A deep salt cavern hydrogen and helium storage leakage identification device and a method of using the same

Abstract

The present application relates to a kind of deep salt cavern hydrogen storage helium storage leakage identification device, the deep salt cavern hydrogen storage helium storage leakage identification device mainly includes injection-production pipe column, distributed optical fiber monitoring unit, several microseismic monitoring units and several laser gas monitors;The one end of the injection-production pipe column passes through the shaft of salt cavern and is located in the cavity of the salt cavern;The distributed optical fiber monitoring unit is arranged on the injection-production pipe;Several microseismic monitoring units are uniformly arranged with the several monitoring wells around the cavity of the salt cavern one by one, and each microseismic monitoring unit is arranged in the corresponding monitoring well;Several laser gas monitors are dispersedly arranged around the wellhead of the salt cavern.The present application also provides a kind of use method of deep salt cavern hydrogen storage helium storage leakage identification device.Compared with the prior art, the deep salt cavern hydrogen storage helium storage leakage identification device of the present application has the advantage of improving the accuracy of leakage identification.

Description

Technical Field

[0001] This invention relates to the field of underground gas storage safety monitoring and multi-source information fusion identification technology, and in particular to a device for identifying leaks in deep salt cavern hydrogen and helium storage and its usage method. Background Technology

[0002] Deep salt caverns possess excellent low permeability, plastic flowability, and sealing properties, making them an important form of underground storage for hydrogen or helium. During long-term cyclic injection and production operations, salt cavern storage facilities are susceptible to various leakage risks due to factors such as wellbore service damage, injection / production string fatigue, casing-cement sheath interface degradation, stress redistribution in the surrounding rock, and localized fracturing. These risks include wellbore leakage, leakage due to surrounding rock failure, and combined leakage.

[0003] Currently, the main methods for monitoring leaks in salt cavern storage facilities include surface gas concentration monitoring, wellbore temperature or acoustic monitoring, and microseismic monitoring. However, these methods suffer from several drawbacks, including high false alarm rates with single monitoring methods, a lack of joint identification mechanisms despite the deployment of multiple methods, a lack of effective bridging between microseismic anomalies and actual leaks, and difficulty in balancing false alarm suppression and location capabilities. In actual engineering projects, real leak events typically generate related anomaly responses at the surface, wellbore, and cavity levels, while isolated anomalies detected by a single monitoring method are more likely to originate from noise disturbances, changes in operating conditions, or external environmental influences.

[0004] Therefore, how to provide a deep salt cavern hydrogen or helium storage leakage detection device that is applicable to deep salt cavern hydrogen or helium storage scenarios, and how to improve the reliability of leakage detection and reduce the false alarm rate based on ground laser gas monitoring, wellbore distributed fiber optic monitoring and cavity microseismic monitoring, is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of the problems existing in the prior art, the technical problem to be solved by the present invention is to provide a deep salt cave hydrogen or helium storage leakage detection device, which is applicable to deep salt cave hydrogen or helium storage scenarios. Based on ground laser gas monitoring, wellbore distributed optical fiber monitoring and cavity microseismic monitoring, it achieves the technical effect of improving the reliability of leakage detection and reducing the false alarm rate in deep salt cave hydrogen or helium storage scenarios.

[0006] To achieve the above objectives, the present invention provides a deep salt cavern hydrogen and helium storage leakage detection device, comprising: an injection-production tubing string, one end of which passes through the wellbore of the salt cavern and is located within the cavity of the salt cavern; a distributed optical fiber monitoring unit, which is arranged on the injection-production tubing string along its axial direction; a plurality of microseismic monitoring units, each corresponding to a plurality of monitoring wells evenly distributed around the cavity of the salt cavern, with each microseismic monitoring unit located within a corresponding monitoring well; and a plurality of laser gas monitors, which are dispersed at the wellhead of the salt cavern. The system includes: a surrounding area; a signal receiving end, the input interface of which is connected to the distributed optical fiber monitoring unit, each of the microseismic monitoring units, and each of the laser gas monitors via a first signal transmission optical cable; a central processing system, which is connected to the output end of the signal receiving end via a second signal transmission optical cable; wherein, the distributed optical fiber monitoring unit includes a DTS distributed temperature measurement subunit and a DAS distributed acoustic wave subunit, which are arranged in parallel; the DTS distributed temperature measurement subunit and the DAS distributed acoustic wave subunit are connected to the input interface of the signal receiving end via the first signal transmission optical cable.

[0007] This invention also provides a method for using a deep salt cavern hydrogen and helium storage leakage identification device, which is used in the aforementioned deep salt cavern hydrogen and helium storage leakage identification device. The method includes: S1, monitoring system deployment: deploying the deep salt cavern hydrogen and helium storage leakage identification device at the deep single-cavity salt cavern gas storage project site; S2, multi-source monitoring data acquisition: within a preset time window, acquiring temperature data along the depth direction of the wellbore through a DTS distributed temperature measurement subunit; acquiring vibration signal data along the depth direction of the wellbore through a DAS distributed acoustic wave subunit; acquiring microseismic event data around the salt cavern cavity through a microseismic monitoring unit; acquiring gas concentration data corresponding to the well site area of ​​the salt cavern through a laser gas monitor; S3, single-source feature extraction and anomaly identification: preprocessing the gas concentration data and calculating concentration anomaly indicators; preprocessing the temperature data and calculating temperature anomaly indicators; and extracting the vibration signal data... The data is preprocessed and vibration anomaly indicators are calculated; the microseismic event data is preprocessed and cavity anomaly indicators are calculated; S4, establish the spatial mapping relationship between the ground, wellbore, and cavity: based on the geometric structure, trajectory, wellhead location, and microseismic location results of the salt cavern, establish the spatial mapping relationship and calculate the spatial correlation indicators; S5, spatiotemporal correlation analysis: within a preset time window, different monitoring signals are allowed to have a sequential order and time lag, and different time weights are set according to different leakage paths to calculate the time correlation indicators; S6, comprehensive leakage identification: based on the concentration anomaly indicators, temperature anomaly indicators, vibration anomaly indicators, cavity anomaly indicators, spatial correlation indicators, and time correlation indicators, a comprehensive leakage identification index is constructed; S7, early warning classification: the comprehensive leakage identification index is corrected by rule constraints, and a three-level early warning classification is performed in combination with the leakage chain identification results.

