Underground oil pipe leakage detection system

Abstract

The invention relates to the technical field of oil pipe leakage detection, in particular to an underground oil pipe leakage detection system, which comprises an underground permanent monitoring unit, which comprises at least one optical fiber sensor, and the optical fiber sensor clings to the outer wall of a production oil pipe or is integrated in an oil pipe coupling and extends along the depth of a whole shaft; the ground signal processing and analyzing unit is in communication connection with the underground permanent monitoring unit; wherein the optical fiber sensor is used as a distributed temperature sensor and a distributed sound wave vibration sensor at the same time, real-time on-line monitoring of oil pipe leakage is achieved, early warning can be given out at the initial stage of leakage, the accuracy of leakage recognition is improved through collaborative analysis of bimodal sensing data, the false alarm situation is effectively reduced, and the reliability of oil pipe leakage detection is improved. A distributed measurement mode provides meter-scale spatial resolution, the depth position of a leakage point is accurately positioned, normal production operation does not need to be interrupted during system operation, and the operation cost of an oil field is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tubing leak detection, in particular to a downhole tubing leak detection system. BACKGROUND

[0002] In the oil industry, the integrity of the oil well production string is the key to ensure safe production, improve recovery and protect the environment. The tubing is subjected to high pressure, high temperature, corrosive medium and alternating stress for a long time in the well, and is prone to leakage due to pipe corrosion and wear.

[0003] In the prior art, the downhole tubing leak is usually detected by wellhead flowmeter comparison method, pressure test method, traditional logging method and single sensing method, but these methods have defects. For example, the wellhead flowmeter comparison method and the pressure test method can only indirectly infer the existence of leakage and cannot accurately locate the depth of the leakage point and evaluate the leakage degree, with high false positive rate and false negative rate. When using the traditional logging method, such as temperature logging and noise logging, the time effectiveness is poor and the cost is high, the production needs to be interrupted and the instrument needs to be lowered by wireline operation, real-time continuous monitoring cannot be realized, the operation cost is high, and intermittent leakage or small signals in the early stage of leakage cannot be captured. SUMMARY

[0004] In order to make up for the deficiencies of the prior art, the present application provides a downhole tubing leak detection system, which solves the technical problems proposed in the background art.

[0005] The technical scheme adopted by the present application to solve its technical problems is: a downhole tubing leak detection system, comprising:

[0006] a downhole permanent monitoring unit, comprising at least one optical fiber sensor, the optical fiber sensor being in close contact with the outer wall of the production tubing or integrated in the tubing coupling and extending along the entire wellbore depth;

[0007] a ground signal processing and analysis unit in communication connection with the downhole permanent monitoring unit;

[0008] Among them, the optical fiber sensor is used as a distributed temperature sensor and a distributed acoustic vibration sensor at the same time.

[0009] Specifically, the ground signal processing and analysis unit comprises:

[0010] a data fusion module for receiving and synchronizing temperature signals and acoustic vibration signals from the same section of optical fiber;

[0011] an artificial intelligence analysis module trained to identify multi-modal feature patterns related to leakage, the multi-modal feature patterns including low temperature anomalies in the temperature signals and corresponding high frequency noise anomalies in the acoustic signals.

[0012] Specifically, the artificial intelligence analysis module is a neural network model based on deep learning, and the training data thereof includes temperature-sound signal pairs recorded in historical leakage cases, simulated leakage experiment data and background noise data under normal downhole conditions.

[0013] Specifically, the downhole tubing leakage detection further comprises a wellbore digital twin model, which receives real-time temperature and sound data and compares them with theoretical values calculated based on well structure, fluid properties and production parameters, and the artificial intelligence analysis module takes the comparison deviation as an auxiliary feature for leakage judgment.

[0014] Specifically, the optical fiber sensor is packaged by metal armor and permanently fixed on the outer wall of the production tubing by chemical welding or mechanical clamps.

