Sand content real-time monitoring device and method

Abstract

The invention discloses a real-time sand content monitoring device and method. The real-time sand content monitoring device comprises a sand content detection module, an automatic sand removal device and a construction device, wherein the sand content detection module comprises an upstream ultrasonic flowmeter, a downstream ultrasonic flowmeter, an upstream vibration sensor, a downstream vibration sensor, a demodulator, a cable and a first slurry passing pipeline; the ultrasonic flowmeter and the vibration sensor are connected to the demodulator through cables. Parameters such as flow, vibration amplitude and the like of sand-containing mud can be obtained in real time and converted into the sand content through equipment; the data acquisition mode is fast and convenient, and is easy for on-site constructors to use.

Description

Technical Field

[0001] The present invention relates to the technical field of construction tools, and more specifically, to a device and method for real-time monitoring of sand content. Background Art

[0002] The demand for large-scale projects such as large factories, high-rise buildings, railways, highways, bridges, offshore platforms and docks has increased dramatically. Such large-scale projects have strict requirements on the bearing capacity of the foundation, and special treatment of the foundation is required to improve the bearing capacity of the foundation. The foundation treated with pile foundation engineering not only has the characteristics of high bearing capacity, small settlement, good stability, etc., but also can adapt to the geological conditions of various environments, and is easy to construct and low in cost. Therefore, pile foundation reinforcement has become the preferred treatment method for the foundation treatment of large buildings.

[0003] At present, pile foundation construction mostly uses two categories: prefabricated piles and cast-in-place piles. Among them, cast-in-place piles can meet the needs of most projects. Therefore, this article takes cast-in-place piles as an example. There are two main methods for cast-in-place pile construction, namely mud wall construction and dry operation construction. Mud wall construction method is often used on site. The stability of the cast-in-place pile hole is directly related to the quality of the pile after concrete pouring, and the hole stability mainly depends on the mud performance. The depth of the cast-in-place pile hole ranges from tens of meters to hundreds of meters. During the hole forming process, corresponding mud parameters need to be used for holes in different strata and different excavation diameters. Therefore, construction personnel are required to dynamically adjust the mud performance indicators according to different geological conditions and construction conditions. Among them, the sand content index in the mud parameter directly affects the thickness of the pile bottom sediment. If the sediment thickness is too thick, the pile quality will decrease and cannot meet the construction quality requirements. However, the existing methods for measuring the sand content in mud are mostly manual measurement. The accuracy of this method depends on the professional skills of the construction personnel, and the measurement process takes a certain amount of time, which cannot reflect the real-time sand content change. Monitoring the changes in sand content can determine the amount of slag discharged during the operation, and then determine the drilling status of the pile head, making it easier for construction personnel to adjust parameters such as drilling rig torque, drilling speed, and drilling pressure to improve construction efficiency and pile quality.

[0004] Therefore, it is necessary to develop a real-time monitoring device and method for the sand content of mud, and to monitor the sand content, slag output and other parameters in the mud in real time, so that construction personnel can adjust the mud performance and drilling rig status during the construction process to ensure that the quality meets the construction requirements. Summary of the invention

[0005] The purpose of the present invention is to provide a real-time monitoring device and method for sand content, which adopts a vibration sensor, an ultrasonic flow meter and a demodulator to jointly determine the sand content and slag output of the mud during construction. If the sand content is higher than a preset value, the sand removal equipment is automatically turned on to separate the mud and sand, thereby reducing the sand content in the mud and ensuring that the pile quality meets the requirements.

[0006] The technical solution adopted by the present invention to solve this technical problem is: a real-time monitoring device for sand content, including: a sand content detection module, an automatic sand removal device and a construction device; wherein the sand content detection module includes: two upstream and downstream ultrasonic flow meters, two upstream and downstream vibration sensors, a demodulator, a cable and a slurry pipeline; the ultrasonic flow meter and the vibration sensor are connected to the demodulator by a cable.

