System and method for detecting sand grain size distribution in underwater wellhead gas pipeline

Abstract

The application provides an underwater wellhead gas transmission pipeline sand particle size distribution detection system and method, which comprises a signal sensing unit, a signal collecting unit, a signal processing unit and a signal communication unit, the signal sensing unit comprises a signal sensing assembly capable of measuring sand particle collision signals in the underwater wellhead gas transmission pipeline, the signal collecting unit is used for collecting the collision signals measured by the signal sensing unit; the signal processing unit analyzes the sand particle collision signals to determine the sand production characteristic frequency band and sand particle size distribution of the underwater gas transmission pipeline sand particle collision signals; and the signal communication unit is used for bidirectional communication of the sand production characteristic signals and control commands between the centralized collection module and the platform master station. The underwater wellhead gas transmission pipeline sand particle size distribution detection system of the application is suitable for underwater sand production monitoring and has high sand production particle size distribution detection precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of sand production management in deepwater oil and gas development, and particularly to a sand particle size distribution detection system and method for a gas pipeline at a subsea wellhead. BACKGROUND

[0002] Sand production is one of the major challenges faced by the oil industry, especially in deepwater environments, where sand production is inevitable. Once sand control fails, it can cause sand burial, production reduction, severe erosion and damage to the downhole string and subsea equipment, and even well shut-in. Through online sand production monitoring, the balance between oil and gas production and sand control can be grasped in real time, ensuring safe and efficient flow of deepwater oil and gas. Real-time sand particle size distribution is one of the key parameters for optimizing sand control completion strategies and developing effective sand management measures. Formation sand is lifted to the subsea wellhead along with the produced fluid, and sand particles often collide with the elbow at the wellhead. By analyzing the acoustic vibration response information excited by this collision process in real time, the particle size distribution characteristics of the sand particles in the pipeline can be obtained. How to effectively identify the weak signal characteristics of sand particles from the complex gas-liquid turbulent flow background noise is the key to determining whether the pipeline sand particle monitoring is accurate.

[0003] In view of the above sand production monitoring problems, and in combination with the small space for offshore oilfield operations, CN105672982A discloses a non-implanted heavy oil well sand production monitoring system, which includes a sand production signal sensing, collecting, processing and calibrating device. The sand production monitoring method includes installing two identical primary instruments on the outer walls of adjacent elbow pipes, using collision signal extraction, characteristic sand production frequency band filtering, mutual correlation flow rate calculation and ultrasonic flow rate measurement comparison methods to denoise the oil flow signal in the sand-containing crude oil signal and extract the sand production signal. This system and method are simple to construct, easy to install and maintain, low in cost and high in efficiency, and can monitor the real-time sand production of heavy oil wells. However, this invention is not suitable for sand production monitoring in deepwater gas wells underwater, and cannot obtain sand particle size characteristics.

[0004] CN111198231A discloses an oil and gas pipeline sand production monitoring experimental device, which includes a signal sensing unit, a signal collecting unit and a signal processing unit. The signal sensing unit includes an acoustic signal sensing assembly that can gather acoustic signals emitted by the pipeline using acoustic signal aggregation principles, a pressure measuring assembly, a flow rate measuring assembly, and a temperature measuring assembly. The signal collecting unit is used to collect acoustic signals, pressure signals, flow rate signals and temperature signals measured by the signal sensing unit. The signal processing unit analyzes the acoustic signals, pressure signals, flow rate signals and temperature signals to determine the sand production in the oil and gas pipeline. This invention can achieve high sand production monitoring accuracy. However, this invention cannot achieve sand particle size monitoring at the subsea wellhead of a deepwater gas well.

[0005] The present application aims to provide a system capable of being applied to online detection of sand particle size distribution of a gas pipeline at a water wellhead, and capable of realizing real-time analysis of sand particle size distribution of a gas well, especially sand particle size distribution monitoring of a deepwater gas well in a complex production environment, and provides a system for detecting sand particle size distribution of a gas pipeline at a water wellhead and an analysis method thereof. SUMMARY

[0006] The present application aims to provide a system capable of being applied to online detection of sand particle size distribution of a gas pipeline at a water wellhead, and capable of realizing real-time analysis of sand particle size distribution of a gas well, especially sand particle size distribution monitoring of a deepwater gas well in a complex production environment, and provides a system for detecting sand particle size distribution of a gas pipeline at a water wellhead and an analysis method thereof.

