Salt cavern gas storage safety monitoring method and system based on optical fiber sensing technology

Abstract

The invention relates to the technical field of salt-cavern gas storage safety monitoring, in particular to a salt-cavern gas storage safety monitoring method and system based on an optical fiber sensing technology. The method comprises the following steps: arranging a plurality of optical fiber sensing networks, and obtaining monitoring data of the salt cavern gas storage during operation through the optical fiber sensing networks; introducing a particle swarm algorithm to construct an optimal parameter screening model, and determining hyper-parameters of a time-frequency analysis algorithm by using the optimal parameter screening model based on the monitoring data; reconstructing the monitoring data through the time-frequency analysis algorithm to obtain reconstructed monitoring data, and analyzing a monitoring index according to the reconstructed monitoring data; and establishing a salt-cavern gas storage safe operation alarm mechanism, and combining the monitoring indexes and the salt-cavern gas storage safe operation alarm mechanism to complete salt-cavern gas storage safe monitoring work. The operation state of the gas storage is comprehensively and accurately monitored in real time through the optical fiber sensing network, and reliable guarantee is provided for safe operation of the salt-cavern gas storage.

Description

Technical Field

[0001] The present invention relates to the technical field of safety monitoring of salt cavern gas storage, and in particular to a safety monitoring method and system for salt cavern gas storage based on optical fiber sensing technology. Background Art

[0002] Global energy demand continues to rise, and the sharp increase in fossil fuel consumption has led to large amounts of carbon dioxide emissions. Supercritical carbon dioxide geological storage technology, as a method of storing carbon dioxide underground for a long time, is seen as one of the key measures to mitigate carbon dioxide emissions. Salt caverns are generally formed in salt layers deep underground. After a long geological period, the structure is stable, and the salt rock has low permeability, which can effectively prevent the leakage of supercritical carbon dioxide. In the supercritical state, the density of carbon dioxide is similar to that of liquid, the viscosity is similar to that of gas, and the fluidity is strong. If there is no stable and well-sealed geological structure, it is easy to leak. The geological stability of salt caverns can accommodate supercritical carbon dioxide for a long time, ensuring the safety of storage.

[0003] The storage and migration behavior of supercritical carbon dioxide in deep salt cavern gas storage has significant particularity. Its physical and chemical properties such as high diffusivity, phase sensitivity, strong solubility and interaction with salt rock-brine system may have a profound impact on the long-term stability, sealing and safety of the gas storage. Injecting supercritical carbon dioxide into a salt cavern gas storage with shallow burial, thin salt layer, many interlayers and low grade will cause the grain boundary sliding and dislocation movement of salt rock to intensify under high temperature and high pressure (usually >31.1℃, >7.38MPa) environment, resulting in an increase in creep rate, especially in salt rock with interlayers, which puts forward strict requirements for the safe operation monitoring of salt cavern gas storage. Traditional monitoring methods such as pressure monitoring and displacement monitoring can only obtain local and limited information, and it is difficult to fully and real-time grasp the overall operation status of salt cavern gas storage. Moreover, when facing complex geological environments, the monitoring accuracy and reliability of these methods are easily affected. Therefore, there is an urgent need for an efficient, accurate and comprehensive salt cavern gas storage safety monitoring technology to ensure its safe and stable operation. Summary of the invention

[0004] In view of the shortcomings of existing methods and the needs of practical applications, in order to timely discover safety hazards in the storage and operation process of supercritical carbon dioxide in deep salt cavern gas storage, solve the problem of real-time, comprehensive and accurate monitoring of the operating status of the gas storage, and provide reliable protection for the safe operation of the salt cavern gas storage. On the one hand, the present invention provides a salt cavern gas storage safety monitoring method based on optical fiber sensing technology, including the following steps: deploying a variety of optical fiber sensing networks, and obtaining monitoring data during the operation of the salt cavern gas storage through the optical fiber sensing network; introducing a particle swarm algorithm to construct an optimal parameter screening model, based on the monitoring data, using the optimal parameter screening model to determine the hyperparameters of the time-frequency analysis algorithm; reconstructing the monitoring data through the time-frequency analysis algorithm to obtain reconstructed monitoring data, and analyzing monitoring indicators based on the reconstructed monitoring data; establishing a salt cavern gas storage safety operation alarm mechanism, combining the monitoring indicators and the salt cavern gas storage safety operation alarm mechanism, to complete the salt cavern gas storage safety monitoring work.

