Well site noise feature map library generation method and device, electronic equipment and medium

Abstract

The application relates to a well site noise feature atlas library generation method and device, electronic equipment and a computer readable medium. The method comprises: performing empirical Fourier decomposition on the well site noise to be analyzed to generate a plurality of noise signals; calculating a plurality of polynomial transformation values of the plurality of noise signals based on a polynomial transformation formula; generating a plurality of instantaneous frequencies of the plurality of noise signals based on the plurality of polynomial transformation values; generating a plurality of generalized synchronous squeezing polynomial transformation values of the plurality of noise signals based on the plurality of instantaneous frequencies; calculating feature indexes of the plurality of noise signals based on the plurality of generalized synchronous squeezing polynomial transformation values; and generating a noise feature atlas library based on the feature indexes of the plurality of noise signals. The well site noise feature atlas library generation method and device can significantly improve the time-frequency energy focusing, perform real-time analysis on the well site noise, and improve the oil field well site noise fault diagnosis precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of signal processing, in particular to a method and device for generating a well site noise feature map library, electronic equipment and computer readable medium. BACKGROUND

[0002] Oil exploration is based on the location of oil reservoirs to lay out exploration wells. In the eastern region of China, the economy is developed and the population is dense. Most of the exploration wells are close to residents. Due to the use of high-power mechanical equipment in drilling operations, and the need for continuous operation day and night, the noise disturbance to residents is more serious. In recent years, more than 10 complaints have been filed by residents around an oilfield every year due to noise exceeding the standard. Once the government intervenes due to noise disturbance, drilling operations and related work are forced to be rectified and regulated, which seriously affects the normal production of the oilfield. Therefore, it is urgent to effectively, quickly and accurately extract and automatically identify the noise features of drilling operations, which has great significance for the protection of oilfield production.

[0003] However, most of the oil wells in the oilfield are scattered, with a range of tens to hundreds of square kilometers. In daily production management, most of them use artificial well inspection to check the noise conditions of each well. However, when equipment failure occurs between two inspections, the on-duty personnel cannot discover it in time. How to use a remote monitoring system to realize real-time diagnosis of oilfield noise is a problem that needs to be solved in the current automatic management of oilfield production. Feature extraction is an important part of noise recognition, which directly determines the accuracy of the recognition result. The noise feature map constructed by high-precision time-frequency analysis method helps to better detect the noise features of the oilfield well site, thereby providing a scientific basis for noise recognition.

[0004] The above information disclosed in the background section is only intended to enhance the understanding of the background of the present application, and therefore it can include information that does not constitute the prior art known to those of ordinary skill in the art. SUMMARY

[0005] Therefore, the present application provides a method and device for generating a well site noise feature map library, electronic equipment and computer readable medium, which can significantly improve the time-frequency energy focusing, analyze the well site noise in real time, and improve the accuracy of oilfield well site noise fault diagnosis.

[0006] Other characteristics and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the present application.

[0007] According to an aspect of the present application, a method for generating a well site noise feature atlas library is provided. The method comprises: performing empirical Fourier decomposition on well site noise to be analyzed to generate a plurality of noise signals; calculating a plurality of polynomial transform values of the plurality of noise signals based on a polynomial transform formula; generating a plurality of instantaneous frequencies of the plurality of noise signals based on the plurality of polynomial transform values; generating a plurality of generalized synchronous squeezing polynomial transform values of the plurality of noise signals based on the plurality of instantaneous frequencies; calculating feature indexes of the plurality of noise signals based on the plurality of generalized synchronous squeezing polynomial transform values; and generating a noise feature atlas library based on the feature indexes of the plurality of noise signals.

[0008] In an exemplary embodiment of the present application, performing empirical Fourier decomposition on well site noise to be analyzed to generate a plurality of noise signals comprises: acquiring the well site noise based on a noise sensor; and performing empirical Fourier decomposition on the well site noise to generate a plurality of noise signals with a single frequency.

[0009] In an exemplary embodiment of the present application, calculating a plurality of polynomial transform values of the plurality of noise signals based on a polynomial transform formula comprises: calculating a plurality of polynomial Chirplet transform values of the plurality of noise signals based on a polynomial Chirplet transform formula.

[0010] In an exemplary embodiment of the present application, calculating a plurality of polynomial Chirplet transform values of the plurality of noise signals based on a polynomial Chirplet transform formula comprises: determining a multi-kernel operator; and calculating a plurality of polynomial Chirplet transform values of the plurality of noise signals based on the multi-kernel operator.