[0007] To achieve the above-mentioned purpose, the present application adopts the technical scheme of:

[0008] A system for detecting sand particle size distribution of a gas pipeline at a water wellhead, comprising:

[0009] A signal sensing unit comprising a plurality of signal sensing components capable of measuring sand particle collision signals in the gas pipeline at the water wellhead;

[0010] A signal acquisition unit for acquiring the collision signals measured by the signal sensing unit, comprising a centralized acquisition module electrically connected to the signal sensing components;

[0011] A signal processing unit comprising a sand production signal identification module electrically connected to the signal acquisition unit, and a sand particle size inversion module; the sand production signal identification module is used for analyzing the sand particle collision signals and multiphase flow signals to determine a sand production characteristic frequency band of the sand particle collision signals in the gas pipeline at the water wellhead; the particle size inversion module is used for analyzing the sand production characteristic frequency band to determine the sand particle size distribution of the gas pipeline at the water wellhead;

[0012] A signal communication unit comprising a half-duplex communication module, a subsea control module for acquiring sand production characteristic signals and receiving platform master station commands; a subsea umbilical module electrically connected to the platform master station and the subsea control module to transmit and feedback the sand production characteristic signals and control signals.

[0013] As a further optimization of the present application, the signal sensing components are subsea primary instruments, and the subsea primary instruments comprise a sealed housing fixedly installed on the gas pipeline at the water wellhead.

[0014] As a further optimization of the present application, the plurality of subsea primary instruments comprise three groups of acceleration sensors, and the acceleration sensors are packaged in the sealed housing, and the three groups of acceleration sensors are respectively arranged at 22.5°, 45° and 72.5° of a downstream elbow of the gas pipeline at the water wellhead.

[0015] As a further optimization of the present application, the subsea primary instruments further comprise a signal transmission hardware module.

[0016] As a further optimization of the application, the sand particle size inversion module is used to perform full-band power spectrum density analysis, adaptive Kalman filter analysis, frequency band energy analysis, and cumulative statistical quantity analysis on the optimal sand characteristic signal to determine the sand particle size distribution of the underwater wellhead gas pipeline.

[0017] The application also provides a sand signal identification method for an underwater wellhead gas pipeline, which uses the sand particle size distribution detection system for an underwater wellhead gas pipeline according to any one of the above, and includes the following steps:

[0018] S1: initializing and self-checking the sand particle size distribution detection system for the underwater wellhead gas pipeline;

[0019] S2: collecting sand particle collision signals sensed by the signal sensing components, and determining whether the system is in a normal working state; if yes, performing step S3; if not, repeating step S2;

[0020] S3: analyzing and processing the sand particle collision signals to obtain a sand-out signal characteristic frequency band;

[0021] S4: reading multiphase flow data to obtain phase flow rates and phase contents in the underwater wellhead gas pipeline;

[0022] S5: performing gas-liquid background noise characteristic analysis based on the multiphase flow data to obtain a noise signal and suppress the noise;

[0023] S6: performing signal-to-noise ratio analysis on the sand-out signal based on the sand-out signal characteristic frequency band and the noise signal to obtain an optimized sand-out characteristic frequency band;

[0024] S7: determining the optimized sand-out characteristic frequency band; if valid, saving the sand-out signal characteristic frequency band; if invalid, repeating steps S2-S7.

[0025] As a further optimization of the application, step S3 further includes performing time-frequency domain joint analysis on the sand particle collision signal, taking the sand particle collision signal as a calibration signal, and obtaining the sand-out signal characteristic frequency band.

[0026] As a further optimization of the application, step S7 further includes comparing the sand-out characteristic frequency band obtained based on the sand particle collision signal analysis with the optimized sand-out characteristic frequency band obtained based on the gas-liquid background noise suppression; if the error between the two is less than or equal to 5%, the sand-out characteristic frequency band is considered valid; otherwise, the sand-out characteristic frequency band is considered invalid.