[0015] Specifically, the downhole tubing leakage detection system further comprises a plurality of discrete sound accelerometers, which are fixed on the tubing by spacing distribution and cross-verified with the sound vibration signals measured by the optical fiber sensor.

[0016] Specifically, the ground signal processing and analysis unit is configured to automatically adjust the signal sampling frequency when potential leakage is detected, to perform high-frequency sampling on the abnormal section and low-frequency sampling on the normal well section.

[0017] The beneficial effects of the present application are as follows:

[0018] The present application synchronously collects temperature and sound vibration signals through the optical fiber sensor extending along the wellbore depth, and realizes real-time accurate positioning of the leakage point and leakage degree evaluation through multi-modal data fusion and artificial intelligence analysis of the ground signal processing and analysis unit, which has the technical effects of real-time continuous monitoring, accurate positioning of the leakage point depth and accurate evaluation of the leakage degree. BRIEF DESCRIPTION OF DRAWINGS

[0019] The present application will be further described below in conjunction with the drawings and embodiments.

[0020] Figure 1 is the system flowchart of the present application. DETAILED DESCRIPTION

[0021] In order to make the technical means, creative features, purposes and effects realized by the present application easy to understand, the present application will be further described below in conjunction with specific embodiments.

[0022] As Figure 1As shown, in one embodiment of the present application, a downhole tubing leak detection system is provided, which comprises a downhole permanent monitoring unit and a ground signal processing and analysis unit, the downhole permanent monitoring unit comprises at least one optical fiber sensor, which is attached to the outer wall of the production tubing or integrated into the tubing coupling, and extends along the entire wellbore depth, the ground signal processing and analysis unit is in communication connection with the downhole permanent monitoring unit, and the optical fiber sensor is used as a distributed temperature sensor and a distributed acoustic vibration sensor at the same time.

[0023] Wherein, the optical fiber sensor attached to the outer wall of the production tubing means that the sensing element forms physical contact with the surface of the tubing, which can be mechanically protected by metal armored packaging, and fixed by chemical welding or mechanical clamps, and this installation mode enhances the sensitivity of the sensor to the vibration signals conducted by the tubing wall, and the extension along the entire wellbore depth means that the sensor is deployed synchronously when the tubing is lowered into the wellbore, forming a continuous sensing path covering the full length of the production string, providing a basis for spatial positioning of the leakage point.

[0024] The simultaneous use of the optical fiber sensor as a distributed temperature sensor and a distributed acoustic vibration sensor means that a single optical fiber realizes double physical quantity measurement through different demodulation techniques, and specifically, the Raman scattering effect can be used to analyze the temperature distribution, and the phase-sensitive optical time domain reflection technology can be used to detect acoustic vibration.

[0025] Specifically, the optical fiber sensor is permanently installed during the tubing lowering operation process, and forms an integrated structure with the tubing, when tubing leakage occurs, the fluid spouted at the leakage point causes local temperature field change, and the high-speed fluid flow produces characteristic acoustic signals, the optical fiber sensor analyzes the temperature distribution curve through Raman scattering, identifies the abnormal temperature section, and the simultaneously collected acoustic vibration signals are phase demodulated to detect the high-frequency noise characteristics, the ground system aligns the temperature and acoustic data in time and space, excludes environmental interference through correlation analysis of the double modal signals, and the spatial coordinates of the abnormal signals are mapped to the actual well depth position by the continuously distributed sensing units along the wellbore, realizing three-dimensional positioning of the leakage point.

[0026] Compared with the prior art, the conventional logging method needs to stop production activities for intermittent detection, while the present application realizes continuous monitoring throughout the life cycle through the permanently installed sensing unit, the single parameter detection system is easily disturbed by the background noise of the wellbore, the present application establishes double criteria through the physical correlation characteristics of temperature and acoustic waves, the discrete arrangement of the sensors has a monitoring blind area, the distributed sensing architecture of the present application realizes full coverage of the wellbore, and the conventional point-type accelerometer needs additional power supply and signal transmission lines, and the present application simplifies the complexity of the downhole equipment by using the passive characteristics of the optical fiber sensing.