[0008] In the second aspect, the preprocessing of the gas concentration data and the calculation of concentration anomaly indicators specifically include: denoising the gas concentration data, correcting background values, and removing outliers; then extracting the gas concentration values; and sequentially calculating the relative background concentration increment, the duration of continuous exceedance, and the ground gas anomaly indicators within the preset time window; the calculation of the relative background concentration increment ΔC(t) is as follows: Wherein, C(t) is the measured concentration at time t; The background concentration at time t; the duration of continuous exceedance. The calculation is as follows: ; wherein, the C th The threshold value for gas concentration increment is Δt; the sampling time interval is Δt; I is an indicator function, which takes the value 1 when the condition is met and 0 otherwise; the ground gas anomaly index G is calculated as follows: ; wherein, the C avg The average concentration within the time window; the ∆Cavg The C represents the average concentration increment within the time window. ref ∆C ref T gref All are reference values; α1, α2, and α3 are empirical weighting coefficients, satisfying α1+α2+α3=1.

[0009] In the second aspect, the preprocessing of the temperature data and the calculation of temperature anomaly indicators specifically include: denoising the temperature data, temperature calibration, and anomaly segment identification; extracting the anomaly depth location; and sequentially calculating the temperature gradient, the anomaly amount of the temperature gradient, the duration of the temperature anomaly, and the temperature anomaly indicators; the temperature gradient is calculated as follows: Where z is the wellbore depth; t is the time; T(z,t) is the wellbore temperature; and the temperature gradient anomaly is calculated as follows: ; wherein, the ▽T b (z,t) represents the background temperature gradient; the duration of the temperature anomaly is the duration of the temperature anomaly within the anomaly depth interval [z1,z2], calculated as follows: ; wherein, the T th The temperature gradient anomaly threshold is used; the temperature anomaly index W1 is calculated as follows: ; wherein, the P z1 The temperature anomaly depth location factor; β1, β2, and β3 are all empirical weighting coefficients, satisfying β1+β2+β3=1.

[0010] In the second aspect, the preprocessing of the vibration signal data and the calculation of vibration anomaly indices specifically include: denoising the vibration signal data, filtering vibration frequency bands, and extracting anomaly depth locations, and sequentially calculating vibration energy, vibration energy anomaly quantity, vibration anomaly duration, and vibration anomaly indices; within the filtered vibration frequency band [f1, f2], the vibration energy is calculated as follows: Where Z is the wellbore depth; t is the time; A(z,t,f) is the vibration response at frequency f; and the vibration energy anomaly is calculated as follows: ; wherein, the E vb (z,t) represents the background vibration energy; the duration of the vibration anomaly is calculated as follows: ; wherein, the E th The vibration energy anomaly threshold is defined as follows: The vibration anomaly index W2 is calculated using the following formula: ; wherein, the P z2 The vibration anomaly depth location factor; γ1, γ2, and γ3 are all empirical weighting coefficients, satisfying γ1+γ2+γ3=1.

[0011] In the second aspect, the preprocessing of the microseismic event data and the calculation of cavity anomaly indicators specifically include: identifying, locating, and inverting the energy of the microseismic data to obtain the event location, energy level, and rupture type; and then sequentially calculating the event density, average event energy, event clustering, and microseismic anomaly indicators. The event density is calculated as follows: ; wherein, the ∆t c The N is a preset time window; c , where is the number of microseismic events recorded within the spatial volume V; the average event energy is calculated as follows: ; wherein, the E i The energy of the i-th event; the event clustering The calculation is as follows: ; ; wherein, the The x represents the average event dispersion. i For the energy of the i-th event, x c The location is the center of the event cluster; ε is a very small positive number; the microseismic anomaly index C is calculated as follows: Wherein, Mc is the fracture type weighting factor; δ1, δ2, δ3, and δ4 are all empirical weighting coefficients, satisfying δ1+δ2+δ3=1.

[0012] In the second aspect, the spatial mapping relationship includes: the spatial correspondence between the location of the wellbore anomaly depth and the location of the cavity microseismic event cluster; the spatial correspondence between the location of the wellbore anomaly depth and the surface anomaly region; the spatial correspondence between the location of the cavity microseismic event cluster and the surface anomaly region; the spatial correlation index S is calculated as follows: ; wherein, the d gw The distance between the center of the abnormal area on the ground and the corresponding location at the wellhead or well site; d wc The distance between the location corresponding to the abnormal depth of the wellbore and the center of the microseismic event cluster; the d gc The distance between the ground anomaly area and the cavity projection or associated area; the L gw L wc L gc All of these are spatial attenuation scale parameters; μ1, μ2, and μ3 are all empirical weighting coefficients, satisfying μ1+μ2+μ3=1.

[0013] In the second aspect, the time-related index is calculated as follows: ; ; ; ; wherein, the The time anomaly is the time anomaly occurrence of ground anomalies; t gThe time when the ground anomaly occurred; the t w The time when the wellbore anomaly occurred; the t c The time of occurrence of microseismic anomalies; the τ gw τ wc τ gc All of these are time decay constants; λ1, λ2, and λ3 are all empirical coefficients.

[0014] In the second aspect, the comprehensive leakage identification index F is calculated as follows: Wherein, η1, η2, η3, η4, η5, and η6 are all empirical weighting coefficients, satisfying η1+η2+η3+η4+η5+η6=1.