[0007] As a further solution of the present invention, the automatic sand removal device includes a mud-sand separator and a controller; wherein the demodulator determines whether the sand content exceeds a predetermined value by integrating the collected signals, and if it exceeds the predetermined value, the mud-sand separator is turned on by the controller.

[0008] As a further solution of the present invention, the construction device is composed of a drilling rig, a drill rod, a drill bit, a slurry pipeline 2, a buried pipe and a slurry pool; The drill pipe is connected to the liquid inlet of the mud-sand separator through a slurry pipe 1, and the liquid outlet of the mud-sand separator is connected to a slurry pipe 2, and the slurry pipe 2 introduces the slurry into the mud pool.

[0009] As a further solution of the present invention, an upstream ultrasonic flow meter is arranged in a straight pipe section of the slurry pipe 1, and an upstream vibration sensor is arranged in a pipe bending section of the slurry pipe 1; The downstream ultrasonic flowmeter is arranged in the straight pipe section of the second slurry pipe, and the downstream vibration sensor is arranged in the pipe bending section of the second slurry pipe.

[0010] As a further solution of the present invention, the ultrasonic flow meter and the vibration sensor are fastened to the pipeline through a stainless steel buckle.

[0011] The present invention also provides a method for utilizing the sand content real-time monitoring device, comprising the following steps: S1. Arrange all components except ultrasonic flowmeter and vibration sensor; S2, using the slurry pipeline 1 and the slurry pipeline 2 as the sand content monitoring section, arranging the upstream ultrasonic flowmeter in the straight pipe section of the slurry pipeline 1, arranging the upstream vibration sensor in the pipe bending section of the slurry pipeline 1, and monitoring the vibration frequency generated by the sand-containing mud hitting the pipe; S3. Arrange a second set of ultrasonic flowmeters and vibration sensors in the sand content monitoring section, and arrange them after the mud-sand separator, wherein the ultrasonic flowmeter is arranged at the straight line of the mud outlet slurry pipeline II, and the vibration sensor is installed at the bend of the mud outlet slurry pipeline II; S4, connecting the above four sensors to the demodulator via cables; S5. Prepare clean mud and inject it into the mud pool. Operate the drilling rig to start drilling. Mud and sand and gravel flow through the inner cavity of the drill pipe to the slurry pipe and then flow into the sand content monitoring section before the mud-sand separator. The mud first flows into the first group of ultrasonic flowmeter measurement areas, which measure the flow velocity and flow parameters of the sand-containing mud. Then, it flows into the first group of vibration sensor measurement areas, which measure the vibration frequency and amplitude parameters generated by the sand-containing mud hitting the pipe. Finally, the mud flows into the mud-sand separator. S6. The demodulator collects the signals transmitted by the first group of ultrasonic flowmeters and vibration sensors, and after filtering, signal reconstruction, and comparison with the calibration function, measures the sand content of the mud before entering the mud-sand separator, and displays the data synchronously on the digital display screen; if the sand content exceeds the preset value, the mud-sand separator is automatically turned on to separate the sand and gravel in the mud; if it does not exceed the preset value, the mud separator is not turned on, and the mud directly flows into the second slurry pipeline to enter the next step; S7, the mud enters the second sand content monitoring section of the slurry pipeline again, and the sand content of the mud after passing through the mud-sand separator is measured; the demodulator receives the signals collected by the second group of ultrasonic and vibration sensors, and simultaneously calculates the sand content, real-time slag discharge and cumulative slag discharge, and displays the data synchronously on the digital display screen; S8. The mud flows into the mud pool and re-enters the borehole to form a mud wall, completing the mud circulation.

[0012] As a further solution of the present invention, the filtering process in step S6 adopts a Chebyshev filter.

[0013] As a further solution of the present invention, the signal reconstruction specifically comprises the following steps: Firstly, the frequency graphs of sand-free mud and sand-containing mud are compared to identify the frequency band of vibration signals generated by sand and gravel hitting the pipeline, extract the frequency band and filter the original vibration signal of the frequency band; Then, the STFT algorithm is used to quickly cut the processed vibration signal and decompose it into several frequency bands. The average amplitude of each frequency band is compared with the overall average amplitude of the processed vibration signal to find the single characteristic frequency that is most closely related to the overall average amplitude. That is, it is considered that this characteristic frequency band can represent the overall vibration amplitude.

