CN115822558A - Oil well pipe column intelligent monitoring and diagnosing method and device based on multi-parameter fusion - Google Patents
Oil well pipe column intelligent monitoring and diagnosing method and device based on multi-parameter fusion Download PDFInfo
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Abstract
The invention provides an oil well pipe column intelligent monitoring and diagnosing method and device based on multi-parameter fusion, which are used for collecting acceleration, downhole flow, pressure and temperature parameters in the pipe column lifting and lowering operation process through a monitoring device, realizing intelligent monitoring and diagnosis of the pipe column lifting and lowering quantity and the downhole operation quality and achieving the purpose of monitoring and managing the downhole operation quality of an oil well. The current underground operation process lacks intelligent supervision and management means, usually adopts manual supervision and management, and has the problems of inadequate supervision and management, lack of data support in the operation process and incapability of visually reflecting the operation quality due to the influence of subjective factors. The invention installs a signal acquisition device on a pipe column and simultaneously trips an oil well together with the oil pipe column, acquires acceleration, flow, pressure and temperature parameters in the tripping process of the pipe column and stores the parameters in the device, performs playback and filtering noise reduction processing on the acquired signals after the operation is finished, and realizes the identification of the tripping number of the pipe column and the underground operation process by using the processed data based on an intelligent monitoring and diagnosis method of multi-parameter fusion.
Description
Technical Field
The invention relates to an oil well pipe column intelligent monitoring and diagnosing method and device based on multi-parameter fusion, which realizes intelligent monitoring of an oil well underground operation process by monitoring and diagnosing the pipe column pulling-out quantity and the pulling-out operation process, and achieves the purpose of standardizing and optimizing the underground operation process and technology.
Background
The underground operation is an important component of oil field production, is an underground maintenance operation technical measure for an oil well, eliminates the fault of a shaft, recovers the normal operation state of the oil-water well, continuously improves the production capacity of the oil field and obtains the best economic benefit through the operation of pulling a pipe column down. The conventional underground operation technical measures of the oil-water well comprise well repairing and pump inspection operation, conventional underground falling object salvage operation, hydraulic sand washing operation and the like; the major repair operation of the oil-water well comprises casing damage repair, complex underground falling object salvage, stuck releasing technical measures and the like; the underground operation technical measures for dredging potential and increasing yield mainly comprise hydraulic fracturing, acidification, water plugging and the like. When the oil field development enters the middle and later stages, the method is an important production increasing means for the underground maintenance operation of the old well, so the supervision and management of the construction quality of the underground operation is more important. The high-quality operation construction can reach the expected underground operation construction standard, unqualified underground operation measures not only waste the fund, but also can not achieve the effect of increasing the yield, and bring harm to the production of oil fields. The underground operation process is usually implemented by a technical means of pulling out and running down a pipe column, and the whole underground operation process can be reversely deduced and verified by monitoring and diagnosing the operation state of the pipe column in the underground operation process, so that the supervision and management of the underground operation process are realized.
The current oil field is based on the manual supervision of supervisors for the underground operation process, and the supervision effect of the operation quality is often dependent on the technical competence and literacy of the supervisors. The manual supervision mode generally has the problems that due to the influence of subjective factors, supervision and management are not in place, data support is lacked in the operation process, the operation quality cannot be reflected visually, and the like, so that the condition that false data are handed over by construction teams sometimes occurs, and the normal production of an oil field is seriously influenced.
To the urgent actual demand of borehole operation quality supervision, CN205297541U provides and has designed a borehole operation monitoring devices, and the device drives steel wire and suction record appearance through the wire rope cylinder and goes into the well, has realized the monitoring to the borehole suction operation through gathering locomotive sound and water tank temperature isoparametric, but this utility model patent is only applicable to the monitoring of borehole suction operation, can't satisfy the monitoring of other operation types. CN203547725U has designed a multipurpose concentric monitoring tubular column and is used for monitoring purpose layer pressure and temperature in the oil and gas field borehole operation process, and the device has only collected pressure and temperature in the operation process, and has not collected acceleration of tubular column borehole process and flow parameter through tubular column central channel, and the device is only suitable for well fluid injection type operation processes such as fracturing.
