A method and device for judging the sand production degree of a gas well

Abstract

The present application discloses a method and device for discriminating the sand production degree of a gas well, which relates to the technical field of oil and gas exploration and development. By decomposing the oil pressure signal at the wellhead of the gas well, extracting the effective intrinsic mode function components, and constructing a Hilbert three-dimensional spectrogram by integrating the time-frequency domain characteristics, the accurate discrimination of the sand production degree is realized. Specifically, it includes: decomposing the oil pressure signal and screening the effective intrinsic mode function components with a preset proportion of high energy density and high frequency concentration in the front; generating an effective modal combined amplitude diagram to capture the amplitude abnormal fluctuation events; drawing a modal frequency diagram to identify the dominant frequency components; analyzing the envelope spectrum numerical diagram to quantify the change of energy distribution; combining the information of the three to form a Hilbert three-dimensional spectrogram, and dynamically judging the sand production degree based on the envelope spectrum value threshold. Compared with the traditional differential pressure limit method, the present application effectively reduces the interference of formation pressure fluctuation, sensor noise, etc. through multi-dimensional feature fusion, and provides a reliable basis for formulating sand control measures in the process of gas field development.

Description

Technical Field

[0001] This application relates to the technical field of oil and gas exploration and development, and particularly to a method and device for discriminating the sand production degree of gas wells. Background Art

[0002] During the development of gas fields, sand production is a common and complex problem, which is directly related to the production efficiency of gas wells, the service life of equipment, and safety production. Accurately judging the sand production degree of gas wells is of great significance for formulating effective sand control measures, optimizing production parameters, and ensuring the long-term stable operation of gas wells.

[0003] Traditional methods for discriminating the sand production degree mainly rely on the pressure difference limit of the wellhead oil pressure signal for judgment, that is, by setting a specific oil pressure fluctuation range as the discrimination basis. However, this method only judges through a single oil pressure fluctuation index and is easily interfered by various non-sand production factors. For example, non-sand production factors such as formation pressure changes (such as production interference from adjacent wells), equipment failures (abnormal pump valves), and sensor noise interfere, resulting in misjudgment or missed judgment. In addition, during the sand production process, there is a coupling of high-frequency particle collision signals and low-frequency pressure fluctuations, and it is difficult for existing technologies to effectively separate the mode mixing components, resulting in the loss of key features.

[0004] Therefore, traditional methods for discriminating the sand production degree based on the pressure difference limit have problems such as poor stability of mode decomposition (such as sensitivity to noise) and insufficient feature fusion. There is an urgent need for a method for discriminating gas well sand production that can comprehensively consider the characteristics in the time domain, frequency domain, and energy domain to improve the accuracy and reliability of sand production discrimination. Summary of the Invention

[0005] In the embodiments of this application, by providing a method for discriminating the sand production degree of gas wells, the problem of insufficient accuracy in discriminating the sand production degree in the prior art is solved.

[0006] First aspect, an embodiment of the present application provides a method for discriminating the sand production degree of a gas well. The method includes: obtaining the oil pressure signal at the wellhead of the gas well; decomposing the oil pressure signal at the wellhead of the gas well to obtain multiple intrinsic mode function components, and extracting effective intrinsic mode function components from the multiple intrinsic mode function components; wherein each effective intrinsic mode function component includes information on different frequency components in the oil pressure signal at the wellhead of the gas well; combining the effective intrinsic mode function components to form an effective mode combined amplitude diagram to display the amplitude changes of different frequency components over time; judging the change of the amplitude in the effective mode combined amplitude diagram, and if the amplitude shows abnormal fluctuations, determining that a sand production event has occurred; determining the sand production time by comparing the amplitude changes of different effective intrinsic mode function components; drawing the frequency distribution diagram of different effective intrinsic mode function components to obtain a modal frequency diagram, and judging the frequency component that dominates in the sand production event; obtaining the envelope line of the effective intrinsic mode function component and performing spectral analysis on it to obtain an envelope spectrum numerical diagram to display the energy distribution of the oil pressure signal at the wellhead of the gas well at different frequencies; comparing the envelope spectrum numerical diagrams of different time periods to judge the change trend of the sand production degree; combining the amplitude changes in the effective mode combined amplitude diagram, the frequency component distribution characteristics of the modal frequency diagram, and the information of the envelope spectrum numerical diagram to form a Hilbert three-dimensional spectrum diagram, setting a preset rule based on the Hilbert three-dimensional spectrum diagram, and judging the sand production degree according to the preset rule.

[0007] In a possible implementation manner, the decomposing the oil pressure signal at the wellhead of the gas well to obtain multiple intrinsic mode function components includes: decomposing Gaussian white noise to obtain multiple intrinsic mode components of Gaussian white noise; adding the oil pressure signal at the wellhead of the gas well to each of the multiple intrinsic mode components of Gaussian white noise respectively to form multiple extended signals; performing the first decomposition on the first extended signal to obtain the first residual component, and obtaining the first intrinsic mode function component through the difference between the oil pressure signal at the wellhead of the gas well and the first residual component; sequentially performing the decomposition processing steps on the subsequent extended signals and residual components, taking the previous residual component as the basis, adding a new intrinsic mode component of Gaussian white noise to form a new extended signal, performing decomposition on the new extended signal to obtain a new residual component, and obtaining the next intrinsic mode function component through the difference between the two consecutive residual components; iteratively executing the decomposition processing steps until a predefined stop criterion is met to obtain multiple intrinsic mode function components.

[0008] In a possible implementation manner, the extracting effective intrinsic mode function components from the multiple intrinsic mode function components includes: sorting the multiple intrinsic mode function components in descending order according to the signal density; selecting the first preset proportion of the sorted intrinsic mode function components as the effective intrinsic mode function components.

[0009] In a possible implementation, the signal density reflects the energy concentration and change rate of the intrinsic mode function components in the time-frequency domain; the signal density is defined using a weighted comprehensive score; the calculation method of the weighted comprehensive score is as follows: ; where is the weighted comprehensive score, is the energy proportion of the th intrinsic mode function component, is the frequency concentration, is the time-domain fluctuation intensity, , and are the weight coefficients; ; where is the th intrinsic mode function component, is the oil wellhead pressure signal; ; where is the main frequency bandwidth, , is the maximum instantaneous frequency of the th intrinsic mode function component, is the minimum instantaneous frequency of the th intrinsic mode function component; ; where is the total number of signal sampling points, is the index, is the time-domain mean value of the th intrinsic mode function component, The larger it is, the stronger the fluctuation.

[0010] In a possible implementation, each effective intrinsic mode function component includes information on different frequency components in the oil wellhead pressure signal, including: performing a Hilbert transform operation on the effective intrinsic mode function component; constructing an analytic function based on the result of the Hilbert transform operation; where the analytic function includes the amplitude and phase information of the oil wellhead pressure signal; extracting the phase information from the analytic function and obtaining the instantaneous frequency based on the phase information; obtaining the Hilbert spectrum according to the correspondence between the signal time, instantaneous frequency, and amplitude to determine the information on different frequency components in the oil wellhead pressure signal.