[0011] In an exemplary embodiment of the present application, generating a plurality of instantaneous frequencies of the plurality of noise signals based on the plurality of polynomial transform values comprises: generating the plurality of instantaneous frequencies of the plurality of noise signals based on a frequency filter and the plurality of polynomial transform values.

[0012] In an exemplary embodiment of the present application, generating the plurality of instantaneous frequencies of the plurality of noise signals based on a frequency filter and the plurality of polynomial transform values comprises: generating the plurality of instantaneous frequencies of the plurality of noise signals based on a Gaussian filter and the plurality of polynomial transform values.

[0013] In an exemplary embodiment of the present application, generating a plurality of generalized synchronous squeezing polynomial transform values of the plurality of noise signals based on the plurality of instantaneous frequencies comprises: generating a plurality of generalized synchronous squeezing polynomial transform values of the plurality of noise signals based on a generalized synchronous squeezing operator and the plurality of instantaneous frequencies.

[0014] In an example embodiment of the present application, the method further comprises: inverse transforming the plurality of generalized synchro-squeeze polynomial transform values to reconstruct the wellsite noise.

[0015] In an example embodiment of the present application, the calculating the feature indicators of the plurality of noise signals based on the plurality of generalized synchro-squeeze polynomial transform values comprises: calculating time domain feature indicators of the plurality of noise signals based on the plurality of generalized synchro-squeeze polynomial transform values; and / or calculating frequency domain feature indicators of the plurality of noise signals based on the plurality of generalized synchro-squeeze polynomial transform values; and / or calculating time-frequency domain feature indicators of the plurality of noise signals based on the plurality of generalized synchro-squeeze polynomial transform values.

[0016] According to an aspect of the present application, there is provided a device for generating a wellsite noise feature atlas library, the device comprising: a decomposition module configured to perform empirical Fourier decomposition on a wellsite noise to be analyzed to generate a plurality of noise signals; a transform module configured to calculate a plurality of polynomial transform values of the plurality of noise signals based on a polynomial transform formula; an instantaneous module configured to generate a plurality of instantaneous frequencies of the plurality of noise signals based on the plurality of polynomial transform values; a squeeze module configured to generate a plurality of generalized synchro-squeeze polynomial transform values of the plurality of noise signals based on the plurality of instantaneous frequencies; an indicator module configured to calculate feature indicators of the plurality of noise signals based on the plurality of generalized synchro-squeeze polynomial transform values; and an atlas module configured to generate a noise feature atlas library based on the feature indicators of the plurality of noise signals.

[0017] According to an aspect of the present application, there is provided an electronic device, comprising: one or more processors; a storage device configured to store one or more programs; and when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0018] According to an aspect of the present application, there is provided a computer readable medium having stored thereon a computer program, which program, when executed by a processor, implements the method as described above.

[0019] According to the well site noise feature map library generation method, device, electronic equipment and computer readable medium, the plurality of noise signals are generated by performing empirical Fourier decomposition on the well site noise to be analyzed; a plurality of polynomial transformation values of the plurality of noise signals are calculated based on a polynomial transformation formula; a plurality of instantaneous frequencies of the plurality of noise signals are generated based on the plurality of polynomial transformation values; a plurality of generalized synchronous squeezing polynomial transformation values of the plurality of noise signals are generated based on the plurality of instantaneous frequencies; and the feature indexes of the plurality of noise signals are calculated based on the plurality of generalized synchronous squeezing polynomial transformation values. The way of generating the noise feature map library based on the feature indexes of the plurality of noise signals can significantly improve the time-frequency energy focusing, analyze the well site noise in real time, and improve the oil field well site noise fault diagnosis precision.

[0020] It should be understood that the foregoing general description and the following detailed description are only examples and are not limiting the present application. BRIEF DESCRIPTION OF DRAWINGS

[0021] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which:

[0022] Figure 1 FIG. 1 is an application scenario diagram of a well site noise feature map library generation method and device according to an example embodiment.

[0023] Figure 2 FIG. 2 is a flowchart of a well site noise feature map library generation method according to an example embodiment.

[0024] Figure 3 FIG. 3 is a simulated oil field well site noise signal diagram.

[0025] Figure 4 FIG. 4 is a single noise signal decomposition diagram according to an example embodiment.

[0026] Figure 5 FIG. 5 is a time-frequency diagram according to an example embodiment.

[0027] Figure 6 FIG. 6 is a feature index diagram according to an example embodiment.

[0028] Figure 7 FIG. 7 is a block diagram of a well site noise feature map library generation device according to an example embodiment.