Disclosure of Invention
The invention provides an oil well pipe column intelligent monitoring and diagnosing method and device based on multi-parameter fusion, which aims to solve the problem that the normal production of an oil well is influenced due to the fact that the quality of underground operation does not reach the standard caused by insufficient manual supervision and management by analyzing documents, realize the digital management of the underground operation process, carry out the intelligent diagnosis and monitoring on the whole underground operation process through a large amount of pipe column operation process data acquired by a monitoring device, effectively avoid the condition that the data flow in the form or insufficient supervision occurs in the personnel supervision and management process, directly prompt a construction party to construct according to the design strictly, and create favorable conditions for further exerting the potential value of the underground operation process data by the digital management of the underground operation process.
In order to achieve the purpose, the invention adopts the technical scheme that: an oil well pipe column intelligent monitoring and diagnosing method and device based on multi-parameter fusion comprises the following steps:
1) The pipe column monitoring device is arranged on the casing pipe, the device has the functions of collecting pressure, temperature, flow and acceleration data, and the collected data comprises X-axis acceleration signals, Y-axis acceleration signals and Z-axis acceleration signals A x 、A y And A z And a casing pressure P, a casing temperature T and a flow Q signal through the oil pipe;
2) In the operation process, the intelligent pipe column monitoring device is in threaded connection with the casing pipe, the well starting and descending process is completed together, pressure, temperature, flow and acceleration data in the well starting and descending process are collected and stored in a memory inside the device; the sampling rate of pressure, temperature and flow signals is f s1 Acceleration signal sampling rate of f s2 ；
3) After the operation is finished, reading pressure, temperature and flow stored in the instrument and acceleration data of an X axis, a Y axis and a Z axis through special data reading software;
4) Intercepting effective data sections of all read signals through the Z-axis acceleration signals to obtain effective data of the well tripping operation process;
5) Carrying out subsection processing on the obtained effective data in the well descending process and the well ascending process, respectively intercepting corresponding data sections, and carrying out data synchronous alignment processing through sampling time;
6) And respectively carrying out denoising and filtering processing on the obtained data of the well descending process and the data of the well starting process, carrying out analysis and diagnosis on the processed data based on a multi-parameter fusion intelligent diagnosis method, and respectively obtaining the number of the well descending pipe columns, the number of the well starting pipe columns and the state parameters of the operation process of each pipe column.
The pipe column monitoring device in the step 1) is divided into an uphole part and a downhole part, wherein the downhole part is provided with a signal acquisition module, a battery power supply module and a data communication processing module, and the signal acquisition module comprises a temperature and pressure sensor, an accelerometer and a flowmeter; the uphole portion includes a data processing computer.
The effective data intercepting method in the step 4): performing data segmentation on the obtained Z-axis acceleration signal, wherein the length of the data segment is a settable variable M, and the total length of the Z-axis acceleration signal data is L, so that the number N of the data segments is represented as:
in the formula
Meaning rounding down.
Z-axis acceleration signal data for each segment
Averaging to obtain average value of Z-axis acceleration signal segmentation data
Setting a mean judgment parameter alpha mean If the judgment condition of the formula (2) is satisfied, the data is indicated to be valid data segments, and all valid data segments are synthesized into valid data A of the Z-axis acceleration signal z_useful (ii) a Obtaining effective data A of acceleration signals of X axis and Y axis according to a data time synchronization intercepting method x_useful 、A y_useful And effective data P of casing pressure signal useful Effective data T of oil pipe temperature useful And flow through tubing valid data Q useful 。
In step 5), a data segmentation method of the downhole process is started: the obtained effective data of the Z-axis acceleration signal is segmented, and the length of the data segment is a settable variable M useful Let the effective data length of the Z-axis acceleration signal data be L useful Then the number of valid data segments N useful Expressed as:
in the formula
Represents rounding up; the length of the last group of data is less than a settable variable M useful Is supplemented with the overall mean of the valid data.