[0011] In a possible implementation, obtaining the envelope of the effective intrinsic mode function component and performing a spectral analysis on it to obtain an envelope spectrum numerical diagram includes: taking the modulus of the analytic function as the envelope signal; performing a Fourier transform on the envelope signal to convert the envelope signal from the time domain to the frequency domain to obtain the envelope of the effective intrinsic mode function component; performing a spectral analysis on the envelope of the intrinsic mode function component to obtain the envelope spectrum numerical diagram.

[0012] In a possible implementation, determining the sand production degree according to a preset rule includes: if the envelope spectrum value in the Hilbert three-dimensional spectrogram is less than 5, it is determined that the gas well is not producing sand during the production time; if the envelope spectrum value in the Hilbert three-dimensional spectrogram is greater than or equal to 5 and less than 15, it is determined that the sand production degree corresponding to the production time is mild sand production; if the envelope spectrum value in the Hilbert three-dimensional spectrogram is greater than or equal to 15 and less than 25, it is determined that the sand production degree corresponding to the production time is moderate sand production; if the envelope spectrum value in the Hilbert three-dimensional spectrogram is greater than or equal to 25, it is determined that the sand production degree corresponding to the production time is severe sand production; the preset rule is determined by mutual verification with the envelope spectrum values in the Hilbert three-dimensional spectrograms of a large number of gas wells with different proven sand production degrees.

[0013] In a second aspect, an embodiment of the present application provides a device for discriminating the sand production degree of a gas well, including: an acquisition module for acquiring the oil pressure signal at the wellhead of the gas well; a decomposition module for decomposing the oil pressure signal at the wellhead of the gas well to obtain a plurality of intrinsic mode function components, and extracting effective intrinsic mode function components from the plurality of intrinsic mode function components; wherein each effective intrinsic mode function component includes information on different frequency components in the oil pressure signal at the wellhead of the gas well; a combination module for combining the effective intrinsic mode function components to form an effective mode combined amplitude diagram to display the amplitude change of different frequency components over time; a determination module for judging the change of the amplitude in the effective mode combined amplitude diagram, and if the amplitude shows abnormal fluctuations, determining that a sand production event has occurred; a determination module for determining the sand production time by comparing the amplitude changes of different effective intrinsic mode function components; a drawing module for drawing the frequency distribution diagram of different effective intrinsic mode function components to obtain a modal frequency diagram and judging the frequency component that dominates in the sand production event; an analysis module for obtaining the envelope line of the effective intrinsic mode function component and performing spectral analysis on it to obtain an envelope spectrum numerical diagram to display the energy distribution of the oil pressure signal at the wellhead of the gas well at different frequencies; a comparison module for comparing the envelope spectrum numerical diagrams of different time periods to judge the change trend of the sand production degree; a judgment module for combining the amplitude change in the effective mode combined amplitude diagram, the frequency component distribution characteristics of the modal frequency diagram, and the information of the envelope spectrum numerical diagram to form a Hilbert three-dimensional spectrogram, setting a preset rule based on the Hilbert three-dimensional spectrogram, and judging the sand production degree according to the preset rule.

[0014] In a third aspect, an embodiment of the present application provides a server for discriminating the sand production degree of a gas well, including a memory and a processor; the memory is used to store computer-executable instructions; the processor is used to execute the computer-executable instructions to implement the method described in the first aspect or any possible implementation manner of the first aspect.

[0015] Fourthly, an embodiment of the present application provides a computer-readable storage medium storing executable instructions, and when a computer executes the executable instructions, the method described in the first aspect or any possible implementation manner of the first aspect can be implemented.

[0016] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects: The embodiments of the present application provide a method for discriminating the sand production degree of a gas well. The prior art only relies on a single index, i.e., the pressure difference limit of the wellhead oil pressure signal, and it is difficult to comprehensively reflect the complex situation of sand production in the gas well. In the present application, the wellhead oil pressure signal of the gas well is decomposed to obtain a plurality of intrinsic mode function components, and the effective intrinsic mode function components containing information of different frequency components are extracted. This multi-component analysis method can carefully analyze the oil pressure signal from multiple frequency dimensions, comprehensively capture the characteristic information at different frequencies in the signal, avoid important sand production-related characteristics that may be ignored by a single index, and provide a rich and comprehensive data basis for accurately judging the sand production situation subsequently. The effective intrinsic mode function components are combined to form an effective mode combined amplitude diagram to display the amplitude change, a modal frequency diagram is drawn to judge the dominant frequency component, an envelope line is obtained for spectrum analysis to obtain an envelope spectrum numerical diagram to display the energy distribution, and finally, these information are combined to form a Hilbert three-dimensional spectrum diagram and preset rules are set to judge the sand production degree. This multi-dimensional comprehensive judgment method comprehensively considers the sand production situation from multiple key angles such as amplitude, frequency, and energy distribution, effectively overcomes the problems of misjudgment or missed judgment caused by the interference of non-sand production factors such as natural changes in formation pressure, production equipment failures, and sensor errors in the prior art due to a single index, and improves the accuracy and reliability of sand production judgment. The present application can not only determine the sand production event by judging the abnormal fluctuation of the amplitude in the effective mode combined amplitude diagram, but also determine the sand production time by comparing the amplitude changes of different effective intrinsic mode function components. At the same time, by comparing the envelope spectrum numerical diagrams of different time periods, the change trend of the sand production degree can be judged. This accurate positioning and analysis ability of the sand production time and degree are difficult to achieve by the prior art relying only on the pressure difference limit, making the sand production judgment result more accurate and detailed, and providing a strong basis for formulating targeted sand control measures. The present application can quickly and efficiently analyze and process the wellhead oil pressure signal of the gas well. Compared with the prior art that requires a large number of repeated tests for judgment, the present application shortens the time for signal processing and analysis, improves the efficiency of sand production judgment, and reduces the consumption of manpower and material resources. Preset rules are set based on the Hilbert three-dimensional spectrum diagram, and the sand production degree can be quickly judged according to the preset rules, making the sand production judgment process more efficient and objective, and capable of giving an accurate judgment result in a short time, providing timely support for decision-making in the gas field development process. It solves the problem of insufficient accuracy in discriminating the sand production degree in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description in the embodiments of the present application or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of a method for judging the sand production degree of a gas well provided by an embodiment of the present application;

[0019] Figure 2 It is a schematic diagram of the wellhead oil pressure signal of Well A806 provided by an embodiment of the present application;

[0020] Figure 3 It is a schematic diagram of multiple intrinsic mode function components of the wellhead oil pressure signal of Well A806 obtained by an embodiment of the present application;

[0021] Figure 4 It is a schematic diagram of an effective modal combination amplitude diagram provided by an embodiment of the present application;

[0022] Figure 5 It is a Hilbert two-dimensional spectrogram obtained by performing a Hilbert transform operation on the effective modal amplitude provided by an embodiment of the present application;

[0023] Figure 6 It is a fluctuation curve diagram of four sets of randomly distributed well oil pressure numbers constructed by an embodiment of the present application;

[0024] Figure 7 It is a three-dimensional signal noise reduction decomposition modal diagram obtained by performing ICEEMDAN decomposition on four sets of randomly distributed well oil pressure numbers provided by an embodiment of the present application;

[0025] Figure 8 It is a schematic diagram of multiple intrinsic mode function components of four sets of randomly distributed well oil pressure numbers obtained by the ICEEMDAN algorithm provided by an embodiment of the present application;

[0026] Figure 9 It is a judgment diagram of the sand production time and sand production degree provided by an embodiment of the present application;

[0027] Figure 10 It is a schematic diagram of a device for judging the sand production degree of a gas well provided by an embodiment of the present application;

[0028] Figure 11 It is a schematic diagram of a server for judging the sand production degree of a gas well provided by an embodiment of the present application. Detailed implementation manners

[0029] 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 part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0030] The following explains some of the technologies related to the embodiments of the present application to facilitate understanding. It should be considered that they are merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described here without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, some descriptions of well-known functions and structures are omitted in the following description.

