Method for identifying mineshaft micro-leakage through distributed temperature gradient sensing data

Abstract

The invention relates to the technical field of geothermal exploitation of petroleum and natural gas, and discloses a method for identifying micro-leakage of a shaft by using distributed temperature gradient sensing data, which comprises the following steps of: S1, laying optical fibers; optical fiber sensors are arranged on the outer wall of the sleeve outside the oil pipe along the whole length of the shaft; s2, data acquisition; s3, data processing: extracting data low-frequency thermal strain data from the DAS, performing temperature gradient processing through LF-DAS data and performing temperature gradient processing through absolute temperature data of DTS data to obtain distributed temperature gradient data; carrying out visualization processing on the DTGS data to generate a heat map of temperature distribution and strain change; s4, DTGS and DTS threshold judgment is carried out in leakage identification, a leakage identification model is established, and the state and the development trend of leakage are predicted in combination with multi-dimensional signal features; and carrying out micro-leakage early-stage identification and early warning. According to the invention, DTGS data are utilized to identify the micro-leakage of the shaft, so that the detection precision of temperature gradient change and the identification capability of the micro-leakage of the low-flow-speed and small-flow shaft are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil, natural gas and geothermal exploitation, and particularly relates to a method for identifying wellbore micro-leakage by using distributed temperature gradient sensing data. Background Art

[0002] With the increasing demand for real-time monitoring of wellbore integrity in the oil and gas and geothermal industries, higher requirements are put forward for the real-time detection and accurate positioning of wellbore leakage. Traditional leakage detection methods, such as point sensors, noise logging tools (Noise Logging Tool, NLT) and production logging tools (Production Logging Tool, PLT), are effective under certain conditions, but generally have limitations such as high layout cost, poor real-time performance, limited coverage, difficulty in identifying micro-leakage or low identification accuracy, and it is difficult to meet the refined monitoring requirements of modern complex downhole environments. In recent years, the development of distributed fiber optic sensing technology (Distributed Fiber Optic Sensing, DFOS) has provided a new solution for wellbore micro-leakage detection.

[0003] The existing literature Sidenko, E., Tertyshnikov, K., Lebedev, M. et al. 2022. Experimental Study of Temperature Change Effect on Distributed Acoustic Sensing Continuous Measurements. Geophysics 87(3): D111–D122. provides the applications of distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) in leakage detection, production profile monitoring and downhole fluid dynamics research. DTS can usually effectively capture large temperature anomalies of the order of 0.1 °C temperature difference, but its ability to identify micro-leakage in wellbores with low flow rate and small flow rate of the order of 0.01 °C temperature difference is still insufficient. Therefore, the ability to identify wellbore micro-leakage based on temperature data still needs to be further improved. Summary of the Invention

[0004] In view of the problems of insufficient accuracy, poor real-time performance, and poor adaptability to complex well conditions in the existing wellbore micro-leakage detection technology, the present invention proposes a method for identifying wellbore micro-leakage by using distributed temperature gradient sensing data. By extracting DTGS data from LF-DAS data and using the processed DTGS data for high-precision identification of wellbore micro-leakage, the detection accuracy of temperature gradient changes and the identification ability for micro-leakage with low flow velocity and small flow rate are significantly improved. The present invention processes the collected DAS raw data for LF-DAS and DTGS to calculate the temperature gradient changes, and combines the DTS absolute temperature analysis to accurately identify the location, fluid velocity, and trend of wellbore micro-leakage. At the same time, by utilizing the long-distance coverage ability of optical fiber sensing and the advantages of multi-dimensional data analysis, real-time monitoring, accurate identification and positioning, and early warning of leakage in complex downhole environments are realized. From the optical fiber layout to signal acquisition and processing, to multi-dimensional feature fusion and leakage detection model construction, the proposed solution forms a complete technical implementation path, and combined with multi-parameter dynamic monitoring, it realizes all-round support for wellbore integrity management.