[0014] As a further solution of the present invention, the comparative calibration function specifically includes: after determining a specific frequency band in which sand and gravel impact the pipeline, only analyzing the parameters of the frequency band; The vibration amplitude-sand content function in the trained neural network model is used as the calibration function, and the vibration amplitude is used to determine the sand content of the mud to be tested; in actual engineering, after the construction work starts, the equipment is turned on to obtain the vibration amplitude of the sand and gravel in the mud hitting the pipeline, and the sand content in the mud is determined by comparing the vibration amplitude-sand content function; The sand content is the percentage of sand mass passing through 1s to the mud mass, and the calculation method is: In the formula, η is the sand content, m 砂 is the mass of sand and gravel, V 泥浆 , ρ 泥浆 is the volume and density of the mud; The real-time slag discharge amount calculation method is: In the formula, m 出渣 To exclude the mass of sand and gravel, η 1 , η 2 are the sand content of the mud before and after the sand removal step, respectively.

[0015] The calculation method of the cumulative slag discharge is: In the formula, m 总 is the total mass of sand and gravel removed during the operation, t 0 ,t 1 The start and end time of the job respectively.

[0016] As a further solution of the present invention, the neural network model is a BP neural network structure, and the model establishment steps are as follows: 1) Collect data sets, take the vibration amplitude generated by the specified mud parameters hitting the elbow under the same environmental parameters as a set of data, and repeatedly test to obtain data sets by adjusting environmental parameters and mud parameters; 2) Normalize the collected data to eliminate the dimension of the data; divide the processed data into training set and test set according to the ratio of 75%-25%; 3) Establish a model, input the training set data into the front end of the neural network for forward propagation, so as to obtain the prediction result of the vibration amplitude and calculate the deviation function; according to the deviation function, use the back propagation algorithm to calculate the coefficient of each parameter on the deviation, and use it to update the parameter proportion to reduce the deviation and promote function convergence until the accuracy meets the requirements; 4) Verify the model, import the test set into the trained model for testing, and evaluate its performance on new data. If the error is large, repeat step 3). If the result error is small, use it as the calibration function.

[0017] The present invention has at least the following beneficial effects: 1) The sensors used in the present invention are all non-invasive sensors, which can collect data without interfering with the original construction process and are very easy to use directly in construction operations.

[0018] 2) The present invention can obtain the flow rate, vibration amplitude and other parameters of sand-containing mud in real time, and convert them into sand content through the equipment. The data acquisition method is fast and convenient, and is easy for on-site construction personnel to use.

[0019] 3) The present invention can obtain the slag discharge amount and cumulative slag discharge amount in the construction operation in real time, and the staff can adjust the drilling parameters such as the torque and speed of the machine according to the data to improve the construction efficiency.

[0020] 4) The device and method for real-time monitoring of the sand content of bored piles provided by the present invention are easy to operate, stable in operation, and reliable in data. They can reflect the changes in key indicators such as the sand content and slag output in the mud in real time. Construction personnel can guide construction according to data changes to improve construction quality.

[0021] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the arrangement of the real-time monitoring device for sand content of the present invention; Figure 2 It is a schematic diagram of the arrangement of sensors in the sand content monitoring section of the present invention; Figure 3 It is the frequency diagram of sandless mud of the present invention; Figure 4 is the frequency diagram of sand-containing mud of the present invention; Figure 5 It is the average amplitude diagram of each frequency of the present invention.

[0023] Among them, 1-drilling rig, 2-drill rod, 3-drill bit, 4-slurry pipeline 2, 5-buried pipe, 6-mud pool, 7-slurry pipeline 1, 8-ultrasonic flow meter, 9-cable, 10-vibration sensor, 11-demodulator, 12-mud-sand separator. DETAILED DESCRIPTION

[0024] The present invention is described in detail and completely below in conjunction with the accompanying drawings. A person of ordinary skill in the art will be able to implement the present invention based on these descriptions. Before describing the present invention in conjunction with the accompanying drawings, it should be particularly pointed out that the technical solutions and technical features provided in each part of the present invention, including the following description, can be combined with each other without conflict.