[0031] The embodiments of the present application propose a method for discriminating the sand production degree of a gas well, and its process is as Figure 1 shown, covering steps S101 to S109.

[0032] It should be emphasized that Figure 1 the shown execution order of the steps is only an exemplary illustration, and not the only feasible execution path of the method. On the premise of ensuring the accuracy of the final discrimination result, the execution order of each step can be flexibly adjusted according to the requirements of the actual application scenario, and even some steps can be implemented in parallel. This design endows the method with high flexibility and adaptability, enabling it to better meet the sand production discrimination requirements under different gas well conditions and production environments, and providing a more efficient and reliable solution for sand production monitoring in gas field development.

[0033] S101: Obtain the oil pressure signal at the wellhead of the gas well.

[0034] Specifically, the oil pressure signal at the wellhead of the gas well is one of the important parameters reflecting the production status of the gas well, which contains the change of the oil pressure during the production process of the gas well. The oil pressure value at the wellhead of the gas well can be measured in real time and accurately by installing a pressure sensor at the wellhead of the gas well.

[0035] S102: Decompose the oil pressure signal at the wellhead of the gas well to obtain multiple intrinsic mode function components, and extract the effective intrinsic mode function components from the multiple intrinsic mode function components. Among them, each effective intrinsic mode function component includes information on different frequency components in the oil pressure signal at the wellhead of the gas well.

[0036] Decompose the wellhead oil pressure signal of the gas well to obtain multiple intrinsic mode function components, including: decompose Gaussian white noise to obtain multiple intrinsic mode components of Gaussian white noise. Add the wellhead oil pressure signal of the gas well to the multiple intrinsic mode components of Gaussian white noise respectively to form multiple extended signals. Perform the first decomposition on the first extended signal to obtain the first residual component, and obtain the first intrinsic mode function component through the difference between the wellhead oil pressure signal of the gas well and the first residual component. Sequentially perform the decomposition processing steps on the subsequent extended signals and residual components. Based on the previous residual component, add the intrinsic mode component of new Gaussian white noise to form a new extended signal, decompose the new extended signal to obtain a new residual component, and obtain the next intrinsic mode function component through the difference between the two consecutive residual components. Iteratively execute the decomposition processing steps until the predefined stop criterion is met to obtain multiple intrinsic mode function components.

[0037] Specifically, for the decomposition of the wellhead oil pressure signal of the gas well in this application, the improved complete ensemble empirical mode decomposition algorithm with adaptive noise (English: Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, abbreviated as ICEEMDAN) can be selected. The ICEEMDAN algorithm is a decomposition method specifically used for processing non-linear and non-stationary signals. During the production process, the wellhead oil pressure signal of the gas well often exhibits strong non-stationarity, which is highly similar to traditional vibration signals.

[0038] Furthermore, decomposing Gaussian white noise to obtain multiple intrinsic mode components of Gaussian white noise is . Among them, is a positive integer representing the total number of intrinsic mode components decomposed from Gaussian white noise.

[0039] The expression for adding the wellhead oil pressure signal of the gas well to the multiple intrinsic mode components of Gaussian white noise respectively to form multiple extended signals is: ; where is the extended signal, is the wellhead oil pressure signal of the gas well, indicating the change of the oil pressure of the gas well over time , , is a constant with a value of 0.02 - 0.2, is the standard deviation of the wellhead oil pressure signal of the gas well, is the standard deviation of the intrinsic mode component of Gaussian white noise, is an index variable, is the th intrinsic mode component of Gaussian white noise. ​

[0040] Furthermore, the first extended signal is subjected to the first decomposition to obtain the first residual component as . . Among them, represents the extended signal minus the first intrinsic mode function component obtained by decomposing the extended signal . The first intrinsic mode function component is the difference between the wellhead oil pressure signal of the gas well and the first residual component, that is , is the number of extended signal samples used to calculate the residual component, is the index variable.

[0041] The second residual component is calculated as: . Among them, , is the standard deviation of the first residual component , is the intrinsic mode component of the second Gaussian white noise, , is the second intrinsic mode function component, is the second residual component.

[0042] And so on, the th residual component is calculated in the same way: . , is the th residual component 's standard deviation, is the th Gaussian white noise's intrinsic mode component, . is the th intrinsic mode function component, is the th residual component. After that, the th Gaussian white noise's intrinsic mode function component can be deduced.

[0043] Specifically, the predefined stopping criterion can be to set a maximum number of iterations and when the residual component is a monotonic function. In practical applications, these two stopping criteria can be used in combination.

[0044] A relatively large maximum number of iterations can be set as a safety margin first. Meanwhile, after each iteration, check whether the residual component is a monotonic function. If the residual component becomes a monotonic function, stop the decomposition process in advance; if the monotonicity condition is still not satisfied after reaching the maximum number of iterations, force it to stop and output the current decomposition result.

[0045] Furthermore, the maximum number of iterations can be 50 times, and the residual component being a monotonic function is defined as: if the amplitude change amount of the residual component between adjacent two iterations is less than 0.1% of the current amplitude in three consecutive iterations of the residual, it can be regarded as approximately monotonic.

[0046] Check the instantaneous frequency stability of the th intrinsic mode function component to ensure no mode aliasing (such as high-frequency noise not being mixed into the low-frequency intrinsic mode function component). Verify through the reconstruction error: . Among them, is the wellhead oil pressure signal of the gas well, is the th intrinsic mode function component, is the th residual component.

[0047] Extract the effective intrinsic mode function components from multiple intrinsic mode function components, including: sort the multiple intrinsic mode function components in descending order according to the signal density. Select the first preset proportion of the intrinsic mode function components after sorting as the effective intrinsic mode function components.

[0048] The signal density reflects the energy concentration and change rate of the intrinsic mode function component in the time-frequency domain.

[0049] Use the weighted comprehensive score as the specific manifestation of the signal density.

[0050] The calculation method of the weighted comprehensive score is: . Among them, is the weighted comprehensive score, is the energy proportion of the th intrinsic mode function component, is the frequency concentration degree, is the time-domain fluctuation intensity, , and are the weight coefficients. The default values of the weight coefficients are , , .

[0051] It should be noted that the default value of the weight coefficient is determined through verification of a large amount of experimental data, balancing the comprehensive effects of energy, frequency concentration, and time-domain fluctuations.

[0052] . Among them, is the th intrinsic mode function component, is the oil wellhead pressure signal of the gas well.

[0053] . Among them, is the main frequency bandwidth, , is the maximum instantaneous frequency of the th intrinsic mode function component, is the minimum instantaneous frequency of the th intrinsic mode function component.

[0054] . Among them, is the total number of signal sampling points, is the index, is the time-domain mean value of the th intrinsic mode function component, The larger it is, the stronger the fluctuation.