[0005] The present invention monitors various indicators of the salt cavern gas storage by deploying a variety of optical fiber sensor networks, and then uses the optimized particle swarm algorithm to quickly and accurately search for the optimal time-frequency analysis algorithm hyperparameters, and then reconstructs the monitoring data to improve data accuracy, effectively solving the problem of real-time, comprehensive and accurate monitoring of the operating status of the gas storage, and providing reliable protection for the safe operation of the salt cavern gas storage.

[0006] Optionally, the introducing of a particle swarm algorithm to construct an optimal parameter screening model comprises the following steps: According to the value range of the hyperparameter, the initial particles are set; the adaptive factor is introduced to improve the inertia coefficient of the particle swarm algorithm; based on the fitness value of the individual particle in the iteration process, the speed update formula of the individual particle is improved. The present invention improves the particle swarm algorithm to construct an optimal parameter screening model, which helps the subsequent steps to quickly and efficiently obtain the optimal time-frequency analysis algorithm to accurately reflect the state of the gas storage reservoir.

[0007] Optionally, the step of setting initial particles according to the value range of the hyperparameter comprises the following steps: Normalize the hyperparameters; decompose the normalized results based on the number of particles, and set the initial particles according to the decomposition results. The present invention normalizes the value range of the hyperparameters and then decomposes them according to the number of particles, which can comprehensively and evenly distribute the initial particles in the solution space, which is conducive to improving the accuracy of the present invention.

[0008] Optionally, the introduction of an adaptive factor to improve the inertia coefficient of the particle swarm algorithm satisfies the following formula: in, represents the improved inertia coefficient, represents the adaptive factor of the i-th particle at the t-th iteration, represents the inertia coefficient, represents the fitness value of the i-th particle at the t-th iteration, represents the fitness value of the optimal particle individual at the tth iteration, Indicates the fitness value of the best individual particle in the iteration history. The present invention dynamically adjusts the inertia coefficient according to the fitness value of the particle, which is conducive to optimizing and iterating for each particle to improve the efficiency of the present invention.

[0009] Optionally, based on the fitness value of the individual particle in the iteration process, the speed update formula of the individual particle is improved to satisfy the following formula: in, represents the velocity of the i-th particle at the t+1th iteration, represents the adaptive factor of the i-th particle at the t-th iteration, represents the velocity of the i-th particle at the t-th iteration, represents the fitness value of the i-th particle at the t-th iteration, represents the fitness value of the optimal particle individual at the tth iteration, represents the fitness value of the best particle individual in the iteration history, represents the position of the i-th particle at the t-th iteration, represents the position of the optimal individual particle in the iterative history, The present invention uses the adaptive factor as the weight factor of the speed, and then adaptively adjusts the moving vector according to the fitness of the particle with the global optimal particle and the historical optimal particle, which is further conducive to improving the accuracy of the present invention.

[0010] Optionally, the determining the hyperparameters of the time-frequency analysis algorithm based on the monitoring data using the optimal parameter screening model comprises the following steps: It is determined that the optimal parameter screening model falls into an optimization deadlock; a group perturbation model is introduced, and the group perturbation model is used to perturb the particle group according to the determination result.

[0011] Optionally, the group disturbance model satisfies the following formula: in, represents the position of the i-th particle after the disturbance, represents the position of the i-th particle, represents the Archimedean spiral coefficient, represents a random number in [0,1], represents the fitness value of the i-th particle individual, represents the fitness value of the best particle individual in the iteration history, represents the judgment threshold, represents the fitness value of the worst individual particle, Indicates the position of the worst individual particle. The present invention distinguishes particles far from the optimal particle and particles close to the optimal particle, and then executes different perturbation strategies, which is conducive to solving the problem of local optimality.