[0025] In addition, the embodiments of the present invention involved in the following description are generally only a part of the embodiments of the present invention, rather than all the embodiments. Therefore, based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] The present invention is further described in detail below in conjunction with the accompanying drawings and implementations, and its specific implementation process is as follows: like Figure 1~2 As shown, the present invention provides a real-time monitoring device for sand content, including: a sand content detection module, an automatic sand removal device and a construction device; wherein the sand content detection module includes: two upstream and downstream ultrasonic flow meters 8, two upstream and downstream vibration sensors 10, a demodulator 11, a cable 9 and a slurry pipeline 7, wherein the ultrasonic flow meter 8 and the vibration sensor 10 are both non-invasive sensors, which can measure pipeline vibration and mud flow rate without affecting construction; the demodulator 11 is a data processor with built-in IEPE (integrated circuit voltage sensor) function, and this instrument can use a cable 9 to realize power supply and signal transmission functions; the ultrasonic flow meter 8 and the vibration sensor 10 are connected to the demodulator 11 by the cable 9.

[0027] The present technical solution may also include the following technical details to better achieve the technical effect: the automatic sand removal device includes a mud-sand separator 12 and a controller; wherein the demodulator 11 determines whether the sand content exceeds a predetermined value by integrating the collected signals, and if it exceeds the predetermined value, the mud-sand separator 12 is automatically opened by the controller to separate the sand and gravel particles in the mud.

[0028] The technical solution may also include the following technical details to better achieve the technical effect: the construction device is composed of a drilling rig 1, a drill rod 2, a drill bit 3, a slurry pipe 4, a buried pipe 5 and a mud pool 6; The drill pipe 2 is connected to the liquid inlet of the mud-sand separator 12 through a slurry pipe 7 , and the liquid outlet of the mud-sand separator 12 is connected to a slurry pipe 4 , and the slurry pipe 4 introduces the slurry into the mud pool 6 .

[0029] The technical solution may also include the following technical details to better achieve the technical effect: the upstream ultrasonic flowmeter 8 is arranged in the straight pipe section of the slurry pipe 7 to measure the flow velocity and flow rate of the sand-containing slurry and other parameters; the upstream vibration sensor 10 is arranged in the pipe bending section of the slurry pipe 7 to monitor the vibration frequency generated by the sand-containing slurry hitting the pipe; The downstream ultrasonic flowmeter 8 is arranged in the straight pipe section of the slurry pipe 2 4, and the downstream vibration sensor 10 is arranged in the pipe bending section of the slurry pipe 2 4 to monitor the vibration frequency generated by the sand-containing mud hitting the pipe.

[0030] The technical solution may also include the following technical details to better achieve the technical effect: the ultrasonic flow meter 8 and the vibration sensor 10 are fastened to the pipeline through a stainless steel buckle. It should be noted that corrosion marks such as rust on the pipeline need to be removed before fixing.

[0031] In another embodiment, a method using a real-time monitoring device for sand content comprises the following steps: S1. Arrange the components except the ultrasonic flowmeter 8 and the vibration sensor 10 according to the schematic diagram; S2. Take the slurry pipe 1 7 and the slurry pipe 2 4 as the sand content monitoring section, arrange the upstream ultrasonic flowmeter 8 in the straight pipe section of the slurry pipe 1 7 to measure the velocity and flow rate of the sand-containing mud, arrange the upstream vibration sensor 10 in the pipe bending section of the slurry pipe 1 7 to monitor the vibration frequency generated by the sand-containing mud hitting the pipe; the detection principle of the vibration sensor 10 is that the solid particles carried in the liquid vibrate when they collide with the pipe at the pipe bending due to inertia, and the vibration sensor 10 in contact with the pipe collects the vibration signal, and then converts the vibration signal into an electrical signal and transmits it to the demodulator 11. The detection principle of the ultrasonic flowmeter 8 is that the ultrasonic flowmeter 8 contains a transmitting sound wave port and a receiving sound wave port. The transmitting port transmits a fixed frequency sound wave to hit the suspended matter (sand and gravel) in the liquid, which is affected by the Doppler effect (when there is relative motion between the wave source and the receiving end, the frequency of the wave received by the receiving end will change) and becomes a signal of different frequency. The receiving port receives the frequency conversion signal after the impact, and the liquid flow rate and cumulative flow rate can be calculated according to the quantitative relationship between the frequency difference and the liquid flow rate.