[0055] Specifically, the preset ratio can be 40%. When there are 10 intrinsic mode function components, the first 4 are selected as effective intrinsic mode function components according to the preset ratio. When the number of effective intrinsic mode function components selected according to the preset ratio is not an integer, rounding can be performed.

[0056] Each effective intrinsic mode function component includes information on different frequency components in the oil wellhead pressure signal of the gas well, including: performing a Hilbert transform operation on the effective intrinsic mode function component. Based on the result of the Hilbert transform operation, an analytic function is constructed. Among them, the analytic function includes the amplitude and phase information of the oil wellhead pressure signal of the gas well. Extract the phase information from the analytic function, and based on the phase information, obtain the instantaneous frequency. According to the correspondence relationship between the signal time, instantaneous frequency, and amplitude, obtain the Hilbert spectrum to determine the information on different frequency components in the oil wellhead pressure signal of the gas well.

[0057] Specifically, the expression for performing a Hilbert transform operation on the effective intrinsic mode function component is: . Among them, is the result of performing a Hilbert transform operation on the effective intrinsic mode function component, is an effective intrinsic mode function component of the oil wellhead pressure signal of the gas well, is 's Hilbert transform signal, is the time variable, is the signal time, is the kernel function of the Hilbert transform operation.

[0058] Specifically, based on the result of the Hilbert transform operation, the expression for constructing the analytic function is: . Wherein, is the analytic function, is the amplitude of the analytic function, representing the instantaneous amplitude of the th effective intrinsic mode function component at time, , is the phase of the analytic function, representing the instantaneous phase of the th effective intrinsic mode function component at time, , is the imaginary unit, satisfying , is the complex exponential function, representing the rotation factor with a phase of .

[0059] Specifically, the phase information is extracted from the analytic function, and the expression for obtaining the instantaneous frequency based on the phase information is: . Wherein, is the instantaneous frequency of the th effective intrinsic mode function component at time, is a constant factor used to convert the rate of change of the phase into frequency, is the phase of the analytic function derivative with respect to the signal time , that is, the rate of change of the phase of the th effective intrinsic mode function component at time.

[0060] Specifically, according to the correspondence relationship between the signal time, the instantaneous frequency, and the amplitude, the Hilbert spectrum is obtained to determine the information of different frequency components in the wellhead oil pressure signal of the gas well.

[0061] The expression of the Hilbert spectrum is: . Wherein, is the Hilbert spectrum, is to take the real part of the complex number, is the total number of effective intrinsic mode function components, is the index variable, is the frequency variable.

[0062] S103: Combine the effective intrinsic mode function components to form an effective mode combined amplitude diagram to display the amplitude change of different frequency components over time.

[0063] Specifically, in order to effectively judge the sand production situation, a method of combining effective intrinsic mode function components into an effective modal combined amplitude diagram is adopted. This step not only reveals the variation law of the amplitudes of different frequency components in the signal over time, but also provides a key basis for identifying sand production characteristics.

[0064] S104: Judge the variation of the amplitude in the effective modal combined amplitude diagram. If the amplitude shows abnormal fluctuations, it is determined that a sand production event has occurred. Specifically, the abnormal fluctuations of the amplitude can be that the change of the amplitude is greater than or equal to 2 or less than or equal to -2.

[0065] S105: Determine the sand production time by comparing the amplitude variations of different effective intrinsic mode function components.

[0066] Specifically, focus on whether the amplitude suddenly increases or decreases at a certain moment. Such abnormal fluctuations are caused by sand production events inside the gas well, because sand production will cause significant changes in the frequency components and amplitudes of the oil pressure signal at the wellhead of the gas well. Analyze the amplitude variations of different effective intrinsic mode function components at the same time point.

[0067] Since different effective intrinsic mode function components correspond to different frequency ranges in the oil pressure signal at the wellhead of the gas well, their responses to sand production events may vary. By comparing the amplitude variations of multiple effective intrinsic mode function components, the specific time point when the sand production event occurs can be determined more accurately, and the influence degree of the sand production event on different frequency components of the oil pressure signal at the wellhead of the gas well can be understood.

[0068] S106: Plot the frequency distribution diagrams of different effective intrinsic mode function components to obtain the modal frequency diagram, and judge the frequency components that dominate in the sand production event.

[0069] Specifically, by analyzing the frequency distribution of different effective intrinsic mode function components in the modal frequency diagram, it can be clarified that when the sand production event occurs, the amplitudes of certain frequency components increase significantly. This indicates that these frequency components dominate in the sand production event.

[0070] S107: Obtain the envelope of the effective intrinsic mode function component and perform spectral analysis on it to obtain the envelope spectrum numerical diagram to show the energy distribution of the oil pressure signal at the wellhead of the gas well at different frequencies.

[0071] Obtaining the envelope of the effective intrinsic mode function component and performing spectral analysis on it to obtain the envelope spectrum numerical diagram includes the following steps.

[0072] Take the modulus of the analytical function as the envelope signal. Perform Fourier transform on the envelope signal to convert the envelope signal from the time domain to the frequency domain, so as to obtain the envelope line of the effective intrinsic mode function component. Conduct spectral analysis on the envelope line of the intrinsic mode function component to obtain the envelope spectrum numerical graph.

[0073] Specifically, the envelope signal reflects the variation trend of the wellhead oil pressure signal of the gas well on a long time scale and is an embodiment of the overall characteristics of the signal. The envelope line of the effective intrinsic mode function component can show the energy distribution of the wellhead oil pressure signal of the gas well at different frequencies.

[0074] The result of the spectral analysis is the envelope spectrum numerical graph, which reflects the change of the frequency components of the wellhead oil pressure signal of the gas well over time and is an important means for analyzing non-stationary signals.

[0075] Furthermore, before performing Fourier transform on the envelope signal, the amplitude spectrum of the envelope signal can also be calculated. In order to eliminate high-frequency noise and fast-changing parts, and thus better highlight the long-time scale characteristics of the wellhead oil pressure signal of the gas well, smooth the amplitude spectrum to make the envelope signal smoother and reduce noise interference. Low-pass filtering is an effective means of smoothing, which can help remove high-frequency noise and retain the main components of the signal. After the initial smoothing process of the amplitude spectrum, in order to further extract the overall trend of the signal, smoothing can be performed again, which helps to make the envelope signal clearer and smoother for subsequent analysis.

[0076] S108: Compare the envelope spectrum numerical graphs of different time periods to judge the change trend of the sand production degree.

[0077] Specifically, the time period can be divided based on the actual operating conditions and monitoring requirements of the gas well, such as dividing by time units such as days, weeks or months. Compare the envelope spectrum numerical graphs of different time periods to obtain the energy distribution of each frequency component.

[0078] By comparing the envelope spectra of different time periods, it can be identified which frequency components' energy changes over time, as well as the direction and amplitude of these changes. According to the change of the energy of the frequency components in the envelope spectrum numerical graph, judge the change trend of the sand production degree. If the energy of certain frequency components continues to increase in multiple time periods, it indicates that the sand production degree is intensifying. On the contrary, if the energy of certain frequency components decreases, it indicates that the sand production degree is alleviating or tending to be stable.