Effective data of Z-axis acceleration of each segment
Calculating the standard deviation to obtain the standard deviation value of each effective segment data
Determining parameter beta by given standard difference value std (ii) a From
To
The data segment by data segment judgment is carried out, and the first data segment number meeting the judgment condition of the formula (4) is the initial position D of the well descending process start From a starting position D start The corresponding data segment is judged backwards, and the first data segment which does not meet the judgment condition of the formula (4) is recorded as the end position D of the well descending process end ，
And with
The data in between correspond to the Z-axis acceleration data of the downhole process.
From the end position D end Corresponding data segment
Initially, the well-raising process start location LF may be obtained according to the same processing method as described above start And the well-tripping-process end position LF end ，
And with
The data between the two is corresponding to Z-axis acceleration data in the well starting process; according to the data time synchronization intercepting method, the data of the process of starting and going down the well of the acceleration signals of the X axis and the Y axis, and the data of the process of starting and going down the well of the casing pressure, the oil pipe temperature and the flow passing through the oil pipe can be respectively obtained.
The effective casing pressure data denoising and filtering method in the multi-parameter fusion diagnosis analysis method in the step 6) comprises the following steps: adopting a sliding mean filtering method for pressure data, wherein the sliding window is W 1 Continuously take W 1 An effective casing pressure signal
Store it into W according to the collection sequence 1 In the dimension array, when the latest casing pressure is read into the array, the casing pressure signal stored earliest is replaced, and so on, the filtered pressure corresponding to the current window is
Wherein the weight coefficient δ l Satisfy the requirements of
The accelerated data denoising and filtering method comprises the following steps: at a given sliding window W 2 If the maximum value and the minimum value of the acceleration amplitude in the window meet the condition of the formula (5), recording the maximum amplitude and the position of a corresponding sampling point, and if the maximum value and the minimum value of the acceleration amplitude in the window do not meet the condition, replacing the data in the window with an integral data mean value;continue according to the distance W 2 Sliding a window backwards, if the data window continuously meeting the condition of the formula (5) is less than or equal to 3, selecting all points meeting the condition window with the maximum amplitude as effective signal points to be reserved, and replacing other data points with an integral data mean value; if the data window continuously satisfying the condition of the formula (5) is larger than 3, selecting the maximum amplitude point of the initial first window and the maximum amplitude point of the last window as effective signals, and replacing other data points with the integral mean value;
where μ denotes the lowest possible amplitude parameter.
The diagnosis method based on the number of casing pressure pipe columns in the multi-parameter fusion diagnosis analysis method in the step 6) comprises the following steps: the number of steps of pressure rise and pressure drop is calculated through a sliding window, and the specific method is as follows: given a sliding window length of W 3 Respectively calculating the mean value and the variance of the internal pressure data of the window, and recording the mean value and the window center position of the data in the window at the moment when the variance is smaller than an settable coefficient epsilon; continuously moving the window, and recording the mean value and the central position of the window when the variance of the data in the window is less than epsilon and the absolute value of the difference value between the mean value and the mean value recorded last time is greater than an adjustable coefficient gamma; and continuously carrying out statistical query on the moving window according to the method until all casing pressure data are queried through the sliding window, wherein the recorded casing pressure mean value number is the tubular column data, and the recorded window center position represents the characteristic time point of tubular column operation.
The method for diagnosing the quantity of the tubular columns based on the acceleration in the multi-parameter fusion diagnosis and analysis method in the step 6) comprises the following steps: three important action characteristics of lifting, starting to lower and completing the lowering are existed in the process of lowering the well through the operation rule of the pipe column; setting the interval between the upper action and the lower action as T 1 The interval between the start of lowering and the completion of lowering is T 2 The interval between the lowering and the lifting of the next pipe string is T 3 (ii) a Calculating the adjacent bits of positive and negative amplitude variation of acceleration in the denoised acceleration signalThe time Interval between the set points is Interval, the set points can be determined as the characteristic positions of the operation of the pipe columns when the condition of the following formula (6) is met, the number of the set points is the number of the pipe columns, and the change position points of the positive and negative amplitudes of the acceleration are recorded.