[0032] S3. Arrange a second set of ultrasonic flowmeters 8 and vibration sensors 10 in the sand content monitoring section, and arrange them behind the mud-sand separator 12, wherein the ultrasonic flowmeter 8 is arranged at the straight line of the mud outlet slurry pipeline 2 4, and the vibration sensor 10 is installed at the bend of the mud outlet slurry pipeline 2 4; S4, connecting the above four sensors to the demodulator 11 through cables, so that the sensors can work normally after being powered on and ensure that data can be transmitted to the demodulator 11; S5, the prepared clean mud is injected into the mud pool 6, the drilling rig 1 is operated to start drilling operation, the mud and sand and gravel flow through the inner cavity of the drill pipe 2 to the slurry pipe 7 and then flow into the sand content monitoring section before the mud-sand separator 12; the mud first flows into the measuring area of ​​the first group of ultrasonic flowmeters 8, which measures the flow velocity and flow parameters of the sand-containing mud; then it flows into the measuring area of ​​the first group of vibration sensors 10, which measures the vibration frequency and amplitude and other parameters generated by the sand-containing mud hitting the pipeline; finally, the mud flows into the mud-sand separator 12; S6, the demodulator 11 collects the signals transmitted by the first group of ultrasonic flowmeters 8 and vibration sensors 10, and after filtering, signal reconstruction, and comparison with the calibration function, measures the sand content of the mud before entering the mud-sand separator 12, and synchronously displays the data on the digital display screen; if the sand content exceeds the predetermined value, the mud-sand separator 12 is automatically turned on to separate the sand and gravel in the mud; if it does not exceed the predetermined value, the mud separator is not turned on, and the mud directly flows into the slurry pipe 2 4 to enter the next step; S7, the mud enters the sand content monitoring section of the slurry pipe 24 again, the purpose of which is to measure the sand content of the mud after passing through the mud-sand separator 12; the demodulator 11 receives the signals collected by the second group of ultrasonic and vibration sensors 10, and simultaneously calculates the sand content, the real-time slag discharge amount and the cumulative slag discharge amount, and synchronously displays the data on the digital display screen; S8, the mud flows into the mud pool 6 and re-enters the borehole to form a mud wall, completing the mud circulation.

[0033] This technical solution may also include the following technical details to better achieve the technical effect: Due to the complexity of on-site construction, some noise will inevitably be mixed in the process of sensor collecting signals, so it is necessary to filter out the interfering noise through filtering. The filtering process can allow specific frequencies in the signal to pass through and greatly attenuate other frequency components.

[0034] The filtering process in step S6 adopts a Chebyshev filter, which is characterized by having the smallest error between the frequency response curve of the filter and the ideal state, and its amplitude-frequency characteristic is in the form of: In the formula, ω 0 is the expected frequency, ε is the coefficient of fluctuation, T n is the n-th order Chebyshev polynomial, which is expressed as: .

[0035] This technical solution may also include the following technical details to better achieve the technical effect: The purpose of signal reconstruction is to select and extract the characteristic frequency that is most closely related to the overall amplitude when sand and gravel hit the pipeline. The signal reconstruction specifically includes the following steps: First, the frequency graphs of sand-free mud and sand-containing mud are compared to identify the frequency band of vibration signals generated by sand hitting the pipeline ( Figure 3~4 ), extract the frequency band and filter the original vibration signal of the frequency band; Then, the STFT algorithm is used to quickly cut the processed vibration signal and decompose it into several frequency bands. The average amplitude of each frequency band is compared with the overall average amplitude of the processed vibration signal to find the single characteristic frequency that is most closely related to the overall average amplitude. It is considered that this characteristic frequency band can represent the overall vibration amplitude ( Figure 5 ).