[0012] Optionally, the time-frequency analysis algorithm includes one of an ensemble empirical mode decomposition algorithm, an empirical mode decomposition algorithm or a variational mode decomposition algorithm. The present invention uses a variety of time-frequency analysis methods, which is further conducive to improving the accuracy of the present invention.

[0013] Optionally, analyzing the monitoring indicators according to the reconstructed monitoring data comprises the following steps: The shrinkage rate of the gas storage cavity is obtained by inversion analysis based on the reconstructed monitoring data; the height of the gas-halogen interface is located using the temperature gradient and the Raman scattering mutation point based on the reconstructed monitoring data; and the carbon dioxide phase is identified through the reconstructed monitoring data. The present invention obtains multiple monitoring indicators by analyzing the reconstructed monitoring data, which effectively improves the comprehensiveness of the monitoring of the present invention.

[0014] In the second aspect, in order to be able to efficiently execute the salt cavern gas storage safety monitoring method based on optical fiber sensing technology provided by the present invention, the present invention also provides a salt cavern gas storage safety monitoring system based on optical fiber sensing technology, including a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the salt cavern gas storage safety monitoring method based on optical fiber sensing technology as described in the first aspect of the present invention. The salt cavern gas storage safety monitoring system based on optical fiber sensing technology of the present invention has a compact structure and stable performance, and can stably execute the salt cavern gas storage safety monitoring method based on optical fiber sensing technology provided by the present invention, further improving the overall applicability and practical application ability of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of a method for safety monitoring of salt cavern gas storage based on optical fiber sensing technology provided by an embodiment of the present invention; Figure 2 A framework diagram of a salt cavern gas storage safety monitoring system based on optical fiber sensing technology provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0017] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0018] See also Figure 1 In order to timely discover safety hazards in the storage and operation process of supercritical carbon dioxide in deep salt cavern gas storage, solve the problem of real-time, comprehensive and accurate monitoring of the operating status of the gas storage, and provide reliable guarantee for the safe operation of the salt cavern gas storage. The present invention provides a salt cavern gas storage safety monitoring method based on optical fiber sensing technology, such as Figure 1 As shown, in one embodiment, the method includes the following steps: S1. Deploy a variety of optical fiber sensor networks, and obtain monitoring data during the operation of the salt cavern gas storage through the optical fiber sensor networks.

[0019] Supercritical carbon dioxide penetrates into the caprock (such as mudstone) through diffusion, reducing the capillary pressure threshold of the caprock and inducing permeability channels. When supercritical carbon dioxide is injected into the salt cavern gas storage, the salt rock will intensify grain boundary sliding and dislocation movement under high temperature and high pressure, leading to an increase in creep rate, which in turn increases the volume shrinkage rate of the cavity and may cause ground subsidence; at the same time, long-term shrinkage may also lead to a reduction in the effective volume of the gas storage, requiring frequent adjustments to the injection and production plan.

[0020] Furthermore, the periodic injection and production of supercritical carbon dioxide will cause pressure fluctuations in the reservoir, triggering stress redistribution in the salt rock mass and shear stress concentration in the caprock and surrounding rock, increasing the risk of crack expansion around the salt caverns, and possibly forming a through-fracture network, inducing microseismic activity.

[0021] Furthermore, carbon dioxide dissolves in brine to form carbonic acid, which further corrodes salt rock, accelerates the development of fractures, and increases the electrochemical corrosion rate of metal casing (such as carbon steel), leading to damage to wellbore integrity.

[0022] Therefore, according to the needs of operational safety risk assessment of the gas storage, spiral BOTDA optical fiber is laid along the surface, and a three-dimensional FBG array is embedded in the inner wall of the cavity. At the same time, DTS-Raman composite optical fiber is vertically suspended, a DAS optical fiber ring network is laid along the fracture zone, and a high-pressure resistant MEMS optical fiber sensor array is deployed in the gas storage to monitor indicators such as ground subsidence, cavity deformation, gas-halogen interface, temperature, pressure and sealing.