Wherein G represents a settable fixed time interval, α 1 Indicating the deviation correction factor, beta, of the interval between the upward movement and the beginning of the downward movement 1 An operation interval deviation correction coefficient, gamma, representing the completion of the lowering from the beginning of the lowering to the lowering 1 And k represents a time interval position serial number.
When the operation of the tubular column is too fast and impact loss occurs in part of the operation process, the tubular column position is identified by adopting the judgment conditions of the formula (7) and the formula (8), and the acceleration positive and negative amplitude change position points meeting the conditions are recorded.
For the initial well descending section of the pipe column, the operation speed is lower than that of an acceleration signal, the acceleration impact characteristic in the operation process is not particularly obvious, the formula (9) can be adopted to diagnose and identify the position of the pipe column, and the change position points of the positive and negative amplitudes of the acceleration meeting the conditions are recorded.
The sum of the number of the pipe columns obtained by the diagnosis and identification method is the number of the pipe columns which are diagnosed to be the final number of the pipe columns based on the acceleration parameters, and the change position points of the positive and negative amplitudes of the speed meeting the conditions are recorded as the characteristic positions of the operation of the pipe columns.
The method for diagnosing the number of the pipe columns based on the casing pressure parameter in the multi-parameter fusion diagnosis analysis method in the step 6) comprises the following steps: through a sliding window W 3 Acquiring pressure data, calculating a Mean value _ p and a variance Var _ p of pressure in a window, if the condition of the formula (10) is satisfied, indicating that a pressure change step exists, namely, indicating that the starting and descending operation process of a pipe column is finished, recording a stable initial position of the pressure step as a pipe column operation finishing position, and marking a starting operation position by a pressure local extreme value.
In which i, j represent different window positions, α p ，β p Respectively, indicating the magnitude of the pressure change threshold and the determination pressure is at the steady state threshold parameter.
The diagnosis result fusion analysis method based on the acceleration, temperature and pressure multi-parameter in the step 6) comprises the following steps: comparison analysis tubular column operation characteristic position list xi obtained based on acceleration signal diagnosis n And obtaining a list psi of operational characteristic locations of the tubular string based on the casing pressure diagnostic c Xi, xi n And psi c Respectively as a set union set to obtain the latest string operation characteristic position list set zeta m Set ζ of feature location list by column operation m The number of pipe string operations and the operating process parameters for each pipe string are available.
Compared with the prior art, the invention has the advantages that: the monitoring device is simple in structure, is directly connected with the oil pipe column through threads, completes the underground operation process together with the pipe column to acquire multi-parameter signals such as underground temperature, pressure, flow and acceleration, is high in accuracy rate of identifying the characteristic position of the pipe column by the multi-parameter fusion intelligent diagnosis method, and can realize accurate diagnosis of the operation number of the pipe column and digital recording management of the operation process.
Drawings
FIG. 1 is a schematic diagram of a downhole tool structure of an intelligent monitoring device for a tubular column;
FIG. 2 is a flow chart of a framework of an intelligent monitoring and diagnosing method for an oil well pipe string based on multi-parameter fusion;
FIG. 3 is a logic diagram for automatic screenshot and trip process division based on Z-axis acceleration effective data;
FIG. 4 is a flow chart of axial acceleration filtering and noise reduction process Z;
FIG. 5 is a logic diagram of an intelligent diagnosis method for pipe string tripping based on acceleration signals;
FIG. 6 is a logic diagram of a string tripping intelligent diagnostic method based on casing pressure signals;
FIG. 7 is a schematic diagram of the intelligent diagnosis result of tripping in and out based on a multi-parameter fusion pipe string.
In the figure: 1-upper tubular column connecting thread 2-lower tubular column connecting thread 3-battery pack 4-circuit board assembly 5-pressure temperature test unit 6-flow test unit 7-wireless communication unit.
Detailed Description
The principles and embodiments of the present invention are described in detail below with reference to the accompanying drawings.
