Shaft leakage detection data processing method and device

Abstract

The invention discloses a shaft leakage detection data processing method and device. Firstly, band-pass filtering processing is carried out on original data, data components in an important frequency range are reserved, effective value processing is carried out on signal data in combination with a data sampling frequency and the important frequency range, and an effective value signal data sequence is obtained; and then processing the effective value data, calculating a first-class demarcation point for converting the static measurement data into the dynamic measurement data and a second-class demarcation point for converting the dynamic measurement data into the static measurement data based on a preset adjacent data point change rate threshold value and a continuous stable data point variance threshold value, and obtaining stable fixed measurement data through the first-class demarcation point and the second-class demarcation point. The static test data are efficiently extracted from the data fragments, and meanwhile invalid dynamic test data are accurately removed.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and device for processing wellbore leakage detection data. Background Art

[0002] During the operation of gas storage wells and other high-pressure gas wells, varying degrees of abnormal annular pressure may occur in the wellbore. This is generally caused by leakage in the wellbore tubing and wellhead, which poses a hidden danger to the safe operation of the gas well. Currently, ultrasonic leak detection technology through the oil pipe can accurately detect and locate leaks in the entire wellbore. Specifically, a leak detection instrument is lowered into the oil pipe. The instrument is equipped with a matching roller noise reduction straightener. Under the traction of the cable, it moves downward and upward along the inner wall of the oil pipe and captures the ultrasonic signals induced by the wellbore leakage to determine the leak location and invert the leak type and path. However, during the walking test of the leak detection instrument, the rolling friction of the roller will generate noise interference, affecting the test results.

[0003] For these reasons, a combination of walking dynamic measurement (dynamic measurement) and static fixed measurement (static measurement) is typically used during wellbore microleak detection. In well sections with abnormal signals, static measurement is performed at the target point and then moved to the next adjacent target point, continuously capturing large amounts of data fragments to accurately analyze and determine the leak location. The large number of continuous signal fragments obtained in this way will contain both static and dynamic measurement data.

[0004] In this test mode, the static measurement data is valid data, and the dynamic measurement data is invalid data captured by walking movement. Therefore, a data processing method is needed to extract the static measurement data while eliminating the invalid dynamic measurement data. Summary of the Invention

[0005] The present invention provides a wellbore leakage detection data processing method and device, which can extract static measurement data from a large number of data fragments and eliminate invalid dynamic measurement data.

[0006] According to one aspect of the present invention, a method for processing wellbore leakage detection data is provided, comprising:

[0007] Obtaining raw logging data sensed by a leak detection instrument, wherein the raw logging data includes alternating static logging data and dynamic logging data, wherein the static logging data is valid data collected when the leak detection instrument is stationary, and the dynamic logging waveform is noisy data collected when the leak detection instrument is moving; performing bandpass filtering on the raw logging data to obtain a discrete signal sequence within a preset frequency range;

[0008] Based on the data sampling frequency of the original well logging data and the preset frequency range, performing effective value processing on the discrete signal sequence to obtain an effective value signal sequence;

[0009] Identifying a first-class demarcation point representing a transition from the static measurement data to the dynamic measurement data and a second-class demarcation point representing a transition from the dynamic measurement data to the static measurement data in the effective value signal sequence;

[0010] The static measurement data in the effective value signal data sequence is extracted based on the first-class dividing point and the second-class classification point.

[0011] Optionally, bandpass filtering is performed on the raw logging data to obtain a discrete signal sequence within a preset frequency range, including:

[0012] Performing Fourier transform on the original logging data to obtain corresponding frequency domain signals;

[0013] retaining frequency domain signal components within a preset frequency range and removing frequency signal components outside the frequency range through a bandpass filter;

[0014] The retained frequency domain signal components are converted into time domain signal components and discretized into the discrete signal sequence.

[0015] Optionally, performing effective value processing on the discrete signal sequence based on the data sampling frequency of the original well logging data and the preset frequency range to obtain an effective value signal sequence includes:

[0016] A first preset number of signal cycles corresponding to the lower limit of the preset frequency range is used as a calculation window, each calculation window includes m data points, and the value of m is determined according to the lower limit of the preset frequency range and the data sampling frequency;

[0017] The effective value of each calculation window is calculated according to m and the total time of the calculation window, and the effective value signal sequence is obtained according to the effective value.