[0029] Figure 8 FIG. 8 is a block diagram of an electronic device according to an example embodiment.

[0030] Figure 9 is a block diagram of a computer readable medium according to an example embodiment. DETAILED DESCRIPTION

[0031] Example embodiments now will be described more fully hereinafter with reference to the accompanying drawings. Example embodiments, however, can be implemented in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. Like reference numerals refer to like elements throughout the several views and, thus, description of the same elements will not be repeated.

[0032] Moreover, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the application can be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, devices, and operations have not been shown or described in detail to avoid obscuring aspects of the application.

[0033] The block diagrams in the drawings show only the functionality of the embodiments and do not necessarily imply a particular arrangement of circuitry and / or software. For example, the functions could be provided in software, hardware, or a combination of both. In an example embodiment, the functions can be provided as a software program for execution by a computer or other electronic device. In this instance, the program need not take the form of a separate program, and can, for example, constitute either part of an operating system or a separate application program.

[0034] The flow diagrams shown in the various figures, which can include a more particular description of the operation of a user computer, are used herein for illustrative purposes only. One skilled in the art will appreciate that exemplary methods can include any number of additional or alternative steps, as desired for a particular application. Moreover, one will appreciate that not all of the steps described in the exemplary methods need be performed, and that some of the steps can be performed in an order other than that described.

[0035] It should be understood that although the terms first, second, third, etc. can be used herein to describe various elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms are only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the present application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0036] Those skilled in the art will understand that the drawings described herein are merely illustrative and should not be construed as limiting the scope of the present application. The modules or processes in the drawings are not necessarily meant to be implemented in the described order, and not all of the steps are necessarily required.

[0037] The inventors of the present application found through analysis that time-frequency analysis converts a one-dimensional non-stationary signal into two dimensions of time and frequency to analyze the signal, and then reflects the frequency domain characteristics of the signal in the local time. Traditional time-frequency analysis methods include Gabor transform, short-time Fourier transform (STFT), wavelet transform (WT), S transform (ST), etc. They all achieve the purpose of time-frequency analysis through windowing transform. Therefore, they are all limited by the Heisenberg uncertainty principle, i.e., the time and frequency resolution cannot be optimal at the same time.

[0038] To improve the time-frequency resolution and further facilitate the analysis of oilfield well site noise, a series of time-frequency post-processing algorithms have been developed, such as time-frequency rearrangement (RM), synchronous squeezing wavelet transform (SSWT), and synchronous extraction transform (SET). RM rearranges energy in the time and frequency directions of the original time-frequency spectrum, thereby improving time-frequency focusing. Inspired by RM, SSWT only rearranges energy in the frequency direction, effectively improving the time-frequency resolution and allowing signal reconstruction. Therefore, it has been extended to other traditional time-frequency analysis methods, such as synchronous squeezing STFT and synchronous squeezing ST. However, they are limited by weak amplitude modulation and frequency modulation, and the time-frequency resolution is not high for strong amplitude modulation and frequency modulation signals. Therefore, many researchers use high-order Taylor expansion to derive high-order instantaneous frequency estimation algorithms to improve time-frequency focusing.

[0039] Although these high-order time-frequency analysis methods effectively improve the time-frequency resolution, they rely on high-order partial derivative information of the original time-frequency representation results. However, when the signal is greatly affected by noise, the instantaneous frequency estimation value will be affected, leading to unstable time-frequency results. Oilfield well site noise signals are themselves greatly disturbed by noise, so there is an urgent need for a high-resolution and stable time-frequency representation method to effectively analyze the characteristic information under different conditions and construct an oilfield well site noise feature map library.

[0040] To overcome the above-mentioned deficiencies in the prior art, the present application provides a method for generating an oilfield well site noise feature map library, which can significantly improve the time-frequency energy focusing and improve the accuracy of oilfield well site noise fault diagnosis.

[0041] The content of the present application will be described in detail below in conjunction with specific embodiments.

[0042] Embodiment 1

[0043] Figure 1 is an application scenario diagram of a method and device for generating an oilfield well site noise feature map library according to an exemplary embodiment.

[0044] As Figure 1As shown, the system architecture 10 can include monitoring devices 101, 102, 103, a network 104 and a server 105. The network 104 is a medium for providing communication links between the monitoring devices 101, 102, 103 and the server 105. The network 104 can include various connection types, such as wired, wireless communication links or fiber optic cables, etc.