[0015] In the second aspect, the calculation for the rule-constrained correction of the comprehensive leakage identification index is as follows: ; Wherein, R is a rule constraint function; F is a comprehensive leakage identification index; and F f The comprehensive identification value is the result of rule constraints; the three-level early warning classification based on the leakage chain identification result specifically includes: setting a first-level early warning threshold θ1 and a second-level early warning discrimination threshold θ2, and judging the comprehensive identification value. Is it less than the first-level warning threshold θ1? If F f If F < θ1, then output a level 1 warning; if F f If the value is greater than θ1, then determine whether a significant temporal and spatial correlation has formed. If no correlation has formed, output a level one warning; if a correlation has formed, then determine the comprehensive identification value. Whether the secondary warning threshold θ2 has been reached, if θ1 <F f If F < θ2, then output a level 2 warning; if F f If θ2 is greater than θ2, then it is determined whether the strong spatiotemporal correlation condition or the leakage chain condition is met. If not, a level-two warning is output; if met, a level-three warning is output. Alternatively, the level-three warning classification based on the leakage chain identification result specifically includes: setting a level-one warning threshold θ1 and a level-two warning discrimination threshold θ2, determining whether a single-source anomaly exists. If not, a level-one warning is output; if it exists, determining whether a significant temporal and spatial correlation has formed. If not, a level-one warning is output; if formed, determining the comprehensive discrimination value. Whether the secondary warning threshold θ2 has been reached, if θ1 <F f If F < θ2, then output a level 2 warning; if F fIf the value is greater than θ2, then it is determined whether the strong spatiotemporal correlation condition or the leakage chain condition is met. If not, a level 2 warning is output; if it is met, a level 3 warning is output. The strong spatiotemporal correlation condition includes that the three-source monitoring anomalies have significant spatiotemporal consistency. The leakage chain condition includes that the time of occurrence of the two single-source features is consistent with the time sequence and that the modified comprehensive leakage identification index meets the chain criterion of composite leakage.

[0016] Beneficial effects This invention provides a device for identifying leaks in deep salt cavern hydrogen and helium storage. The device mainly includes an injection-production tubing, a distributed optical fiber monitoring unit, several microseismic monitoring units, and several laser gas monitors. One end of the injection-production tubing passes through the wellbore of the salt cavern and is located inside the cavity of the salt cavern, while the other end is located at the wellhead of the salt cavern. The distributed optical fiber monitoring unit is deployed inside the wellbore of the salt cavern and can be a permanently deployed distributed optical fiber monitoring system outside the injection-production tubing. The distributed optical fiber monitoring unit includes a DTS distributed temperature measurement subunit and a DAS distributed acoustic wave subunit. The DTS distributed temperature measurement subunit is used to acquire... The system collects information on temperature gradient anomalies, anomaly depth locations, and anomaly durations within the wellbore; a DAS distributed acoustic subunit is used to acquire vibration energy anomalies, anomaly depth locations, and anomaly durations within the wellbore; a microseismic monitoring unit is deployed within the monitoring well to target the microseismic cavity of the salt cavern, acquiring information on the location, energy level, density or clustering of microseismic events, and types of microseismic ruptures in the surrounding rock of the salt cavern cavity; a laser gas monitor, which can be a pan-tilt-zoom type, is used to scan the wellhead, well site perimeter, and potential escape areas of the salt cavern to detect changes in hydrogen or helium concentrations; several laser... Optical gas monitors are deployed on the ground near the wellhead of the salt cavern to collect ground-based hydrogen or helium concentration values, relative background concentration increments, and durations of continuous exceedances. The distributed fiber optic monitoring units, several microseismic monitoring units, and several laser gas monitors constitute a multi-source collaborative sensing system based on the ground, wellbore, and cavity, improving the accuracy of hydrogen or helium leak detection and avoiding false alarms. The central processing system performs preprocessing, feature extraction, single-source anomaly identification, and spatiotemporal correlation analysis on the multi-source data collected by the distributed fiber optic monitoring units, several microseismic monitoring units, and several laser gas monitors. The central processing system analyzes, comprehensively identifies, and outputs early warnings. In addition, it displays multi-source anomaly states, comprehensive identification results, and risk trends. It can calibrate the collected multi-source data using a unified time reference and output first-level, second-level, and third-level early warnings. The central processing system establishes a three-layer leakage identification framework—single-source identification, spatiotemporal optical linkage, and comprehensive identification—for processing and analyzing multi-source data. This framework can accurately determine the leakage location, time, and path, enabling chain identification of wellbore leaks, cavity damage leaks, and complex leaks in salt caverns, further improving the accuracy of leakage identification. It outputs three levels of early warnings for different leakage situations, making the warning levels more precise. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a structural diagram of a deep salt cavern hydrogen and helium storage leakage detection device according to the present invention.

[0019] Figure 2 This is a flowchart illustrating the usage method of a deep salt cavern hydrogen and helium storage leakage detection device according to the present invention.

[0020] Figure 3 This is the three-layer logic diagram of the present invention: single-source identification, spatiotemporal correlation, and comprehensive judgment.

[0021] Figure 4 This is a three-level early warning identification logic diagram of the present invention.

[0022] Figure 5 This is another three-level early warning identification logic diagram of the present invention.

[0023] Figure label: 1. Injection and production tubing; 2. Salt cavern cavity; 3. Distributed fiber optic monitoring unit; 4. Microseismic monitoring unit; 5. Monitoring well; 6. Laser gas monitor; 7. Signal receiver; 8. First signal transmission fiber optic cable; 9. Central processing system; 10. Second signal transmission fiber optic cable. Detailed Implementation

[0024] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0025] Example 1 like Figure 1As shown, this invention provides a deep salt cavern hydrogen and helium storage leakage detection device, which includes: an injection-production tubing 1, one end of which passes through the wellbore of the salt cavern and is located within the cavity 2 of the salt cavern; a distributed optical fiber monitoring unit 3, which is arranged on the injection-production tubing 1 along the axial direction of the injection-production tubing 1; several microseismic monitoring units 4, which are arranged one-to-one with several monitoring wells 5 evenly distributed around the cavity 2 of the salt cavern, with each microseismic monitoring unit 4 located within a corresponding monitoring well 5; and several laser gas monitors 6, which are dispersed around the wellhead of the salt cavern. The signal receiving end 7 has its input interface connected to the distributed optical fiber monitoring unit 3, each of the microseismic monitoring units 4, and each of the laser gas monitors 6 via a first signal transmission optical cable 8. The central processing system 9 is connected to the output end of the signal receiving end 7 via a second signal transmission optical cable 10. The distributed optical fiber monitoring unit 3 includes a DTS distributed temperature measurement subunit and a DAS distributed acoustic wave subunit, which are arranged in parallel. The DTS distributed temperature measurement subunit and the DAS distributed acoustic wave subunit are connected to the input interface of the signal receiving end 7 via the first signal transmission optical cable 8.