[0018] Optionally, the first type of classification point is identified by:

[0019] Starting from the starting point of the effective value signal sequence, the change rate of adjacent data points is calculated in sequence;

[0020] When the change rate of a data point exceeds a preset change rate threshold, the data point is determined to be a class dividing point.

[0021] Optionally, the second-class classification points are identified by:

[0022] After identifying the first-class dividing point, calculate the rate of change of the subsequent data points of the first-class dividing point. When the rate of change of a certain data point is less than the change rate threshold, calculate the variance of the data point and the second consecutive preset number of data points thereafter. If the variance is less than the preset variance threshold, determine that the data point is the second-class dividing point.

[0023] Optionally, extracting the static measurement data from the effective value signal data sequence based on the first-class dividing point and the second-class classification point includes:

[0024] The data points between the adjacent first-class dividing points and the second-class dividing points are assigned a value of 0 to obtain the static measurement data.

[0025] Optionally, the method further includes:

[0026] When the first calculation window is static measurement data, the data points before the first category one dividing point are retained as static measurement data; when the last dividing point is the category two dividing point, all data points after the category two dividing point are retained as static measurement data.

[0027] According to another aspect of the present invention, a wellbore leakage detection data processing device is provided, comprising:

[0028] a bandpass filtering unit configured to obtain raw logging data sensed by a leak detection instrument, wherein the raw logging data includes alternating static logging data and dynamic logging data, wherein the static logging data is valid data collected when the leak detection instrument is stationary, and the dynamic logging waveform is noisy data collected when the leak detection instrument is moving; and performing bandpass filtering on the raw logging data to obtain a discrete signal sequence within a preset frequency range;

[0029] an effective value processing unit, configured to perform effective value processing on the discrete signal sequence based on the data sampling frequency of the original well logging data and the preset frequency range to obtain an effective value signal sequence;

[0030] a classification point identification unit, configured to identify, in the effective value signal sequence, a first-class dividing point representing a transition from the static measurement data to the dynamic measurement data and a second-class dividing point representing a transition from the dynamic measurement data to the static measurement data;

[0031] A static measurement data extraction unit is used to extract the static measurement data in the effective value signal data sequence based on the first-class dividing point and the second-class classification point.

[0032] According to another aspect of the present invention, an electronic device is provided, comprising:

[0033] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the wellbore leakage detection data processing method described in any embodiment of the present invention.

[0034] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the wellbore leakage detection data processing method according to any embodiment of the present invention when executed.

[0035] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the computer program implements the wellbore leakage detection data processing method according to any embodiment of the present invention.

[0036] The technical solution of the embodiment of the present invention is to first perform bandpass filtering on the original data to retain the data components within the key frequency range, and then perform effective value processing on the signal data in combination with the data sampling frequency and the key frequency range to obtain an effective value signal data sequence; then the effective value data is processed, and based on the preset adjacent data point change rate threshold and the continuous stable data point variance threshold, a first-class dividing point for the transition from static measurement data to dynamic measurement data and a second-class dividing point for the transition from dynamic measurement data to static measurement data are calculated, and stable fixed measurement data are obtained through the first-class dividing point and the second-class dividing point, thereby realizing efficient extraction of static measurement data from data fragments and accurate elimination of invalid dynamic measurement data.

[0037] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 This is a flow chart of a method for processing wellbore leakage detection data provided by the first embodiment of the present invention;

[0040] Figure 2 This is a working diagram of a leak detection instrument provided by Example 1 of the present invention;

[0041] Figure 3 This is a flow chart of a bandpass filtering method provided by the second embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram of comparing static and dynamic measurement waveform data provided by the second embodiment of the present invention;

[0043] Figure 5 This is a flow chart of a method for performing effective value processing on a discrete signal sequence provided by the third embodiment of the present invention;

[0044] Figure 6 This is a schematic diagram of a data sequence after effective value conversion provided by the third embodiment of the present invention;

[0045] Figure 7 This is a flow chart of a classification point recognition method provided by the fourth embodiment of the present invention;

