribute parameter may include: porosity, permeability, reservoir thickness, recovery rate, etc.

[0037] In the embodiment of the present application, the historical attribute parameters reflect the physical and / or chemical properties of the first historical reservoir and the second historical reservoir, and the reservoir connectivity coefficient reflects the connectivity between the first historical reservoir and the second historical reservoir. Therefore, the reservoir connectivity coefficient is used as a label of the historical attribute parameters to generate training samples. After the connectivity detection model is obtained based on the training samples, the model can detect the connectivity of the two superimposed reservoirs, and the detection result is relatively accurate.

[0038] It should be noted that the first historical reservoir, the second historical reservoir, and the historical attribute parameters are the names of the reservoirs and sample data of the training samples. The first target reservoir and the second target reservoir that appear later are the names of the two superimposed reservoirs to be detected, and the target attribute parameters are the attribute parameters of the two superimposed reservoirs to be detected.

[0039] Superimposed reservoirs refer to two different reservoirs, one of which is located above the other, with no stable mudstone barrier between the two reservoirs. For superimposed reservoirs, their connectivity needs to consider the physical properties of the two reservoirs and the interference of wells producing in the two reservoirs.

[0040] S120, training a connectivity detection model to be trained based on the training samples to obtain a connectivity detection model, so as to perform connectivity detection on the first target reservoir and the second target reservoir based on the connectivity detection model.

[0041] Specifically, after establishing the connectivity detection model to be trained, the connectivity detection model to be trained is trained based on the training samples to obtain the trained connectivity detection model. Further, the target attribute parameters of the first target reservoir and the second target reservoir are obtained, and the target attribute parameters are input into the trained connectivity detection model to obtain the connectivity detection results of the first target reservoir and the second target reservoir.

[0042] The technical solution of the embodiment of the present application includes: taking the reservoir connectivity coefficient of the produced well as the label of the corresponding historical attribute parameter, and generating training samples according to the historical attribute parameter and the label; the reservoir connectivity coefficient reflects the reservoir connectivity between the first historical reservoir and the second historical reservoir of the produced well; the first historical reservoir and the second historical reservoir are in a superimposed relationship in space; based on the training samples, the connectivity detection model to be trained is trained to obtain a connectivity detection model, so as to perform connectivity detection on the first target reservoir and the second target reservoir based on the connectivity detection model. The technical solution takes the reservoir connectivity coefficient as the label of the corresponding historical attribute parameter, generates training samples, and obtains a model with connectivity detection capability. Based on the model, the connectivity coefficient between superimposed reservoirs can be accurately determined, and the connectivity coefficient can provide theoretical guidance for dynamic analysis, development deployment and adjustment of superimposed oil reservoirs.

[0043] Embodiment 2

[0044] Figure 2 This is a flow chart of a method for detecting connectivity of stacked reservoirs provided in Example 2 of the present application. This embodiment of the present application is optimized based on the above embodiment.

[0045] In the embodiment of the present application, for example, the first historical reservoir is a sandstone reservoir, and the second historical reservoir is a buried hill reservoir; accordingly, the first target reservoir and the second target reservoir are also sandstone reservoirs and buried hill reservoirs, respectively. This setting can train the model based on the attribute parameters of the sandstone reservoir and the buried hill reservoir, and then identify the connectivity between the superimposed sandstone reservoir and the buried hill reservoir based on the trained model, so that the connectivity detection result is more accurate.

[0046] like Figure 2 As shown, the method of the embodiment of the present application specifically includes the following steps:

[0047] S210, taking the first sand-subsurface connectivity coefficient and the second sand-subsurface connectivity coefficient of the produced well as labels of corresponding historical attribute parameters, and generating training samples according to the historical attribute parameters and the labels; the first historical reservoir and the second historical reservoir are in an overlapping relationship in space.

[0048] In the embodiment of the present application, optionally, the first historical reservoir is a sandstone reservoir, and the second historical reservoir is a buried hill reservoir; accordingly, the reservoir connectivity coefficient includes: a first sandstone buried hill connectivity coefficient and a second sandstone buried hill connectivity coefficient. The historical attribute parameters are the attribute parameters of the first historical reservoir and the second historical reservoir.

[0049] The first sand-submersible connectivity coefficient reflects the connectivity of oil and gas from the buried-hill reservoir to the sandstone reservoir. The second sand-submersible connectivity coefficient reflects the connectivity of oil and gas from the sandstone reservoir to the buried-hill reservoir.