[0027] The application further provides an underwater wellhead gas pipeline sand particle size inversion method, which utilizes the underwater wellhead gas pipeline sand particle size distribution detection system in any one of the above aspects, and comprises the following steps:

[0028] S1: input the sand-out signal characteristic frequency band;

[0029] S2: perform full-band power spectrum density analysis and adaptive Kalman filter analysis on the sand-out signal characteristics to adaptively allocate the energy of each sand-out characteristic frequency band;

[0030] S3: perform energy analysis and sand particle quantity analysis on the sand-out characteristic frequency band to obtain sand particle size distribution;

[0031] S4: respectively correct the sand particle size distributions obtained by the three groups of acceleration sensors, wherein the average value of each group of particle size distribution results is obtained, and the final sand particle size analysis result is obtained by coupling the analysis results of the three groups;

[0032] S5: repeat steps S2-S4 four times;

[0033] S6: perform consistency judgment on the sand-out particle size distribution, if valid, output the sand-out particle size distribution result, and if invalid, repeat steps S2-S5.

[0034] Compared with the prior art, the application has the advantages and positive effects that:

[0035] 1: the underwater wellhead gas pipeline sand particle size distribution detection system avoids installation position error and information loss caused by performance degradation of a single sensor, realizes real-time capture of a collision signal set on a two-dimensional pipeline wall surface, improves the identification reliability of the sand-out characteristic frequency band by analyzing the multi-source pipeline collision signals sensed by the multiple signal sensing components, and further improves the detection accuracy of the sand-out particle size distribution;

[0036] 2: the underwater wellhead gas pipeline sand particle size distribution detection system comprises an underwater primary instrument with a pressure-resistant and corrosion-resistant shell, and can meet the conditions of underwater 500-meter high pressure, sealing and corrosion resistance compared with the existing sand-out signal sensing component;

[0037] 3. The sand production signal identification method for the underwater wellhead gas pipeline of the present application, which collects multiple sand particle collision signals by setting multiple signal sensing components, applies a time-frequency domain joint analysis method to comprehensively analyze the characteristic frequency band of the multiple-channel sand particle collision signals, takes the sand particle collision signal as a calibration signal to further calibrate the sand production characteristic frequency band, acquires phase flow rate, phase content rate and other information in the pipeline by reading multiphase flow data to judge the influence of gas-liquid background noise on the sand production signal, and improves the analysis accuracy of the sand production signal characteristic frequency band; and further obtains the optimal sand production characteristic frequency band through sand production signal SNR analysis.

[0038] 4. The sand particle size inversion method for the underwater wellhead gas pipeline of the present application, which comprehensively analyzes multiple groups of sand production noise frequency bands, performs full-band power spectral density analysis on the sand production signal, applies Kalman adaptive filtering to adaptively distribute the energy of each sand production characteristic frequency band, further analyzes the sand particle quantity and sand particle energy, and then obtains the sand particle size distribution result. Compared with the single sensor sand production detection system, the present application respectively applies the sand production signals obtained by each sensor to calculate the particle size distribution result, further couples and corrects the particle size distribution result, and improves the accuracy of the particle size distribution detection. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 Fig. 1 is a structural schematic diagram of the underwater wellhead gas pipeline sand particle size distribution detection system of the present application;

[0040] Figure 2 Fig. 3 is a distribution schematic diagram of the signal sensing component in the present application;

[0041] Figure 3 Fig. 4 is a structural block diagram of the underwater wellhead gas pipeline sand particle size distribution detection system of the present application;

[0042] Figure 4 Fig. 5 is a flowchart of the sand production signal identification method for the underwater wellhead gas pipeline of the present application;

[0043] Figure 5 Fig. 6 is a flowchart of the sand production particle size inversion method for the underwater wellhead gas pipeline of the present application;

[0044] Figure 6 Fig. 7 is an effect display diagram of experimental verification by using the method of the present application.

[0045] In the above figures: 1-9, signal sensing component; 10, underwater wellhead gas pipeline; 11, centralized collection module; 12, sand production signal identification module; 13, half-duplex communication module; 14, underwater control module; 15, underwater umbilical cable module; 16, semi-submersible oil storage platform; 17, sand production particle size inversion module. DETAILED DESCRIPTION

[0046] The application will be described in greater detail below with reference to exemplary embodiments. It should be understood, however, that elements, structures and features in one embodiment can be beneficially incorporated in other embodiments without further recitation.