[0046] Figure 8 is a schematic diagram of a static measurement data sequence provided by the fourth embodiment of the present invention;

[0047] Figure 9 This is a structural diagram of a wellbore leakage detection data processing device provided by the fifth embodiment of the present invention;

[0048] Figure 10 It is a structural diagram of an electronic device for implementing the wellbore leakage detection data processing method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0050] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0051] Example 1

[0052] Figure 1 This is a flow chart of a method for processing wellbore leakage detection data provided by the first embodiment of the present invention. This embodiment is applicable to extracting valid static data from the logging data measured by the leakage detection instrument. The method can be executed by a wellbore leakage detection data processing device. The wellbore leakage detection data processing device can be implemented in the form of hardware and / or software. The wellbore leakage detection data processing device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0053] S110. Obtain original logging data sensed by the leakage detection instrument, wherein the original logging data includes alternating static measurement data and dynamic measurement data, the static measurement data is valid data collected when the leakage detection instrument is stationary, and the dynamic measurement waveform is noisy data collected when the leakage detection instrument is moving; perform bandpass filtering on the original logging data to obtain a discrete signal sequence within a preset frequency range.

[0054] Figure 2 This is a working diagram of a leakage detection instrument provided in Example 1 of the present invention, wherein 1 is an oil pipe, 2 is a roller centralizer, 3 is a cable, and 4 is a leakage detection instrument. The leakage detection instrument is used to implement ultrasonic leakage detection technology for oil pipes, and can accurately detect and locate leakage in the entire wellbore. Specifically, the leakage detection instrument is lowered into the oil pipe. The detection instrument is equipped with a matching roller noise reduction centralizer. Under the traction of the cable, it moves downward and upward along the inner wall of the oil pipe and captures the ultrasonic signals induced by the wellbore leakage to determine the leak location and invert the leak type and path. Although the leakage detection instrument is equipped with a roller noise reduction centralizer, the rolling friction of the roller still generates noise interference during the walking test of the instrument.

[0055] Specifically, static data is collected when the leak detection instrument reaches a preset detection point and remains stationary. Since the instrument is not moving at this time, the collected data is less susceptible to external interference and more accurately reflects the presence of leaks at the detection point, thus being considered valid data. Dynamic data is collected while the leak detection instrument is moving—that is, from one detection point to the next. Since the instrument's movement is affected by factors such as roller friction and the instrument's own vibration, the collected data can be mixed with a significant amount of noise, resulting in a high level of interference.

[0056] Bandpass filtering is the process of filtering out signals within a specific frequency range from the raw data, removing interference from other frequencies and thus improving the quality and validity of the data. A specific frequency range is pre-set based on actual detection needs and experience. For example, in wellbore leak detection, the preset frequency range is determined to be 30-50kHz based on the characteristics of the leakage signal. The raw logging data is bandpass filtered to retain only signal components within the preset frequency range and filter out signals at other frequencies. After filtering, the originally continuous analog signal is converted into a discrete digital signal sequence, discretizing the signal to facilitate subsequent calculations and analysis. A discrete signal sequence can be represented by a set of numbers, each of which represents the signal value at a specific point in time.

[0057] S120 , performing effective value processing on the discrete signal sequence based on the data sampling frequency and the preset frequency range of the original logging data to obtain an effective value signal sequence.

[0058] The sampling frequency of raw logging data refers to the number of times the signal is sampled per unit time. For example, a sampling frequency of 600kHz means 600,000 data points are collected per second. The sampling frequency determines the temporal resolution of the raw signal. The higher the frequency, the more accurately the original signal can be restored. As mentioned above, the preset frequency range is a predefined frequency interval based on actual detection requirements and the characteristics of the leakage signal. For example, a setting of 30-50kHz allows bandpass filtering to retain signals within this frequency range while removing interference from other frequencies. A discrete signal sequence is a discrete digital signal sequence converted from the continuous raw logging signal through bandpass filtering. Each data point represents the signal value at a specific point in time. RMS processing is a statistical analysis method that measures the average power of a signal over a period of time. For time-varying signals, the RMS value more accurately reflects the signal's energy characteristics. An RMS signal sequence is a new signal sequence obtained after RMS processing. Each value in the sequence represents the RMS value of the signal during the corresponding time period. In the embodiment of the present invention, compared with the original discrete signal sequence, the effective value processing can highlight the characteristics of the signal, thereby amplifying the difference between the static measurement data and the dynamic measurement data.