[0036] For example: compare the frequency of collected mud without sand and gravel with mud containing sand and gravel Figure 3 , 4 , it was found that in the case of sand and gravel, there are obvious signal characteristics in the 12-25KHz frequency band, that is, it is believed that the signal frequency of sand and gravel hitting and rubbing the pipeline is mainly concentrated in the 12-25KHz high frequency area. The 0-12KHz frequency is cut using the STFT algorithm, leaving only the 10-25KHz frequency band, and then the frequency is cut into 12-14KHz, 14-16Hz, 16-18KHz, 18-20KHz, 20-22KHz, 22-24KHz, and 24-25Khz segments in units of 2KHz. After calculation, the average amplitude of the 7 frequency bands is obtained, as shown in Figure 5 By comparing the closeness of the 7-band average amplitude with the 12-25KHz frequency band average amplitude, it is determined that the sand and gravel mainly affect a certain frequency. It should be noted that in order to pursue higher accuracy, the frequency band can be divided into smaller sections, but this will also increase the workload and require on-site personnel to make trade-offs.

[0037] This technical solution can also include the following technical details to better achieve the technical effect: The comparative calibration function specifically includes: after determining the specific frequency band that is mainly affected by the impact of sand and gravel on the pipeline, only the parameters of this frequency band are analyzed; it should be noted that the specific frequency band is not a fixed frequency band, but the frequency band that is most relevant to the change in the vibration amplitude of sand-containing materials. The specific relevance depends on the size and material of the sand and gravel particles. At present, the impact of sand and gravel on vibration in domestic and foreign papers is generally above 10KHZ. The example in this article is that the vibration of sand-containing mud in the 10-25KHZ frequency band has obvious fluctuations compared with sand-free mud, so it is believed that the main vibration amplitude when adding sand and gravel is 10-25KHZ. In order to reduce the amount of calculation, it is necessary to divide a large amount of data into multiple groups of small amounts of data, and select the group of data that is most closely related to the change of the original data in the small amount of data to analyze the change law of the vibration frequency. In this article, the example is cut according to 2KHZ as a unit. This 2KHZ unit is not fixed and can be changed according to the situation. The closeness of the change is determined by observing the amplitude after segmentation and the overall amplitude. The different properties of each type of sand and gravel result in inconsistent closeness in the vibration frequency domain, and it is impossible to give a clear range of the frequency domain.

[0038] The vibration amplitude-sand content function in the trained neural network model is used as the calibration function, and the vibration amplitude is used to determine the sand content of the mud to be tested; in actual engineering, after the construction work starts, the equipment is turned on to obtain the vibration amplitude of the sand and gravel in the mud hitting the pipeline, and the sand content in the mud is determined by comparing the vibration amplitude-sand content function; The sand content is the percentage of sand mass passing through 1s to the mud mass, and the calculation method is: In the formula, η is the sand content, m 砂 is the mass of sand and gravel, V 泥浆 , ρ 泥浆 is the volume and density of the mud; The real-time slag discharge amount calculation method is: In the formula, m 出渣 To exclude the mass of sand and gravel, η 1 , η 2 are the sand content of the mud before and after the sand removal step, respectively.

[0039] The calculation method of the cumulative slag discharge is: In the formula, m 总 is the total mass of sand and gravel removed during the operation, t 0 ,t 1 The start and end time of the job respectively.