[0045] The monitoring devices 101, 102, 103 can be arranged at any location in an oilfield, and interact with the server 105 through the network 104 to receive or send messages, etc. The monitoring devices 101, 102, 103 can be installed with various monitoring applications. The monitoring devices 101, 102, 103 can be various electronic devices with monitoring and wireless transmission functions, and the present application is not limited thereto.

[0046] The server 105 can be a server providing various services, such as a background management server for analyzing signals transmitted by the monitoring devices 101, 102, 103. The background management server can analyze and process received signals, and feed back the processing results (such as fault analysis results) to an administrator.

[0047] The server 105 can perform empirical Fourier decomposition on wellsite noise transmitted by the monitoring devices 101, 102, 103 to generate a plurality of noise signals; the server 105 can calculate a plurality of polynomial transform values of the plurality of noise signals based on a polynomial transform formula; the server 105 can generate a plurality of instantaneous frequencies of the plurality of noise signals based on the plurality of polynomial transform values; the server 105 can generate a plurality of generalized synchrosqueezing polynomial transform values of the plurality of noise signals based on the plurality of instantaneous frequencies; the server 105 can calculate characteristic indexes of the plurality of noise signals based on the plurality of generalized synchrosqueezing polynomial transform values; and the server 105 can generate a noise feature map library based on the characteristic indexes of the plurality of noise signals.

[0048] The server 105 can be a server of one entity, or can be composed of a plurality of servers. It should be noted that the method for generating a wellsite noise feature map library provided in the embodiments of the present application can be executed by the server 105, and accordingly, the device for generating a wellsite noise feature map library can be arranged in the server 105. The application end for monitoring is generally located in the monitoring devices 101, 102, 103.

[0049] Embodiment 2

[0050] Figure 2 is a flowchart of a method for generating a wellsite noise feature map library according to an exemplary embodiment. The method 20 for generating a wellsite noise feature map library at least includes steps S202 to S212.

[0051] like Figure 2 As shown, in S202, the well site noise to be analyzed is subjected to empirical Fourier decomposition to generate multiple noise signals. The well site noise can be obtained based on a noise sensor; empirical Fourier decomposition is performed on the well site noise to generate multiple noise signals with a single frequency.

[0052] In a specific embodiment, the oilfield well site noise signal to be analyzed is x(t), where t represents time.

[0053] The original signal x(t) can be decomposed into several individual noise signals x using empirical Fourier decomposition. i (t), calculated as follows:

[0054]

[0055] Where t and ω represent time and frequency, respectively, i = (1, 2, ..., n) is the number of single noise signals, and n is a positive integer. The imaginary part of a complex number is the unit. Fourier transform of the original signal x(t):

[0056]

[0057] In S204, multiple polynomial transform values ​​of the multiple noise signals are calculated based on polynomial transform formulas. The multiple polynomial Chirplet transform values ​​of the multiple noise signals can be calculated based on polynomial Chirplet transform formulas.

[0058] More specifically, calculating multiple polynomial Chirplet transform values ​​of the multiple noise signals based on the polynomial Chirplet transform formula includes: determining a multi-core operator; and calculating multiple polynomial Chirplet transform values ​​of the multiple noise signals based on the multi-core operator.

[0059] Using the polynomial Chirplet transform formula, several single noise signals x i (t) Calculate the polynomial Chirplet transform value of the window function g(t).

[0060]

[0061] in, Indicates a multi-core operator, The imaginary part of a complex number is the unit. Let g(t) represent the complex conjugate of the function g(t), where g(t) is a Gaussian window function, and its specific mathematical expression is:

[0062]

[0063] wherein the multi-kernel operator, whose specific mathematical expression is:

[0064]

[0065] wherein, denotes a frequency rotation operator, denotes a frequency shift operator, denotes a phase shift operator, Γ = (c1, c2,..., c n ) is the parameter of the multi-kernel operator.

[0066] In S206, a plurality of instantaneous frequencies of the plurality of noise signals are generated based on the plurality of polynomial transform values. The plurality of instantaneous frequencies of the plurality of noise signals can be generated based on a frequency filter and the plurality of polynomial transform values.

[0067] The frequency filter is h(ω), and the estimated signal x i (t) in the polynomial Chirplet transform whose specific expression is:

[0068]

[0069] wherein, denotes taking the real part of a complex number, denotes the polynomial Chirplet transform value under the window function tg(t), and |·| denotes the determinant of a matrix;

[0070] More specifically, the plurality of instantaneous frequencies of the plurality of noise signals are generated based on a Gaussian filter and the plurality of polynomial transform values.