[0023] Specifically, the optical fiber sensor network is precisely laid in the cavity wall, surrounding rock, and key connection locations according to the design plan. During the laying process, the optical fiber sensor network is ensured to be in close contact with the monitored object to ensure effective signal collection.

[0024] It can be understood that through the distributed optical fiber sensing network, the cavity wall, surrounding rock and key connection parts of the salt cavern gas storage can be fully monitored, and the change information of various physical quantities such as temperature, strain and pressure can be obtained in real time, so as to timely grasp the operating status of the salt cavern gas storage. The optical fiber sensing system has a simple structure and a long service life, which can meet the needs of long-term safety monitoring of the salt cavern gas storage and reduce maintenance costs.

[0025] At the same time, fiber optic sensing technology has extremely high measurement accuracy and can accurately sense tiny changes in physical quantities, effectively improving the accuracy of salt cavern gas storage safety monitoring and reducing the missed detection rate of potential safety hazards. Furthermore, fiber optic sensors are not subject to electromagnetic interference and can work stably in complex underground environments, ensuring the reliability and stability of monitoring signals.

[0026] By setting preset safety thresholds, a safe operation alarm mechanism for salt cavern gas storage is established. Based on real-time monitoring data and analysis of alarm index values, early warning signals can be issued promptly and accurately, providing operators with sufficient time to take measures and effectively prevent the occurrence of safety accidents.

[0027] Furthermore, a high-performance fiber optic signal acquisition card is selected as the signal acquisition device, and the acquisition frequency is set, preferably 10Hz, to ensure that the physical quantity changes during the operation of the salt cavern gas storage can be captured in real time. Wavelength division multiplexing technology is used to transmit optical signals of different wavelengths in the same optical fiber to improve the utilization rate of the optical fiber. During the signal transmission process, an optical amplifier is set at a certain distance to enhance the signal strength and ensure that the signal can be transmitted stably.

[0028] S2. Introduce a particle swarm algorithm to construct an optimal parameter screening model, and based on the monitoring data, use the optimal parameter screening model to determine the hyperparameters of the time-frequency analysis algorithm.

[0029] Specifically, the time-frequency analysis algorithm includes one of an ensemble empirical mode decomposition algorithm, an empirical mode decomposition algorithm or a variational mode decomposition algorithm. A variety of reconstructed monitoring data are obtained through a variety of time-frequency analysis algorithms, thereby further improving the accuracy of the data.

[0030] In the embodiment, the hyperparameters of the time-frequency analysis algorithm include the number of decompositions and the penalty factor.

[0031] The appropriate number of decompositions can accurately decompose the fiber optic sensor monitoring signal of the salt cavern gas storage into a corresponding number of intrinsic mode function (IMF) components according to the actual physical process and characteristics. For example, if the number of decompositions is too small, the complex signal characteristics cannot be fully analyzed, and key information such as slight deformation of the cavity and abnormal changes in local temperature may be missed; if the number of decompositions is too large, redundant components will be introduced, interfering with the judgment of the actual state. The optimal number of decompositions can ensure that each IMF component carries unique and valuable information on the operating status of the gas storage, such as stress changes in specific parts, changes in temperature fields caused by gas leakage, etc., providing a reliable basis for subsequent analysis.

[0032] The penalty factor controls the degree of constraint of each modal bandwidth. When the penalty factor is optimal, the decomposed IMF components can have good characteristics. On the one hand, it can balance the frequency distribution between the modes and avoid modal aliasing, that is, signal components with different physical meanings are mistakenly mixed in one IMF component. For example, in the monitoring of salt cavern gas storage, it prevents the confusion of signal characteristics caused by cavity deformation and pressure changes, so that each IMF component can clearly correspond to a certain physical change. On the other hand, it can ensure the stability of the decomposition results, reduce abnormal decomposition caused by signal fluctuations or noise interference, and make the decomposition results truly reflect the actual operating conditions of the salt cavern gas storage.