[0047] In the description of the application, it should be noted that the non-collision vibration array signal and the collision signal generated by the collision of the multiphase fluid of the pipeline with the pipe wall are collected from the pipeline measurement; the collision signal includes the fluid signal generated by the collision of the fluid with the pipe wall, and the sand production characteristic signal generated by the collision of the sand particles with the pipe wall; the terms "inner", "outer", "upper", "lower", "front", "rear" and the like indicate the positional or positional relationship based on the positional relationship shown in the drawings, and are only for the convenience of describing the application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the application. In addition, the terms "first" and "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0048] As shown in Figure 1 The application provides an underwater wellhead gas pipeline sand particle size distribution detection system, which comprises a signal sensing assembly 1-9, a centralized collection module 11, a sand production signal identification module 12, a half-duplex communication module 13, an underwater control module 14, an underwater umbilical cable module 15, a semi-submersible oil storage platform 16, and a main control station sand production particle size inversion module 17. The signal sensing assembly 1-9 is used to measure the collision signal of the underwater wellhead gas pipeline 10. Specifically, according to the fluid flow direction shown in the figure, the signal sensing assembly 1-9 comprises a signal sensing assembly 7-9 installed at a downstream elbow 22.5° of the underwater wellhead gas pipeline 10, a signal sensing assembly 4-6 installed at a downstream elbow 45° of the underwater wellhead gas pipeline 10, and a signal sensing assembly 1-3 installed at a downstream elbow 77.5° of the underwater wellhead gas pipeline 10. The signal sensing assembly 1-9 can measure the pipeline collision signal at multiple angles of the underwater wellhead gas pipeline 10. In this embodiment, the signal sensing assembly 1-9 is an underwater primary instrument. The centralized collection module 11 is used to collect the impact signal measured by the signal sensing assembly 1-9, and the centralized collection module 11 is electrically connected to the signal sensing assembly 1-9. The sand production signal identification module 12 is electrically connected to the centralized collection module 11 and completes the identification of the sand production characteristic frequency band.

[0049] The sand grain size distribution detection system of the underwater wellhead gas pipeline of the application, compared with the single measuring instrument installed at the pipeline elbow in the prior art, collects pipeline collision signals at multiple angles, analyzes the multiple pipeline collision signals measured by the signal sensing assembly 1-9, further improves the identification reliability of the sand production characteristic frequency band, and further improves the detection accuracy of the sand grain size distribution.

[0050] It should be noted that, in order to further improve the accuracy of the sand production characteristic frequency band identification, the collection and acquisition module 11 can read the pipeline phase flow rate, phase content rate and other data measured by the underwater multiphase flow meter to judge the flow state of the fluid in the underwater wellhead gas pipeline 10, such as laminar flow, bubble flow, annular flow and slug flow, analyze the influence of the gas-liquid fluid noise signal on the collision signal of the underwater wellhead gas pipeline 10 under each flow type, and then optimize the sand production characteristic frequency band to improve the frequency band analysis accuracy.

[0051] Continuing to refer to Figure 1 The half-duplex communication module 13 integrated with the sand production signal identification module 12 can transmit the sand production characteristic signal to the underwater control module 14, and at the same time, can transmit the response control command to the collection and acquisition module 11 and the signal sensing assembly 1-9 in reverse. The underwater umbilical cable module 15 can transmit the sand production characteristic signal to the main control station of the semi-submersible oil storage platform 16, and the sand production grain size inversion module 17 of the main control station can perform energy analysis on the sand production characteristic signal to judge the sand production grain size distribution in the underwater wellhead gas pipeline 10.

[0052] Referring to Figure 2 The signal sensing assemblies 1-3 are uniformly distributed on the outer surface of the 77.5° section downstream of the pipeline elbow, and are installed on the underwater wellhead gas pipeline 10 using a non-implanted installation method respectively using clamps; similarly, the signal sensing assemblies 4-6 and the signal sensing assemblies 7-9 are uniformly distributed on the outer surfaces of the 45° and 22.5° sections downstream of the elbow of the underwater wellhead gas pipeline 10; it should be noted that the signal sensing assemblies 1, 4 and 7 are located on the same generatrix of the pipeline 10, similarly, the signal sensing assemblies 2, 5 and 8 are located on the same generatrix of the pipeline 10, and the signal sensing assemblies 3, 6 and 9 are located on the same generatrix of the pipeline 10, and the three generatrices are equidistantly distributed.

[0053] As a preferred, the signal sensing assemblies 1-9 have pressure-resistant and corrosion-resistant shells, which can meet the conditions of deep water high pressure, sealing and corrosion resistance.