[0026] This invention provides a device for identifying leaks in deep salt cavern hydrogen and helium storage. The device mainly includes an injection-production tubing, a distributed optical fiber monitoring unit, several microseismic monitoring units, and several laser gas monitors. One end of the injection-production tubing passes through the wellbore of the salt cavern and is located inside the cavity of the salt cavern, while the other end is located at the wellhead of the salt cavern. The distributed optical fiber monitoring unit is deployed inside the wellbore of the salt cavern and can be a permanently deployed distributed optical fiber monitoring system outside the injection-production tubing. The distributed optical fiber monitoring unit includes a DTS distributed temperature measurement subunit and a DAS distributed acoustic wave subunit. The DTS distributed temperature measurement subunit is used to acquire... The system collects information on temperature gradient anomalies, anomaly depth locations, and anomaly durations within the wellbore; a DAS distributed acoustic subunit is used to acquire vibration energy anomalies, anomaly depth locations, and anomaly durations within the wellbore; a microseismic monitoring unit is deployed within the monitoring well to target the microseismic cavity of the salt cavern, acquiring information on the location, energy level, density or clustering of microseismic events, and types of microseismic ruptures in the surrounding rock of the salt cavern cavity; a laser gas monitor, which can be a pan-tilt-zoom laser gas monitor, is used to scan the wellhead, well site perimeter, and potential escape areas of the salt cavern to detect changes in hydrogen or helium concentrations; several Laser gas monitors are deployed on the ground near the wellhead of the salt cavern to collect ground-based hydrogen or helium concentration values, relative background concentration increments, and durations of continuous exceedances. The distributed fiber optic monitoring units, several microseismic monitoring units, and several laser gas monitors constitute a multi-source collaborative sensing system based on the ground, wellbore, and cavity, improving the accuracy of hydrogen or helium leak detection and avoiding false alarms. The central processing system performs preprocessing, feature extraction, single-source anomaly identification, and spatiotemporal correlation on the multi-source data collected by the distributed fiber optic monitoring units, several microseismic monitoring units, and several laser gas monitors. The system analyzes, comprehensively identifies, and outputs early warnings. In addition, the central processing system displays multi-source anomaly states, comprehensive identification results, and risk trends. It can calibrate the collected multi-source data using a unified time reference and output first-level, second-level, and third-level early warnings. The central processing system establishes a three-layer leakage identification framework—single-source identification, spatiotemporal optical linkage, and comprehensive identification—for processing and analyzing multi-source data. This framework can accurately determine the leakage location, time, and path, enabling chain identification of wellbore leaks, cavity damage leaks, and complex leaks in salt caverns, further improving identification accuracy. It outputs three levels of early warnings for different leakage situations, making the warning level more precise. Furthermore, this invention provides a deep salt cavern hydrogen and helium storage leakage identification device that effectively suppresses single-source false alarms caused by non-leakage factors such as sudden wind direction changes, emissions from surrounding production activities, injection-production mode switching, normal flow noise, and construction blasting. This reduces the false alarm rate and improves the ability to identify real leakage events. It also has good applicability to salt cavern-type small molecule storage facilities, such as those for hydrogen and helium storage, where high sealing requirements are necessary.

[0027] Example 2 like Figures 1-5 As shown, Embodiment 2 of the present invention provides a method for using a deep salt cavern hydrogen and helium storage leakage identification device, which is used in the application of the deep salt cavern hydrogen and helium storage leakage identification device of Embodiment 1. The method includes: S1, monitoring system deployment: deploying the deep salt cavern hydrogen and helium storage leakage identification device at the deep single-cavity salt cavern gas storage project site; S2, multi-source monitoring data acquisition: within a preset time window, acquiring temperature data along the depth direction of the wellbore through the DTS distributed temperature measurement subunit; acquiring vibration signal data along the depth direction of the wellbore through the DAS distributed acoustic wave subunit; acquiring microseismic event data around the salt cavern cavity through the microseismic monitoring unit; acquiring gas concentration data corresponding to the well site area of ​​the salt cavern through the laser gas monitor; S3, single-source feature extraction and anomaly identification: preprocessing the gas concentration data and calculating concentration anomaly indicators; preprocessing the temperature data and calculating temperature anomaly indicators; and extracting the... The vibration signal data is preprocessed and vibration anomaly indicators are calculated; the microseismic event data is preprocessed and cavity anomaly indicators are calculated; S4, establish the spatial mapping relationship between the ground, wellbore, and cavity: based on the geometric structure, trajectory, wellhead location, and microseismic location results of the salt cavern, establish the spatial mapping relationship and calculate the spatial correlation indicators; S5, spatiotemporal correlation analysis: within a preset time window, different monitoring signals are allowed to have a sequential order and time lag, and different time weights are set according to different leakage paths to calculate the time correlation indicators; S6, comprehensive leakage identification: based on the concentration anomaly indicators, temperature anomaly indicators, vibration anomaly indicators, cavity anomaly indicators, spatial correlation indicators, and time correlation indicators, construct a comprehensive leakage identification index; S7, early warning classification: the comprehensive leakage identification index is modified by rule constraints, and a three-level early warning classification is performed in combination with the leakage chain identification results.

