Signal detection method and device, readable storage medium and electronic equipment

Abstract

The embodiment of the invention provides a signal detection method and device, a readable storage medium and electronic equipment, relates to the technical field of exploration, can improve the accuracy and efficiency of oil well ground perforation signal detection, and solves the problem that the detonation condition of a perforator is difficult to determine. The method comprises the steps that perforation operation of a target oil well is monitored, and a to-be-detected signal is obtained; acquiring a signal feature corresponding to the to-be-detected signal; inputting the signal features into a first network layer of a neural network model to obtain a plurality of feature matrixes; inputting the plurality of feature matrixes into a second network layer of the neural network model to obtain a plurality of feature values; and taking the to-be-detected signal as a target perforation signal under the condition that the plurality of feature values meet a preset condition.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of exploration technologies, and in particular, to a signal detection method, apparatus, readable storage medium, and electronic device. Background Art

[0002] Currently, in the oil industry, during the perforation operation of an oil well, high-energy perforating charges, detonating cords, and fracturing gunpowder are installed in a perforating gun in a certain quantity. After the perforating gun is lowered to the target formation underground, it is detonated. The high-energy perforating charges after detonation form a high-temperature and high-pressure liquid metal jet. After penetrating the perforating gun, it further penetrates the casing and cement sheath of the wellbore into the formation, making the formation communicate with the oil well, so as to achieve the purpose that formation fluids can flow smoothly into the oil well.

[0003] Generally, during the perforating completion operation, the determination of the detonation situation of the perforator mainly relies on the detection of perforation signals in the received signals. However, since it may be impossible to accurately distinguish the difference between the perforation signals and noise signals in the received signals, it may be impossible to determine the detonation situation of the perforator. Summary of the Invention

[0004] Embodiments of the present application provide a signal detection method, apparatus, readable storage medium, and electronic device, which can solve the problem of difficult determination of the detonation situation of the perforator.

[0005] To achieve the above object, the embodiments of the present application adopt the following technical solutions:

[0006] In a first aspect, embodiments of the present application provide a signal detection method, including:

[0007] Monitoring the perforation operation of a target oil well to obtain a signal to be detected;

[0008] Obtaining signal characteristics corresponding to the signal to be detected;

[0009] Inputting the signal characteristics into a first network layer of a neural network model to obtain a plurality of feature matrices;

[0010] Inputting the plurality of feature matrices into a second network layer of the neural network model to obtain a plurality of feature values;

[0011] When the plurality of feature values meet a preset condition, regarding the signal to be detected as a target perforation signal.

[0012] Optionally, the step of monitoring the perforation operation of a target oil well to obtain a signal to be detected includes:

[0013] Monitoring the perforation operation of the target oil well, and sequentially obtaining a plurality of wave field signals at preset time sequences within a target time period;

[0014] Take the wave field signal with the highest preset time sequence within the target time period as the target wave field signal;

[0015] When the wave field signal characteristics of the target wave field signal match the preset signal characteristics, extract the target wave field signal from the multiple wave field signals as the signal to be detected.

[0016] Optionally, the first network layer includes: a convolutional network layer; the convolutional network layer includes multiple convolutional kernels; among them, the convolutional parameters corresponding to each convolutional kernel are different; the convolutional parameters include at least one of the following: the step size of the convolution, the size of the convolutional kernel;

[0017] The step of inputting the signal characteristics into the first network layer of the neural network model to obtain multiple feature matrices includes:

[0018] Input the signal characteristics into the convolutional network layer;

[0019] Enable each convolutional kernel in the convolutional network layer to perform feature extraction on the signal characteristics and output the feature matrices respectively.

[0020] Optionally, the second network layer includes: a pooling layer;

[0021] The step of inputting the multiple feature matrices into the second network layer of the neural network model to obtain multiple eigenvalues includes:

[0022] Input the multiple feature matrices into the pooling layer;

[0023] Enable the pooling layer to perform pooling processing on each feature matrix and output multiple eigenvalues respectively.