[0033] Based on the optimal number of decompositions and penalty factors, high-quality IMF components can be obtained, which can more accurately extract physical quantity characteristics such as temperature, strain, and pressure, which is conducive to accurately judging the changes in characteristics and reducing false alarms and missed alarms. For example, when a certain area of ​​the salt cavern gas storage reservoir causes strain changes due to abnormal pressure increase, it can trigger an early warning in a timely and accurate manner, providing strong support for ensuring the safe and stable operation of the salt cavern gas storage reservoir.

[0034] Furthermore, the introduction of the particle swarm algorithm to construct the optimal parameter screening model includes the following steps: S21. Set initial particles according to the value range of the hyperparameters.

[0035] First, the hyperparameters are normalized.

[0036] The range of the number of decompositions is usually determined according to the specific signal characteristics and analysis requirements, and is generally between 2 and 10. For the fiber optic sensor signal of the salt cavern gas storage, the initial range of the number of decompositions can be set to [3,8]. This is because too few decompositions cannot fully analyze the complex characteristics of the signal, while too many may introduce redundant information.

[0037] The penalty factor is usually between 100 and 2000, depending on the noise level and complexity of the signal. For salt cavern gas storage monitoring signals, if the noise is relatively low, the value is between 200 and 800; if the noise is high, a larger value may be required to suppress the noise impact, and the setting range is [200,1000].

[0038] By normalizing the hyperparameters, it is convenient to uniformly process and compare different parameters during model training and optimization, thus improving the stability and convergence speed of the algorithm. At the same time, limiting the value range to [0,1] also helps to avoid numerical calculation problems caused by parameter values ​​that are too large or too small.

[0039] Next, the normalized result is decomposed based on the number of particles, and the initial particles are set according to the decomposition result.

[0040] The particle swarm algorithm needs to set the group size parameter, that is, the number of particles. The larger the number of particles, the wider the coverage of the algorithm search space, the more comprehensive the exploration of the solution space, and the greater the possibility of finding the global optimal solution, but it will also increase the amount of calculation and time. On the contrary, the smaller the number of particles, the faster the calculation speed, but it may cause the algorithm to converge prematurely and fall into the local optimal solution. The number of particles is usually determined based on the complexity of the problem, the size of the search space, and the computing power, and the general range is between 20-100.

[0041] Specifically, the normalized hyperparameters are divided equally according to the number of particles, and the combination of the division points of all hyperparameters is the set initial particle.

[0042] S22. Introduce an adaptive factor to improve the inertia coefficient of the particle swarm algorithm.

[0043] In an embodiment, the inertia coefficient of the particle swarm algorithm is improved by introducing an adaptive factor, which satisfies the following formula: in, represents the improved inertia coefficient, represents the adaptive factor of the i-th particle at the t-th iteration, represents the inertia coefficient, represents the fitness value of the i-th particle at the t-th iteration, represents the fitness value of the optimal particle individual at the tth iteration, Represents the fitness value of the best individual particle in the iteration history.

[0044] In the embodiment, the fitness value of the individual particle refers to the minimum value of the envelope entropy of the IMF component of the monitoring signal decomposed by the particle corresponding time-frequency analysis algorithm. The envelope entropy reflects the sparsity of the signal. The less noise contained in the IMF component, the smaller the envelope entropy value.

[0045] S23. Based on the fitness value of the individual particle during the iteration process, improve the speed update formula of the individual particle.

[0046] Specifically, based on the fitness value of the individual particle in the iteration process, the speed update formula of the individual particle is improved to satisfy the following formula: in, represents the velocity of the i-th particle at the t+1th iteration, represents the adaptive factor of the i-th particle at the t-th iteration, represents the velocity of the i-th particle at the t-th iteration, represents the fitness value of the i-th particle at the t-th iteration, represents the fitness value of the optimal particle individual at the tth iteration, represents the fitness value of the best particle individual in the iteration history, represents the position of the i-th particle at the t-th iteration, represents the position of the optimal individual particle in the iterative history, Represents the position of the optimal individual particle at the tth iteration.