[0028] Specifically, the present invention discloses a method for using a deep salt cavern hydrogen and helium storage leakage identification device. This method involves collecting temperature data along the depth direction of the wellbore, vibration signal data along the depth direction of the wellbore, microseismic event data around the salt cavern cavity, and gas concentration data corresponding to the well site area of ​​the salt cavern. This constructs a multi-source collaborative sensing information acquisition system based on the ground, wellbore, and cavity. Single-source feature extraction is performed on the obtained multi-source data to obtain anomaly indicators for each single-source feature. Anomaly identification is then performed by comparing the data to be judged based on these single-source features with the anomaly indicators. Based on the geometric relationship, trajectory, wellhead location, and microseismic location results of the salt cavern, a spatial mapping relationship is established. This spatial mapping relationship includes the location of the abnormal depth of the wellbore and... Spatial correspondences of cavity microseismic event cluster locations; spatial correspondences between wellbore anomaly depth locations and surface anomaly areas; spatial correspondences between cavity microseismic event cluster locations and surface anomaly areas; through multiple spatial location relationships of surface-identified areas of abnormal gas concentration, wellbore temperature anomalies, wellbore vibration anomalies, and cavity microseismic event cluster locations, spatial correlation indices of the surface-wellbore-cavity are calculated. A larger spatial correlation index indicates stronger consistency among multi-source anomalies, suggesting a higher likelihood of gas leakage. Furthermore, based on spatial mapping relationships, accurate identification of chains of wellbore leakage, cavity damage leakage, and complex leakage can be achieved; the gas leakage path is not... Similarly, while the timing of anomalies in individual source data varies across multi-source data points as gas flows from the cavity to the ground, they exhibit correlation. By calculating time correlation indices, the strength of temporal synchronicity or timing matching of multi-source anomalies can be determined; a higher time correlation index indicates stronger temporal synchronicity or timing matching. Based on the anomaly indices of each individual source, spatial correlation indices, and temporal correlation indices, a comprehensive leak identification index is derived. This comprehensive leak identification index serves as the score for outputting early warnings. Furthermore, based on the anomaly indices of each individual source, spatial correlation indices, and temporal correlation indices, the chain of leaks—including wellbore leaks, cavity failure leaks, and complex leaks—can be identified, ensuring the time consistency of multi-source monitoring anomalies. Consistency, spatial consistency, and leakage chain consistency improve the ability to identify real leakage events. To reduce the impact of single-source false alarms on the comprehensive identification results, the comprehensive leakage identification index needs to be modified by rule constraints. The modified comprehensive leakage identification index is compared with the set first-level and second-level warning thresholds. The warning is graded and output based on whether there is a significant temporal and spatial correlation, whether there are monitoring anomalies of two or three sources within the preset time window, whether the three-source monitoring anomalies have significant spatiotemporal consistency, whether the timing of the anomalies of two single-source features is consistent with the time sequence, and whether the modified comprehensive leakage identification index meets the chain criteria for composite leakage.The present invention discloses a method for using a deep salt cavern hydrogen and helium storage leakage identification device. By identifying single-source anomalies and combining spatial mapping relationships, spatiotemporal correlation analysis, and comprehensive leakage identification, a three-layer leakage identification framework of single-source identification, spatiotemporal correlation, and comprehensive identification is constructed. The identification mechanism, which combines rule-based and scoring methods, improves the accuracy of monitoring and early warning. At the same time, it has good applicability to salt cavern-type small molecule storage facilities, such as hydrogen and helium storage facilities, which have high sealing requirements.

[0029] In some possible implementations, the preprocessing of the gas concentration data and the calculation of concentration anomaly indicators specifically include: denoising the gas concentration data, correcting background values, and removing outliers; then extracting the gas concentration values; and sequentially calculating the relative background concentration increment, the duration of continuous exceedance, and the ground gas anomaly indicators within the preset time window; the calculation of the relative background concentration increment ΔC(t) is as follows: Wherein, C(t) is the measured concentration at time t; The background concentration at time t; the duration of continuous exceedance. The calculation is as follows: ; wherein, the C th The threshold value for gas concentration increment is Δt; the sampling time interval is Δt; I is an indicator function, which takes the value 1 when the condition is met and 0 otherwise; the ground gas anomaly index G is calculated as follows: ; wherein, the C avg The average concentration within the time window; the ∆C avg The C represents the average concentration increment within the time window. ref ∆C ref T gref All are reference values; α1, α2, and α3 are empirical weighting coefficients, satisfying α1+α2+α3=1.

[0030] Specifically, when the ground concentration of a gas is greater than the ground gas anomaly index, it is identified as a ground gas concentration anomaly.

[0031] In some possible implementations, the preprocessing of the temperature data and the calculation of temperature anomaly indicators specifically include: denoising the temperature data, temperature calibration, and anomaly segment identification; extracting the anomaly depth location; and sequentially calculating the temperature gradient, the anomaly amount of the temperature gradient, the duration of the temperature anomaly, and the temperature anomaly indicators; the temperature gradient is calculated as follows: Where z is the wellbore depth; t is the time; T(z,t) is the wellbore temperature; and the temperature gradient anomaly is calculated as follows: ; wherein, the ▽T b(z,t) represents the background temperature gradient; the duration of the temperature anomaly is the duration of the temperature anomaly within the anomaly depth interval [z1,z2], calculated as follows: ; wherein, the T th The temperature gradient anomaly threshold is used; the temperature anomaly index W1 is calculated as follows: ; wherein, the P z1 The temperature anomaly depth location factor; β1, β2, and β3 are all empirical weighting coefficients, satisfying β1+β2+β3=1.

[0032] Specifically, when the wellbore temperature exceeds the temperature anomaly index, it is identified as a wellbore temperature anomaly.