[0024] Optionally, the step of taking the signal to be detected as the target perforation signal when the multiple eigenvalues meet the preset conditions includes:

[0025] Obtain the probability value that the signal to be detected is the perforation signal according to the multiple eigenvalues;

[0026] When the probability value is greater than or equal to the preset probability threshold, take the signal to be detected as the target perforation signal;

[0027] Among them, the preset probability threshold is determined according to whether each eigenvalue is greater than the preset feature threshold.

[0028] (1)Based on the above embodiments, the present application provides a signal detection method, which monitors the perforation operation of a target oil well to obtain a signal to be detected; acquires the signal characteristics corresponding to the signal to be detected; inputs the signal characteristics into the first network layer of a neural network model to obtain multiple feature matrices; inputs the multiple feature matrices into the second network layer of the neural network model to obtain multiple eigenvalues; and in the case where the multiple eigenvalues meet a preset condition, takes the signal to be detected as a target perforation signal. Since the signal characteristics corresponding to the signal to be detected can be acquired, and the neural network model performs multiple feature extractions on the signal characteristics to obtain higher-dimensional features, it is thus possible to determine whether to take the signal to be detected as a target perforation signal according to the higher-dimensional features of the eigenvalues, and accurately take the signal to be detected as a target perforation signal when the higher-dimensional features meet the preset conditions. Therefore, the situation where it is impossible to accurately distinguish between the perforation signal and the noise signal in the received signal can be avoided, so that the detonation situation of the perforation can be accurately determined, the signal detection of surface perforation is realized, and the signal detection can be extended to the detection of various perforation signals, having the advantages of multi-functional signal detection.

[0029] (2)Based on the above embodiments, the accuracy and efficiency of surface perforation signal detection of oil wells can be improved, which further helps to partially or completely solve the problem of difficult determination of the detonation situation of perforators.

[0030] In a second aspect, an embodiment of the present application provides a signal detection device, including:

[0031] A signal acquisition module, configured to monitor the perforation operation of a target oil well to obtain a signal to be detected;

[0032] A feature acquisition module, configured to acquire the signal characteristics corresponding to the signal to be detected;

[0033] A processing module, configured to input the signal characteristics into the first network layer of a neural network model to obtain multiple feature matrices, and input the multiple feature matrices into the second network layer of the neural network model to obtain multiple eigenvalues;

[0034] A signal determination module, configured to take the signal to be detected as a target perforation signal in the case where the multiple eigenvalues meet a preset condition.

[0035] Optionally, the signal acquisition module includes:

[0036] A wave field monitoring module, configured to monitor the perforation operation of a target oil well and sequentially acquire multiple wave field signals at preset time sequences within a target time period;

[0037] A target acquisition module, configured to take the wave field signal with the highest preset time sequence within the target time period as a target wave field signal;

[0038] A matching module, configured to extract the target wave field signal from multiple wave field signals as the signal to be detected when the wave field signal feature of the target wave field signal matches a preset signal feature.

[0039] Optionally, the first network layer includes: a convolutional network layer; the convolutional network layer includes multiple convolutional kernels; wherein, the convolutional parameters corresponding to each convolutional kernel are different; the convolutional parameters include at least one of the following: the stride of the convolution, the size of the convolutional kernel; the second network layer includes: a pooling layer; the processing module includes:

[0040] A first input module, configured to input the signal feature into the convolutional network layer;

[0041] A first sub-processing module, configured to enable each convolutional kernel in the convolutional network layer to perform feature extraction on the signal feature and output the feature matrix respectively;

[0042] A second input module, configured to input the multiple feature matrices into the pooling layer;

[0043] A second sub-processing module, configured to enable the pooling layer to perform pooling processing on each feature matrix and output multiple feature values respectively.

[0044] In a third aspect, an embodiment of the present application provides a readable storage medium, in which instructions are stored. When a computer executes the instructions, the computer executes the steps of the signal detection method in any of the above embodiments.

[0045] In a fourth aspect, an embodiment of the present application provides an electronic device, which includes: a processor and a communication interface; the communication interface is coupled to the processor, and the processor is configured to run a computer program or instructions to implement the steps of the signal detection method in any of the above embodiments.

[0046] In a fifth aspect, an embodiment of the present application provides a computer program product containing instructions. When the computer program product runs on a signal detection device, the signal detection device is enabled to execute the steps of the signal detection method in any of the above embodiments.