[0047] Furthermore, the method of determining the hyperparameters of the time-frequency analysis algorithm based on the monitoring data using the optimal parameter screening model comprises the following steps: S24, determining whether the optimal parameter screening model is stuck in optimization.

[0048] Specifically, a threshold A of the number of consecutive iterations is set. When the global optimal solution of the model is not updated in consecutive A iterations, or the change in the objective function value is less than a preset minimum value, the algorithm is considered to be in an optimization deadlock. In other embodiments, judgment can also be made by statistical diversity indicators, analyzing particle states, and observing iteration curves.

[0049] S25. Introduce a group disturbance model, and use the group disturbance model to disturb the particle group according to the judgment result.

[0050] Specifically, the group disturbance model satisfies the following formula: in, represents the position of the i-th particle after the disturbance, represents the position of the i-th particle, represents the Archimedean spiral coefficient, represents a random number in [0,1], represents the fitness value of the i-th particle individual, represents the fitness value of the best particle individual in the iteration history, Indicates the judgment threshold, represents the fitness value of the worst individual particle, Indicates the position of the worst individual particle.

[0051] The judgment threshold is used to judge the distance between the current particle individual and the optimal particle individual, and can be determined according to the value range of the fitness function.

[0052] S3. Reconstruct the monitoring data using the time-frequency analysis algorithm to obtain reconstructed monitoring data, and analyze monitoring indicators based on the reconstructed monitoring data.

[0053] Based on the time-frequency analysis algorithm with optimal hyperparameters, the monitoring data is decomposed in the time domain to obtain the corresponding K modal components, which are then divided into several sub-frequency bands according to the relationship between the components in different frequency ranges and the characteristic differences between each modal component and the original signal in the time domain. The MPE algorithm is used to calculate these decomposed modal components, and the specific value of the MPE is determined for each modal component. If the MPE value of the modal component is not less than the preset value, the modal component is determined to be a noise function. If the MPE value of the modal component is less than the preset value, the modal component IMF is determined to be a noise-free function. Then, the modal components with MPE values ​​less than the preset value are reconstructed and combined to obtain the reconstructed monitoring data.

[0054] Further, analyzing the monitoring indicators according to the reconstructed monitoring data comprises the following steps: S31. Obtain the shrinkage rate of the gas storage cavity according to the inversion analysis of the reconstructed monitoring data.

[0055] In the embodiment, the cavity deformation vector is calculated based on the cavity stress monitoring data, and then the current cavity volume contraction threshold is obtained. Then, the volume of the salt cavern gas storage cavity is detected regularly, and the cavity volume contraction rate is calculated in combination with the cavity monitoring data.

[0056] In other embodiments, the change of brine pressure in the dissolution cavity can also be used to analyze the shrinkage deformation of the cavity volume, and the main creep parameters of the creep constitutive equation of the salt layer in the gas storage area can be inversely analyzed to obtain the cavity shrinkage rate.

[0057] S32. Based on the reconstructed monitoring data, the height of the gas-halogen interface is located using the temperature gradient and the Raman scattering mutation point.

[0058] Specifically, the Raman scattered light in the optical fiber backscattered light is used as the signal demodulation light, and the temperature parameter is obtained by calculating the ratio of the light intensity of the Stokes light and the anti-Stokes light.

[0059] The collected temperature data is processed and analyzed, and the temperature gradient curve is drawn to find the mutation point in the temperature gradient curve, which is the potential location of the gas-liquid interface. According to the location of the temperature gradient mutation point, combined with the downhole geology and fluid distribution characteristics, the exact height of the gas-halogen interface is further determined.

[0060] S33. Identify the phase state of carbon dioxide by reconstructing the monitoring data.

[0061] Small temperature and pressure fluctuations of carbon dioxide near the critical point can lead to sudden phase changes. Pressure and temperature data are extracted from the reconstructed monitoring data, and the phase state of carbon dioxide can be determined based on pressure and temperature.