The invention relates to a method and a device for intelligently monitoring and diagnosing an oil well pipe column based on multi-parameter fusion, wherein the structural diagram of a downhole instrument of an intelligent pipe column monitoring device is shown in figure 1, an upper pipe column connecting thread 1 and a lower pipe column thread 2 are arranged to realize effective connection with the oil pipe column so as to realize the completion of a downhole operation process together with the oil pipe column, the downhole instrument and the oil pipe column adopt a concentric structure design, when liquid is injected into a well bottom through the pipe column, the liquid can directly flow through a central channel, the flow passing through the liquid is tested through a flow testing unit 6, meanwhile, a pressure and temperature testing unit 5 can test the casing pressure and temperature at the position of the downhole instrument, a circuit board component 4 collects the triaxial acceleration of the downhole instrument of the intelligent pipe column monitoring device in the whole operation process according to a set sampling rate, besides the collection of the pressure and temperature flow data, and collected signals are stored in a downhole instrument storage chip. After the operation is finished, the data acquired by the test is played back and read from the underground instrument, the read data is analyzed and diagnosed by using an intelligent diagnosis method, and the overall monitoring and diagnosis process frame flow chart is shown in fig. 2 and mainly comprises the parts of field test data acquisition, effective data self-adaptive identification and interception, data filtering and noise reduction processing, multi-parameter fusion intelligent identification diagnosis and evaluation result output.
In the specific implementation process, two-well-time underground operation is respectively carried out in the first area and the fourth area of the three factories in the Daqing oil field, and the underground instrument of the intelligent column monitoring device and the operation tubular column complete the data acquisition of the whole operation process together. The implementation is described in detail by taking one of the wells 2-6 as an example, the well is about 1300 meters deep, the operation type is channeling, and the pressure is required to be applied in the period. And after the operation is finished, the data playback is realized through the wireless transmission module. The effective data reading and well tripping data dividing method based on the Z-axis acceleration effective data automatic screenshot and the well tripping process dividing method shown in the figure 3 are adopted to carry out effective data reading and well tripping data dividing on the original data, and the mean value judgment parameter alpha in the effective data judgment method in the implementation process mea =0.1, data segment length variable M =9000, and standard deviation value judgment parameter β in the method for segmenting and dividing downhole data std =0.05, preliminary preprocessing of the raw data by interception of the useful data and subdivision of the trip operation data.
Noise reduction and filtering processing is carried out on the casing pressure based on a sliding window averaging method, wherein a sliding window W is formed 1 The value is 20. In the noise reduction processing for the acceleration signal, a sliding window W 2 Taking the value as 35, taking the value of the lowest amplitude parameter mu as 0.05, and carrying out filtering processing on the original data of the well trip by adopting the acceleration filtering and noise reduction processing method shown in figure 4 to obtain the acceleration signal after noise reduction, wherein the acceleration signal after noise reduction has obvious tubular column operation characteristics as shown in figure 7. Respectively adopting the intelligent diagnosis methods for lifting and lowering the pipe column shown in the figures 5 and 6 to obtain the characteristic position of the operation of the pipe column and the number of the operation pipe columns by utilizing casing pressure and acceleration signals after noise reduction, and in the intelligent diagnosis method for lifting and lowering the pipe column based on the acceleration signals, the interval parameter T between the lifting action and the lowering action 1 Set to 70, start to put completed operation interval parameter T 2 Set to 230, interval parameter T between the completion of lowering and the lifting of the next tubular string 3 Set to 450, judging the threshold parameter alpha 1 、β 1 、γ 1 G is set to 1.2, 1.3,2 and 40, using the diagnosis judgment method shown in fig. 5 to obtain a list of characteristic positions corresponding to the operation of the pipe column, wherein the diagnosis result of the number of pipe columns is 132; sliding window W in intelligent diagnostic method based on casing pressure 3 The value is 25, and the pressure step judgment parameter alpha p And beta p The values are respectively 0.06 and 0.1, 123 tubular columns are obtained based on the diagnosis method of fig. 6, and a characteristic position list corresponding to the operation is obtained, and the diagnosis results of the two methods are shown in fig. 7. As can be seen from fig. 7, the result of the diagnosis of a single parameter is a missing or multiple identification of the operating position of the pipe string. And finally, through fusion diagnosis of the two methods, more accurate results can be obtained through mutual verification, the number of the fused tubular column diagnosis operations is 127, the difference between the fused tubular column diagnosis operations and the actual operation number is 128, and the accuracy is 99.2%. According to the implementation process, the diagnosis results of other wells are shown in table 1, and the average accuracy of the diagnosis results of the number of tubular column operations based on the multi-parameter fusion intelligent diagnosis method is 98.4% in the downhole operation process of 4 wells.