[0005] The present invention provides the following technical solutions:

[0006] The present invention provides a method for identifying wellbore micro-leakage by using distributed temperature gradient sensing data, which includes the following steps:

[0007] S1: Optical fiber layout

[0008] An optical fiber sensor is laid along the entire length of the outer wall of the casing outside the tubing in the wellbore. The optical fiber sensor includes a single-mode optical fiber and a multi-mode optical fiber. The single-mode optical fiber is used to collect DAS data, the multi-mode optical fiber is used to collect DTS data, and the ends of the single-mode optical fiber and the multi-mode optical fiber are respectively connected to a DAS demodulator and a DTS demodulator;

[0009] S2: Data acquisition

[0010] The temperature distribution data along the optical fiber path, that is, the DTS data, is obtained through the DTS demodulator; the dynamic strain and broadband vibration signal data, that is, the DAS data, is obtained through the DAS demodulator;

[0011] S3: Data processing

[0012] The low-frequency thermal strain data, that is, the LF-DAS data, is extracted from the DAS. The temperature gradient is processed through the LF-DAS data and the absolute temperature data of the DTS data is used for temperature gradient processing to obtain the distributed temperature gradient data, that is, the DTGS data; and the DTGS data is visually processed to generate a heat map of temperature distribution and strain changes, and is stored for backup;

[0013] S4: Leakage identification

[0014] Perform DTGS and DTS threshold judgments, establish a leakage identification model based on the DTGS data extracted by LF-DAS, and combine multi-dimensional signal features to predict the state and development trend of leakage; perform early identification and warning of micro-leakage.

[0015] According to some embodiments, step S3 includes the following steps:

[0016] S31: LF-DAS data extraction, including the following processing steps:

[0017] S311: Low-pass filtering: Apply a low-pass filter of 0 - 0.5 Hz to remove high-frequency noise and retain the low-frequency signals related to thermal strain; after low-pass filtering of 0 - 0.5 Hz, the original DAS data is extracted as LF-DAS signals, and the plume-like signals on its two-dimensional image represent the fluid flow velocity;

[0018] S312: Downsampling: Reduce the sampling rate of the original signal to 1 Hz;

[0019] S313: Median filtering: Eliminate possible impulse noise;

[0020] S314: DC drift correction: Perform DC drift correction on the low-frequency components in the signal to eliminate the signal offset caused by device noise;

[0021] S32: Distributed temperature gradient data extraction

[0022] Calculate the temperature gradient of the absolute temperature data in the DTS data to obtain the DTGS data; assume that the temperature T is a function of time t and position L, the rate of change of temperature with time is the time gradient of temperature, and the calculation of the temperature gradient uses a discrete form. The discrete time gradient formula is as follows:

[0023]

[0024] In the above formula, T is the temperature change, t 1、 t2 represents different times, Δt is the time change, Tt2 is the temperature at time t2, and Tt1 is the temperature at time t1;

[0025] Calculate the temperature gradient of the LF-DAS data to obtain DTGS data to accurately characterize the temperature gradient change, thereby realizing the early detection of micro-leakage or temperature fluctuations. Consider the LF-DAS data as DTS data and apply formula (1) to extract the DTGS data.

[0026] According to some embodiments, the following steps are further included:

[0027] S33: Data Visualization and Storage. Visualize the DTGS data and DTS data, draw a time series graph to reflect the dynamic process of leakage development in detail; and store and back up the DTGS data and DTS data.