[0054] Referring to Figure 3The application provides a sand signal identification method for a gas pipeline at a water wellhead, which is integrated in a sand signal identification module 12, and uses a pipeline sand signal obtained by the sand particle size distribution detection system to perform feature identification, specifically as follows:

[0055] S1: initializing and self-checking the sand particle size distribution detection system for the gas pipeline at the water wellhead;

[0056] S2: measuring pipeline collision signals by the signal sensing components 1-9 respectively, storing the pipeline collision signals by the centralized collection module 11, and judging whether the distributed sensors are in a normal working state according to the consistency of the time domain response characteristics of the signal sensing components 1-9; if yes, performing step S3, and if not, repeating steps S1-S2;

[0057] Consistency judgment of time domain response characteristics of signal sensing components:

[0058]

[0059] In the formula, x i (t) is the time domain amplitude of the pipeline collision signal detected by the signal sensing component i at time t;

[0060] S3: storing and analyzing the pipeline collision signals, applying a short-time Fourier time-frequency domain joint analysis method, comprehensively analyzing results of multiple channels in a sand frequency band, judging a frequency domain range corresponding to a maximum value of a time-frequency spectrum matrix, and obtaining a sand signal characteristic frequency band;

[0061] Short-time Fourier time-frequency joint analysis method:

[0062]

[0063] In the formula, i is the serial number of the signal sensing components 1-9, STFT(t, f) is a frequency spectrum of a signal x(t) at time t obtained by the time-frequency joint analysis method, ω(τ-t) is a window function, and a time-frequency spectrum matrix obtained by the short-time Fourier time-frequency joint analysis method is as follows:

[0064]

[0065] Sand signal characteristic frequency band:

[0066] f sand =f(max(STFT m×n ))

[0067] S4: reading phase flow velocity and phase content rate in the gas pipeline 10 at the water wellhead by a multiphase flow meter to judge a flow state of fluid in the pipeline;

[0068] S5: analyzing the influence of the gas-liquid fluid noise signal on the pipeline collision signal through the flow state of the fluid in the underwater wellhead gas transmission pipeline 10, judging the overlap degree of the fluid noise signal characteristic frequency band and the sand production signal characteristic frequency band, and summarizing the peak value, root mean square value, skewness, kurtosis and other time domain statistical characteristics and main frequency variation law of the fluid noise signal excited by the change of the sand-carrying medium flow pattern (single-phase liquid, dispersed bubble flow, slug flow, annular flow, stirred flow, single-phase gas) ;

[0069] S6: performing signal-to-noise ratio analysis on the sand production signal by using the sand production signal characteristic frequency band and the fluid noise signal to further obtain an optimal sand production characteristic frequency band;

[0070] Sand production signal signal-to-noise ratio calculation method:

[0071]

[0072] In the formula, SNR m represents the signal-to-noise ratio of the sand production characteristic frequency band f m , P sand is the power spectrum of the characteristic frequency band signal, and P noise is the corresponding noise signal power spectrum. According to the signal-to-noise ratio calculation result, the frequency band corresponding to max(SNR m ) is selected as the sand production characteristic frequency band.

[0073] S7: judging the sand production characteristic frequency band, comparing the sand production characteristic frequency band obtained by analyzing the sand particle collision signal with the optimized sand production characteristic frequency band obtained by suppressing the gas-liquid background noise, and if the error between the two is less than or equal to 5%, it is considered that the sand production characteristic frequency band is effective, and the sand production signal characteristic frequency band is saved. Otherwise, it is considered that the sand production characteristic frequency band is invalid, and steps S2-S7 are repeated.

[0074] The underwater wellhead gas transmission pipeline sand production signal identification method of the application comprehensively analyzes the characteristic frequency band of the multi-channel sand particle collision signal by collecting the sand production collision signals measured by the 9 signal sensing components and applying the time-frequency domain joint analysis method. The sand production characteristic frequency band is further calibrated by using the sand particle collision signal as a calibration signal. The influence of the gas-liquid background noise on the sand production signal is judged by reading the multiphase flow data to obtain the phase flow rate, phase content rate and other information in the pipeline, thereby improving the analysis accuracy of the sand production signal characteristic frequency band. The sand production characteristic frequency band is further obtained through sand production signal signal-to-noise ratio analysis.