[0047] In a sixth aspect, an embodiment of the present application provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor, and the processor is configured to run a computer program or instructions to implement the steps of the signal detection method in any of the above embodiments.

[0048] Optionally, the chip provided in the embodiment of the present application further includes a memory for storing computer programs or instructions.

[0049] In a seventh aspect, an embodiment of the present application provides a monitoring device for a perforator, which is characterized by comprising: a detonation judgment device and a signal detection device as described in any of the above embodiments;

[0050] The signal detection device is further configured to output a target perforation signal to the detonation judgment device in real time;

[0051] The detonation judgment device is configured to monitor the target perforation signal, and determine that the perforator detonates when the pulse height value of the target perforation signal is greater than a preset pulse height threshold.

[0052] Compared with the prior art, the devices or products provided in the second to seventh aspects of the embodiments of the present application can all implement the steps of the signal detection method in any of the above embodiments, and also have all the advantages of the signal detection method in any of the above embodiments.

[0053] In an eighth aspect, an embodiment of the present application provides a monitoring method for a perforator, which is characterized by comprising:

[0054] Monitoring the perforation operation of a target oil well to obtain a signal to be detected;

[0055] Obtaining signal characteristics corresponding to the signal to be detected;

[0056] Inputting the signal characteristics into a first network layer of a neural network model to obtain a plurality of feature matrices;

[0057] Inputting the plurality of feature matrices into a second network layer of the neural network model to obtain a plurality of feature values;

[0058] When the plurality of feature values meet a preset condition, taking the signal to be detected as a target perforation signal;

[0059] When the pulse height value of the target perforation signal is greater than a preset pulse height threshold, determining that the perforator detonates.

[0060] Compared with the prior art, the monitoring method for a perforator provided in the eighth aspect of the embodiments of the present application can implement the steps of the signal detection method in any of the above embodiments, and also have all the advantages of the signal detection method in any of the above embodiments. Moreover, since the above signal detection method can improve the accuracy and efficiency of detecting perforation signals on the ground of an oil well, the above embodiments can partially or completely solve the problem of difficultly determining the detonation situation of a perforator. Description of the Drawings

[0061] The drawings are only for reference and illustration, and are not intended to limit the protection scope of the embodiments of the present application.

[0062] Figure 1One of the flow diagrams of a signal detection method provided by an embodiment of the present application;

[0063] Figure 2 Two of the flow diagrams of a signal detection method provided by an embodiment of the present application;

[0064] Figure 3 Three of the flow diagrams of a signal detection method provided by an embodiment of the present application;

[0065] Figure 4 Four of the flow diagrams of a signal detection method provided by an embodiment of the present application;

[0066] Figure 5 Structural diagram of a signal detection device provided by an embodiment of the present application;

[0067] Figure 6 Structural diagram of another signal detection device provided by an embodiment of the present application;

[0068] Figure 7 Structural diagram of a chip provided by an embodiment of the present application. Detailed implementation manners

[0069] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the embodiments of the present application.

[0070] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone.

[0071] The terms "first" and "second" in the description of the embodiments of the present application are used to distinguish different objects or different processes for the same object, rather than to describe a specific order of the objects.

[0072] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the embodiments of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes other steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0073] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0074] In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality of" refers to two or more.

[0075] Currently, in the oil industry, in well perforation operations, high-energy perforating charges, detonating cords and fracturing gunpowder are installed in a perforating gun in a certain quantity. After the perforating gun is lowered to the target formation underground, it is detonated. After detonation, the high-energy perforating charge forms a high-temperature and high-pressure liquid metal jet. After penetrating the perforating gun, it further penetrates the casing and cement sheath of the wellbore and enters the formation, making the formation communicate with the oil well, so as to achieve the purpose that formation fluids can flow smoothly into the oil well. Generally, in perforating completion operations, the determination of the detonation situation of the perforator mainly relies on the detection of perforation signals in the received signals. However, since it may be impossible to accurately distinguish the perforation signals from the noise signals in the received signals, it may be impossible to determine the detonation situation of the perforator.