[0050] In the embodiment of the present application, the ratio of the first oil and gas flow rate to the oil and gas production of the sandstone reservoir is determined as the first sand-submerged connectivity coefficient; the first oil and gas flow rate is the flow from the buried hill reservoir into the sandstone reservoir.

[0051] The first sand potential connectivity coefficient is determined by the following formula:

[0052]

[0053] Among them, f 1 is the first sand potential connectivity coefficient, Q 潜山流入砂层 is the first oil and gas flow rate, Q 砂 The oil and gas production of sandstone reservoirs.

[0054] In the embodiment of the present application, the ratio of the second oil and gas flow rate to the oil and gas production of the buried hill reservoir is determined as the second sandstone-buried hill connectivity coefficient; the second oil and gas flow rate is the flow from the sandstone reservoir into the buried hill reservoir.

[0055] The second sand potential connectivity coefficient is determined by the following formula:

[0056]

[0057] f 2 is the second sand potential connectivity coefficient, Q 砂层流入潜山 is the second oil and gas flow rate, Q 潜 is the oil and gas production of the buried hill reservoir.

[0058] It should be noted that the units of the first oil and gas flow rate and the second oil and gas flow rate are m 3 / s, the unit of oil and gas production is also m 3 / s.

[0059] In an embodiment of the present application, optionally, the historical attribute parameters include at least one of the following: sand layer porosity (SPO), buried hill matrix porosity (BMPO), buried hill fracture porosity (BFPO), sandstone permeability (SPE), buried hill matrix permeability (BMPE), buried hill fracture permeability (BFPE), sandstone thickness (ST), buried hill thickness (BT), well spacing (WP), number of fractures (FN), sand layer recovery rate (SFR) and buried hill layer recovery rate (BFR).

[0060] In an embodiment of the present application, optionally, the method further includes: preprocessing the historical attribute parameters; the preprocessing includes at least one of the following: outlier processing, missing value processing, and standardization processing.

[0061] Specifically, outlier detection is performed on the basic attribute parameters based on the interquartile range method and the Z-score method. If an outlier exists, the outlier can be deleted.

[0062] For example, regarding outlier processing, when data exceeds a reasonable range, it can generally be identified as an outlier. In addition to the given range, it can also be detected by IQR and Z-score methods.

[0063] If it is detected that there are missing values ​​in the historical attribute parameters, the historical attribute parameters corresponding to the missing values ​​are deleted, or the missing values ​​are interpolated.

[0064] For example, regarding the missing value processing, the records with missing values ​​can be directly deleted or interpolated. Common interpolation methods include mean interpolation, median interpolation, mode interpolation, linear interpolation, front value interpolation and back value interpolation.

[0065] Furthermore, each training sample is standardized, and the specific processing method may be normalization processing, etc.

[0066] For example, standardization can speed up the gradient descent, eliminate the influence of units and scales between features, and speed up model learning, so standardization can make the model learn better to a certain extent. Commonly used standardization methods include Min-max, Z-score, absolute value, and robustness.

[0067] S220, training a connectivity detection model to be trained based on the training samples to obtain a connectivity detection model, so as to perform connectivity detection on the first target reservoir and the second target reservoir based on the connectivity detection model.

[0068] In an embodiment of the present application, optionally, the method further includes: training the connectivity detection model to be trained based on at least one of the following regression algorithms to obtain a connectivity detection model; the regression algorithms include: multivariate linear regression, random forest regression, BP neural network regression and XGBoost regression.

[0069] Specifically, multiple linear regression, random forest regression, BP neural network regression and XGBoost regression can be used as the regression algorithm of the model, the model can be trained to obtain multiple connectivity detection models, and each model can be evaluated to determine the final connectivity detection model.

[0070] In the embodiment of the present application, grid search is optionally used to optimize the hyperparameters of the machine learning model, and Table 1 is the result of grid search optimization of hyperparameters. The grid search method creates a grid of possible values ​​for the hyperparameters, and each iteration tries the combination of hyperparameters in a specific order, fits the model on each possible hyperparameter combination, records the performance of the model, and finally returns the best model with the best hyperparameters.

[0071] Table 1 Grid search optimization hyperparameter results

[0072]

[0073]

[0074] In an embodiment of the present application, optionally, the method further includes: during the training process of the connectivity detection model, using a generalization ability evaluation index to evaluate the generalization ability of the model to obtain an evaluation result; the generalization ability evaluation index includes at least one of the following: mean absolute error, root mean square error and determination coefficient; based on the evaluation result, determining the regression algorithm adopted by the connectivity detection model.