[0071] The frequency filter h(ω) can be a Gaussian filter, whose specific mathematical expression is:

[0072]

[0073] Using this filter h(ω), the inner products and are respectively defined as:

[0074]

[0075]

[0076] In the formula, * represents a convolution operator with respect to the frequency ω.

[0077] In S208, a plurality of generalized synchrosqueezing polynomial transform values of the plurality of noise signals are generated based on the plurality of instantaneous frequencies. The plurality of generalized synchrosqueezing polynomial transform values of the plurality of noise signals can be generated based on a generalized synchrosqueezing operator and the plurality of instantaneous frequencies. The plurality of generalized synchrosqueezing polynomial transform values can also be inverse transformed to reconstruct the wellsite noise.

[0078] With the instantaneous frequencies, the result at the time-frequency location (t, ω) is superimposed at to obtain the generalized synchrosqueezing polynomial Chirplet transform value TP x (t, ω).

[0079] The generalized synchrosqueezing polynomial Chirplet transform is:

[0080]

[0081] where δ represents the Dirichlet function, is the generalized synchrosqueezing operator, which is used to squeeze to the estimated time-frequency location .

[0082] The inverse transform of TP x (t, ω) can be performed to reconstruct the wellsite noise x(t) with the following equation:

[0083]

[0084] In S210, feature indicators of the plurality of noise signals are calculated based on the plurality of generalized synchrosqueezing polynomial transform values. For example, time-domain feature indicators of the plurality of noise signals can be calculated based on the plurality of generalized synchrosqueezing polynomial transform values; frequency-domain feature indicators of the plurality of noise signals can be calculated based on the plurality of generalized synchrosqueezing polynomial transform values; time-frequency-domain feature indicators of the plurality of noise signals can be calculated based on the plurality of generalized synchrosqueezing polynomial transform values.

[0085] Feature indicators of different noise signals x i (t) are calculated: time-domain feature indicators (kurtosis indicator C lf , impulse indicator C f ), frequency-domain feature indicators (mean frequency ω mean represents the mean frequency of the signal x i (t), frequency bandwidth represents the frequency range of the signal x i (t) whose frequency is greater than the mean frequency), time-frequency-domain feature indicators (energy entropy H ω , power spectral density PSD):

[0086]

[0087]

[0088]

[0089]

[0090] wherein p j represents the proportion of the energy of the jth frequency to the energy.

[0091] In S212, a noise feature spectrum library is generated based on the feature indexes of the plurality of noise signals. The feature spectrum library is constructed, and different noise characteristics of the oilfield well site are analyzed.

[0092] According to the generation method of the well site noise feature spectrum library, the plurality of noise signals are generated by performing empirical Fourier decomposition on the well site noise to be analyzed; a plurality of polynomial transform values of the plurality of noise signals are calculated based on a polynomial transform formula; a plurality of instantaneous frequencies of the plurality of noise signals are generated based on the plurality of polynomial transform values; a plurality of generalized synchronous squeezing polynomial transform values of the plurality of noise signals are generated based on the plurality of instantaneous frequencies; the feature indexes of the plurality of noise signals are calculated based on the plurality of generalized synchronous squeezing polynomial transform values; and the noise feature spectrum library is generated based on the feature indexes of the plurality of noise signals. The way of generating the noise feature spectrum library based on the feature indexes of the plurality of noise signals can significantly improve the time-frequency energy focusing, analyze the well site noise in real time, and improve the fault diagnosis accuracy of the oilfield well site noise.

[0093] The generation method of the well site noise feature spectrum library can solve the problems of large deviation in instantaneous frequency estimation and poor stability of time-frequency focusing of high-order synchronous squeezing transformation in multi-component non-stationary nonlinear complex signals.

[0094] First, the original oilfield well site noise signal to be analyzed is input and acquired by using empirical Fourier decomposition transformation to obtain a single noise signal; according to the polynomial Chirplet transform principle, the polynomial Chirplet transform results of a plurality of single noise signals are calculated; then a suitable frequency filter is selected, and a kind of instantaneous frequency estimation quantity with statistical effect is constructed by convolution; and according to the synchronous squeezing principle, the polynomial Chirplet transform results are squeezed to the estimated instantaneous frequency to obtain the generalized synchronous squeezing polynomial Chirplet transform results; in addition, the original signal can also be reconstructed by inverse transformation; finally, the time domain, frequency domain and time-frequency domain feature indexes of different noise signals are calculated, and the oilfield well site noise feature spectrum library is constructed.

[0095] The well site noise feature atlas library generation method of the application can significantly improve the time-frequency energy focusing, obtain a strong and stable time-frequency representation result, provide a more comprehensive oil field well site noise feature information library, and thus provide a reliable basis for oil field well site fault diagnosis analysis.