[0033] In some possible implementations, the preprocessing of the vibration signal data and the calculation of vibration anomaly indices specifically include: denoising the vibration signal data, filtering vibration frequency bands, and extracting anomaly depth locations, and sequentially calculating vibration energy, vibration energy anomaly quantity, vibration anomaly duration, and vibration anomaly indices; within the filtered vibration frequency band [f1, f2], the vibration energy is calculated as follows: Where Z is the wellbore depth; t is the time; A(z,t,f) is the vibration response at frequency f; and the vibration energy anomaly is calculated as follows: ; wherein, the E vb (z,t) represents the background vibration energy; the duration of the vibration anomaly is calculated as follows: ; wherein, the E th The vibration energy anomaly threshold is defined as follows: The vibration anomaly index W2 is calculated using the following formula: ; wherein, the P z2 The vibration anomaly depth location factor; γ1, γ2, and γ3 are all empirical weighting coefficients, satisfying γ1+γ2+γ3=1.

[0034] Specifically, when the wellbore vibration signal data is greater than the vibration anomaly index, it is identified as a wellbore vibration anomaly.

[0035] In some possible implementations, the preprocessing of the microseismic event data and the calculation of cavity anomaly indicators specifically include: identifying, locating, and inverting the energy of the microseismic data to obtain event location, energy level, and rupture type; and then sequentially calculating event density, average event energy, event clustering, and microseismic anomaly indicators. The event density is calculated as follows: ; wherein, the ∆t c The N is a preset time window; c , where is the number of microseismic events recorded within the spatial volume V; the average event energy is calculated as follows: ; wherein, the E i The energy of the i-th event; the event clustering The calculation is as follows: ; ; wherein, the The x represents the average event dispersion. i For the energy of the i-th event, x c The location is the center of the event cluster; ε is a very small positive number; the microseismic anomaly index C is calculated as follows: Wherein, Mc is the fracture type weighting factor; δ1, δ2, δ3, and δ4 are all empirical weighting coefficients, satisfying δ1+δ2+δ3=1.

[0036] Specifically, when the microseismic event data is greater than the microseismic anomaly index, it is identified as a cavity anomaly.

[0037] In some possible implementations, the spatial mapping relationship includes: the spatial correspondence between the location of the wellbore anomaly depth and the location of the cavity microseismic event cluster; the spatial correspondence between the location of the wellbore anomaly depth and the surface anomaly region; the spatial correspondence between the location of the cavity microseismic event cluster and the surface anomaly region; the spatial correlation index S is calculated as follows: ; wherein, the d gw The distance between the center of the abnormal area on the ground and the corresponding location at the wellhead or well site; d wc The distance between the location corresponding to the abnormal depth of the wellbore and the center of the microseismic event cluster; the d gc The distance between the ground anomaly area and the cavity projection or associated area; the L gw L wc L gc All of these are spatial attenuation scale parameters; μ1, μ2, and μ3 are all empirical weighting coefficients, satisfying μ1+μ2+μ3=1.

[0038] Specifically, the location of anomalies in wellbore depth includes the location of anomalies in wellbore temperature and the location of anomalies in wellbore vibration; the location of microseismic event clusters in the cavity includes the location of clusters of microseismic events in the cavity; the anomaly area on the surface includes the area of ​​anomaly in gas concentration identified on the surface; when the spatial correlation index S is larger, it indicates that the spatial consistency of multi-source anomalies is stronger.

[0039] In some possible implementations, the time-related metric is calculated as follows: ; ; ; ; wherein, the The time anomaly is the time anomaly occurrence of ground anomalies; t g The time when the ground anomaly occurred; the t wThe time when the wellbore anomaly occurred; the t c The time of occurrence of microseismic anomalies; the τ gw τ wc τ gc All of these are time decay constants; λ1, λ2, and λ3 are all empirical coefficients.

[0040] Specifically, the larger the time correlation index T is, the stronger the time synchronization or time sequence matching of multi-source anomalies.

[0041] In some possible implementations, the comprehensive leakage identification index F is calculated as follows: Wherein, η1, η2, η3, η4, η5, and η6 are all empirical weighting coefficients, satisfying η1+η2+η3+η4+η5+η6=1.

[0042] Specifically, the comprehensive leakage identification index F can be used as a score for outputting early warnings.

[0043] In some possible implementations, the calculation for the rule-constrained correction of the comprehensive leakage identification index is as follows: ; Wherein, R is a rule constraint function; F is a comprehensive leakage identification index; and F f The comprehensive identification value is the result of rule constraints; the three-level early warning classification based on the leakage chain identification result specifically includes: setting a first-level early warning threshold θ1 and a second-level early warning discrimination threshold θ2, and judging the comprehensive identification value. Is it less than the first-level warning threshold θ1? If F f If F < θ1, then output a level 1 warning; if F f If the value is greater than θ1, then determine whether a significant temporal and spatial correlation has formed. If no correlation has formed, output a level one warning; if a correlation has formed, then determine the comprehensive identification value. Whether the secondary warning threshold θ2 has been reached, if θ1 <F f If F < θ2, then output a level 2 warning; if F f If θ2 is greater than θ2, then it is determined whether the strong spatiotemporal correlation condition or the leakage chain condition is met. If not, a level-two warning is output; if met, a level-three warning is output. Alternatively, the level-three warning classification based on the leakage chain identification result specifically includes: setting a level-one warning threshold θ1 and a level-two warning discrimination threshold θ2, determining whether a single-source anomaly exists. If not, a level-one warning is output; if it exists, determining whether a significant temporal and spatial correlation has formed. If not, a level-one warning is output; if formed, determining the comprehensive discrimination value. Whether the secondary warning threshold θ2 has been reached, if θ1 <F f If F < θ2, then output a level 2 warning; if F fIf the value is greater than θ2, then it is determined whether the strong spatiotemporal correlation condition or the leakage chain condition is met. If not, a level 2 warning is output; if it is met, a level 3 warning is output. The strong spatiotemporal correlation condition includes that the three-source monitoring anomalies have significant spatiotemporal consistency. The leakage chain condition includes that the time of occurrence of the two single-source features is consistent with the time sequence and that the modified comprehensive leakage identification index meets the chain criterion of composite leakage.