[0062] S4. Establish a safe operation alarm mechanism for the salt cavern gas storage, and complete the safety monitoring work of the salt cavern gas storage by combining the monitoring indicators and the safe operation alarm mechanism for the salt cavern gas storage.

[0063] According to the influence of ground subsidence, cavity deformation, gas-halogen interface, temperature pressure and sealing on the safe operation of gas storage, a safe operation alarm mechanism for salt cavern gas storage and corresponding monitoring index thresholds are designed, and then early warning is issued through the monitoring indicators analyzed according to the reconstructed monitoring data.

[0064] In an embodiment, the salt cavern gas storage safe operation alarm mechanism includes: Level 1 (yellow): cavity shrinkage rate > 0.1% / month or pressure fluctuation > 10%; Level 2 (orange): daily migration of the gas-halogen interface > 1m or a sudden increase in the frequency of microseismic events; Level 3 (red): The crack is connected or carbon dioxide is leaking.

[0065] See also Figure 2In an embodiment, in order to efficiently execute the salt cavern gas storage safety monitoring method based on optical fiber sensing technology provided by the present invention, the present invention also provides a salt cavern gas storage safety monitoring system based on optical fiber sensing technology, including: an input device, an output device, a processor, and a memory, wherein the input device, the output device, the processor, and the memory are interconnected, and the memory contains program instructions, and the program instructions are used for the steps of the salt cavern gas storage safety monitoring method based on optical fiber sensing technology. The salt cavern gas storage safety monitoring system based on optical fiber sensing technology of the present invention has a compact structure and stable performance, and can stably execute the salt cavern gas storage safety monitoring method based on optical fiber sensing technology of the present invention, further improving the overall applicability and practical application ability of the present invention.

[0066] In an embodiment, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. An input device may be used to obtain data information. An output device may be used to output the results obtained by storing program instructions contained in a computer program in a memory provided by the present invention. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory.

[0067] In one possible implementation, the memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, and at least one application required for a function, etc.; the data storage area may store data created during use. In addition, the memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0068] The embodiment also provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned salt cavern gas storage safety monitoring method based on optical fiber sensing technology are implemented.

[0069] The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0070] In summary, the present invention monitors various indicators of the salt cavern gas storage by deploying various optical fiber sensor networks, and then uses the optimized particle swarm algorithm to quickly and accurately search for the optimal hyperparameters of the time-frequency analysis algorithm, and then reconstructs the monitoring data to improve data accuracy, effectively solving the problem of real-time, comprehensive and accurate monitoring of the operating status of the gas storage, and providing reliable protection for the safe operation of the salt cavern gas storage.

[0071] Therefore, the present invention effectively overcomes various shortcomings in the prior art and has high industrial utilization value.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope recorded in the present invention.

Claims

1. A method for safety monitoring of salt cavern gas storage based on optical fiber sensing technology, characterized in that: The salt cavern gas storage safety monitoring method based on optical fiber sensing technology comprises the following steps: Deploy a variety of optical fiber sensor networks to obtain monitoring data during the operation of the salt cavern gas storage reservoir through the optical fiber sensor networks; Introducing a particle swarm algorithm to construct an optimal parameter screening model, and based on the monitoring data, using the optimal parameter screening model to determine the hyperparameters of the time-frequency analysis algorithm; Reconstructing the monitoring data by using the time-frequency analysis algorithm to obtain reconstructed monitoring data, and analyzing monitoring indicators according to the reconstructed monitoring data; Establish a safe operation alarm mechanism for salt cavern gas storage, and complete the safety monitoring work of salt cavern gas storage by combining the monitoring indicators and the safe operation alarm mechanism for salt cavern gas storage.