[0096] It should be clearly understood that the application describes how to form and use specific examples, but the principles of the application are not limited to any details of these examples. On the contrary, based on the teachings of the disclosure of the application, these principles can be applied to many other embodiments.

[0097] Embodiment 3

[0098] Taking an analog oil field well site noise signal as an example, the well site noise signal is as shown in Figure 3 . Figure 4 are four single noise signals obtained after performing empirical Fourier decomposition transformation. Figure 5 is a time-frequency spectrum obtained by generalized synchronous extrusion polynomial Chirplet transformation. In the figure, the abscissa represents time, the ordinate represents frequency, and the right color bar represents energy value. Figure 6 is a feature index diagram extracted. The embodiment proves that the result diagram obtained after the method of the application is processed has higher time-frequency resolution and more concentrated energy, obtains a strong and stable time-frequency representation result, provides a more comprehensive oil field well site noise feature information library, and thus provides a reliable basis for oil field well site fault diagnosis analysis.

[0099] Those skilled in the art can understand that all or part of the steps of the above embodiments are implemented as computer programs executed by a CPU. When the computer program is executed by the CPU, the above functions defined by the above method provided by the application are executed. The program can be stored in a computer readable storage medium, which can be a read-only memory, a magnetic disk or an optical disk, etc.

[0100] In addition, it should be noted that the above figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the application, and are not for limiting purposes. It is easy to understand that the processes shown in the above figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be executed synchronously or asynchronously, for example, in multiple modules.

[0101] Embodiment 4

[0102] The following is a device embodiment of the application, which can be used to execute the method embodiments of the application. For details not disclosed in the device embodiments of the application, please refer to the method embodiments of the application.

[0103] Figure 7 is a block diagram of a well site noise feature atlas library generation device according to an exemplary embodiment. As Figure 7As shown, the well site noise feature atlas library generation device 70 comprises a decomposition module 702, a transformation module 704, an instantaneous module 706, a squeezing module 708, an index module 710, and an atlas module 712.

[0104] The decomposition module 702 is configured to perform empirical Fourier decomposition on the well site noise to be analyzed to generate a plurality of noise signals, and to obtain the well site noise based on a noise sensor.

[0105] The transformation module 704 is configured to calculate a plurality of polynomial transformation values of the plurality of noise signals based on a polynomial transformation formula, and to calculate a plurality of polynomial Chirplet transformation values of the plurality of noise signals based on a polynomial Chirplet transformation formula.

[0106] The instantaneous module 706 is configured to generate a plurality of instantaneous frequencies of the plurality of noise signals based on the plurality of polynomial transformation values, and to generate the plurality of instantaneous frequencies of the plurality of noise signals based on a frequency filter and the plurality of polynomial transformation values.

[0107] The squeezing module 708 is configured to generate a plurality of generalized synchronous squeezing polynomial transformation values of the plurality of noise signals based on the plurality of instantaneous frequencies, and to generate the plurality of generalized synchronous squeezing polynomial transformation values of the plurality of noise signals based on a generalized synchronous squeezing operator and the plurality of instantaneous frequencies.

[0108] The index module 710 is configured to calculate feature indexes of the plurality of noise signals based on the plurality of generalized synchronous squeezing polynomial transformation values, to calculate time-domain feature indexes of the plurality of noise signals based on the plurality of generalized synchronous squeezing polynomial transformation values, and / or to calculate frequency-domain feature indexes of the plurality of noise signals based on the plurality of generalized synchronous squeezing polynomial transformation values, and / or to calculate time-frequency-domain feature indexes of the plurality of noise signals based on the plurality of generalized synchronous squeezing polynomial transformation values.

[0109] The atlas module 712 is configured to generate a noise feature atlas library based on the feature indexes of the plurality of noise signals.

[0110] The generation device of the well site noise feature map library according to the application generates a plurality of noise signals by performing empirical Fourier decomposition on the well site noise to be analyzed; calculates a plurality of polynomial transformation values of the plurality of noise signals based on a polynomial transformation formula; generates a plurality of instantaneous frequencies of the plurality of noise signals based on the plurality of polynomial transformation values; generates a plurality of generalized synchronous squeezing polynomial transformation values of the plurality of noise signals based on the plurality of instantaneous frequencies; calculates feature indexes of the plurality of noise signals based on the plurality of generalized synchronous squeezing polynomial transformation values; and generates a noise feature map library based on the feature indexes of the plurality of noise signals. The way of generating the noise feature map library based on the feature indexes of the plurality of noise signals can significantly improve the time-frequency energy focusing, analyze the well site noise in real time, and improve the fault diagnosis precision of the oil field well site noise.