[0075] Among them, the definition of Mean Absolute Error (MAE) is:

[0076]

[0077] Among them, n is the number of test set samples, y i ′ is the predicted value, y i is the true value.

[0078] The definition of Root Mean Absolute Error (RMSE) is:

[0079]

[0080] Coefficient of Determination (R 2 ) is defined as:

[0081]

[0082] in, is the average of the true values.

[0083] Specifically, a generalization ability evaluation index is used to evaluate the generalization ability of the model to obtain an evaluation result, and according to the evaluation result, a final connectivity detection model is determined in the models corresponding to each regression algorithm.

[0084] Exemplarily, FIG3 is a cross-plot of the predicted value and the true value of the test set of the first sand potential connectivity coefficient, and FIG3(a) is a cross-plot of the predicted value and the true value corresponding to the multivariate linear regression algorithm. FIG3(b) is a cross-plot of the predicted value and the true value corresponding to the random forest algorithm. FIG3(c) is a cross-plot of the predicted value and the true value corresponding to the BP neural network algorithm. FIG3(d) is a cross-plot of the predicted value and the true value corresponding to XGBoost.

[0085] Table 2 shows the evaluation results of various model evaluation indicators of the test set of the first sand potential connectivity coefficient, that is, the evaluation indicators of generalization ability.

[0086] Table 2 Model evaluation index of the first sand potential connectivity coefficient test set

[0087]

[0088] Combining Figure 3 and Table 2, it can be seen that for the fitting of the first sand potential connectivity coefficient, the overfitting phenomenon of the four algorithms is not obvious, and the prediction accuracy and generalization performance are good. Among them, the random forest and XGBoost models have similar effects and are better than the other two algorithms.

[0089] Exemplarily, the intersection of the true value and the predicted value of the second sand potential connectivity coefficient is shown in Figure 4, and Figure 4 (a) is the intersection diagram of the predicted value and the true value corresponding to the multivariate linear regression algorithm. Figure 4 (b) is the intersection diagram of the predicted value and the true value corresponding to the random forest algorithm. Figure 4 (c) is the intersection diagram of the predicted value and the true value corresponding to the BP neural network algorithm. Figure 4 (d) is the intersection diagram of the predicted value and the true value corresponding to XGBoost. Further, the mean absolute error, root mean square error and determination coefficient of the corresponding training set and prediction set are shown in Table 3.

[0090] Table 3 Model evaluation indexes of the second sand potential connectivity coefficient test set

[0091]

[0092]

[0093] Combining Figure 4 and Table 3, it can be seen that for the fitting of the second sand potential connectivity coefficient, the four algorithms have relatively good results, among which the random forest model has a serious overfitting phenomenon, while the BP neural network model has outstanding prediction accuracy and generalization performance, and the other three algorithms have similar results.

[0094] In the embodiment of the present application, optionally, after obtaining the connectivity detection model, the method further includes: acquiring target attribute parameters of the first target reservoir and the second target reservoir; inputting the target attribute parameters into the connectivity detection model to obtain a connectivity detection result.

[0095] The first target reservoir and the second target reservoir are the sandstone reservoir and the buried hill reservoir to be tested respectively.

[0096] The technical solution of the embodiment of the present application includes: taking the first sandstone-submerged connectivity coefficient and the second sandstone-submerged connectivity coefficient of the produced well as labels of the corresponding historical attribute parameters, and generating training samples based on the historical attribute parameters and the labels; the first historical reservoir and the second historical reservoir are in an overlapping relationship in space; training the connectivity detection model to be trained based on the training samples to obtain a connectivity detection model, so as to perform connectivity detection on the first target reservoir and the second target reservoir based on the connectivity detection model. Based on the model, the technical solution can accurately determine the connectivity coefficient between the superimposed sandstone reservoir and the buried hill reservoir, and the connectivity coefficient can provide theoretical guidance for dynamic analysis, development deployment and adjustment of superimposed oil reservoirs; it can also quantitatively characterize the connectivity relationship between the sandstone and buried hill reservoirs, reflecting the degree of interference of production wells between the two types of reservoirs.