[0027] That is, the present application realizes real-time online monitoring of oil pipe leakage, can issue a pre-warning at the initial stage of leakage, the cooperative analysis of the dual-mode sensing data improves the accuracy of leakage identification, effectively reduces the false alarm situation, the distributed measurement mode provides a meter-level spatial resolution, accurately locates the depth position of the leakage point, does not need to interrupt normal production operation during system operation, reduces the oil field operation cost, and the application of the optical fiber sensing technology avoids the power supply and maintenance requirements of the downhole electronic components, and improves the reliability of the present application in a high temperature and high pressure environment.

[0028] As another embodiment of the present application, the ground signal processing and analysis unit includes a data fusion module and an artificial intelligence analysis module, the data fusion module receives and synchronizes the temperature signal and the acoustic vibration signal from the same section of optical fiber, and the artificial intelligence analysis module is trained to identify a multi-modal feature pattern related to leakage, the multi-modal feature pattern including a low-temperature anomaly in the temperature signal and a high-frequency noise anomaly in the corresponding acoustic signal;

[0029] The data fusion module is a hardware or software unit for realizing the spatio-temporal alignment of multi-source signals, and can be realized by using a time stamp synchronization technology and a signal interpolation algorithm, so as to ensure that the temperature and acoustic signals are matched in the same well section and the same time window, and the data fusion module eliminates the time delay difference of signal collection, and provides an accurate spatio-temporal reference for subsequent correlation analysis.

[0030] The artificial intelligence analysis module is a pattern recognition system based on a machine learning algorithm, and can be realized by using a hybrid model of a convolutional neural network and a long short-term memory network, and the spatio-temporal coupling features of temperature drop and high-frequency noise in leakage are extracted by inputting the temperature-acoustic signal pair after synchronization, the artificial intelligence analysis module learns the multi-modal features jointly, and the noise resistance to a complex downhole interference environment is enhanced.

[0031] Further, the data fusion module first pre-processes the original signals collected by the optical fiber sensor, divides the signal segments by using a sliding time window, eliminates the collection time deviation of the temperature sensing and acoustic sensing by using a clock synchronization protocol, for each well section unit, the local low-temperature anomaly in the temperature signal is preliminarily detected by calculating the adjacent well section temperature difference threshold, at the same time, the high-frequency energy features of the acoustic signal are extracted by using a fast Fourier transform, the artificial intelligence analysis module receives the signal segments after spatio-temporal alignment, calculates the joint probability distribution of the temperature gradient distribution and the acoustic frequency spectrum features by using the trained neural network model, and when the spatial position coincidence degree of the low-temperature anomaly region and the high-frequency noise region exceeds a set threshold, a leakage warning is triggered, and the process realizes the dual physical evidence verification of leakage by fusing the phase change endothermic effect of the temperature sensing and the turbulent vibration features of the acoustic sensing.

[0032] Therefore, the application can solve the misjudgment problem caused by single sensing signal analysis, such as distinguishing the local temperature fluctuation caused by well repair operation from the real leakage, and excluding the single signal anomaly caused by non-leakage factors by synchronously correlating the space-time characteristics of temperature and acoustic wave signals. For example, the collision of downhole tools only produces acoustic signal mutation without temperature anomaly, so the application is particularly suitable for identifying the initial stage of micro-leakage, at which the leakage amount is small, resulting in weak temperature change, but the high-frequency acoustic characteristics of the corresponding position can still be accurately detected.