[0079] When comparing the envelope spectrum numerical graphs, those frequency components that may increase significantly during sand production events should be focused on. The energy of a frequency component refers to the signal energy in a certain frequency or frequency range, which reflects the intensity or amplitude size of the signal at that frequency.

[0080] S109: Combine the amplitude changes in the effective modal combination amplitude diagram, the frequency component distribution characteristics of the modal frequency diagram, and the information in the envelope spectrum numerical diagram to form a Hilbert three-dimensional spectrum diagram. Set a preset rule based on the Hilbert three-dimensional spectrum diagram, and judge the sand production degree according to the preset rule.

[0081] Judging the sand production degree according to the preset rule includes: If the envelope spectrum value in the Hilbert three-dimensional spectrum diagram is less than 5, it is judged that the gas well is not producing sand during the production time. If the envelope spectrum value in the Hilbert three-dimensional spectrum diagram is greater than or equal to 5 and less than 15, it is judged that the sand production degree corresponding to the production time is mild sand production. If the envelope spectrum value in the Hilbert three-dimensional spectrum diagram is greater than or equal to 15 and less than 25, it is judged that the sand production degree corresponding to the production time is moderate sand production. If the envelope spectrum value in the Hilbert three-dimensional spectrum diagram is greater than or equal to 25, it is judged that the sand production degree corresponding to the production time is severe sand production.

[0082] The preset rule is determined by mutual verification with the envelope spectrum values in the Hilbert three-dimensional spectrum diagrams of a large number of gas wells with proven different sand production degrees.

[0083] Specifically, for example, there have been 50 gas wells with moderate sand production in a certain block historically, and the range of envelope spectrum values obtained by this application is between 15 - 25. The sand production degrees of other types are also determined by the method of this application.

[0084] In the Hilbert three-dimensional spectrum analysis, in order to more intuitively compare the relative strengths of different frequency components, the wellhead oil pressure signal of the gas well will be normalized. This processing can more clearly show the relative strength relationship between frequency components, which helps to more accurately analyze the characteristics of the signal. The normalized envelope spectrum values are dimensionless, and they represent the energy distribution of the wellhead oil pressure signal of the gas well at different frequencies.

[0085] Specifically, the starting time of sand production can be judged according to the starting time of the abnormal increase in amplitude in the amplitude diagram, combined with the time points of significant changes in the energy of frequency components in the frequency diagram and the envelope spectrum diagram. The ending time of sand production can be judged by determining whether the amplitude, frequency, and energy changes tend to be stable or return to normal.

[0086] Regarding the sand production phenomenon occurring in Well A806, and based on the obtained sand samples, the method of this application based on the envelope spectrum value in the Hilbert three-dimensional spectrum diagram is used to judge the sand production degree and the sand production time. The ICEEMDAN decomposition is performed on the wellhead oil pressure signal of Well A806 to obtain the effective intrinsic mode function components.

[0087] Figure 2 This is a schematic diagram of the wellhead oil pressure signal of Well A806 provided by the embodiment of this application. The abscissa represents the production time, and the ordinate represents the oil pressure.

[0088] Figure 2The purpose of interpolating data is to transform the actually possibly discontinuous production data into continuous data, which is convenient for the next step of modal decomposition. Since the production data related to the wellhead oil pressure signal of the gas well actually collected may be discontinuous or non - continuous due to sensor failures, transmission losses, etc. These discontinuity points will interfere with the subsequent signal processing and analysis processes. Therefore, the purpose of interpolating data is to transform the actually possibly discontinuous production data into continuous data, which is convenient for the next step of modal decomposition. Modal decomposition (ICEEMDAN decomposition) is a technique that decomposes a complex signal into multiple simple modal functions. It usually requires the input signal to be continuous and smooth in order to effectively extract different characteristic components in the signal.

[0089] By interpolating data, it is possible to estimate or predict the values of missing data points using the existing data points, thereby transforming the discontinuous production data into a continuous data sequence. Interpolating data can fill in the missing values in the data, making the data sequence more complete and accurate. A continuous data sequence can ensure that the modal decomposition algorithm works effectively and extracts different modal components in the signal. A continuous data sequence is also convenient for other subsequent analyses, such as spectral analysis, time - frequency analysis, etc.

[0090] Figure 3 This is a schematic diagram of multiple intrinsic mode function components of the wellhead oil pressure signal of Well A806 obtained in the embodiment of the present application. The present application selects the first four intrinsic mode function components (Mode 1, Mode 2, Mode 3, Mode 4) as effective intrinsic mode function components. Figure 3 The horizontal and vertical coordinates of Figure 2 are consistent.

[0091] Figure 4 This is a schematic diagram of the effective modal combination amplitude diagram provided by the embodiment of the present application. By judging the change of the amplitude, it is possible to preliminarily judge the abnormal periods in the signal, and these periods are related to the sand production events. Figure 4 The abscissa in Figure 4 is the normalized production time, ranging from 0 to 1, representing the time proportion in the whole production process. Figure 4 The ordinate in

[0092] Figure 5 is the effective modal amplitude, representing the amplitude size of the signal at different times. Figure 5 Two periods with relatively large amplitude changes are circled in

[0092] Figure 5 This is the Hilbert two - dimensional spectrogram obtained by performing the Hilbert transform operation on the effective modal amplitude provided by the embodiment of the present application. Figure 5The abscissa in it is the normalized time, the ordinate is the modal frequency value, and different colors represent different envelope spectrum values. According to the change of frequency components in the Hilbert two-dimensional spectrogram and combined with the time period when the amplitude in the effective modal combination amplitude diagram increases abnormally, the sand production time can be judged. By analyzing the distribution characteristics of the envelope spectrum values corresponding to the modal frequencies, the sand production degree can be further determined. For Well A806, the analysis results of this application show that: sand production phenomenon began to occur in July 2015 and developed into severe sand production in August 2019.

[0093] By comparing the spectrum analysis results with the actual situation, the accuracy of the analysis results can be verified. For Well A806, the spectrum analysis results are consistent with the actual situation, proving the effectiveness of this application. This application can accurately identify the sand production time and sand production degree of gas wells, providing an important basis for taking timely sand control measures. By continuously monitoring and analyzing the wellhead oil pressure signal of gas wells, real-time monitoring and early warning of the sand production situation of gas wells can be realized, which helps to ensure the safe production of gas wells.

[0094] The following lists an embodiment to further illustrate the method for judging the sand production degree of gas wells in this application.

[0095] In order to simulate the oil pressure change situation under different sand production degrees, four groups of random distribution numbers of gas well oil pressure are constructed.

[0096] (1) 96 → 91, oil pressure signal fluctuation .

[0097] (2) 91 → 76, oil pressure signal fluctuation .

[0098] (3) 76 → 51, oil pressure signal fluctuation .

[0099] (4) 51 → 16, oil pressure signal fluctuation .

[0100] Specifically, the oil pressure signal fluctuation can also be understood as the oil pressure change.

[0101] Figure 6 is the fluctuation curve diagram of the four groups of well oil pressure random distribution numbers constructed for the embodiment of this application. This application takes the four groups of well oil pressure random distribution numbers constructed as input data, uses the ICEEMDAN method to decompose the oil pressure change signal, and obtains a series of intrinsic mode function (IMF) components. Each component contains the information of different frequency components in the original signal. Figure 6 The abscissa in it is the production time, and the ordinate is the oil pressure. Figure 6 Intuitively shows the fluctuation of the oil pressure over time.