[0044] Specifically, in order to reduce the impact of single-source false alarms on the comprehensive identification results, the comprehensive leakage identification index F needs to be modified by rule constraints; the existence of only a single-source anomaly indicates that there is an anomaly only on the ground, in the well, or in the cavity; the existence of two-source related anomalies indicates that there is an anomaly in two of the ground, well, and cavity; the existence of three-source significantly related anomalies indicates that there is an anomaly in the ground, well, and cavity.

[0045] It should be noted that the method of using the deep salt cavern hydrogen and helium storage leakage detection device in this embodiment is used in the same way as the deep salt cavern hydrogen and helium storage leakage detection device described in embodiment one. Therefore, the performance principle of the deep salt cavern hydrogen and helium storage leakage detection device will not be repeated here, and the undescribed parts can be referred to embodiment one.

[0046] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A device for detecting leaks in deep salt cavern hydrogen and helium storage, characterized in that, The deep salt cave hydrogen and helium storage leakage detection device includes: The injection-production tubing has one end passing through the wellbore of the salt cavern and located within the cavity of the salt cavern. A distributed optical fiber monitoring unit is installed on the injection-production pipe and arranged along the axial direction of the injection-production pipe column. Several microseismic monitoring units are arranged in a one-to-one correspondence with several monitoring wells evenly distributed around the cavity of the salt cavern, with each microseismic monitoring unit located in a corresponding monitoring well. Several laser gas monitors are distributed around the wellhead of the salt cavern. The signal receiving end has its input interface connected to the distributed optical fiber monitoring unit, each of the microseismic monitoring units, and each of the laser gas monitors via a first signal transmission optical cable. A central processing system, wherein the central processing system is connected to the output end of the signal receiving end via a second signal transmission optical cable; The distributed optical fiber monitoring unit includes a DTS distributed temperature measurement subunit and a DAS distributed acoustic wave subunit, which are arranged in parallel. The DTS distributed temperature measurement subunit and the DAS distributed acoustic wave subunit are respectively connected to the input interface of the signal receiving end through the first signal transmission optical cable.

2. A method of using a deep salt cavern hydrogen and helium storage leakage detection device, used in accordance with the deep salt cavern hydrogen and helium storage leakage detection device as described in claim 1, characterized in that, The method of use includes: S1. Monitoring system deployment: Deploy deep salt cavern hydrogen and helium storage leakage detection devices at the deep single-cavity salt cavern gas storage project site; S2. Multi-source monitoring data acquisition: Within a preset time window, temperature data along the depth direction of the wellbore is acquired through the DTS distributed temperature measurement subunit; vibration signal data along the depth direction of the wellbore is acquired through the DAS distributed acoustic wave subunit; microseismic event data around the salt cavern cavity is acquired through the microseismic monitoring unit; and concentration data of the corresponding gas in the well site area of ​​the salt cavern is acquired through the laser gas monitor. S3. Single-source feature extraction and anomaly identification: Preprocessing the gas concentration data and calculating concentration anomaly indicators; preprocessing the temperature data and calculating temperature anomaly indicators; preprocessing the vibration signal data and calculating vibration anomaly indicators; preprocessing the microseismic event data and calculating cavity anomaly indicators. S4. Establish the spatial mapping relationship between the surface, wellbore, and cavity: Based on the geometric structure, trajectory, wellhead location, and microseismic positioning results of the salt cavern, establish the spatial mapping relationship and calculate the spatial correlation index; S5. Spatiotemporal correlation analysis: Within a preset time window, different monitoring signals collected are allowed to have a sequential order and time lag, and different time weights are set according to different leakage paths to calculate time correlation indicators. S6. Comprehensive Leakage Identification: Based on the concentration anomaly index, the temperature anomaly index, the vibration anomaly index, the cavity anomaly index, the spatial correlation index, and the time correlation index, a comprehensive leakage identification index is constructed. S7. Early Warning Classification: The comprehensive leakage identification index is modified by rule constraints, and a three-level early warning classification is carried out in combination with the leakage chain identification results.

3. The method of using the deep salt cavern hydrogen and helium storage leakage detection device as described in claim 2, characterized in that, The preprocessing of the gas concentration data and the calculation of concentration anomaly indicators specifically include: denoising the gas concentration data, correcting the background value and removing outliers, then extracting the gas concentration value, and sequentially calculating the relative background concentration increment, the duration of continuous exceeding the threshold and the ground gas anomaly indicators within the preset time window. The relative background concentration increment ΔC(t) is calculated as follows: ； Wherein, C(t) is the measured concentration at time t; Let be the background concentration at time t; The duration of continuous overthreshold The calculation is as follows: ； Wherein, the C th is the gas concentration increment threshold; ∆t is the sampling time interval; I is an indicator function, which takes the value 1 when the condition is met, and 0 otherwise. The ground gas anomaly index G is calculated as follows: ； Wherein, the C avg The average concentration within the time window; the ∆C avg The C represents the average concentration increment within the time window; ref ∆C ref T gref All are reference values; α1, α2, and α3 are empirical weighting coefficients, satisfying α1+α2+α3=1.

4. The method of using the deep salt cavern hydrogen and helium storage leakage detection device as described in claim 3, characterized in that, The preprocessing of the temperature data and the calculation of temperature anomaly indicators specifically include: denoising the temperature data, temperature calibration and anomaly segment identification, extracting the anomaly depth location, and sequentially calculating the temperature gradient, the anomaly amount of the temperature gradient, the duration of the temperature anomaly and the temperature anomaly indicators. The temperature gradient is calculated as follows: ； Where z is the wellbore depth; t is the time; and T(z,t) is the wellbore temperature. The temperature gradient anomaly is calculated as follows: ； Among them, the ▽T b (z,t) represents the background temperature gradient; The duration of the temperature anomaly is the duration of the temperature anomaly within the anomaly depth interval [z1, z2], and is calculated as follows: ； Wherein, the T th This is the threshold for temperature gradient anomalies; The temperature anomaly index W1 is calculated as follows: ； Wherein, P z1 The temperature anomaly depth location factor; β1, β2, and β3 are all empirical weighting coefficients, satisfying β1+β2+β3=1.