[0097] Embodiment 3

[0098] Figure 5 This is a schematic diagram of the structure of a connectivity detection device for stacked reservoirs provided in the third embodiment of the present application. The device can execute the connectivity detection method for stacked reservoirs provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. Figure 5 As shown, the device comprises:

[0099] The training sample generation module 310 is used to use the reservoir connectivity coefficient of the produced well as the label of the corresponding historical attribute parameter, and generate a training sample according to the historical attribute parameter and the label; the reservoir connectivity coefficient reflects the reservoir connectivity between the first historical reservoir and the second historical reservoir of the produced well; the first historical reservoir and the second historical reservoir are in a superimposed relationship in space;

[0100] The connectivity detection model determination module 320 is used to train the connectivity detection model to be trained based on the training samples to obtain the connectivity detection model, so as to perform connectivity detection on the first target reservoir and the second target reservoir based on the connectivity detection model.

[0101] The technical solution of the embodiment of the present application includes: a training sample generation module, which is used to use the reservoir connectivity coefficient of the produced well as the label of the corresponding historical attribute parameter, and generate training samples based on the historical attribute parameter and the label; the reservoir connectivity coefficient reflects the reservoir connectivity between the first historical reservoir and the second historical reservoir of the produced well; the first historical reservoir and the second historical reservoir are in a superimposed relationship in space; a connectivity detection model determination module, which is used to train the connectivity detection model to be trained based on the training sample to obtain a connectivity detection model, so as to perform connectivity detection on the first target reservoir and the second target reservoir based on the connectivity detection model. The technical solution uses the reservoir connectivity coefficient as the label of the corresponding historical attribute parameter to generate training samples and obtain a model with connectivity detection capability. Based on the model, the connectivity coefficient between superimposed reservoirs can be accurately determined, and the connectivity coefficient can provide theoretical guidance for dynamic analysis, development deployment and adjustment of superimposed oil reservoirs.

[0102] Optionally, the first historical reservoir is a sandstone reservoir, and the second historical reservoir is a buried hill reservoir;

[0103] Correspondingly, the reservoir connectivity coefficient includes: a first sand-submerged connectivity coefficient and a second sand-submerged connectivity coefficient;

[0104] Accordingly, the ratio of the first oil and gas flow rate to the oil and gas production of the sandstone reservoir is determined as the first sandstone-submerged connectivity coefficient; the first oil and gas flow rate is the flow from the buried hill reservoir into the sandstone reservoir;

[0105] The ratio of the second oil and gas flow rate to the oil and gas production of the buried hill reservoir is determined as the second sandstone-buried hill connectivity coefficient; the second oil and gas flow rate is the flow from the sandstone reservoir into the buried hill reservoir.

[0106] Optionally, the historical attribute parameters include at least one of the following: sand layer porosity, buried hill matrix porosity, buried hill fracture porosity, sandstone permeability, buried hill matrix permeability, buried hill fracture permeability, sandstone thickness, buried hill thickness, well spacing, number of fractures, sand layer recovery rate, and buried hill layer recovery rate.

[0107] Optionally, the device further comprises:

[0108] The preprocessing module is used to preprocess the historical attribute parameters; the preprocessing includes at least one of the following: outlier processing, missing value processing and standardization processing.

[0109] Optionally, the device further comprises: a model training module based on a regression algorithm, used for training the connectivity detection model to be trained based on at least one of the following regression algorithms to obtain a connectivity detection model;

[0110] The regression algorithm includes:

[0111] Multiple linear regression, random forest regression, BP neural network regression and XGBoost regression.

[0112] Optionally, the device further comprises:

[0113] An evaluation result determination module is used to evaluate the generalization ability of the model using a generalization ability evaluation index during the training process of the connectivity detection model to obtain an evaluation result; the generalization ability evaluation index includes at least one of the following: mean absolute error, root mean square error and determination coefficient;

[0114] The regression algorithm determination module is used to determine the regression algorithm used by the connectivity detection model according to the evaluation result.

[0115] Optionally, the device further comprises:

[0116] A target attribute parameter acquisition module, used to acquire target attribute parameters of the first target reservoir and the second target reservoir;

[0117] The connectivity detection result determination module is used to input the target attribute parameters into the connectivity detection model to obtain the connectivity detection result.

[0118] A connectivity detection device for stacked reservoirs provided in an embodiment of the present application can execute a connectivity detection method for stacked reservoirs provided in any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0119] Embodiment 4

[0120] Figure 6 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0121] like Figure 6As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. 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.