[0059] S130 , identifying a first-type demarcation point representing a transition from static measurement data to dynamic measurement data and a second-type demarcation point representing a transition from dynamic measurement data to static measurement data in the effective value signal sequence.

[0060] The first-class dividing point represents the position where the static measurement data changes to the dynamic measurement data. Before the first-class dividing point, the signal is in the static measurement stage and the effective value is relatively stable; after passing the first-class dividing point, the signal enters the dynamic measurement stage and the effective value begins to fluctuate greatly. Correspondingly, the second-class dividing point represents the position where the dynamic measurement data changes to the static measurement data. Before the second-class dividing point, the signal is in the dynamic measurement stage and the effective value fluctuates greatly; after passing the second-class dividing point, the signal enters the static measurement stage and the effective value tends to be stable. Identifying the first-class dividing point and the second-class dividing point is used to distinguish the static measurement data from the dynamic measurement data in the effective value signal sequence.

[0061] S140. Extract static measurement data from the effective value signal data sequence based on the first-class dividing point and the second-class classification point.

[0062] Because the first-class dividing points and the second-class dividing points appear alternately in the effective value signal data sequence, the effective value signal data sequence can be divided into different intervals according to the dividing points, and then whether it is static measurement data can be determined according to the nature of the interval.

[0063] The technical solution of the embodiment of the present invention is to first perform bandpass filtering on the original data to retain the data components within the key frequency range, and then perform effective value processing on the signal data in combination with the data sampling frequency and the key frequency range to obtain an effective value signal data sequence; then the effective value data is processed, and based on the preset adjacent data point change rate threshold and the continuous stable data point variance threshold, a first-class dividing point for the transition from static measurement data to dynamic measurement data and a second-class dividing point for the transition from dynamic measurement data to static measurement data are calculated, and stable fixed measurement data are obtained through the first-class dividing point and the second-class dividing point, thereby realizing efficient extraction of static measurement data from data fragments and accurate elimination of invalid dynamic measurement data.

[0064] Example 2

[0065] Figure 3 This is a flow chart of a bandpass filtering method provided by the second embodiment of the present invention. This embodiment further explains the above-mentioned first embodiment. Figure 2 As shown, the method includes:

[0066] S310: Perform Fourier transform on the original logging data to obtain corresponding frequency domain signals.

[0067] Raw logging data is recorded in the time domain, making it difficult to directly analyze the frequency components of the signal. Converting this data to the frequency domain using a Fourier transform allows the determination of the signal's frequency and the amplitude of each frequency component. During testing, the first waveform sequence is typically a stationary waveform. Assume that the value of the raw waveform at time t is f(t), and the total number of data points is n. First, perform a Fourier transform on the raw waveform to obtain the frequency domain signal.

[0068] S320: retain frequency domain signal components within a preset frequency range and remove frequency signal components outside the frequency range through a bandpass filter.

[0069] Based on the characteristics of wellbore leakage signals, past detection experience, and theoretical analysis, the preset frequency range is preferably 30-50kHz, which is common in wellbore leakage detection. This range can include as many effective signal frequencies related to leakage as possible while avoiding the frequencies of noise and interference signals as much as possible.

[0070] The frequency domain signal obtained by Fourier transform is input into the designed bandpass filter, retaining its components in the frequency range of 30-50kHz and eliminating the components in the rest of the frequency range. Bandpass filtering is achieved by the following formula:

[0071] X(f)=∫f(t)e (-jft) dt (1)

[0072]

[0073] S330: Convert the retained frequency domain signal components into time domain signal components and discretize them into discrete signal sequences.

[0074] Although the time domain signal is obtained after the inverse Fourier transform, it is still in the form of a continuous analog signal. Discretization is to sample the continuous time domain signal at a certain time interval and convert it into a series of discrete data points. The discretized signal can better meet the requirements of subsequent data processing algorithms.