[0033] As another embodiment of the application, the artificial intelligence analysis module is a neural network model based on deep learning, and the training data thereof includes temperature-acoustic signal pairs recorded in historical leakage cases, simulated leakage experimental data, and background noise data under normal downhole conditions. Specifically, the neural network model based on deep learning refers to a machine learning model that automatically extracts data features through multiple nonlinear transformations. It can be implemented using a convolutional neural network or a recurrent neural network structure, and can capture leakage-related spatiotemporal correlation features from multidimensional signals.

[0035] Among them, the temperature-acoustic signal pairs recorded in historical leakage cases refer to the temperature and acoustic vibration data synchronously collected when leakage occurs in the actual downhole. They can be obtained from the oilfield historical database and used to establish the physical feature mapping relationship under the real leakage scenario.

[0036] And the simulated leakage experimental data refers to the leakage signals generated by ground or downhole simulation devices. It can be obtained through combination experiments of different pipe diameters, pressures, and leakage rates, and used to expand the recognition ability of the model for different leakage conditions.

[0037] Among them, the background noise data under normal downhole conditions refers to the continuous monitoring data collected under the non-leakage state, which can be obtained by continuously recording during the daily operation of the production well, and is used to establish the environmental noise benchmark and improve the anti-interference ability of the model.

[0038] Specifically, by jointly training the historical leakage data and the simulated experimental data, the neural network model can learn the coupling characteristics between temperature drop and high-frequency acoustic vibration, and master the signal intensity variation law corresponding to different leakage rates. During the model training process, the normal condition data is used as negative sample input, and the sensitivity of the model to abnormal signals is enhanced through the contrast learning mechanism. In the real-time monitoring stage, the model performs multi-scale feature extraction on the input mixed signal, and uses the weight parameters obtained through training to perform correlation analysis on the low-temperature abnormal region in the temperature signal and the high-frequency noise in the acoustic signal. When the two types of signals show correlation in the space-time dimension, a leakage warning is triggered.

[0039] Through the technical scheme, the downhole tubing leakage signal is accurately recognized, real leakage and background noise can be distinguished in the complex working conditions of high temperature and high pressure and multi-device operation, the frequency of unnecessary workover caused by misjudgment is reduced, and the detection sensitivity of micro leakage and intermittent leakage is improved.

[0040] As another embodiment of the application, the downhole tubing leakage detection system comprises a wellbore digital twin model, which receives real-time temperature and acoustic wave data and compares them with theoretical values calculated based on well structure, fluid properties and production parameters, and an artificial intelligence analysis module takes the comparison deviation as an auxiliary feature for leakage judgment.

[0041] The wellbore digital twin model is a virtual wellbore system constructed by three-dimensional modeling technology, which can be dynamically updated by combining real-time data flow using finite element analysis method, and is used to simulate the real state of downhole physical field. The wellbore digital twin model integrates geometric features such as casing size and tubing wall thickness in well structure parameters to establish a stress distribution model consistent with the actual wellbore. Fluid property parameters include physical property data such as crude oil viscosity and gas composition, which are used to calculate heat transfer coefficient and acoustic wave propagation velocity. Production parameters include real-time variables such as oil pressure and fluid production, which are used to dynamically adjust theoretical prediction values. The comparison deviation refers to the difference between measured data and theoretical model prediction values, which can be quantified by calculating the standard deviation of temperature gradient or the energy offset of acoustic spectrum.

[0042] During normal production of the oil well, the wellbore digital twin model continuously receives temperature and acoustic wave signals uploaded by the optical fiber sensor, generates a geometric model according to the current well structure parameters, derives thermodynamic equations combined with fluid property parameters, and calculates theoretical temperature distribution and acoustic wave characteristics according to real-time production parameters. When tubing leakage occurs, the temperature signal at the leakage point will show local low temperature anomaly, accompanied by high frequency acoustic noise. The digital twin model can identify abnormal areas outside the normal fluctuation range by comparing the deviation between measured temperature curve and theoretical temperature curve. The artificial intelligence analysis module takes temperature deviation amplitude, acoustic spectrum offset and other parameters as additional features input, and matches them with multi-modal features in historical leakage cases, so as to distinguish between normal signal fluctuation caused by production adjustment and real leakage.