[0111] Embodiment 5

[0112] Figure 8 is a block diagram of an electronic device according to an exemplary embodiment.

[0113] The electronic device 800 according to this embodiment of the application will be described below with reference to Figure 8 Figure 8 The displayed electronic device 800 is only an example and should not impose any limitation on the function and use range of the embodiments of the application.

[0114] As shown in Figure 8 , the electronic device 800 is in the form of a general computing device. The components of the electronic device 800 can include, but are not limited to, at least one processing unit 810, at least one storage unit 820, a bus 830 connecting different system components (including the storage unit 820 and the processing unit 810), a display unit 840, etc.

[0115] The storage unit stores program codes which can be executed by the processing unit 810, so that the processing unit 810 performs the steps described in the specification according to various exemplary embodiments of the application. For example, the processing unit 810 can perform the steps as shown in Figure 2

[0116] The storage unit 820 can include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 8201 and / or a cache memory unit 8202, and can further include a read-only memory (ROM) 8203.

[0117] ​​The storage unit 820 can also include the programs / utilities 8204 having a set (at least one) of program modules 8205, such as an operating system, one or more application programs, other program modules, and program data, and each or a combination thereof can include an implementation of a network environment.

[0118] The bus 830 can be representative of one or more of several types of bus structures, including a storage unit bus or bus controller, a peripheral bus, a graphics acceleration port, a processing unit bus, or a local bus using any of a variety of bus architectures.

[0119] The electronic device 800 can also communicate with one or more external devices 800' such as a keyboard, a pointing device, a Bluetooth device, or a Universal Serial Bus (USB) device, among other devices. This communication can occur via the input / output (I / O) interface 850. Additionally, the electronic device 800 can communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or the Internet, among other networks, via the network adapter 860. The network adapter 860 can communicate with the other modules of the electronic device 800 via the bus 830. It should be appreciated that although not shown, other hardware and / or software modules could be used in conjunction with the electronic device 800. Such hardware would include, but is not limited to, a microcode, a device driver, a redundant processing unit, external disk drive arrays, a RAID system, a tape drive, and data archival storage system, among other modules.

[0120] From the foregoing description, it will be apparent to those skilled in the art that the example embodiments described herein can be implemented in software and / or hardware. Based on the description herein, a person of ordinary skill in the art will be able to implement the example embodiments without undue experimentation. Figure 9 As shown, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or a network, and includes a plurality of instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above-mentioned method according to the embodiments of the present application.

[0121] The software product can employ any combination of one or more computer-readable media. The computer-readable media can be a computer-readable storage medium or a computer-readable signal medium. The computer-readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection having one or more wires, a portable disc, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0122] The computer-readable storage medium can include data signals on a carrier wave, propagated over a propagation medium, in which the computer-readable program code is embodied. Such propagated signals can take a wide variety of forms, including but not limited to, electro-magnetic signals, optical signals, or any suitable combination thereof. The computer-readable storage medium can also be any computer-readable medium other than a computer-readable storage medium that can be a source of computer-readable program code, which can be read by the instruction execution system, apparatus, or device, and used, used in combination, or to manufacture the instruction execution system, apparatus, or device. The computer-readable program code embodied on the computer-readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0123] The program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++, or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider.

[0124] The computer readable medium carries one or more programs, when the one or more programs are executed by the device, the computer readable medium enables the following functions: performing empirical Fourier decomposition on wellsite noise to be analyzed to generate a plurality of noise signals; calculating a plurality of polynomial transform values of the plurality of noise signals based on a polynomial transform formula; generating a plurality of instantaneous frequencies of the plurality of noise signals based on the plurality of polynomial transform values; generating a plurality of generalized synchrosqueezing polynomial transform values of the plurality of noise signals based on the plurality of instantaneous frequencies; calculating feature indexes of the plurality of noise signals based on the plurality of generalized synchrosqueezing polynomial transform values; and generating a noise feature atlas library based on the feature indexes of the plurality of noise signals.

[0125] Those skilled in the art can understand that the above modules can be distributed in the device according to the description of the embodiments, and can also be changed in one or more devices different from the embodiments. The modules of the above embodiments can be combined into one module, or further split into multiple sub-modules.

[0126] Through the above description of the embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or a network, and includes a plurality of instructions to make a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) execute the method according to the embodiments of the present application.