[0028] According to some embodiments, step S4 specifically includes the following steps:

[0029] S41: DTGS and DTS Threshold Judgment

[0030] Based on the DTGS data extracted from the LF-DAS data, establish a leakage identification model, which specifically includes location identification and status prediction, that is: locate the leakage point x through the spatial distribution of the peak values of the DTGS and DTS data signals leak , and combine multi-dimensional signal features, including leakage location, fluid velocity, and absolute temperature change, to predict the status and development trend of the leakage. The leakage identification model is shown in the following formula:

[0031]

[0032] In the above formula, P leak is the amplitude peak value of the leakage point, and x leak is the maximum value among the peak values;

[0033] S42: Early Identification and Warning of Wellbore Micro-Leakage

[0034] Set the warning threshold P threshold , when the fusion signal strength exceeds the warning threshold, use the identification model to identify the wellbore micro-leakage phenomenon and location, and trigger an early warning. The identification model is shown in the following formula:

[0035]

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention proposes a method for identifying wellbore micro-leakage using distributed temperature gradient sensing data. Using DTGS data to identify wellbore micro-leakage significantly improves the detection accuracy of temperature gradient changes and the identification ability of wellbore micro-leakage with low flow velocity and small flow rate. The present invention overcomes the limitations of traditional wellbore leakage detection methods in terms of real-time performance, accuracy, and adaptability to complex well conditions, significantly improving the reliability, stability, and economy of monitoring, and providing an innovative solution for wellbore integrity management. Specifically, it is reflected in the following aspects:

[0038] (1) High-precision Leakage Detection

[0039] The present invention accurately identifies the phenomenon of micro-leakage in the wellbore through DTS absolute temperature data and DTGS data extracted from LF-DAS data. The temperature gradient calculation and analysis of DTGS significantly improve the spatial and temporal resolution of leakage detection, and have higher leakage identification accuracy in complex well conditions. The method of the present invention for processing DTGS based on LF-DAS realizes the accurate identification and dynamic monitoring of micro-leakage in the wellbore by extracting the low-frequency strain characteristics caused by temperature changes and using them to calculate the dynamic changes of the temperature gradient; an automatic leakage identification method based on DTS absolute temperature data and DTGS data extracted from LF-DAS data is established, which significantly improves the identification accuracy and reliability of the micro-leakage position, fluid velocity and development trend in the wellbore, realizes the real-time monitoring and early warning of the micro-leakage phenomenon, and provides efficient and reliable technical support for wellbore integrity management.

[0040] (2) High efficiency and economy

[0041] Compared with traditional point sensors and production logging tools (such as PLT, NLT), the present invention adopts distributed fiber optic sensing technology, which can provide continuous monitoring information along the entire path of the optical fiber without additional hardware deployment, greatly reducing the system construction and maintenance costs. Brief description of the drawings

[0042] Figure 1 It is a flow chart of the method for identifying micro-leakage in the wellbore using distributed temperature gradient sensing data provided by an embodiment of the present invention.

[0043] Figure 2 It is a comparison waterfall chart of DTGS data (a) extracted from LF-DAS and DTGS data (b) extracted from DTS provided by an embodiment of the present invention. Detailed implementation manners

[0044] To solve the problems of insufficient accuracy, poor real-time performance and poor adaptability to complex well conditions in the existing wellbore micro-leakage detection technology, the present invention proposes a method for identifying micro-leakage in the wellbore using distributed temperature gradient sensing (DTGS) data. Optical fibers can be arranged along the entire length of the wellbore. First, low-frequency distributed acoustic sensing (LF-DAS) data is extracted from the original DAS data as temperature (thermal strain) data, then DTGS data is extracted from the LF-DAS data, and finally, threshold judgment is carried out by combining DTGS and DTS data to conduct real-time monitoring, accurate identification and positioning, and early warning of micro-leakage in the wellbore.

[0045] The present invention will be described in detail below in conjunction with embodiments and the accompanying drawings. However, it should be understood that the embodiments and the drawings are only used for exemplary description of the present invention and do not constitute any limitation to the protection scope of the present invention. All reasonable transformations and combinations within the scope of the inventive concept of the present invention fall within the protection scope of the present invention.