[0043] In existing technologies, traditional methods rely solely on single sensor data or static threshold judgments, which cannot effectively handle signal interference caused by complex downhole conditions. For example, during the water injection development phase, changes in the phase state of downhole fluids can cause natural fluctuations in the temperature field, which existing technologies may easily misinterpret as leakage signals. However, this invention uses a digital twin model to dynamically generate theoretical benchmark values, which can eliminate the influence of normal operating condition changes on the detection results. At the same time, existing technologies lack quantitative analysis of the stress distribution of the wellbore structure, making it difficult to locate minute leak points. This invention, through coupled calculations of geometric and fluid models, can accurately identify the weak areas of the tubing string corresponding to the leak location.

[0044] Therefore, this invention can effectively reduce the misjudgment rate caused by dynamic changes in downhole operating conditions. Even in complex scenarios such as oil well production fluctuations and water injection operations, it can still accurately identify real leakage signals. By converting theoretical calculation deviations into auxiliary judgment features, it enhances the ability to capture weak leakage features. In particular, in the scenario of micro-leakage generated in the early stage of pipe corrosion, it can provide early warning of potential risks.

[0045] In another embodiment of the present invention, the fiber optic sensor is encapsulated in metal armor and permanently fixed to the outer wall of the production pipeline by chemical welding or mechanical clamping.

[0046] It should be noted that metal armor encapsulation refers to the use of metal materials to wrap the external structure of the fiber optic sensor. Specifically, stainless steel or corrosion-resistant alloys can be used. Its function is to protect the fiber optic cable from physical impact and chemical corrosion in the downhole environment through the mechanical strength and corrosion resistance of the metal. Chemical welding, on the other hand, refers to the bonding of the metal armor layer to the outer wall of the tubing through high-temperature brazing or molten metal joining processes. Nickel-based or copper-based solders can be used. Its function is to form a fixed metallurgical interface, preventing the sensor from detaching due to vibration or fluid erosion.

[0047] Mechanical clamps refer to the use of metal clips or bolts to press the armored sensor against the outer wall of the oil pipe. Specifically, they can be implemented using split clamps or ring clamps. Their function is to provide adjustable clamping force to accommodate the deformation of the oil pipe caused by temperature changes, while maintaining the stability of the contact between the sensor and the pipe wall.

[0048] In actual operation, the metal armor layer is designed as a fully enclosed structure, and the fiber optic sensor is completely wrapped inside the metal sheath. The thickness of the sheath can be selected according to the downhole pressure level. Before the tubing is run into the well, the metal armor layer is permanently connected to the outer wall of the tubing through a chemical welding process, and the welding area covers the entire length of the sensor; or a split mechanical clamp is used, and a ring clamp is installed at the tubing coupling position. The sensor is pressed against the surface of the pipe wall by applying pre-tightening force with bolts. Both fixing methods can eliminate the gap between the sensor and the pipe wall and ensure the effective transmission of acoustic vibration signals.

[0049] Therefore, this invention solves the problem of signal distortion caused by fixation failure of fiber optic sensors under complex downhole conditions, ensuring that the sensor continuously and stably collects temperature and vibration data during the service life of the tubing, providing a reliable data source for leak detection.