[0102] Figure 7This is a three-dimensional signal denoising decomposition mode diagram obtained by performing ICEEMDAN decomposition on the random distribution numbers of the oil pressure of four groups of wells provided by the embodiments of this application. The X-axis represents the production time, indicating the change of the oil pressure signal over time. The Y-axis represents the mode number, indicating different intrinsic mode function components. The Z-axis represents the mode amplitude, indicating the amplitude size of each intrinsic mode function component.

[0103] Figure 7 There are multiple curves in it, and each curve represents the change of the amplitude of an intrinsic mode function component over time. Different intrinsic mode function components are distinguished by different colors. From Figure 7 it can be seen that the amplitudes of different intrinsic mode function components show different changing trends over time. The amplitudes of some components are larger in the initial stage of production and then gradually decrease; the amplitudes of some components remain relatively stable throughout the production period.

[0104] Figure 8 This is a schematic diagram of multiple intrinsic mode function (IMF) components of the random distribution numbers of the oil pressure of four groups of wells obtained by the ICEEMDAN algorithm provided by the embodiments of this application. These components are sorted in descending order according to the signal density, and each component accurately captures the dynamic characteristics in a specific frequency range of the oil pressure signal at the gas well wellhead.

[0105] Specifically, the high-frequency components, that is, the intrinsic mode function components with a larger signal density, can reflect the rapid pressure fluctuations during the gas well production process, while the low-frequency components, that is, the intrinsic mode function components with a smaller signal density, reveal the long-term trends and slow changes. Each IMF component not only shows a high degree of energy concentration in the time-frequency domain but also provides key sand production characteristic information through its rate of change. This multi-scale signal decomposition lays the foundation for subsequent multi-dimensional feature fusion analysis, enabling more accurate identification of sand production events and determination of the sand production degree.

[0106] Figure 9 This is a judgment diagram of the sand production time and the sand production degree provided by the embodiments of this application. Figure 9 In (a) of it is the effective mode combined amplitude diagram. Figure 9 In (b) of it is the Hilbert two-dimensional spectrum diagram. Figure 9Among them, (c) is the Hilbert three-dimensional spectrogram. The effective modal combination amplitude diagram shows the variation of the amplitudes of different effective intrinsic mode function components over time. The intervals of non-sanding, mild sanding, moderate sanding, and severe sanding are distinguished in the diagram. By judging the change of the amplitude, the start time and the degree of sanding can be determined. For example, a sudden increase in the amplitude indicates the occurrence of a sanding event. The Hilbert two-dimensional spectrogram shows the frequency and amplitude variations of the signal within a certain time period in the form of a two-dimensional diagram, which is used for further analysis of sanding characteristics. The distribution characteristics of sanding events in terms of frequency and time can be obtained through the two-dimensional diagram. That is, the Hilbert two-dimensional spectrogram shows the distribution characteristics of the frequency components of the modal frequency diagram. The Hilbert three-dimensional spectrogram shows the relationship between the frequency, time, and amplitude of the signal over the entire time period in the form of a three-dimensional diagram, providing more comprehensive sanding information. That is, the Hilbert three-dimensional spectrogram shows the information of the envelope spectrum numerical diagram. By combining the amplitude change in the effective modal combination amplitude diagram, the distribution characteristics of the frequency components of the modal frequency diagram, and the information of the envelope spectrum numerical diagram, the sanding time and the degree of sanding can be accurately determined.

[0107] Based on the previously established preset rules for the degree of sanding, this application made a comprehensive determination of the sanding time and the degree of sanding for 22 gas wells in Block A and 26 gas wells in Block B of a gas field in the Tarim Basin. By using the ICEEMDAN algorithm to decompose the wellhead oil pressure signal of the gas wells, the effective intrinsic mode function components containing information on different frequency components were extracted. Further, through the multi-dimensional feature analysis of the effective modal combination amplitude diagram, the modal frequency diagram, and the envelope spectrum numerical diagram, the accurate identification of sanding events was achieved.

[0108] Specifically, the effective modal combination amplitude diagram clearly shows the amplitude change of the wellhead signal of the gas wells, the modal frequency diagram reveals the distribution characteristics of the dominant frequency components in the sanding events, and the envelope spectrum numerical diagram quantifies the energy distribution at different frequencies. By integrating this information, this application not only accurately determines the occurrence time of the sanding events, but also quantitatively evaluates the degree of sanding, providing a scientific and reliable basis for the sanding monitoring and sand control measure formulation of the gas field.

[0109] Table 1 and Table 2 respectively show the sand production degree discrimination results of 22 gas wells in Block A and 26 gas wells in Block B by the method based on the envelope spectrum values in the Hilbert three-dimensional spectrogram of this application. The results show that the coincidence rate of the method of this application with the actual production situation reaches more than 90%, fully verifying its high reliability and accuracy in practical applications. Through multi-dimensional feature fusion (amplitude change, frequency distribution, energy distribution) and dynamic adaptability analysis, the method of this application can accurately identify sand production events and classify the degree of sand production, effectively overcoming the problems of misjudgment and missed judgment caused by single indicators in traditional methods. The high coincidence rate indicates that the method of this application can not only accurately reflect the actual sand production situation of gas wells, but also provide an innovative solution for the classification of sand production degree in gas field development. This achievement provides a scientific basis for formulating targeted sand control measures, optimizing production parameters, and ensuring the long-term stable operation of gas wells, and has important practical application value.

[0110] Table 1 Discrimination Results Table of Sand Production Degree of 22 Gas Wells in Block A

[0111]

[0112] Table 2 Discrimination Results Table of Sand Production Degree of 26 Gas Wells in Block B

[0113]

[0114] The prior art only relies on a single index, i.e., the differential pressure limit of the wellhead oil pressure signal, and it is difficult to comprehensively reflect the complex situation of sand production in gas wells. In this application, the wellhead oil pressure signal of the gas well is decomposed to obtain multiple intrinsic mode function components, and the effective intrinsic mode function components containing information of different frequency components are extracted. This multi-component analysis method can conduct a detailed analysis of the oil pressure signal from multiple frequency dimensions, comprehensively capture the characteristic information at different frequencies in the signal, avoid important sand production-related characteristics that may be ignored by a single index, and provide a rich and comprehensive data basis for accurately judging the sand production situation subsequently. The effective intrinsic mode function components are combined to form an effective mode combined amplitude diagram to display the amplitude change, a modal frequency diagram is drawn to judge the dominant frequency component, the envelope line is obtained for spectral analysis to get an envelope spectrum numerical diagram to display the energy distribution, and finally, these information are combined to form a Hilbert three-dimensional spectrum diagram and preset rules are set to judge the sand production degree. This multi-dimensional comprehensive judgment method comprehensively considers the sand production situation from multiple key angles such as amplitude, frequency, and energy distribution, effectively overcomes the problems of misjudgment or missed judgment caused by the interference of non-sand production factors such as natural changes in formation pressure, production equipment failures, and sensor errors in the prior art due to a single index, and improves the accuracy and reliability of sand production judgment. This application can not only determine the sand production event by judging the abnormal fluctuation of the amplitude in the effective mode combined amplitude diagram, but also determine the sand production time by comparing the amplitude changes of different effective intrinsic mode function components. At the same time, by comparing the envelope spectrum numerical diagrams of different time periods, the change trend of the sand production degree can be judged. This accurate positioning and analysis ability of the sand production time and degree are difficult to achieve by the prior art relying only on the differential pressure limit, making the sand production judgment result more accurate and detailed, and providing a strong basis for formulating targeted sand control measures. This application can quickly and efficiently analyze and process the wellhead oil pressure signal of the gas well. Compared with the method of the prior art that requires a large number of repeated tests for judgment, this application shortens the time for signal processing and analysis, improves the efficiency of sand production judgment, and reduces the consumption of manpower and material resources. Preset rules are set based on the Hilbert three-dimensional spectrum diagram, and the sand production degree can be quickly judged according to the preset rules, making the sand production judgment process more efficient and objective, and being able to give an accurate judgment result in a short time, providing timely support for decision-making in the process of gas field development. It solves the problem of insufficient accuracy in discriminating the sand production degree in the prior art.