[0046] Embodiment 1

[0047] As Figure 1 , this embodiment provides a method for identifying wellbore micro-leakage using distributed temperature gradient sensing data, which includes the following steps:

[0048] S1: Optical fiber layout

[0049] Optical fiber sensors are laid along the entire length of the wellbore to ensure that the monitoring range covers all potential leakage areas. In this embodiment, the optical fiber sensors used for data collection are coupled to the outer wall of the casing outside the tubing. The optical fiber sensors include single-mode optical fibers and multi-mode optical fibers. The single-mode optical fibers are used to collect DAS data, and the multi-mode optical fibers are used to collect DTS data. The ends of the single-mode optical fibers and multi-mode optical fibers are correspondingly connected to the DAS and DTS demodulators, and the collected data enters the next step for processing.

[0050] The optical fiber sensors are responsible for sensing, and the main tasks of the two DAS and DTS demodulators are: one is to use DTS technology to record the temperature distribution data along the optical fiber path, providing basic information for DTGS processing based on DTS; the other is to use DAS technology to collect dynamic strain and broadband vibration signals, and extract LF-DAS low-frequency thermal strain data from the original DAS data and convert it into temperature change data, providing key data support for DTGS processing based on LF-DAS. The optical fiber layout needs to ensure the continuity and high resolution of data collection, adapt to the long-term stable operation under the complex well conditions of the wellbore, so as to meet the requirements of high-precision leakage monitoring. With its high sensitivity to low-frequency signals and anti-interference ability, LF-DAS can more accurately capture the tiny temperature fluctuations caused by micro-leakage, and combined with DTGS data, realize real-time monitoring and early warning of the location, fluid velocity and development trend of wellbore micro-leakage. Compared with traditional methods, it shows higher sensitivity and adaptability in complex downhole environments, providing an innovative and efficient solution for wellbore integrity management and oil and gas production optimization.

[0051] S2: Data collection

[0052] Data collection is the basic link of the present invention. Monitoring data is obtained through DTS demodulators and DAS demodulators to achieve high-frequency acquisition of multi-dimensional signals, providing basic support for subsequent DTGS processing. It includes the following steps:

[0053] S21: DAS data collection

[0054] The working principle of DAS is based on the Rayleigh scattering effect of optical fibers. By recording acoustic signal (dynamic strain) signals, it reflects the vibration and strain changes caused by wellbore leakage. The DAS system adopts a high sampling rate of 10 kHz, which can not only capture subtle strain changes along the optical fiber path but also ensure a sufficiently high time resolution to meet the requirements of dynamic monitoring. The collected raw data, after being processed by LF-DAS, mainly includes vibration signals (with frequencies below 0.5 Hz) and dynamic strain changes, which provide key data support for the real-time monitoring and analysis of leakage.

[0055] S22: DTS Data Acquisition

[0056] The DTS system monitors the temperature distribution along the optical fiber path and captures the changes in temperature gradients in real time to identify abnormal temperature behaviors in the leakage area. Its core principle is based on the scattering characteristics of Stokes light and anti-Stokes light in the optical fiber, and the intensities of these optical signals are directly related to the temperature changes along the optical fiber path. By monitoring and analyzing these signals, the temperature distribution at each point along the optical fiber path can be obtained.

[0057] To meet the temperature monitoring requirements in different scenarios, the DTS system adopts an adjustable sampling rate to achieve long-term stable temperature recording while ensuring data accuracy. A high sampling rate is suitable for the real-time monitoring of dynamic temperature changes, while a lower sampling rate is suitable for long-term trend tracking and static environment monitoring.

[0058] S23: Data Storage and Transmission

[0059] All the collected data is transmitted to the ground control center through a real-time data transmission system to ensure the integrity and real-time nature of the data during transmission. After transmission, the data is stored in high-capacity storage devices, which can support the large amount of data generated during long-term and continuous acquisition processes and have high reliability to ensure storage security. At the same time, the system designs a perfect data backup mechanism to effectively prevent data loss through multiple backup means, thus ensuring the smooth progress of subsequent analysis work. This complete data management process provides reliable data support for real-time monitoring and subsequent analysis.