[0040] The technical solution may also include the following technical details to better achieve the technical effect: the neural network model is a BP neural network structure, which can derive nonlinear functional relationships under complex conditions. The model establishment steps are as follows: 1) Collect data sets. The vibration amplitude generated by the specified mud parameters (mud consistency, mud flow rate, sand and gravel mesh number, sand and gravel ratio, and sand content) hitting the elbow under the same environmental parameters (pipeline outer diameter and inner diameter, elbow wall thickness, elbow curvature radius, and ambient temperature) is taken as a set of data. The data sets are obtained by repeatedly testing by adjusting the environmental parameters and mud parameters. 2) Normalization: To ensure the reliability of the data, the collected data is normalized to eliminate the dimension of the data; the processed data is divided into training set and test set according to the ratio of 75%-25%; 3) Establish a model, input the training set data into the front end of the neural network for forward propagation, so as to obtain the prediction result of the vibration amplitude and calculate the deviation function; according to the deviation function, use the back propagation algorithm to calculate the coefficient of each parameter on the deviation, and use it to update the parameter proportion to reduce the deviation and promote function convergence until the accuracy meets the requirements; 4) Verify the model, import the test set into the trained model for testing, and evaluate its performance on new data. If the error is large, repeat step 3). If the result error is small, use it as the calibration function.

[0041] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the embodiments shown and described herein.

Claims

1. A real-time monitoring device for sand content, characterized in that: include: Sand content detection module, automatic sand removal device and construction device; wherein, the sand content detection module includes: two upstream and downstream ultrasonic flow meters, two upstream and downstream vibration sensors, a demodulator, a cable and a slurry pipeline; the ultrasonic flow meter and the vibration sensor are connected to the demodulator by a cable.

2. The real-time monitoring device for sand content according to claim 1, characterized in that: The automatic sand removal device includes a mud-sand separator and a controller; wherein the demodulator determines whether the sand content exceeds a preset value by integrating the collected signals, and if it exceeds the preset value, the mud-sand separator is turned on by the controller.

3. The real-time monitoring device for sand content as claimed in claim 2, characterized in that: The construction device is composed of a drilling rig, a drill rod, a drill bit, a slurry pipeline, a buried pipe and a slurry pool; The drill pipe is connected to the liquid inlet of the mud-sand separator through a slurry pipe 1, and the liquid outlet of the mud-sand separator is connected to a slurry pipe 2, and the slurry pipe 2 introduces the slurry into the mud pool.

4. The real-time monitoring device for sand content as claimed in claim 3, characterized in that: The upstream ultrasonic flowmeter is arranged in the straight pipe section of the slurry pipe 1, and the upstream vibration sensor is arranged in the pipe bending section of the slurry pipe 1; The downstream ultrasonic flowmeter is arranged in the straight pipe section of the second slurry pipe, and the downstream vibration sensor is arranged in the pipe bending section of the second slurry pipe.

5. The real-time monitoring device for sand content as claimed in claim 4, characterized in that: The ultrasonic flow meter and vibration sensor are fastened to the pipeline through stainless steel buckles.

6. A method for using the real-time monitoring device for sand content as claimed in any one of claims 4 to 5, characterized in that: The following steps are involved: S1. Arrange all components except ultrasonic flowmeter and vibration sensor; S2, using the slurry pipeline 1 and the slurry pipeline 2 as the sand content monitoring section, arranging the upstream ultrasonic flowmeter in the straight pipe section of the slurry pipeline 1, arranging the upstream vibration sensor in the pipe bending section of the slurry pipeline 1, and monitoring the vibration frequency generated by the sand-containing mud hitting the pipe; S3. Arrange a second set of ultrasonic flowmeters and vibration sensors in the sand content monitoring section, and arrange them after the mud-sand separator, wherein the ultrasonic flowmeter is arranged at the straight line of the mud outlet slurry pipeline II, and the vibration sensor is installed at the bend of the mud outlet slurry pipeline II; S4, connecting the above four sensors to the demodulator via cables; S5. Prepare clean mud and inject it into the mud pool. Operate the drilling rig to start drilling. Mud and sand and gravel flow through the inner cavity of the drill pipe to the slurry pipe and then flow into the sand content monitoring section before the mud-sand separator. The mud first flows into the first group of ultrasonic flowmeter measurement areas, which measure the flow velocity and flow parameters of the sand-containing mud. Then, it flows into the first group of vibration sensor measurement areas, which measure the vibration frequency and amplitude parameters generated by the sand-containing mud hitting the pipe. Finally, the mud flows into the mud-sand separator. S6. The demodulator collects the signals transmitted by the first group of ultrasonic flowmeters and vibration sensors, and after filtering, signal reconstruction, and comparison with the calibration function, measures the sand content of the mud before entering the mud-sand separator, and displays the data synchronously on the digital display screen; if the sand content exceeds the preset value, the mud-sand separator is automatically turned on to separate the sand and gravel in the mud; if it does not exceed the preset value, the mud separator is not turned on, and the mud directly flows into the second slurry pipeline to enter the next step; S7, the mud enters the second sand content monitoring section of the slurry pipeline again, and the sand content of the mud after passing through the mud-sand separator is measured; the demodulator receives the signals collected by the second group of ultrasonic and vibration sensors, and simultaneously calculates the sand content, real-time slag discharge and cumulative slag discharge, and displays the data synchronously on the digital display screen; S8. The mud flows into the mud pool and re-enters the borehole to form a mud wall, completing the mud circulation.