[0115] An embodiment of this application also provides a device 1000 for discriminating the sand production degree of a gas well, as Figure 10 shown. The device includes: an acquisition module 1001, a decomposition module 1002, a combination module 1003, a determination module 1004, a determination module 1005, a drawing module 1006, an analysis module 1007, a comparison module 1008, and a judgment module 1009.

[0116] The acquisition module 1001 is used to acquire the wellhead oil pressure signal of the gas well.

[0117] The decomposition module 1002 is used to decompose the oil pressure signal at the wellhead of the gas well to obtain multiple intrinsic mode function components, and extract the effective intrinsic mode function components from the multiple intrinsic mode function components. Each effective intrinsic mode function component includes information on different frequency components in the oil pressure signal at the wellhead of the gas well.

[0118] The combination module 1003 is used to combine the effective intrinsic mode function components to form an effective mode combined amplitude diagram to display the amplitude changes of different frequency components over time.

[0119] The determination module 1004 is used to judge the change of amplitude in the effective mode combined amplitude diagram. If abnormal fluctuations occur in the amplitude, it is determined that a sand production event has occurred.

[0120] The determination module 1005 is used to determine the sand production time by comparing the amplitude changes of different effective intrinsic mode function components.

[0121] The plotting module 1006 is used to plot the frequency distribution diagrams of different effective intrinsic mode function components to obtain a modal frequency diagram and judge the frequency components that dominate in the sand production event.

[0122] The analysis module 1007 is used to obtain the envelope of the effective intrinsic mode function components and perform spectral analysis on it to obtain an envelope spectrum numerical diagram to display the energy distribution of the oil pressure signal at the wellhead of the gas well at different frequencies.

[0123] The comparison module 1008 is used to compare the envelope spectrum numerical diagrams of different time periods to judge the changing trend of the sand production degree.

[0124] The judgment module 1009 is used to combine the amplitude changes in the effective mode combined amplitude diagram, the frequency component distribution characteristics of the modal frequency diagram, and the information of the envelope spectrum numerical diagram to form a Hilbert three-dimensional spectrum diagram, set a preset rule based on the Hilbert three-dimensional spectrum diagram, and judge the sand production degree according to the preset rule.

[0125] Some modules in the device described in this application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. This application can also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0126] The devices or modules described in the above application embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. For the convenience of description, when describing the above devices, they are divided into various modules according to functions and described separately. When implementing the application embodiments, the functions of each module can be implemented in the same or multiple software and / or hardware. Of course, the module implementing a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0127] The methods, devices or modules described in this application can be implemented in the form of computer-readable program codes. The controller can be implemented in any appropriate manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program codes (such as software or firmware) executable by the (micro)processor, logic gates, switches, application specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program codes, the method steps can be logically programmed to enable the controller to be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, and embedded microcontrollers to achieve the same function. Therefore, such a controller can be regarded as a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and the structures within the hardware component.

[0128] As Figure 11 shown, the embodiment of the present application also provides a gas well sand production degree discrimination server, including a memory 1101 and a processor 1102; the memory 1101 is used to store computer-executable instructions; the processor 1102 is used to execute the computer-executable instructions to implement a gas well sand production degree discrimination method described above in the embodiment of the present application.

[0129] The embodiment of the present application also provides a computer-readable storage medium, and the computer-readable storage medium stores executable instructions. When a computer executes the executable instructions, it can implement a gas well sand production degree discrimination method described above in the embodiment of the present application.

[0130] As can be seen from the description of the above embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware. Based on such an understanding, the technical solution of this application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product or can also be reflected in the implementation process of data migration. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the method described in the embodiments of this application.

[0131] The various embodiments in this specification are described in a progressive manner. For the same or similar parts between the various embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments. All or part of this application can be used in many general or special computer system environments or configurations.

[0132] The above embodiments are only used to illustrate the technical solution of this application, rather than to limit this application; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solution recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solution to deviate from the scope of the technical solution of this application.

Claims

1. A method for judging the degree of sand production in a gas well, characterized in that, Including: Obtaining the oil pressure signal at the wellhead of the gas well; Using the ICEEMDAN algorithm to decompose the oil pressure signal at the wellhead of the gas well, obtaining multiple intrinsic mode function components, and extracting effective intrinsic mode function components from the multiple intrinsic mode function components; wherein, each effective intrinsic mode function component includes information on different frequency components in the oil pressure signal at the wellhead of the gas well; The extracting effective intrinsic mode function components from the multiple intrinsic mode function components includes: sorting the multiple intrinsic mode function components in descending order according to the signal density; selecting the first preset proportion of the sorted intrinsic mode function components as the effective intrinsic mode function components; The signal density reflects the energy concentration and change rate of the intrinsic mode function components in the time-frequency domain; the signal density is defined using a weighted comprehensive score; the calculation method of the weighted comprehensive score is as follows: ; where is the weighted comprehensive score, is the energy proportion of the th intrinsic mode function component, is the frequency concentration, is the time-domain fluctuation intensity, , and are the weight coefficients; ; where is the th intrinsic mode function component, is the oil pressure signal at the wellhead of the gas well; ; where is the main frequency bandwidth, , is the maximum instantaneous frequency of the th intrinsic mode function component, is the minimum instantaneous frequency of the th intrinsic mode function component; ; where is the total number of signal sampling points, is the index, is the time-domain mean value of the th intrinsic mode function component, The larger it is, the stronger the fluctuation is; Combining the effective intrinsic mode function components to form an effective mode combined amplitude diagram to display the amplitude change of different frequency components over time; Judging the change of the amplitude in the effective mode combined amplitude diagram, if the amplitude shows abnormal fluctuations, it is determined that a sand production event has occurred; Determining the sand production time by comparing the amplitude changes of different effective intrinsic mode function components; Drawing the frequency distribution diagram of different effective intrinsic mode function components to obtain the mode frequency diagram and judging the frequency component that dominates in the sand production event; Obtaining the envelope line of the effective intrinsic mode function component and performing spectrum analysis on it to obtain the envelope spectrum numerical diagram to display the energy distribution of the oil pressure signal at the wellhead of the gas well at different frequencies; Comparing the envelope spectrum numerical diagrams of different time periods to judge the change trend of the sand production degree; Combining the amplitude change in the effective mode combined amplitude diagram, the frequency component distribution characteristics of the mode frequency diagram, and the information of the envelope spectrum numerical diagram to form a Hilbert three-dimensional spectrum diagram, setting preset rules based on the Hilbert three-dimensional spectrum diagram, and judging the sand production degree according to the preset rules; The judging the sand production degree according to the preset rules includes: if the envelope spectrum value in the Hilbert three-dimensional spectrum diagram is less than 5, it is judged that the gas well is not producing sand during the production time; if the envelope spectrum value in the Hilbert three-dimensional spectrum diagram is greater than or equal to 5 and less than 15, it is judged that the sand production degree corresponding to the production time is mild sand production; if the envelope spectrum value in the Hilbert three-dimensional spectrum diagram is greater than or equal to 15 and less than 25, it is judged that the sand production degree corresponding to the production time is moderate sand production; if the envelope spectrum value in the Hilbert three-dimensional spectrum diagram is greater than or equal to 25, it is judged that the sand production degree corresponding to the production time is severe sand production; the preset rules are determined by mutual verification with the envelope spectrum values in the Hilbert three-dimensional spectrum diagrams of a large number of gas wells with different proven sand production degrees.