[0060] S3: Data Processing

[0061] Data processing is one of the core links for the present invention to achieve high-precision monitoring of wellbore micro-leakage. This link preprocesses, extracts features, and performs multi-dimensional fusion on the collected raw data, and finally extracts the key information related to leakage. It specifically includes the following steps:

[0062] S31: LF-DAS Data Extraction, including the following processing steps:

[0063] S311: Low-pass filtering: Apply a 0-0.5 Hz low-pass filter to remove high-frequency noise and retain low-frequency signals related to thermal strain. After 0-0.5 Hz low-pass filtering, the original DAS data is extracted as LF-DAS signals, and the plume-like signals on its two-dimensional image can represent the fluid flow velocity;

[0064] S312: Downsampling: reducing the sampling rate of the original signal to 1 Hz to improve the efficiency of subsequent data analysis and reduce the pressure of data storage and calculation;

[0065] S313: Median filtering: eliminate possible impulse noise to ensure the smoothness and stability of data;

[0066] S314: DC drift correction: perform DC drift correction on the low-frequency components in the signal to eliminate the signal offset caused by equipment noise;

[0067] S32: Distributed Temperature Gradient (DTGS) Data Extraction

[0068] Data processing based on LF-DAS and DTS can further extract temperature gradient information. The core of DTGS technology is to infer the temperature gradient by analyzing the time derivative of optical fiber data in the time dimension.

[0069] DTS data records a complete temperature curve that is nearly continuous in space and time. Therefore, the temperature gradient of DTS absolute temperature data can be obtained to obtain DTGS data. Assuming that temperature T is a function of time t and position L, in practical applications, we usually focus on the rate of change of temperature over time, that is, the time gradient of temperature. In practical applications, the calculation of temperature gradient is usually discrete. The discrete time gradient formula is as follows:

[0070]

[0071] In the above formula, T is the temperature change, t 1、 t2 represents different time, Δt is the time change, Tt2 is the temperature at time t2, and Tt1 is the temperature at time t1.

[0072] LF-DAS can indirectly reflect the relative temperature change in the near-wellbore area along the optical fiber by detecting the thermal strain signal related to the temperature change in the low-frequency acoustic signal. By calculating the temperature gradient of LF-DAS, DTGS data can also be obtained to accurately characterize the temperature gradient change, thereby achieving early detection of small leaks or temperature fluctuations. Therefore, LF-DAS data can be regarded as DTS data and formula (1) can be used to extract DTGS data. The DTGS of DTS can resolve the absolute value of the temperature, and the DTGS of DAS can see the temperature change value, which is more accurate than DTS.

[0073] Compared with the existing DTS-based DTGS technology, the data processing process of step S3 provided in this embodiment has the following technical advantages:

[0074] ① The detection sensitivity is significantly improved. The LF-DAS technology shows excellent sensitivity in detecting temperature gradient changes and can effectively capture small temperature changes in complex downhole environments;

[0075] ② The temperature gradient resolution is greatly improved. The DTGS temperature gradient resolution obtained by using the LF-DAS technology can reach 0.01 °C / s, which is one order of magnitude higher than 0.1 °C / s of the traditional DTS-DTGS;

[0076] ③ Further, when using a DAS demodulator device optimized for low-frequency response signal-to-noise ratio, the obtained DTGS data temperature gradient resolution can reach ±0.001 °C / s, enabling refined analysis of temperature anomaly points and improving the reliability of anomaly detection.

[0077] The above technical solution not only overcomes the technical problem of insufficient detection sensitivity of DTS temperature gradient in the prior art, but also realizes high-precision detection and characterization of small temperature changes in complex downhole environments through the low-frequency signal capture ability of the LF-DAS technology, with significant technical advantages.

[0078] S33: Data visualization and storage

[0079] After the data processing is completed, the obtained DTGS data and DTS data are used to generate heat maps of temperature distribution and strain changes through data visualization technology to intuitively display the leakage location. In addition, a time series graph is drawn to detail the dynamic process of leakage development, providing an intuitive reference for further analysis. The processed data is stored in a high-capacity storage device to ensure long-term preservation and availability, and a real-time backup mechanism is adopted to prevent data loss and ensure the smooth progress of subsequent analysis work.