[0076] To solve the problem in the prior art that it is impossible to determine the detonation situation of the perforator, the embodiments of the present application provide a signal detection method, which is characterized by including: monitoring the perforation operation of a target oil well to obtain a signal to be detected; acquiring the signal characteristics corresponding to the signal to be detected; inputting the signal characteristics into the first network layer of a neural network model to obtain a plurality of feature matrices; inputting the plurality of feature matrices into the second network layer of the neural network model to obtain a plurality of eigenvalues; and when the plurality of eigenvalues meet a preset condition, regarding the signal to be detected as a target perforation signal. Since the signal characteristics corresponding to the signal to be detected can be obtained, and the signal characteristics are successively subjected to multiple feature extractions by the first network layer and the second network layer of the neural network model to obtain higher-dimensional features, it is thus possible to determine whether to regard the signal to be detected as a target perforation signal based on the higher-dimensional features of the eigenvalues, and accurately regard the signal to be detected as a target perforation signal when the higher-dimensional features meet the preset condition. Therefore, the situation where it is impossible to accurately distinguish the perforation signals from the noise signals in the received signals can be avoided, and thus the detonation situation of the perforation can be accurately determined. Moreover, the embodiments of the present application achieve signal detection for surface perforation, and the signal detection can be extended to the detection of various perforation signals, having the advantages of multi-functional signal detection.

[0077] Based on the above embodiments, the accuracy and efficiency of well surface perforation signal detection can be improved, which helps to partially or completely solve the problem of difficult to determine the detonation situation of perforators.

[0078] Figure 1 One of the schematic flowcharts of a signal detection method provided by an embodiment of this application. As Figure 1 shown, the signal detection method provided by the embodiment of this application may include the following steps S101 to S105.

[0079] Step S101: Monitor the perforation operation of the target well to obtain the signal to be detected.

[0080] In some optional embodiments, the signal to be detected can be obtained by monitoring the wave field signal of the perforation operation of the target well.

[0081] Among them, the target well can be a well that needs to detect the detonation situation of the perforator.

[0082] Step S102: Obtain the signal characteristics corresponding to the signal to be detected.

[0083] In the embodiment of this application, the signal to be detected can be the received wave field signal. The signal characteristics can at least include: the pulse height of the wave field signal.

[0084] Among them, the signal characteristics can use the voltage value of the signal to characterize the pulse height of the signal to be detected.

[0085] Step S103: Input the signal characteristics into the first network layer of the neural network model to obtain a plurality of feature matrices.

[0086] Among them, the neural network model can be obtained by inputting the signal characteristics of more than a preset number of sample perforation signals and the signal characteristics of sample noise signals into the initial neural network model and performing training for more than a preset number of times.

[0087] In some optional embodiments, the signal characteristics can be input into the first network layer of the neural network model so that the first network layer extracts features from the signal characteristics to obtain a plurality of feature matrices.

[0088] Step S104: Input the plurality of feature matrices into the second network layer of the neural network model to obtain a plurality of eigenvalues.

[0089] In some optional embodiments, the plurality of feature matrices can be input into the second network layer of the neural network model so that the second network side extracts features from the plurality of feature matrices to obtain a plurality of eigenvalues.

[0090] Step S105: When the multiple eigenvalues meet the preset conditions, use the signal to be detected as the target perforation signal.

[0091] In some alternative embodiments, the signal to be detected can be used as the target perforation signal when multiple eigenvalues are greater than a preset eigenvalue threshold.

[0092] Through the above embodiments, the present application provides a signal detection method for monitoring the perforation operation of a target oil well, obtaining a signal to be detected, acquiring signal features corresponding to the signal to be detected, inputting the signal features into the first network layer of a neural network model to obtain multiple feature matrices, inputting the multiple feature matrices into the second network layer of the neural network model to obtain multiple eigenvalues, and using the signal to be detected as the target perforation signal when the multiple eigenvalues meet the preset conditions. Since the signal features corresponding to the signal to be detected can be acquired and the signal features are subjected to multiple feature extractions through the neural network model to obtain higher-dimensional features, it is possible to determine whether to use the signal to be detected as the target perforation signal based on the higher-dimensional features of the eigenvalues, and accurately use the signal to be detected as the target perforation signal when the higher-dimensional features meet the preset conditions. Therefore, the situation where it is impossible to accurately distinguish between the perforation signal and the noise signal in the received signal can be avoided, and thus the detonation situation of the perforation can be accurately determined. Moreover, the embodiments of the present application implement signal detection for surface perforation, and the signal detection can be extended to the detection of various perforation signals, having the advantages of multi-functional signal detection.