[0127] The example embodiments of the present application are specifically shown and described above. It should be understood that the present application is not limited to the detailed structure, arrangement or implementation method described herein; on the contrary, the present application is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the appended claims.

Claims

1. A method of generating a library of wellsite noise signatures, the method comprising: The method comprises: performing empirical Fourier decomposition on wellsite noise to be analyzed to generate a plurality of noise signals; calculating a plurality of polynomial transform values of the plurality of noise signals based on a polynomial transform formula; generating a plurality of instantaneous frequencies of the plurality of noise signals based on the plurality of polynomial transform values; generating a plurality of generalized synchrosqueezed polynomial transform values of the plurality of noise signals based on the plurality of instantaneous frequencies; calculating feature indexes of the plurality of noise signals based on the plurality of generalized synchrosqueezed polynomial transform values; generating a noise feature atlas library based on the feature indexes of the plurality of noise signals.

2. The method of claim 1, wherein, The method of performing empirical Fourier decomposition on wellsite noise to be analyzed to generate a plurality of noise signals comprises: acquiring the wellsite noise based on a noise sensor; performing empirical Fourier decomposition on the wellsite noise to generate a plurality of noise signals with a single frequency.

3. The method of claim 1, wherein, The method of calculating a plurality of polynomial transform values of the plurality of noise signals based on a polynomial transform formula comprises: calculating a plurality of polynomial Chirplet transform values of the plurality of noise signals based on a polynomial Chirplet transform formula.

4. The method of claim 3, wherein, The method of calculating a plurality of polynomial Chirplet transform values of the plurality of noise signals based on a polynomial Chirplet transform formula comprises: determining a multi-kernel operator; calculating a plurality of polynomial Chirplet transform values of the plurality of noise signals based on the multi-kernel operator.

5. The method of claim 1, wherein, The method of generating a plurality of instantaneous frequencies of the plurality of noise signals based on the plurality of polynomial transform values comprises: generating the plurality of instantaneous frequencies of the plurality of noise signals based on a frequency filter and the plurality of polynomial transform values.

6. The method of claim 5, wherein, The method of generating the plurality of instantaneous frequencies of the plurality of noise signals based on a frequency filter and the plurality of polynomial transform values comprises: generating the plurality of instantaneous frequencies of the plurality of noise signals based on a Gaussian filter and the plurality of polynomial transform values.

7. The method of claim 1, wherein, The method of generating a plurality of generalized synchrosqueezed polynomial transform values of the plurality of noise signals based on the plurality of instantaneous frequencies comprises: generating a plurality of generalized synchrosqueezed polynomial transform values of the plurality of noise signals based on a generalized synchrosqueezing operator and the plurality of instantaneous frequencies.

8. The method of claim 7, wherein, The method further comprises: performing inverse transformation on the plurality of generalized synchrosqueezed polynomial transform values to reconstruct the wellsite noise.

9. The method of claim 1, wherein, The method of calculating feature indexes of the plurality of noise signals based on the plurality of generalized synchrosqueezed polynomial transform values comprises: calculating time-domain feature indexes of the plurality of noise signals based on the plurality of generalized synchrosqueezed polynomial transform values; and / or calculating frequency-domain feature indexes of the plurality of noise signals based on the plurality of generalized synchrosqueezed polynomial transform values; and / or calculating time-frequency-domain feature indexes of the plurality of noise signals based on the plurality of generalized synchrosqueezed polynomial transform values.

10. A device for generating a well site noise characteristic spectrum library, characterized in that, The method comprises: a decomposition module configured to perform empirical Fourier decomposition on wellsite noise to be analyzed to generate a plurality of noise signals; a transform module configured to calculate a plurality of polynomial transform values of the plurality of noise signals based on a polynomial transform formula; an instantaneous module configured to generate a plurality of instantaneous frequencies of the plurality of noise signals based on the plurality of polynomial transform values; an extrusion module configured to generate a plurality of generalized synchronous extrinsic polynomial transform values of the plurality of noise signals based on the plurality of instantaneous frequencies; an index module configured to calculate feature indexes of the plurality of noise signals based on the plurality of generalized synchronous extrinsic polynomial transform values; a graph module configured to generate a noise feature graph library based on the feature indexes of the plurality of noise signals.

11. An electronic device, comprising: comprising: one or more processors; a memory device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1-9.

12. A computer readable medium having stored thereon a computer program, characterized in that, the program is executed by the processor to implement the method according to any one of claims 1-9. the program is executed by the processor to implement the method according to any one of claims 1-9.

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