[0080] S4: Leakage identification, which includes the following steps:

[0081] S41: DTGS and DTS threshold judgment

[0082] Based on the DTGS data extracted by LF-DAS, a leakage identification model is established, specifically including location identification and state prediction, that is: the leakage point x is located through the spatial distribution of the peak values of the DTGS and DTS data signals leak , and combined with multi-dimensional signal features (such as leakage location, fluid velocity, absolute temperature change), the state and development trend of the leakage are predicted. This model is shown in the following formula and can be optimized through machine learning algorithms to further improve the detection accuracy.

[0083]

[0084] In the above formula, P leak is the amplitude peak value of the leakage point, and x leak is the maximum value among the peak values.

[0085] S42: Early identification and warning of wellbore micro-leakage

[0086] Through continuous dynamic analysis, the system monitors the behavior and location changes of wellbore micro-leakage in real time to ensure timely response.

[0087] Set the warning threshold P threshold , when the integrated signal strength exceeds the set range, the wellbore micro-leakage phenomenon and location are automatically identified, and an early warning is triggered in a timely manner, as shown in the following formula:

[0088]

[0089] When the amplitude intensity of the monitored leakage point exceeds the preset value, a warning is issued.

[0090] After the warning is triggered, the system automatically generates a leakage report and provides decision-making support for management personnel.

[0091] Embodiment 2

[0092] Through experiments and practical applications, the present invention verifies the accuracy and reliability of extracting DTGS data from the low-frequency thermal strain data of LF-DAS in the identification of wellbore micro-leakage.

[0093] During gas production in a certain gas well, the B annulus was under pressure. Although the pressure fluctuation was small, wellbore micro-leakage was suspected. Using the permanent optical fiber deployed outside the casing of this well, DAS and DTS demodulator devices were connected, and high-quality DAS and DTS data were collected during the gas injection test in the A annulus. According to the technical solution proposed by the present invention, the DAS and DTS data were processed. First, the LF-DAS low-frequency thermal strain (temperature) data was extracted from the original DAS data, and then the DTGS data with high precision and high temperature resolution was extracted from the LF-DAS data; in addition, the DTGS data was also extracted from the original DTS temperature data.

[0094] Figure 2 (a) shows the waterfall plot of the DTGS data extracted from LF-DAS, Figure 2 (b) shows the waterfall plot of the DTGS data extracted from DTS, and the same temperature color scale is used for both. It can be seen that:

[0095] (1) After 20:12:00, both the DTGS data extracted from LF-DAS and the DTGS data extracted from DTS showed a slight temperature change of only 0.65°C in the depth range of 0-100 m. This temperature change was caused by the temperature difference caused by the gas injected from annulus A into annulus B, indicating that both can identify micro-leakage in the wellbore.

[0096] (2) The temperature changes in the waterfall chart of the DTGS data extracted from LF-DAS are relatively fine, and the resolution of temperature changes in depth and time dimensions is on the order of 0.01°C; while the waterfall chart of the DTGS data extracted from DTS is relatively smooth and lacks rich details, indicating that its resolution of temperature changes is only maintained at the order of 0.1°C; therefore, compared with the DTGS data extracted from DTS, the DTGS data extracted from LF-DAS has a higher temperature resolution and is more suitable for identifying small temperature changes caused by micro-leaks.