2. The method for judging the sand production degree of a gas well according to claim 1, wherein The using the ICEEMDAN algorithm to decompose the oil pressure signal at the wellhead of the gas well and obtaining multiple intrinsic mode function components includes: Decomposing Gaussian white noise to obtain multiple intrinsic mode components of Gaussian white noise; Adding the oil pressure signal at the wellhead of the gas well to the multiple intrinsic mode components of Gaussian white noise respectively to form multiple extended signals; Performing the first decomposition on the first extended signal to obtain the first residual component, and obtaining the first intrinsic mode function component through the difference between the oil pressure signal at the wellhead of the gas well and the first residual component; Perform decomposition processing steps on subsequent extended signals and residual components in sequence. Based on the previous residual component, add an inherent mode component of new Gaussian white noise to form a new extended signal, decompose the new extended signal, obtain a new residual component, and obtain the next inherent mode function component through the difference between the two consecutive residual components. Iteratively execute the decomposition processing steps until a predefined stop criterion is met to obtain multiple inherent mode function components.

3. The sand production degree discrimination method of a gas well according to claim 1, wherein Each effective inherent mode function component includes information on different frequency components in the wellhead oil pressure signal of the gas well, including: Perform a Hilbert transform operation on the effective inherent mode function component. Based on the result of the Hilbert transform operation, construct an analytic function; wherein, the analytic function includes the amplitude and phase information of the wellhead oil pressure signal of the gas well. Extract the phase information from the analytic function and obtain the instantaneous frequency based on the phase information. According to the correspondence relationship between signal time, instantaneous frequency, and amplitude, obtain the Hilbert spectrum to determine the information on different frequency components in the wellhead oil pressure signal of the gas well.

4. The method for judging the sand production degree of a gas well according to claim 3, characterized in that The obtaining the envelope of the effective inherent mode function component and performing spectral analysis on it to obtain an envelope spectrum numerical diagram includes: Take the modulus of the analytic function as the envelope signal. Perform a Fourier transform on the envelope signal to convert the envelope signal from the time domain to the frequency domain to obtain the envelope of the effective inherent mode function component. Perform spectral analysis on the envelope of the inherent mode function component to obtain an envelope spectrum numerical diagram.

5. A device for judging the sand production degree of a gas well, characterized in that, Include: An acquisition module for acquiring the wellhead oil pressure signal of the gas well. A decomposition module for decomposing the wellhead oil pressure signal of the gas well using the ICEEMDAN algorithm to obtain multiple inherent mode function components, and extracting effective inherent mode function components from the multiple inherent mode function components; wherein, each effective inherent mode function component includes information on different frequency components in the wellhead oil pressure signal of the gas well. The extracting effective inherent mode function components from the multiple inherent mode function components includes: sorting the multiple inherent mode function components in descending order according to the signal density; selecting the first preset proportion of the sorted inherent mode function components as the effective inherent mode function components. The signal density reflects the energy concentration and rate of change of the intrinsic mode function components in the time-frequency domain; the signal density is defined using a weighted comprehensive score; the calculation method of the weighted comprehensive score is as follows: ; where is the weighted comprehensive score, is the energy proportion of the th intrinsic mode function component, is the frequency concentration, is the time-domain fluctuation intensity, , and are the weight coefficients; ; where is the th intrinsic mode function component, is the oil pressure signal at the wellhead of the gas well; ; where is the main frequency bandwidth, , is the maximum instantaneous frequency of the th intrinsic mode function component, is the minimum instantaneous frequency of the th intrinsic mode function component; ; where is the total number of signal sampling points, is the index, is the time-domain mean value of the th intrinsic mode function component, The larger it is, the stronger the fluctuation; A combination module for combining the effective inherent mode function components to form an effective mode combined amplitude diagram to display the amplitude change of different frequency components over time. A determination module for judging the change of the amplitude in the effective mode combined amplitude diagram. If abnormal fluctuations occur in the amplitude, it is determined that a sand production event has occurred. A determination module for determining the sand production time by comparing the amplitude changes of different effective inherent mode function components. A plotting module for plotting the frequency distribution diagram of different effective inherent mode function components to obtain a modal frequency diagram and judging the frequency component that dominates in the sand production event. An analysis module for obtaining the envelope of the effective inherent mode function component and performing spectral analysis on it to obtain an envelope spectrum numerical diagram to display the energy distribution of the wellhead oil pressure signal of the gas well at different frequencies. A comparison module for comparing the envelope spectrum numerical diagrams of different time periods to judge the change trend of the sand production degree. A judgment module, configured to combine the amplitude change in the effective modal combination amplitude diagram, the frequency component distribution characteristics of the modal frequency diagram, and the information of the envelope spectrum numerical diagram to form a Hilbert three-dimensional spectrum diagram, set a preset rule based on the Hilbert three-dimensional spectrum diagram, and judge the sand production degree according to the preset rule; The judging the sand production degree according to the preset rule includes: if the envelope spectrum value in the Hilbert three-dimensional spectrum diagram is less than 5, it is judged that the gas well is not sand-producing during the production time; if the envelope spectrum value in the Hilbert three-dimensional spectrum diagram is greater than or equal to 5 and less than 15, it is judged that the sand production degree corresponding to the production time is mild sand production; if the envelope spectrum value in the Hilbert three-dimensional spectrum diagram is greater than or equal to 15 and less than 25, it is judged that the sand production degree corresponding to the production time is moderate sand production; if the envelope spectrum value in the Hilbert three-dimensional spectrum diagram is greater than or equal to 25, it is judged that the sand production degree corresponding to the production time is severe sand production; the preset rule is determined by mutual verification with the envelope spectrum values in the Hilbert three-dimensional spectrum diagrams of a large number of gas wells with different proven sand production degrees.

6. A sand production degree discrimination server for a gas well, characterized in that, It includes a memory and a processor; The memory is used to store computer-executable instructions; The processor is used to execute the computer-executable instructions to implement the method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores executable instructions, and when the computer executes the executable instructions, it can implement the method according to any one of claims 1-4.

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