[0050] As another embodiment of the present invention, the downhole tubing leak detection system also includes multiple discrete acoustic accelerometers. The acoustic accelerometers are fixed on the tubing with a spacing distribution and cross-validated with the acoustic vibration signals measured by the fiber optic sensors. The discrete acoustic accelerometers refer to vibration sensing devices independently installed on the surface of the tubing, using piezoelectric or MEMS sensors. Each accelerometer has an independent signal acquisition and transmission channel, capable of capturing the detailed vibration features of a local area. Its high sensitivity can effectively identify high-frequency vibration signals. The spacing distribution refers to setting the sensor interval according to the tubing length and the leakage signal propagation characteristics. Specifically, it can be implemented using an equal-spacing or variable-spacing installation mode. The spacing between adjacent accelerometers can be set to 10-50 meters. This distribution can cover the monitoring blind spots that may exist in the fiber optic sensors, forming a spatially complementary sensing network. Cross-validation refers to performing correlation analysis on the vibration signals collected by different sensors in the same spatiotemporal dimension. Specifically, it can be implemented using time-frequency domain feature comparison or signal propagation path verification algorithms. By comparing the vibration signal characteristics of the fiber optic sensors and accelerometers in the same location or adjacent areas, the consistency of signal propagation is identified, thereby eliminating environmental noise interference.

[0051] In actual operation, acoustic accelerometers are fixedly installed along the pipeline axis at preset intervals, forming discrete monitoring nodes. Each node independently collects vibration signals at its location. When a leak occurs in the pipeline, the high-frequency vibration signal generated at the leak point propagates along the pipeline. Fiber optic sensors obtain the distributed characteristics of the vibration signal through continuous measurement, while acoustic accelerometers capture local vibration details. The ground signal processing unit performs time synchronization and spatial matching of the data from the two sensors, and verifies the attenuation law and frequency component changes of the vibration signal during propagation through time-frequency analysis. For example, when the fiber optic sensor detects high-frequency noise anomalies in a certain section of the pipeline, the system retrieves the acoustic accelerometer data near the corresponding location. If both show consistent propagation attenuation characteristics in time-frequency features, it is determined to be a real leak signal; if only a single sensor shows an anomaly, it is identified as environmental noise interference.

[0052] Through the above-mentioned solution, the present invention can reduce the risk of misjudgment caused by environmental interference of a single sensor, improve the accuracy of leak detection by multi-source data fusion, achieve accurate positioning and reliable identification of leak signals, and enhance the anti-interference capability of the system under complex well conditions.

[0053] Furthermore, this invention proposes a ground signal processing and analysis unit configuration that automatically adjusts the signal sampling frequency when a potential leak is detected, performing high-frequency sampling on abnormal sections while maintaining low-frequency sampling on normal well sections.

[0054] Specifically, automatic signal sampling frequency adjustment refers to dynamically changing the data acquisition rate based on real-time monitoring data. This can be achieved through a triggering algorithm and frequency switching circuit working together. By setting threshold conditions to detect potential leaks, the sampling mode is switched. This feature enables the system to implement differentiated data acquisition strategies for different well sections, solving the resource waste problem caused by fixed sampling frequencies.

[0055] High-frequency sampling in abnormal sections refers to acquiring signals at a rate higher than the conventional rate in the well section that triggers the alarm. Specifically, this can be achieved by using multiplexing technology combined with a high-speed analog-to-digital conversion module, such as increasing the sampling rate from the conventional 10Hz to 1kHz. High-frequency sampling can capture transient signal characteristics in the early stage of leakage, such as high-frequency vibration noise accompanied by a sudden drop in temperature, avoiding signal distortion caused by traditional low-frequency sampling.

[0056] Maintaining low-frequency sampling in normal well sections refers to maintaining the basic monitoring frequency for well sections that have not triggered alarms. Specifically, time-division multiplexing technology can be used to reduce the activation frequency of data acquisition channels, such as controlling the sampling rate below 1Hz. Low-frequency sampling reduces the system storage and transmission load by reducing redundant data, thereby optimizing the allocation of monitoring resources.