[0093] To pre-screen the signal in advance and improve the signal, the wave field signal obtained during the perforation operation of the target oil well can be processed to obtain a signal to be detected that meets the preliminary screening requirements of the target perforation signal. In one possible implementation, referring to Figure 2 , Figure 2 is the second schematic flowchart of a signal detection method provided by the embodiments of the present application. Combining Figure 1 ,as Figure 2 shown, the embodiments of the present application also provide a method for obtaining a signal to be detected, which specifically includes:

[0094] Step S101a: Monitor the perforation operation of the target oil well, and sequentially acquire multiple wave field signals at preset time sequences within a target time period.

[0095] Among them, the target time period can be set periodically.

[0096] Exemplarily, the target time period can be a time period that alternates every 0.5 seconds.

[0097] Exemplarily, 10 wave field signals can be sequentially acquired within each target time period.

[0098] Step S101b: Use the wave field signal with the highest preset time sequence within the target time period as the target wave field signal.

[0099] Among them, the wave field signal with the highest preset time sequence can be the first wave field signal among multiple sequentially acquired wave field signals.

[0100] Step S101c: When the wave field signal characteristics of the target wave field signal match the preset signal characteristics, extract the target wave field signal from the multiple wave field signals as the signal to be detected.

[0101] Through the above embodiments, when the wave field signal characteristics of the target wave field signal match the preset signal characteristics, the first wave field signal among multiple sequentially acquired wave field signals can be used as the signal to be detected.

[0102] In a possible implementation manner, referring to Figure 3 , Figure 3 is the third flowchart of a signal detection method provided by an embodiment of the present application. Combining Figure 1 , as Figure 3 shown, the first network layer includes: a convolutional network layer; the convolutional network layer includes multiple convolutional kernels; among them, the convolutional parameters corresponding to each convolutional kernel are different; the convolutional parameters include at least one of the following: the stride of the convolution, the size of the convolutional kernel. Correspondingly, an embodiment of the present application also provides a method for obtaining a feature matrix, which specifically includes:

[0103] Step S103a: Input the signal characteristics into the convolutional network layer.

[0104] Step S103b: Enable each convolutional kernel in the convolutional network layer to perform feature extraction on the signal characteristics respectively, and output the feature matrix respectively.

[0105] Among them, the signal characteristics can be input into the convolutional network layer, so that each convolutional kernel in the multiple convolutional kernels of the convolutional network layer performs feature extraction on the signal characteristics respectively, so as to obtain one feature matrix output by each convolutional kernel.

[0106] It can be understood that the convolutional network layer can perform multiple convolutional operations on the signal characteristics, and then each channel outputs a feature matrix, so that multiple feature matrices can be obtained. These multiple feature matrices are high-order features of the signal characteristics. Correspondingly, referring to Figure 4 , Figure 4 is the fourth flowchart of a signal detection method provided by an embodiment of the present application. Combining Figure 1 , as Figure 4As shown in the figure, an embodiment of the present application also provides a method for obtaining eigenvalues, which specifically includes:

[0107] Step S104a: Input the multiple feature matrices into the pooling layer.

[0108] Step S104b: Enable the pooling layer to perform pooling processing on each feature matrix, and respectively output multiple eigenvalues.

[0109] It can be understood that the pooling layer can perform pooling processing on each feature matrix to obtain an eigenvalue, thereby obtaining multiple eigenvalues, and these multiple eigenvalues are higher-order features.

[0110] Specifically, the eigenvalue can at least characterize the pulse height of the signal to be detected. Further, the dimension of the eigenvalue used to characterize the pulse height of the signal to be detected is higher than the dimension of the signal feature used to characterize the pulse height of the signal to be detected.

[0111] In a possible implementation manner, an embodiment of the present application also provides a method for obtaining a target perforation signal, which specifically includes:

[0112] Step S105a: Obtain the probability value that the signal to be detected is the perforation signal according to the multiple eigenvalues.