TABLE 1 oil well downhole operation diagnosis result comparison table
In order to prevent the problem that the normal production of an oil well is influenced by the fact that the quality of underground operation does not reach the standard due to insufficient manual supervision and management, the intelligent monitoring and diagnosing method and the intelligent monitoring and diagnosing device for the oil well pipe column based on multi-parameter fusion can effectively realize supervision of the underground operation process through intelligent monitoring and diagnosis of the pipe column operation process in the underground operation process, and have an important role in standardizing management of the underground operation process.
Claims (10)
1. The invention relates to an oil well pipe column intelligent monitoring and diagnosing method and device based on multi-parameter fusion, which is characterized in that:
1) The pipe column monitoring device is arranged on the sleeve, the device has the functions of collecting pressure, temperature, flow and acceleration data, and the collected data comprises an X axis and a Y axis in the movement process of the deviceAnd Z-axis acceleration signal A x 、A y And A z And downhole casing pressure P, casing temperature T and flow Q signal through the tubing;
2) In the operation process, the intelligent pipe column monitoring device is in threaded connection with the casing pipe, the well starting and descending process is completed together, pressure, temperature, flow and acceleration data in the well starting and descending process are collected and stored in a memory inside the device; the sampling rate of pressure, temperature and flow signals is f s1 Acceleration signal sampling rate of f s2 ；
3) After the operation is finished, reading pressure, temperature and flow stored in the instrument and acceleration data of an X axis, a Y axis and a Z axis through special data reading software;
4) Intercepting effective data segments of all read signals through the Z-axis acceleration signals to obtain effective data of the process of the well tripping operation;
5) Carrying out subsection processing on the obtained effective data in the well descending process and the well ascending process, respectively intercepting corresponding data sections, and carrying out data synchronous alignment processing through sampling time;
6) And respectively carrying out denoising and filtering processing on the obtained data of the well descending process and the data of the well starting process, carrying out analysis and diagnosis on the processed data based on a multi-parameter fusion intelligent diagnosis method, and respectively obtaining the number of the well descending pipe columns, the number of the well starting pipe columns and the state parameters of the operation process of each pipe column.
2. The intelligent monitoring and diagnosing method and device for the oil well pipe string based on the multi-parameter fusion as claimed in claim 1, wherein the pipe string monitoring device in step 1) is divided into an uphole part and a downhole part, the downhole part is provided with a signal acquisition module, a battery power supply module and a data communication processing module, wherein the signal acquisition module comprises a temperature and pressure sensor, an accelerometer and a flowmeter; the uphole portion includes a data processing computer.