[0075] Specifically, the frequency-domain signal H(f) is converted into a time-domain signal, denoted as signal(t). Then, according to the number of data points n of the original signal, signal(t) is discretized and denoted as signal(i), where i = 1, 2, 3, ..., n. The original signal sampling frequency is Fo, which is a fixed value. Figure 4 This is a schematic diagram of a comparison of static and dynamic waveform data provided by the second embodiment of the present invention. Figure 4 As shown, 5 is the static measurement data waveform; 6 is the dynamic measurement data waveform.

[0076] Example 3

[0077] Figure 5 This is a flow chart of a method for performing effective value processing on a discrete signal sequence provided by the third embodiment of the present invention. This embodiment further explains and illustrates the above-mentioned first embodiment. Figure 5 As shown, the method includes:

[0078] S510: Use a first preset number of signal cycles corresponding to the lower limit of the preset frequency range as a calculation window, each calculation window contains m data points, and the value of m is determined according to the lower limit of the preset frequency range and the data sampling frequency.

[0079] Signals are composed of components of different frequencies, and signals of different frequencies have different periods. In wellbore leakage detection, the preset frequency range (such as 30-50kHz mentioned above) is determined based on the characteristics of the leakage signal, where the signal period corresponding to the lower limit frequency is relatively fixed. Combining a certain number (i.e., the first preset number) of lower limit frequency signal periods into a calculation window is to perform statistical analysis on the signal within this relatively stable time period, such as calculating the effective value, so as to more accurately reflect the energy characteristics of the signal over a period of time, because a single data point or a signal in a very short time may be interfered with by factors such as noise and cannot represent the overall characteristics of the signal.

[0080] Assume that the lower limit frequency of the preset frequency range is Fmin. According to the relationship between period T and frequency F, the signal period corresponding to the lower limit frequency can be obtained. For example, if the lower limit frequency is 30000Hz, the signal period is The first preset number is determined based on the actual situation and analysis requirements. In order to ensure the accuracy and stability of statistics, at least 10 signal cycles with a lower frequency limit are selected as a calculation window, that is, the first preset number is 10. According to the frequency lower limit of 30kHz, the number of data points of 10 signal cycles is taken, recorded as m, and the total time is T m .

[0081] S520 , calculating the effective value of each calculation window according to m and the total time of the calculation window, and obtaining an effective value signal sequence according to the effective value.

[0082] The effective value of a calculation window is calculated according to the following formula:

[0083]

[0084] The total number of signal segments after the effective value conversion is recorded as p, and a new data series formed by the conversion is recorded as: RMS(i), i = 1, 2, 3....., p.

[0085] Figure 6 This is a schematic diagram of a data sequence after effective value conversion provided by the third embodiment of the present invention. It can be seen that after the effective value calculation, the difference between the static measurement data and the dynamic measurement data is significantly widened.

[0086] Example 4

[0087] Figure 7 This is a flow chart of a classification point recognition method provided by the fourth embodiment of the present invention. This embodiment further explains the above-mentioned first embodiment. Figure 7 As shown, the method includes:

[0088] S710 , starting from the starting point of the effective value signal sequence, sequentially calculating the change rates of adjacent data points.

[0089] S720: When the change rate of a certain data point exceeds a preset change rate threshold, determine that the data point is a first-class dividing point.

[0090] Based on the rule that the effective value of the static measurement signal fluctuates less and the effective value of the dynamic measurement signal fluctuates more, the RMS(i) effective value data is processed and the dividing point between the static measurement signal and the dynamic measurement signal is found (defined as a type of dividing point). First, the dividing point where the static measurement data changes to the dynamic measurement data is determined, and the change rate of the subsequent data is calculated with the previous data:

[0091] change_rate=|(RMS(i)-RMS(i-1)) / RMS(i-1)| (5)

[0092] If the above-mentioned change rate exceeds a preset fluctuation change threshold at a certain data point (meaning that the data fluctuation begins to increase), it can be determined that this point is the dividing point between the static measurement signal and the dynamic measurement signal.

[0093] S730. After identifying a first-class dividing point, calculate the rate of change of the data points following the first-class dividing point. When the rate of change of a certain data point is less than the rate of change threshold, calculate the variance of the data point and the second preset number of consecutive data points thereafter. If the variance is less than the preset variance threshold, determine that the data point is a second-class dividing point.