[0057] Furthermore, the ground signal processing and analysis unit analyzes the temperature and acoustic signals transmitted by the distributed fiber optic sensors in real time. When an abnormal temperature gradient or acoustic spectrum energy exceeds a preset threshold is detected, it is determined to be a potential leak. At this time, the system automatically activates the abnormal section location algorithm, determines the spatial coordinates of the suspected leak area, and starts a high-frequency sampling mode for that area, while simultaneously reducing the sampling frequency of other normal well sections. For example, if a section of tubing is detected to have a temperature drop of 2°C and a sudden increase in acoustic signal energy in the 5kHz frequency band, the system marks that section as an abnormal section and increases its sampling frequency from the usual 20Hz to 500Hz, while reducing the sampling frequency of other well sections from 20Hz to 5Hz. This process achieves millisecond-level response through an embedded control chip and can complete the mode switching without manual intervention.

[0058] Compared with existing technologies, this invention achieves high-resolution data capture in abnormal sections by dynamically adjusting the sampling frequency, while reducing the amount of data collected in normal areas. For example, when the abnormal section accounts for only 5% of the total length of the wellbore, the overall data volume of the system can be reduced by about 70%, while the signal resolution in the abnormal section is improved by 25 times, effectively solving the contradiction between resource waste and signal distortion in the fixed sampling mode.

[0059] Therefore, this invention achieves intelligent allocation of monitoring resources during tubing leak detection, significantly reducing data storage and transmission pressure while ensuring high-precision capture of leak characteristics. High-frequency sampling in abnormal sections can completely record the rapid temperature change process and transient acoustic waveforms, providing reliable data support for leak location and severity assessment; low-frequency sampling in normal well sections avoids overloading data acquisition in leak-free areas, enabling the system to achieve continuous monitoring of the entire wellbore under limited bandwidth conditions. This solution can switch sampling modes without interrupting production operations, overcoming the drawback of traditional logging methods that require production shutdown.

[0060] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A downhole tubing leak detection system, characterized in that: include: A downhole permanent monitoring unit includes at least one fiber optic sensor, which is attached to the outer wall of the production tubing or integrated into the tubing coupling and extends along the entire depth of the wellbore. The ground signal processing and analysis unit is communicatively connected to the downhole permanent monitoring unit; The fiber optic sensor is used as both a distributed temperature sensor and a distributed acoustic vibration sensor.

2. The downhole tubing leakage detection system according to claim 1, characterized in that: The ground signal processing and analysis unit includes: The data fusion module is used to receive and synchronize temperature signals and acoustic vibration signals from the same optical fiber segment; An artificial intelligence analysis module is trained to identify multimodal feature patterns associated with leaks, including low-temperature anomalies in temperature signals and their corresponding high-frequency noise anomalies in acoustic signals.

3. The downhole tubing leakage detection system according to claim 2, characterized in that: The artificial intelligence analysis module is a neural network model based on deep learning. Its training data includes temperature-sound signal pairs recorded in historical leakage cases, simulated leakage experimental data, and background noise data under normal downhole operating conditions.

4. The downhole tubing leakage detection system according to claim 2 or 3, characterized in that: The downhole tubing leak detection also includes a wellbore digital twin model, which receives real-time temperature and acoustic data and compares them with theoretical values ​​calculated based on well structure, fluid properties and production parameters. The artificial intelligence analysis module uses the comparison deviation as an auxiliary feature for leak judgment.

5. The downhole tubing leakage detection system according to claim 1, characterized in that: The fiber optic sensor is encapsulated in metal armor and permanently fixed to the outer wall of the production pipeline by chemical welding or mechanical clamping.

6. The downhole tubing leakage detection system according to claim 1, characterized in that: The downhole tubing leak detection system also includes multiple discrete acoustic accelerometers, which are fixed to the tubing in a spaced manner and cross-validated with the acoustic vibration signals measured by the fiber optic sensor.

7. The downhole tubing leakage detection system according to claim 1, characterized in that: The ground signal processing and analysis unit is configured to automatically adjust the signal sampling frequency when a potential leak is detected, performing high-frequency sampling on abnormal sections and maintaining low-frequency sampling on normal well sections.

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