[0113] Step S105b: When the probability value is greater than or equal to a preset probability threshold, use the signal to be detected as the target perforation signal.

[0114] Wherein, the preset probability threshold is determined according to whether each eigenvalue is greater than a preset feature threshold.

[0115] Wherein, the preset feature threshold can be determined in advance according to the eigenvalue relationship between the sample perforation signal and the sample noise signal.

[0116] In an optional example, the eigenvalues of the sample perforation signal and the sample noise signal can be obtained through pre-training of a neural network model.

[0117] Through the above embodiments, the probability value that the signal to be detected is the perforation signal can be obtained by using eigenvalues, and the accuracy of judging whether the signal to be detected is the perforation signal can be improved through higher-dimensional feature judgment.

[0118] Figure 5 The structural schematic diagram of a signal detection device provided by an embodiment of the present application is shown. As Figure 5 shown, based on the same inventive concept, an embodiment of the present application also provides a signal detection device 40, which specifically may include:

[0119] A signal acquisition module 41 for monitoring the perforation operation of a target oil well to obtain a signal to be detected;

[0120] A feature acquisition module 42 for acquiring signal features corresponding to the signal to be detected;

[0121] A processing module 43 for inputting the signal features into the first network layer of a neural network model to obtain multiple feature matrices, and inputting the multiple feature matrices into the second network layer of the neural network model to obtain multiple feature values;

[0122] A signal determination module 44 for taking the signal to be detected as a target perforation signal when the multiple feature values meet a preset condition.

[0123] In a possible implementation manner, an embodiment of the present application further provides a signal acquisition module, including:

[0124] A wave field monitoring module for monitoring the perforation operation of a target oil well and sequentially acquiring multiple wave field signals at preset time sequences within a target time period;

[0125] A target acquisition module for taking the wave field signal with the highest preset time sequence within the target time period as a target wave field signal;

[0126] A matching module for intercepting the target wave field signal from the multiple wave field signals as the signal to be detected when the wave field signal features of the target wave field signal match preset signal features.

[0127] In a possible implementation manner, the first network layer includes: a convolutional network layer; the convolutional network layer includes multiple convolutional kernels. Among them, the convolutional parameters corresponding to each convolutional kernel are different. The convolutional parameters include at least one of the following: the stride of convolution, the size of the convolutional kernel. The second network layer includes: a pooling layer. Correspondingly, an embodiment of the present application further provides a processing module, including:

[0128] A first input module for inputting the signal features into the convolutional network layer.

[0129] A first sub-processing module for enabling each of the convolutional kernels in the convolutional network layer to perform feature extraction on the signal features and respectively output the feature matrices.

[0130] A second input module for inputting the multiple feature matrices into the pooling layer.

[0131] A second sub-processing module for enabling the pooling layer to perform pooling processing on each of the feature matrices and respectively output multiple feature values.

[0132] When implemented by hardware, the signal acquisition module 41, the feature acquisition module 42, the processing module 43, and the signal judgment module 44 in the embodiments of the present application can be integrated on a processor. The specific implementation manner is as Figure 6 shown.

[0133] Figure 6 It is a schematic structural diagram of another signal detection device provided by the embodiments of the present application. As Figure 6 shown, the signal detection device includes: a processor 302 and a communication interface 303. The processor 302 is used to control and manage the operations of the signal detection device. For example, it executes the steps performed by the above-mentioned signal acquisition module 41, feature acquisition module 42, processing module 43, and signal judgment module 44, and / or is used to execute other processes of the technologies described herein. The communication interface 303 is used to support the communication of the signal detection device with other network entities. The signal detection device may further include a memory 301 and a bus 304. The memory 301 is used to store the program code and data of the signal detection device.

[0134] Among them, the memory 301 may be a memory in the signal detection device, etc. The memory may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk, or a solid-state drive; the memory may further include a combination of the above types of memories.

[0135] The above-mentioned processor 302 may be a variety of exemplary logic blocks, modules, and circuits implemented or executed in combination with the disclosed content of the embodiments of the present application. The processor may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in combination with the disclosed content of the embodiments of the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0136] The bus 304 may be an Extended Industry Standard Architecture (EISA) bus, etc. The bus 304 may be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 6 only one thick line is shown in , but it does not mean that there is only one bus or one type of bus.