[0075] Referring to Figure 5 , the application further proposes a kind of underwater wellhead gas transmission pipeline sand particle diameter inversion method, which is integrated in the sand production particle diameter inversion module 17 of the master control station, and sand particle diameter is inverted using the aforementioned sand production characteristic frequency band, comprising the following steps:

[0076] S1: input the sand production signal characteristic frequency band to the sand production particle size inversion module 17 of the master station through the underwater umbilical cable module 15;

[0077] S2: adaptively allocate the energy of each sand production characteristic frequency band by power spectral density analysis and Kalman adaptive filtering analysis on the sand production signal characteristic full frequency band;

[0078] Full frequency band power spectral density calculation formula:

[0079]

[0080] Adaptive Kalman filtering calculation method:

[0081] z(k+1) = H(k+1)x(k) + v(k);

[0082] wherein z(k) is the observation value, x(k) is the sand production signal, and v(k) is the noise signal. The optimal estimation state matrix H(k) of the sand production signal is obtained by applying the above Kalman filtering calculation method, and the energy of each sand production characteristic frequency band is obtained by further applying the power spectral density calculation formula;

[0083] S3: energy analysis and sand particle number analysis are performed on the sand production characteristic frequency band to obtain the sand particle size distribution, and the results are expressed in percentage size: d5, d10, d20,..., d90; d90 is the corresponding particle size when the cumulative mass fraction reaches 90%;

[0084] Sand particle characteristic frequency band energy analysis algorithm:

[0085]

[0086] Sand particle size distribution statistical method:

[0087]

[0088] S4: repeat steps S2-S3 four times;

[0089] S5: consistency judgment is performed on the sand particle size distribution, and the five sand particle size distribution inversion results are compared. If the error is less than or equal to 5%, the sand particle size distribution is considered valid, the sand particle size distribution result is output, otherwise, the sand particle size distribution is considered invalid. If valid, repeat steps S2-S5.

[0090] The sand particle size inversion method of the underwater wellhead gas pipeline of the application is characterized in that: the sand signal is subjected to full-band power spectrum density analysis by comprehensively collecting multiple groups of sanding noise frequency bands; the Kalman adaptive filter is applied to adaptively distribute the energy of each sanding characteristic frequency band; the sand particle quantity ratio and sand particle energy are further analyzed to obtain the sand particle size distribution result.

[0091] Embodiment

[0092] The embodiment is used to illustrate the sanding signal feature recognition method and the sand particle size distribution detection and analysis method of the application, and the pipe wall collision signal sensed by one sensor in the distributed sensor array is taken as an example for illustration.

[0093] (1) Collect the pipe collision signal, such as Figure 6 , and perform time-frequency domain joint analysis on the signal, as shown in Figure 6 , and it is obtained from the figure that the sanding signal characteristic frequency band is 30-50 kHz;

[0094] (2) Take the sand particle collision signal as the calibration signal, and perform signal-to-noise ratio analysis on the sanding signal according to the phase flow rate and phase content rate of the multiphase flowmeter to further optimize the sanding characteristic frequency band.

[0095] (3) Repeat steps (1) and (2) to complete the sanding characteristic signal recognition of the signals collected by the 9 sensors, perform consistency judgment, and complete the correction of the sanding characteristic signal;

[0096] (4) Based on the sanding characteristic frequency band obtained in step (3), the sanding signal is subjected to adaptive Kalman filtering, the full-band power spectrum density of the sanding characteristic signal is calculated, and the energy of each sanding frequency band is adaptively distributed, and the energy analysis result is shown in the third graph in Figure 6 ;

[0097] (5) On the basis of the calculation result of step (4), further statistical quantity analysis is performed to obtain the sanding particle size distribution result, as shown in the fourth graph in Figure 6 ;

[0098] (6) Repeat steps (4) and (5) to complete the sand particle size analysis result of the signals collected by the 9 sensors, respectively correct the sand particle size distribution obtained by the three groups of sensors 1-3, 4-6 and 7-9 at different installation levels, wherein the average value of each group of particle size distribution results is calculated, and the final sand particle size analysis result is obtained according to the three groups of analysis results;

[0099] (7) Repeat step (6) for 5 times to complete the sanding particle size distribution analysis.