[0094] Then, the dividing point where the dynamic measurement data changes to the static measurement data is determined (defined as the second-class dividing point), and the change rate of the subsequent data is calculated with the previous data, and the change rate is as described above.

[0095] If the above-mentioned rate of change is less than the preset fluctuation change threshold at a certain data point (meaning that the data fluctuation begins to decrease), then the variance value of 50 points starting from this point is judged. If the variance value of the 50 points is less than the preset variance threshold, then the point is judged as the dividing point between the dynamic measurement signal and the static measurement signal.

[0096] Among them, the setting of the fluctuation change threshold and the variance threshold needs to take into account multiple factors. For example, the moving speed of the leakage detection instrument during dynamic measurement will affect the data fluctuation. The faster the moving speed, the greater the interference may be, and the more obvious the signal fluctuation. At the same time, the degree of friction between the roller and the well wall is different, and the noise size and frequency also vary, which in turn affects the fluctuation characteristics of the data; the accuracy of the speed measurement, the trajectory of the wellbore, and whether the wellbore is tilted are all closely related to the fluctuation of the data. The accuracy of the speed measurement will affect the judgment of the fluctuation of the dynamic measurement data; when the wellbore trajectory is complex or there is a tilt, the external forces and interferences to the instrument during operation are more complex, making the data fluctuation more complex. In summary, in order to reasonably set the fluctuation change threshold and the variance threshold, it is necessary to consider the above factors comprehensively.

[0097] In an embodiment of the present invention, extracting static measurement data from an effective value signal data sequence based on a first-class dividing point and a second-class classification point includes:

[0098] The data points between the adjacent first-class dividing points and second-class dividing points are assigned a value of 0 to obtain static measurement data.

[0099] Because dynamic measurement data contains a significant amount of noise, it contributes little to effective information for wellbore leak detection. To more accurately analyze whether a wellbore leak exists, dynamic measurement data must be removed from the raw data. The first and second cutoff points define the boundaries between static and dynamic measurement data. The data between adjacent first and second cutoff points falls within the dynamic measurement data interval. Assigning a value of 0 to data points within this interval effectively removes the dynamic measurement data, leaving the remaining data as relatively pure static measurement data. Figure 8 3 is a schematic diagram of a static measurement data sequence provided by the fourth embodiment of the present invention, where two adjacent static measurement data segments are separated by an amplitude of 0.

[0100] In an embodiment of the present invention, the method may further include the following steps:

[0101] When the first calculation window is static measurement data, the data points before the first Class I cutoff point are retained as static measurement data; when the last cutoff point is a Class II cutoff point, all data points after the Class II cutoff point are retained as static measurement data.

[0102] When the first calculation window is static measurement data, retain the data before the first class I cutoff point

[0103] If the first calculation window contains static data, it indicates that the instrument was stationary from the start of the test until the first Class I demarcation point. The data collected during this period is minimally disturbed and provides accurate and reliable data reflecting the wellbore condition. The Class I demarcation point marks the transition from static to rotating measurements. Before the first Class I demarcation point, the data is considered static. To ensure the integrity of the static data, the data points before the first Class I demarcation point are retained.

[0104] If the last demarcation point is a Class II demarcation point, it indicates that the instrument has been collecting data in a static state since then, and the data has returned to a stable state, representing static measurement data. If the data in the final stage shows significant fluctuations before stabilizing, this indicates a transition from dynamic measurement to static measurement, and this portion of the data should be retained. Retaining all data points after the Class II demarcation point ensures that no valid static measurement data is missed. When analyzing wellbore leaks, complete static measurement data improves accuracy; ignoring this data can lead to misjudgments or omissions of leaks.

[0105] In summary, the technical solutions of the embodiments of the present invention include at least the following advantages and improvements:

[0106] 1. Accurately separate static and dynamic measurement data, effectively remove dynamic measurement noise interference, and obtain pure static measurement data. Based on the difference in the effective value fluctuations of static and dynamic measurement signals, combined with bandpass filtering, effective value processing and demarcation point identification, it can accurately capture weak signals related to leakage, reduce misjudgments and missed judgments, and provide a reliable basis for accurately determining the location, type and extent of wellbore leakage, thereby ensuring the safe operation of gas wells.