[0137] Figure 7 It is a schematic structural diagram of a chip 170 provided by the embodiments of the present application. The chip 170 includes one or more than two (including two) processors 1710 and a communication interface 1730.

[0138] Optionally, the chip 170 further includes a memory 1740, which may include a read-only memory and a random access memory, and provides operation instructions and data to the processor 1710. A part of the memory 1540 may further include a non-volatile random access memory (NVRAM).

[0139] In some embodiments, the memory 1740 stores the following elements, execution modules or data structures, or subsets or extended sets thereof.

[0140] In the embodiments of the present application, by invoking the operation instructions stored in the memory 1740 (the operation instructions may be stored in the operating system), corresponding operations are executed.

[0141] Wherein, the above-mentioned processor 1710 may implement or execute various exemplary logic blocks, units and circuits described in connection with the disclosed content of the embodiments of the present application. The processor may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, transistor logic device, hardware component or any combination thereof. It may implement or execute various exemplary logic blocks, units and circuits described in connection with the disclosed content of the embodiments of the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0142] The memory 1740 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk or a solid-state drive; the memory may further include a combination of the above types of memories.

[0143] The bus 1720 may be an Extended Industry Standard Architecture (EISA) bus or the like. The bus 1720 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 only one line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0144] Through the description of the above embodiments, those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0145] The embodiment of the present application provides a computer program product containing instructions. When the computer program product runs on a computer, it causes the computer to execute the perforation signal detection method in the foregoing method embodiment.

[0146] The embodiment of the present application provides a readable storage medium. The readable storage medium stores instructions, and is characterized in that when a computer executes the instructions, the computer executes the steps of the signal detection method in any of the foregoing embodiments.

[0147] Among them, the readable storage medium is the computer-readable storage medium.

[0148] Among them, the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a register, a hard disk, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above, or any other form of computer-readable storage medium well-known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). In the embodiment of the present application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0149] An embodiment of the present application provides a computer program product including instructions, which, when the instructions run on a computer, cause the computer to execute the signal detection method as described in Figures 1 to 5 the following.

[0150] An embodiment of the present application provides a monitoring device for a perforator, which is characterized by including: a detonation judgment device and a signal detection device in any of the above embodiments;

[0151] The signal detection device is further configured to output a target perforation signal to the detonation judgment device in real time;

[0152] The detonation judgment device is configured to monitor the target perforation signal, and when the pulse height value of the target perforation signal is greater than a preset pulse height threshold, determine that the perforator detonates.

[0153] Specifically, the pulse height value of the target perforation signal or the preset pulse height threshold can be characterized by the voltage value of the pulse signal.

[0154] Exemplarily, when the pulse height value of the target perforation signal is characterized by the voltage value of the pulse signal, the preset pulse height threshold can be 800V. When it is detected that the pulse height value of the target perforation signal is greater than 800V, it is determined that the perforator detonates, and then the detonating time and the current state of the perforator during the perforation operation can be connected by the staff.

[0155] For the device embodiment or the product embodiment, since it is basically similar to the method embodiment, the description is relatively simple. The technical effects that can be obtained can refer to the above method embodiment, and the relevant parts can refer to the partial description of the method embodiment. The embodiments of the present application will not be elaborated here.

[0156] In several embodiments provided by the embodiments of the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in electrical, mechanical or other forms.

[0157] In the embodiments of the present application, the units described as separation components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] In addition, in each embodiment of the present application, each functional unit may be integrated in a processing unit, may exist physically separately for each unit, or two or more units may be integrated in one unit.

[0159] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0160] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a device, or a computer program product. Therefore, the embodiments of the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0161] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal devices generate for implementing in the process Figure 1 a process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0162] These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing terminal devices to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements in the process Figure 1 a process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable terminal device provide for implementing the process Figure 1 in one process or multiple processes and / or blocks Figure 1 the steps of the functions specified in one block or multiple blocks.

[0164] Although the preferred embodiments of the embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the embodiments of the present application.

[0165] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, article or terminal device comprising the element.