[0097] The above embodiments are only preferred implementations of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for identifying micro-leakage in a wellbore using distributed temperature gradient sensing data, characterized in that: It includes the following steps: S1: Optical fiber layout An optical fiber sensor is laid along the entire length of the outer wall of the casing outside the tubing in the wellbore. The optical fiber sensor includes a single-mode optical fiber and a multi-mode optical fiber. The single-mode optical fiber is used to collect DAS data, and the multi-mode optical fiber is used to collect DTS data. The ends of the single-mode optical fiber and the multi-mode optical fiber are respectively connected to a DAS demodulator and a DTS demodulator; S2: Data acquisition The temperature distribution data along the optical fiber path, that is, the DTS data, is obtained through the DTS demodulator; the dynamic strain and broadband vibration signal data, that is, the DAS data, is obtained through the DAS demodulator; S3: Data processing Extract the low-frequency thermal strain data from the DAS, that is, LF-DAS data. Through the LF-DAS data, the temperature gradient is processed and the absolute temperature data of the DTS data is processed for temperature gradient to obtain the distributed temperature gradient data, that is, DTGS data; and the DTGS data is visually processed to generate a heat map of temperature distribution and strain change, and is stored and backed up; S4: Leakage identification Perform DTGS and DTS threshold judgments. Based on the DTGS data extracted from LF-DAS, a leakage identification model is established, and combined with multi-dimensional signal characteristics, the state and development trend of leakage are predicted; early identification and warning of micro-leakage are carried out.

2. The method for identifying micro-leakage in a wellbore using distributed temperature gradient sensing data according to claim 1, wherein: Step S3 includes the following steps: S31: LF-DAS data extraction, including the following processing steps: S311: Low-pass filtering: Apply a low-pass filter of 0 - 0.5 Hz to remove high-frequency noise and retain the low-frequency signal related to thermal strain; after low-pass filtering of 0 - 0.5 Hz, the original DAS data is extracted as an LF-DAS signal, and the plume-like signal on its two-dimensional image represents the fluid flow velocity; S312: Downsampling: Reduce the sampling rate of the original signal to 1 Hz; S313: Median filtering: Eliminate possible pulse noise; S314: DC drift correction: Perform DC drift correction on the low-frequency components in the signal to eliminate the signal offset caused by equipment noise; S32: Distributed temperature gradient data extraction Calculate the temperature gradient of the absolute temperature data in the DTS data to obtain the DTGS data; assume that the temperature T is a function of time t and position L. The rate of change of temperature with time is the time gradient of temperature. The calculation of the temperature gradient uses a discrete form, and the discrete time gradient formula is as follows: In the above formula, T is the temperature change, and t 1、 t2 represents different times, Δt is the time change, Tt2 is the temperature at time t2, and Tt1 is the temperature at time t1; Calculate the temperature gradient of the LF-DAS data to obtain DTGS data to accurately characterize the temperature gradient change, so as to realize the early detection of micro-leakage or temperature fluctuation. Regard the LF-DAS data as DTS data and apply formula (1) to extract the DTGS data.

3. The method for identifying wellbore micro-leakage using distributed temperature gradient sensing data according to claim 2, wherein: It also includes the following steps: S33: Data visualization and storage. Visually process the DTGS data and DTS data, and draw a time series graph to reflect in detail the dynamic process of leakage development; and store and back up the DTGS data and DTS data.

4. The method for identifying wellbore micro-leakage by using distributed temperature gradient sensing data according to claim 2, characterized in that: Step S4 specifically includes the following steps: S41: DTGS and DTS threshold judgment Based on the DTGS data extracted from the LF-DAS data, a leakage identification model is established, specifically including location identification and status prediction, that is: the leakage point x is located through the spatial distribution of the peak values of the DTGS and DTS data signals leak , and combined with multi-dimensional signal features, including leakage location, fluid velocity, and absolute temperature change, the status and development trend of the leakage are predicted. The leakage identification model is shown in the following formula: In the above formula, P leak is the amplitude peak value of the leakage point, and x leak is the maximum value among the peak values; S42: Early identification and warning of wellbore micro-leakage Set the warning threshold P threshold , when the integrated signal strength exceeds the warning threshold, use the recognition model to identify the wellbore micro-leakage phenomenon and location, and trigger an early warning. The recognition model is shown in the following formula:

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