Claims

1. A system for detecting the particle size distribution of sand particles in an underwater wellhead gas transmission pipeline, characterized in that: The system comprises: a signal sensing unit comprising a plurality of signal sensing components capable of measuring sand particle collision signals in the underwater wellhead gas pipeline; a signal acquisition unit for acquiring the collision signals measured by the signal sensing unit, comprising a centralized acquisition module electrically connected to the signal sensing components; a signal processing unit comprising a sand production signal identification module electrically connected to the signal acquisition unit, and a sand particle size inversion module; the sand production signal identification module is used to analyze the sand particle collision signals and multiphase flow signals to determine the sand production characteristic frequency band of the sand particle collision signals in the underwater wellhead gas pipeline; the particle size inversion module is used to analyze the sand production characteristic frequency band to determine the sand production particle size distribution of the underwater wellhead gas pipeline; a signal communication unit comprising a half-duplex communication module, and an underwater control module for acquiring sand production characteristic signals and receiving platform master station commands; an underwater umbilical module electrically connected to the platform master station and the underwater control module to transmit and feedback sand production characteristic signals and control signals; the plurality of signal sensing components comprise three groups of acceleration sensors, which are packaged in the sealed shell and arranged on the outer surface of the downstream elbow of the underwater wellhead gas pipeline at 22.5°, 45° and 72.5°, respectively; each group of acceleration sensors is located on the same generatrix of the pipeline; the sand production particle size inversion module is used to perform full-band power spectral density analysis, adaptive Kalman filter analysis, frequency band energy analysis, and cumulative statistical quantity analysis on the optimal sand production characteristic signals to determine the sand production particle size distribution of the underwater wellhead gas pipeline.

2. The sand grain size distribution monitoring system for subsea wellhead gas export pipelines of claim 1, wherein: The signal sensing component is an underwater primary instrument, which comprises a sealed shell fixedly installed on the underwater wellhead gas pipeline.

3. The sand grain size distribution monitoring system for subsea wellhead gas export pipelines of claim 1, wherein: The underwater primary instrument further comprises a signal transmission hardware module.

4. A method for identifying sand production signals in a subsea wellhead gas pipeline, using a subsea wellhead gas pipeline sand grain size distribution detection system as claimed in any one of claims 1 to 3, characterized in that: The system comprises the following steps: S1: initializing and self-checking the underwater wellhead gas pipeline sand particle size distribution detection system; S2: acquiring sand particle collision signals sensed by the plurality of signal sensing components, and determining whether the system is in a normal working state; if yes, executing step S3; if not, repeating step S2; S3: analyzing and processing the sand particle collision signals to obtain sand production signal characteristic frequency bands; S4: reading multiphase flow data to obtain phase flow rates and phase contents in the underwater wellhead gas pipeline; S5: performing gas-liquid background noise characteristic analysis based on the multiphase flow data to obtain noise signals and suppress noise; S6: performing signal-to-noise ratio analysis on sand production signals based on the sand production signal characteristic frequency bands and the noise signals to obtain optimized sand production characteristic frequency bands; S7: determining the optimized sand production characteristic frequency bands; if valid, saving the sand production signal characteristic frequency bands; if invalid, repeating steps S2-S7. In step S3, the sand particle collision signals are further subjected to time-frequency domain joint analysis to obtain sand production signal characteristic frequency bands using the sand particle collision signals as calibration signals.

5. The method of claim 4, wherein: ​ 6. The method of claim 5, wherein: The step S7 further comprises comparing the sand production characteristic frequency band obtained according to the sand particle collision signal analysis with the optimized sand production characteristic frequency band obtained according to the gas-liquid background noise suppression, and if the error between the two is less than or equal to 5%, the sand production characteristic frequency band is considered valid, otherwise, the sand production characteristic frequency band is considered invalid.

7. A method for sand grain size inversion of a subsea wellhead gas pipeline, using the subsea wellhead gas pipeline sand grain size distribution detection system as claimed in any one of claims 1 to 3, characterized in that, Comprise the following steps: S1: input the sand production signal characteristic frequency band; S2: perform full-band power spectral density analysis and adaptive Kalman filter analysis on the sand production signal characteristics to adaptively allocate the energy of each sand production characteristic frequency band; S3: perform energy analysis and sand particle number analysis on the sand production characteristic frequency band to obtain sand particle size distribution; S4: respectively correct three groups of acceleration sensors to obtain sand particle size distribution, wherein the average value of each group of particle size distribution results is obtained, and the final sand particle size analysis result is obtained by coupling three groups of analysis results; S5: repeat steps S2-S4 four times; S6: perform consistency judgment on the sand production particle size distribution, if valid, output the sand production particle size distribution result, if invalid, repeat steps S2-S5.

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