[0107] 2. The use of discrete integration and preset thresholds to process data makes the calculation process relatively simple and efficient. The reasonable method of determining the calculation window and the number of data points reduces unnecessary calculations and improves data processing efficiency.

[0108] Example 5

[0109] Figure 9 This is a structural diagram of a wellbore leakage detection data processing device provided by the fifth embodiment of the present invention. Figure 9 As shown, the device includes:

[0110] The bandpass filtering unit 910 is used to obtain the original logging data sensed by the leakage detection instrument, wherein the original logging data includes static measurement data and dynamic measurement data that appear alternately. The static measurement data is the valid data collected when the leakage detection instrument is stationary, and the dynamic measurement waveform is the noisy data collected when the leakage detection instrument is moving. The original logging data is bandpass filtered to obtain a discrete signal sequence within a preset frequency range.

[0111] The effective value processing unit 920 is used to perform effective value processing on the discrete signal sequence based on the data sampling frequency and the preset frequency range of the original well logging data to obtain an effective value signal sequence.

[0112] The classification point identification unit 930 is used to identify the first-class dividing point representing the transition from static measurement data to dynamic measurement data and the second-class dividing point representing the transition from dynamic measurement data to static measurement data in the effective value signal sequence.

[0113] The static measurement data extraction unit 940 is used to extract the static measurement data in the effective value signal data sequence based on the first-class dividing point and the second-class classification point.

[0114] Optionally, when performing bandpass filtering on the original well logging data to obtain a discrete signal sequence within a preset frequency range, the bandpass filtering unit 910 executes:

[0115] Perform Fourier transform on the original logging data to obtain the corresponding frequency domain signal;

[0116] The frequency domain signal components within a preset frequency range are retained by a bandpass filter and the frequency signal components outside the frequency range are eliminated;

[0117] The retained frequency domain signal components are converted into time domain signal components and discretized into discrete signal sequences.

[0118] Optionally, the effective value processing unit 920 is configured to execute:

[0119] A first preset number of signal cycles corresponding to the lower limit of the preset frequency range is used as a calculation window, each calculation window contains m data points, and the value of m is determined according to the lower limit of the preset frequency range and the data sampling frequency;

[0120] According to m and the total time of the calculation window, the effective value of each calculation window is calculated, and the effective value signal sequence is obtained according to the effective value.

[0121] Optionally, the classification point identification unit 930 is configured to identify a type of classification point by:

[0122] Starting from the starting point of the effective value signal sequence, the change rate of adjacent data points is calculated in sequence;

[0123] When the change rate of a data point exceeds a preset change rate threshold, the data point is determined to be a class dividing point.

[0124] Optionally, the classification point identification unit 930 is configured to identify the second-class classification points in the following manner:

[0125] After identifying a first-class dividing point, calculate the rate of change of the data points subsequent to the first-class dividing point. When the rate of change of a certain data point is less than the change rate threshold, calculate the variance of the data point and the second preset number of consecutive data points thereafter. If the variance is less than the preset variance threshold, determine that the data point is a second-class dividing point.

[0126] Optionally, the static measurement data extraction unit 940 is configured to perform:

[0127] The data points between the adjacent first-class dividing points and second-class dividing points are assigned a value of 0 to obtain static measurement data.

[0128] Optionally, the static measurement data extraction unit 940 is further configured to execute:

[0129] When the first calculation window is static measurement data, the data points before the first category I cutoff point are retained as static measurement data; when the last cutoff point is a category II cutoff point, all data points after the category II cutoff point are retained as static measurement data.

[0130] The wellbore leakage detection data processing device provided in the embodiment of the present invention can execute the wellbore leakage detection data processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0131] Example 6

[0132] Figure 10A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0133] like Figure 10 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0134] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0135] Processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. Processor 11 executes the various methods and processes described above, such as the wellbore leakage detection data processing method.

[0136] In some embodiments, the wellbore leakage detection data processing method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the wellbore leakage detection data processing method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the wellbore leakage detection data processing method in any other suitable manner (e.g., via firmware).