7. The real-time monitoring device for sand content as claimed in claim 6, characterized in that: The filtering process in step S6 adopts a Chebyshev filter.

8. The real-time monitoring device for sand content as claimed in claim 6, characterized in that: The signal reconstruction specifically comprises the following steps: Firstly, the frequency graphs of sand-free mud and sand-containing mud are compared to identify the frequency band of vibration signals generated by sand and gravel hitting the pipeline, extract the frequency band and filter the original vibration signal of the frequency band; Then, the STFT algorithm is used to quickly cut the processed vibration signal and decompose it into several frequency bands. The average amplitude of each frequency band is compared with the overall average amplitude of the processed vibration signal to find the single characteristic frequency that is most closely related to the overall average amplitude. That is, it is considered that this characteristic frequency band can represent the overall vibration amplitude.

9. The real-time monitoring device for sand content according to claim 6, characterized in that: The comparative calibration function specifically includes: after determining the specific frequency band where the sand and gravel hit the pipeline, only the parameters of this frequency band are analyzed; The vibration amplitude-sand content function in the trained neural network model is used as the calibration function, and the vibration amplitude is used to determine the sand content of the mud to be tested; in actual engineering, after the construction work starts, the equipment is turned on to obtain the vibration amplitude of the sand and gravel in the mud hitting the pipeline, and the sand content in the mud is determined by comparing the vibration amplitude-sand content function; The sand content is the percentage of sand mass passing through 1s to the mud mass, and the calculation method is: In the formula, η is the sand content, m 砂 is the mass of sand and gravel, V 泥浆 , ρ 泥浆 is the volume and density of the mud; The real-time slag discharge amount calculation method is: In the formula, m 出渣 To remove the mass of sand and gravel, η1 and η2 are the sand content of the mud before and after the sand removal step, respectively; The calculation method of the cumulative slag discharge is: In the formula, m 总 is the total mass of sand and gravel removed during the operation, and t0 and t1 are the start and end time of the operation respectively.

10. The real-time monitoring device for sand content as claimed in claim 9, characterized in that: The neural network model is a BP neural network structure, and the model establishment steps are as follows: 1) Collect data sets, take the vibration amplitude generated by the specified mud parameters hitting the elbow under the same environmental parameters as a set of data, and repeatedly test to obtain data sets by adjusting environmental parameters and mud parameters; 2) Normalize the collected data to eliminate the dimension of the data; divide the processed data into training set and test set according to the ratio of 75%-25%; 3) Establish a model, input the training set data into the front end of the neural network for forward propagation, so as to obtain the prediction result of the vibration amplitude and calculate the deviation function; according to the deviation function, use the back propagation algorithm to calculate the coefficient of each parameter on the deviation, and use it to update the parameter proportion to reduce the deviation and promote function convergence until the accuracy meets the requirements; 4) Verify the model, import the test set into the trained model for testing, and evaluate its performance on new data. If the error is large, repeat step 3). If the result error is small, use it as the calibration function.

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