Claims

1. A signal detection method, characterized in that: include: Monitor the perforation operation of the target oil well and obtain the signal to be detected; Obtaining signal characteristics corresponding to the signal to be detected; Inputting the signal features into the first network layer of the neural network model to obtain multiple feature matrices; Inputting a plurality of the feature matrices into the second network layer of the neural network model to obtain a plurality of feature values; When the plurality of characteristic values ​​meet preset conditions, the signal to be detected is used as a target perforation signal.

2. The method according to claim 1, characterized in that The step of monitoring the perforation operation of the target oil well to obtain the signal to be detected includes: Monitoring the perforation operation of the target oil well, and sequentially acquiring a plurality of wave field signals in a preset time sequence within a target time period; Taking the wavefield signal with the highest preset time sequence within the target time period as the target wavefield signal; In a case where the wavefield signal feature of the target wavefield signal matches the preset signal feature, the target wavefield signal is intercepted from the plurality of wavefield signals as the signal to be detected.

3. The method according to claim 1, characterized in that The first network layer includes: a convolutional network layer; the convolutional network layer includes a plurality of convolution kernels; wherein each convolution kernel corresponds to a different convolution parameter; the convolution parameter includes at least one of the following: a convolution step size, a convolution kernel size; The step of inputting the signal features into the first network layer of the neural network model to obtain a plurality of feature matrices comprises: Inputting the signal features into the convolutional network layer; The convolution kernels in the convolutional network layer are configured to extract features from the signal features and output the feature matrices.

4. The method according to claim 1, characterized in that: The second network layer includes: a pooling layer; The step of inputting the plurality of feature matrices into the second network layer of the neural network model to obtain a plurality of feature values ​​comprises: Inputting the plurality of feature matrices into the pooling layer; The pooling layer performs pooling processing on each of the feature matrices and outputs a plurality of the feature values ​​respectively.

5. The method according to claim 1, characterized in that: The step of using the signal to be detected as a target perforation signal when the multiple characteristic values ​​meet a preset condition comprises: According to the plurality of characteristic values, obtaining a probability value that the signal to be detected is the perforation signal; When the probability value is greater than or equal to a preset probability threshold, taking the signal to be detected as the target perforation signal; The preset probability threshold is determined based on whether each of the characteristic values ​​is greater than a preset characteristic threshold.

6. A signal detection device, characterized in that: include: A signal acquisition module is used to monitor the perforation operation of the target oil well and obtain the signal to be detected; A feature acquisition module, used to acquire signal features corresponding to the signal to be detected; A processing module, used for inputting the signal features into a first network layer of a neural network model to obtain a plurality of feature matrices, and inputting the plurality of feature matrices into a second network layer of the neural network model to obtain a plurality of eigenvalues; The signal judgment module is used to use the signal to be detected as a target perforation signal when the multiple characteristic values ​​meet preset conditions.

7. The device according to claim 6, characterized in that The signal acquisition module comprises: The wave field monitoring module is used to monitor the perforation operation of the target oil well and obtain multiple wave field signals in sequence according to a preset time sequence within the target time period; A target acquisition module, configured to take the wavefield signal with the highest preset time sequence within the target time period as the target wavefield signal; The matching module is used to extract the target wavefield signal from the plurality of wavefield signals as the signal to be detected when the wavefield signal feature of the target wavefield signal matches the preset signal feature.

8. The device according to claim 6, characterized in that The first network layer includes: a convolutional network layer; the convolutional network layer includes a plurality of convolutional kernels; wherein each convolutional kernel corresponds to a different convolutional parameter; the convolutional parameter includes at least one of the following: a convolution step size and a convolutional kernel size; the second network layer includes: a pooling layer; the processing module includes: A first input module, used for inputting the signal features into the convolutional network layer; A first sub-processing module, configured to enable each of the convolution kernels in the convolution network layer to extract features from the signal features respectively, and output the feature matrices respectively; A second input module, used for inputting the plurality of feature matrices into the pooling layer; The second sub-processing module is used to enable the pooling layer to perform pooling processing on each of the feature matrices and output a plurality of the feature values ​​respectively.

9. A readable storage medium, wherein instructions are stored in the readable storage medium, characterized in that: When a computer executes the instruction, the computer executes the steps of the signal detection method described in any one of claims 1 to 5.

10. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the signal detection method according to any one of claims 1 to 5 are implemented.

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