2. According to claim 1, the method for safety monitoring of salt cavern gas storage based on optical fiber sensing technology is characterized in that: The method of introducing the particle swarm algorithm to construct the optimal parameter screening model includes the following steps: Set the initial particles according to the value range of the hyperparameters; Introducing adaptive factors to improve the inertia coefficient of particle swarm algorithm; Based on the fitness value of the individual particle during the iteration process, the speed update formula of the individual particle is improved.

3. According to claim 2, the method for safety monitoring of salt cavern gas storage based on optical fiber sensing technology is characterized in that: The step of setting the initial particles according to the value range of the hyperparameters comprises the following steps: Normalizing the hyperparameters; The normalized result is decomposed based on the number of particles, and the initial particles are set according to the decomposition result.

4. According to claim 2, the method for safety monitoring of salt cavern gas storage based on optical fiber sensing technology is characterized in that: The inertia coefficient of the particle swarm algorithm is improved by introducing an adaptive factor, which satisfies the following formula: in, represents the improved inertia coefficient, represents the adaptive factor of the i-th particle at the t-th iteration, represents the inertia coefficient, represents the fitness value of the i-th particle at the t-th iteration, represents the fitness value of the optimal particle individual at the tth iteration, Represents the fitness value of the best individual particle in the iteration history.

5. According to claim 2, the method for safety monitoring of salt cavern gas storage based on optical fiber sensing technology is characterized in that: Based on the fitness value of the individual particle in the iteration process, the speed update formula of the individual particle is improved to satisfy the following formula: in, represents the velocity of the i-th particle at the t+1th iteration, represents the adaptive factor of the i-th particle at the t-th iteration, represents the velocity of the i-th particle at the t-th iteration, represents the fitness value of the i-th particle at the t-th iteration, represents the fitness value of the optimal particle individual at the tth iteration, represents the fitness value of the best particle individual in the iteration history, represents the position of the i-th particle at the t-th iteration, represents the position of the optimal individual particle in the iterative history, Represents the position of the optimal individual particle at the tth iteration.

6. The method for safety monitoring of salt cavern gas storage based on optical fiber sensing technology according to claim 1 is characterized in that: Determining the hyperparameters of the time-frequency analysis algorithm based on the monitoring data using the optimal parameter screening model comprises the following steps: Determining that the optimal parameter screening model is stuck in optimization; A group disturbance model is introduced, and the group disturbance model is used to disturb the particle group according to the judgment result.

7. The method for safety monitoring of salt cavern gas storage based on optical fiber sensing technology according to claim 6 is characterized in that: The group disturbance model satisfies the following formula: in, represents the position of the i-th particle after the disturbance, represents the position of the i-th particle, represents the Archimedean spiral coefficient, represents a random number in [0,1], represents the fitness value of the i-th particle individual, represents the fitness value of the best particle individual in the iteration history, represents the judgment threshold, represents the fitness value of the worst individual particle, Indicates the position of the worst individual particle.

8. The method for safety monitoring of salt cavern gas storage based on optical fiber sensing technology according to claim 1 is characterized in that: The time-frequency analysis algorithm includes one of an ensemble empirical mode decomposition algorithm, an empirical mode decomposition algorithm or a variational mode decomposition algorithm.

9. The method for safety monitoring of salt cavern gas storage based on optical fiber sensing technology according to claim 1 is characterized in that: The step of analyzing the monitoring indicators according to the reconstructed monitoring data comprises the following steps: Obtaining the shrinkage rate of the gas storage cavity according to the inversion analysis of the reconstructed monitoring data; Based on the reconstructed monitoring data, the height of the gas-halogen interface is located using the temperature gradient and the Raman scattering mutation point; The phase state of carbon dioxide is identified through the reconstructed monitoring data.

10. A salt cavern gas storage safety monitoring system based on optical fiber sensing technology, characterized in that: The salt cavern gas storage safety monitoring system based on optical fiber sensing technology comprises: an input device, an output device, a processor, and a memory, wherein the input device, the output device, the processor, and the memory are interconnected, and the memory comprises program instructions, and the program instructions are used to execute the salt cavern gas storage safety monitoring method based on optical fiber sensing technology according to any one of claims 1 to 9.

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