3. The intelligent monitoring and diagnosing method and device for the oil well pipe column based on the multi-parameter fusion as claimed in claim 1, wherein the effective data intercepting method in step 4) is as follows: performing data segmentation on the obtained Z-axis acceleration signal, wherein the length of the data segment is a settable variable M, and the total length of the Z-axis acceleration signal data is L, so that the number N of the data segments is represented as:
in the formula
Represents rounding down;
z-axis acceleration signal data for each segment
Averaging to obtain average value of Z-axis acceleration signal segmentation data
Setting a mean judgment parameter alpha mean If the judgment condition of the formula (2) is satisfied, the data is indicated to be valid data segments, and all valid data segments are synthesized into valid data A of the Z-axis acceleration signal z_useful (ii) a Obtaining effective data A of acceleration signals of X axis and Y axis according to a data time synchronization intercepting method x_useful 、A y_useful And effective data P of casing pressure signal useful 、
Effective data T of oil pipe temperature useful And flow effective data Q through oil pipe useful ；
4. The intelligent monitoring and diagnosing method and device for the oil well pipe string based on the multi-parameter fusion as claimed in claim 1, wherein the step 5) of segmenting the data of the downhole process comprises the following steps: the obtained effective data of the Z-axis acceleration signal is segmented and the dataSegment length being settable variable M useful Let the effective data length of the Z-axis acceleration signal data be L useful Then the number of valid data segments N useful Expressed as:
in the formula
Represents rounding up; the length of the last group of data is less than a settable variable M useful Is supplemented with the overall mean of the valid data;
effective data of Z-axis acceleration of each segment
Calculating the standard deviation to obtain the standard deviation value of each effective segment data
Determining parameter beta by giving standard deviation value std (ii) a From
To
The data segment by data segment judgment is carried out, and the first data segment number meeting the judgment condition of the formula (4) is the initial position D of the well descending process start From a starting position D start The corresponding data segment is judged backwards, and the first data segment which does not meet the judgment condition of the formula (4) is recorded as the end position D of the well descending process end ，
And
the data between the two points are corresponding to Z-axis acceleration data in the well descending process;
from the end position D end Corresponding data segment
Initially, the well-raising process start location LF may be obtained according to the same processing method as described above start And the well-tripping-process end position LF end ，
And
the data between the two is corresponding to Z-axis acceleration data in the well starting process; according to the data time synchronization intercepting method, the data of the process of starting and going down the well of the acceleration signals of the X axis and the Y axis, and the data of the process of starting and going down the well of the casing pressure, the oil pipe temperature and the flow passing through the oil pipe can be respectively obtained.
5. The intelligent monitoring and diagnosis method and device for the oil well pipe column based on the multi-parameter fusion as claimed in claim 1, wherein the effective casing pressure data denoising and filtering method in the multi-parameter fusion diagnosis and analysis method in step 6) is as follows: adopting a sliding mean filtering method for pressure data, wherein the sliding window is W 1 Continuously take W 1 An effective casing pressure signal
Store it into W according to the collection sequence 1 In the dimension array, when the latest casing pressure is read into the array, the casing pressure signal stored earliest is replaced, and so on, the filtered pressure corresponding to the current window is
Wherein the weight coefficient δ l Satisfy the requirement of
6. The intelligent monitoring and diagnosis method and device for the oil well pipe column based on the multi-parameter fusion as claimed in claim 1, wherein the effective acceleration data denoising and filtering method in the multi-parameter fusion diagnosis and analysis method in step 6) is as follows: at a given sliding window W 2 If the maximum value and the minimum value of the acceleration amplitude in the window meet the condition of the formula (5), recording the maximum amplitude and the position of a corresponding sampling point, and if the maximum value and the minimum value of the acceleration amplitude in the window do not meet the condition, replacing the data in the window with an integral data mean value; continue according to the distance W 2 Sliding a window backwards, if the data window continuously satisfying the condition of the formula (5) is less than or equal to 3, selecting all points satisfying the condition window with the maximum amplitude as effective signal points to be reserved, and replacing other data points with an integral data mean value; if the data window continuously satisfying the condition of the formula (5) is larger than 3, selecting the maximum amplitude point of the initial first window and the maximum amplitude point of the last window as effective signals, and replacing other data points with the integral mean value;
where μ denotes the lowest possible amplitude parameter.
7. The intelligent monitoring and diagnosis method and device for the oil well string based on the multi-parameter fusion as claimed in claim 1, wherein the diagnosis method based on the number of casing pressure strings in the multi-parameter fusion diagnosis and analysis method of step 6) comprises the following steps: the number of steps of pressure rise and pressure drop is calculated through a sliding window, and the specific method is as follows: given a sliding window length of W 3 Separately calculating the in-window crimping dataWhen the variance is smaller than the settable coefficient epsilon, recording the mean value and the window center position of the data in the window at the moment; continuously moving the window, and recording the mean value and the central position of the window when the variance of the data in the window is less than epsilon and the absolute value of the difference value between the mean value and the mean value recorded last time is greater than an adjustable coefficient gamma; and continuously carrying out statistical query on the moving window according to the method until all casing pressure data are queried through the sliding window, wherein the recorded casing pressure mean value quantity is the tubular column data, and the recorded window center position represents the characteristic time point of tubular column operation.