[0137] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0138] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0139] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0140] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0141] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0142] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0143] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0144] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for processing wellbore leakage detection data, characterized in that: include: Obtaining raw logging data sensed by a leak detection instrument, wherein the raw logging data includes alternating static logging data and dynamic logging data, wherein the static logging data is valid data collected when the leak detection instrument is stationary, and the dynamic logging waveform is noisy data collected when the leak detection instrument is moving; performing bandpass filtering on the raw logging data to obtain a discrete signal sequence within a preset frequency range; Based on the data sampling frequency of the original well logging data and the preset frequency range, performing effective value processing on the discrete signal sequence to obtain an effective value signal sequence; Identifying a first-class demarcation point representing a transition from the static measurement data to the dynamic measurement data and a second-class demarcation point representing a transition from the dynamic measurement data to the static measurement data in the effective value signal sequence; The static measurement data in the effective value signal data sequence is extracted based on the first-class dividing point and the second-class classification point.

2. The method according to claim 1, characterized in that The method of acquiring raw logging data sensed by the leakage detection instrument and performing bandpass filtering on the raw logging data to obtain a discrete signal sequence within a preset frequency range includes: Performing Fourier transform on the original logging data to obtain corresponding frequency domain signals; retaining frequency domain signal components within a preset frequency range and removing frequency signal components outside the frequency range through a bandpass filter; The retained frequency domain signal components are converted into time domain signal components and discretized into the discrete signal sequence.

3. The method according to claim 1, characterized in that The performing effective value processing on the discrete signal sequence based on the data sampling frequency of the original well logging data and the preset frequency range to obtain an effective value signal sequence includes: A first preset number of signal cycles corresponding to the lower limit of the preset frequency range is used as a calculation window, each calculation window includes m data points, and the value of m is determined according to the lower limit of the preset frequency range and the data sampling frequency; The effective value of each calculation window is calculated according to m and the total time of the calculation window, and the effective value signal sequence is obtained according to the effective value.

4. The method according to claim 1, wherein The first type of classification points are identified by: Starting from the starting point of the effective value signal sequence, the change rate of adjacent data points is calculated in sequence; When the change rate of a data point exceeds a preset change rate threshold, the data point is determined to be a class dividing point.

5. The method according to claim 4, characterized in that The second-class classification points are identified as follows: After identifying the first-class dividing point, calculate the rate of change of the subsequent data points of the first-class dividing point. When the rate of change of a certain data point is less than the change rate threshold, calculate the variance of the data point and the second consecutive preset number of data points thereafter. If the variance is less than the preset variance threshold, determine that the data point is the second-class dividing point.

6. The method according to claim 1, wherein The step of extracting the static measurement data from the effective value signal data sequence based on the first-class dividing point and the second-class classification point includes: The data points between the adjacent first-class dividing points and the second-class dividing points are assigned a value of 0 to obtain the static measurement data.

7. The method according to claim 6, characterized in that The method further comprises: When the first calculation window is static measurement data, the data points before the first category one dividing point are retained as static measurement data; when the last dividing point is the category two dividing point, all data points after the category two dividing point are retained as static measurement data.

8. A wellbore leakage detection data processing device, characterized in that: include: a bandpass filtering unit configured to obtain raw logging data sensed by a leak detection instrument, wherein the raw logging data includes alternating static logging data and dynamic logging data, wherein the static logging data is valid data collected when the leak detection instrument is stationary, and the dynamic logging waveform is noisy data collected when the leak detection instrument is moving; and performing bandpass filtering on the raw logging data to obtain a discrete signal sequence within a preset frequency range; an effective value processing unit, configured to perform effective value processing on the discrete signal sequence based on the data sampling frequency of the original well logging data and the preset frequency range to obtain an effective value signal sequence; a classification point identification unit, configured to identify, in the effective value signal sequence, a first-class dividing point representing a transition from the static measurement data to the dynamic measurement data and a second-class dividing point representing a transition from the dynamic measurement data to the static measurement data; A static measurement data extraction unit is used to extract the static measurement data in the effective value signal data sequence based on the first-class dividing point and the second-class classification point.

9. An electronic device, characterized in that: The electronic device comprises: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the wellbore leakage detection data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the wellbore leakage detection data processing method according to any one of claims 1 to 7 when executed.

11. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the wellbore leakage detection data processing method according to any one of claims 1 to 7.

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