[0122] A number of 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, speakers, etc.; a storage unit 18, such as a 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.

[0123] The processor 11 may be a variety of general and / or special 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 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 executes the various methods and processes described above, such as a connectivity detection method for stacked reservoirs.

[0124] In some embodiments, the connectivity detection method of the stacked reservoirs may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on 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 connectivity detection method of the stacked reservoirs described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the connectivity detection method of the stacked reservoirs in any other appropriate manner (e.g., by means of firmware).

[0125] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0126] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0127] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0128] To provide interaction with a user, the systems and techniques described herein may 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 trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0129] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may 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), a blockchain network, and the Internet.

[0130] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may 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 difficult management and weak business scalability in traditional physical hosts and VPS services.

[0131] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0132] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting connectivity of stacked reservoirs, characterized in that: include: Using the reservoir connectivity coefficient of the produced well as a label of the corresponding historical attribute parameter, and generating a training sample according to the historical attribute parameter and the label; The reservoir connectivity coefficient reflects the reservoir connectivity between the first historical reservoir and the second historical reservoir of the produced well; the first historical reservoir and the second historical reservoir are in a superimposed relationship in space; The connectivity detection model to be trained is trained based on the training samples to obtain a connectivity detection model, so as to perform connectivity detection on the first target reservoir and the second target reservoir based on the connectivity detection model.

2. The method according to claim 1, characterized in that The first historical reservoir is a sandstone reservoir, and the second historical reservoir is a buried hill reservoir; Correspondingly, the reservoir connectivity coefficient includes: a first sand-submerged connectivity coefficient and a second sand-submerged connectivity coefficient; Accordingly, the ratio of the first oil and gas flow rate to the oil and gas production of the sandstone reservoir is determined as the first sandstone-submerged connectivity coefficient; the first oil and gas flow rate is the flow from the buried hill reservoir into the sandstone reservoir; The ratio of the second oil and gas flow rate to the oil and gas production of the buried hill reservoir is determined as the second sandstone-buried hill connectivity coefficient; the second oil and gas flow rate is the flow from the sandstone reservoir into the buried hill reservoir.

3. The method according to claim 1, characterized in that: The historical attribute parameters include at least one of the following: sand layer porosity, buried hill matrix porosity, buried hill fracture porosity, sandstone permeability, buried hill matrix permeability, buried hill fracture permeability, sandstone thickness, buried hill thickness, well spacing, fracture number, sand layer recovery rate, and buried hill layer recovery rate.

4. The method according to claim 1, characterized in that: The method further comprises: The historical attribute parameters are preprocessed; the preprocessing includes at least one of the following: outlier processing, missing value processing, and standardization processing.

5. The method according to claim 1, characterized in that The method further includes: training the connectivity detection model to be trained based on at least one of the following regression algorithms to obtain a connectivity detection model; The regression algorithm includes: Multiple linear regression, random forest regression, BP neural network regression and XGBoost regression.

6. The method according to claim 5, characterized in that The method further comprises: In the process of training the connectivity detection model, a generalization ability evaluation index is used to evaluate the generalization ability of the model to obtain an evaluation result; the generalization ability evaluation index includes at least one of the following: mean absolute error, root mean square error and determination coefficient; According to the evaluation result, a regression algorithm adopted by the connectivity detection model is determined.

7. The method according to claim 1, characterized in that After obtaining the connectivity detection model, the method further includes: Acquire target attribute parameters of the first target reservoir and the second target reservoir; The target attribute parameters are input into the connectivity detection model to obtain a connectivity detection result.

8. A device for detecting connectivity of stacked reservoirs, characterized in that: include: A training sample generation module, used to use the reservoir connectivity coefficient of the produced well as a label of the corresponding historical attribute parameter, and generate a training sample according to the historical attribute parameter and the label; The reservoir connectivity coefficient reflects the reservoir connectivity between the first historical reservoir and the second historical reservoir of the produced well; the first historical reservoir and the second historical reservoir are in a superimposed relationship in space; The connectivity detection model determination module is used to train the connectivity detection model to be trained based on the training samples to obtain the connectivity detection model, so as to perform connectivity detection on the first target reservoir and the second target reservoir based on the connectivity detection model.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, 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 so that the at least one processor can perform the method for detecting connectivity of superimposed reservoirs according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the connectivity detection method for superimposed reservoirs according to any one of claims 1 to 7 when executed.

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