8. The intelligent monitoring and diagnosis method and device for the oil well string based on the multi-parameter fusion as claimed in claim 1, wherein the diagnosis method based on the number of acceleration strings in the multi-parameter fusion diagnosis and analysis method of step 6) is as follows: three important action characteristics of lifting, starting to lower and completing the lowering are existed in the process of lowering the well through the operation rule of the pipe column; setting the interval parameter between the upper action and the lower action as T 1 The parameter of the operation interval from the beginning of the lowering to the completion of the lowering is T 2 The interval parameter between the lowering and the lifting of the next pipe string is T 3 (ii) a Calculating the Interval between adjacent position points with acceleration positive and negative amplitude changes in the denoised acceleration signal, determining the position as a tubular column operation characteristic position if the condition of the following formula (6) is met, and recording the position points with the acceleration positive and negative amplitude changes, wherein the quantity of the position points is the number of the tubular columns;
wherein G represents a settable fixed time interval, α 1 Indicating the deviation correction factor, beta, of the interval between the lifting action and the beginning of the lowering action 1 An operation interval deviation correction coefficient, gamma, representing the completion of the lowering from the beginning of the lowering to the lowering 1 The interval deviation correction coefficient between the lowering completion and the lifting action of the next pipe column is shown, and k represents a time interval position serial number;
when the operation pipe column acts too fast and impact loss occurs in part of the operation process, the judgment conditions of the formula (7) and the formula (8) are adopted to identify the position of the pipe column, and the change position points of the positive and negative amplitudes of the acceleration meeting the conditions are recorded;
for the initial well descending section of the pipe column, the operation speed is slower than an acceleration signal, the acceleration impact characteristic is not particularly obvious in the operation process, the formula (9) can be adopted for diagnosing and identifying the position of the pipe column, and the change position points of the positive and negative amplitudes of the acceleration meeting the conditions are recorded;
the sum of the number of the pipe columns obtained by the diagnosis and identification method is the number of the pipe columns which are diagnosed to be the final number of the pipe columns based on the acceleration parameters, and the change position points of the positive and negative amplitudes of the speed meeting the conditions are recorded as the characteristic positions of the operation of the pipe columns.
9. The intelligent monitoring and diagnosis method and device for the oil well pipe string based on the multi-parameter fusion as claimed in claim 1, wherein the diagnosis method for the number of pipe strings based on the casing pressure parameter in the multi-parameter fusion diagnosis and analysis method in step 6) is as follows: through the sliding window W 3 Acquiring pressure data, calculating a Mean value Mean _ p and a variance Var _ p of pressure in a window, if the condition of formula (10) is met, indicating that a pressure change step exists, namely, indicating that the starting and descending operation process of a pipe column is finished, recording a stable initial position of the pressure step as a pipe column operation completion position, and marking a starting operation position by a pressure local extreme value;
where i, j represent different window positions, α p ，β p Respectively, indicating the magnitude of the pressure change threshold and the determination pressure is at the steady state threshold parameter.
10. The intelligent monitoring and diagnosis method and device for the oil well pipe column based on the multi-parameter fusion as claimed in claim 1, wherein the fusion analysis method for the diagnosis results based on the acceleration, temperature and pressure multi-parameters in step 6 is as follows: comparison analysis tubular column operation characteristic position list xi obtained based on acceleration signal diagnosis n And obtaining a list psi of operational characteristic locations of the tubular string based on the casing pressure diagnostics c Xi, xi n And psi c Respectively as a set union set to obtain the latest string operation characteristic position list set zeta m Set ζ of feature location list by column operation m The number of operational strings and the operational process parameters of each string can be obtained.
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