5. The method of using the deep salt cavern hydrogen and helium storage leakage detection device as described in claim 4, characterized in that, The preprocessing of the vibration signal data and the calculation of vibration anomaly indicators specifically include: denoising the vibration signal data, filtering vibration frequency bands and extracting the anomaly depth location, and sequentially calculating the vibration energy, vibration energy anomaly amount, vibration anomaly duration and vibration anomaly indicators; Within the selected vibration frequency band [f1, f2], the vibration energy is calculated as follows: ； Where Z is the wellbore depth; t is the time; and A(z,t,f) is the vibration response at frequency f. The abnormal amount of vibration energy is calculated as follows: ； Wherein, E vb (z,t) represents the background vibrational energy; The duration of the vibration anomaly is calculated as follows: ； Wherein, E th The threshold for abnormal vibration energy; The vibration anomaly index W2 is calculated as follows: ； Wherein, P z2 The vibration anomaly depth location factor; γ1, γ2, and γ3 are all empirical weighting coefficients, satisfying γ1+γ2+γ3=1.

6. The method of using the deep salt cavern hydrogen and helium storage leakage detection device as described in claim 5, characterized in that, The preprocessing of the microseismic event data and the calculation of cavity anomaly indicators specifically include: identifying, locating, and inverting the energy of the microseismic data to obtain the event location, energy level, and rupture type, and then performing event density calculation, average event energy calculation, event clustering calculation, and microseismic anomaly indicator calculation in sequence. The event density is calculated as follows: ； Wherein, the ∆t c The N is a preset time window; c , represents the number of microseismic events recorded within the spatial volume V; The average event energy is calculated as follows: ； Wherein, E i The energy of the i-th event; The event clustering The calculation is as follows: ； ； Among them, the The x represents the average event dispersion. i For the energy of the i-th event, x c The location is the center of the event cluster; ε is a very small positive number. The microseismic anomaly index C is calculated as follows: ； Wherein, Mc is the fracture type weighting factor; δ1, δ2, δ3, and δ4 are all empirical weighting coefficients, satisfying δ1+δ2+δ3=1.

7. The method of using the deep salt cavern hydrogen and helium storage leakage detection device as described in claim 6, characterized in that, The spatial mapping relationships include: the spatial correspondence between the location of anomaly depth in the wellbore and the location of microseismic event clusters in the cavity; the spatial correspondence between the location of anomaly depth in the wellbore and the anomaly area on the ground; and the spatial correspondence between the location of microseismic event clusters in the cavity and the anomaly area on the ground. The spatial correlation index S is calculated as follows: ； Wherein, the d gw The distance between the center of the abnormal area on the ground and the corresponding location at the wellhead or well site; d wc The distance between the location corresponding to the abnormal depth of the wellbore and the center of the microseismic event cluster; the d gc The distance between the ground anomaly area and the cavity projection or associated area; the L gw L wc L gc All of these are spatial attenuation scale parameters; μ1, μ2, and μ3 are all empirical weighting coefficients, satisfying μ1+μ2+μ3=1.

8. The method of using the deep salt cavern hydrogen and helium storage leakage detection device as described in claim 7, characterized in that, The time-related indicators are calculated as follows: ； ； ； ； Among them, the The time anomaly is the time anomaly occurrence of ground anomalies; t g The time when the ground anomaly occurred; the t w The time when the wellbore anomaly occurred; the t c The time of occurrence of microseismic anomalies; the τ gw τ wc τ gc All of these are time decay constants; λ1, λ2, and λ3 are all empirical coefficients.

9. The method of using the deep salt cavern hydrogen and helium storage leakage detection device as described in claim 8, characterized in that, The comprehensive leakage identification index F is calculated as follows: ； Wherein, η1, η2, η3, η4, η5, and η6 are all empirical weighting coefficients, satisfying η1+η2+η3+η4+η5+η6=1.

10. The method of using the deep salt cavern hydrogen and helium storage leakage detection device as described in claim 9, characterized in that, The calculation for the rule-constrained correction of the comprehensive leakage identification index is as follows: ； ； Wherein, R is a rule constraint function; F is a comprehensive leakage identification index; and F f The comprehensive judgment value after rule constraints; The three-level early warning classification based on the leakage chain identification results specifically includes: setting a first-level early warning threshold θ1 and a second-level early warning discrimination threshold θ2, and judging the comprehensive discrimination value. Is it less than the first-level warning threshold θ1? If F f If F < θ1, then output a level 1 warning; if F f If the value is greater than θ1, then determine whether a significant temporal and spatial correlation has formed. If no correlation has formed, output a level one warning; if a correlation has formed, then determine the comprehensive identification value. Whether the secondary warning threshold θ2 has been reached, if θ1 <F f If F < θ2, then output a level 2 warning; if F f If the value is greater than θ2, then determine whether the strong spatiotemporal correlation condition or leakage chain condition is met. If not, output a level 2 warning; if it is met, output a level 3 warning. or, The three-level early warning classification based on the leakage chain identification results specifically includes: setting a first-level early warning threshold θ1 and a second-level early warning discrimination threshold θ2; determining whether a single-source anomaly exists; if not, outputting a first-level early warning; if it exists, determining whether a significant temporal and spatial correlation has formed; if not, outputting a first-level early warning; if so, determining the comprehensive discrimination value. Whether the secondary warning threshold θ2 has been reached, if θ1 <F f If F < θ2, then output a level 2 warning; if F f If the value is greater than θ2, then determine whether the strong spatiotemporal correlation condition or leakage chain condition is met. If not, output a level 2 warning; if it is met, output a level 3 warning. The strong spatiotemporal correlation condition includes significant spatiotemporal consistency of the three-source monitoring anomalies; the leakage chain condition includes either the timing of the anomalies in the two single-source features conforming to the time sequence or the modified comprehensive leakage identification index conforming to the chain criterion of composite leakage.

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