Compressed air energy storage warehouse cavity sealing performance monitoring and evaluating method

Abstract

The invention discloses a compressed air energy storage warehouse cavity sealing performance monitoring and evaluation method, and relates to the field of wall sealing, and the method comprises the steps: S1, collecting cover layer properties and cover layer parameters of a block, carrying out the analysis and evaluation of the collected cover layer properties and cover layer parameters, and evaluating the cover layer sealing performance grade of the block; s2, monitoring the energy storage cover layer and the energy storage chamber in real time to obtain real-time monitoring data; and S3, collecting cavity properties and cavity parameters of the cavity, carrying out analysis and evaluation by adopting an XGBoost machine learning algorithm aiming at the cavity properties, the cavity parameters, the cover layer properties and the cover layer parameters, and evaluating the energy storage library sealing performance grade of the block through a classification model of the XGBoost machine learning algorithm. Therefore, measures are taken in time, and downtime and potential safety risks are reduced.

Description

Technical Field

[0001] The invention relates to the field of wall sealing, and in particular to a method for monitoring and evaluating the sealing performance of a compressed air energy storage cavity. Background Art

[0002] Gas storage is an energy storage technology, usually used to store energy such as natural gas for future use. Energy storage, such as compressed air, is usually used for grid-side matching to achieve frequency and peak regulation of energy or power systems. The present invention relates to a method for monitoring the sealing of energy storage cavity, involving the fields of energy reserves, geology, environmental science and engineering safety, and in particular, proposes a comprehensive evaluation and monitoring method for the sealing of the cover layer and cavity of an energy storage reservoir, and also involves the configuration of sensors and monitoring equipment to monitor the performance and sealing of compressed air energy storage reservoirs in real time, as well as the construction and maintenance of energy storage reservoirs, including underground engineering, safety measures, pipeline systems and equipment, etc.

[0003] A method for transforming the cavity of a gas storage reservoir with publication number CN202310980880.2 is disclosed, which uses a solidifying material to displace and clean the impurities of the residual gas-liquid mixture in the cavity, thereby increasing the cavity capacity; and after the solidifying material is completely solidified, a cavity boundary protection layer with high compressive strength, strong density and good stability is formed, which effectively ensures the sealing of the energy storage reservoir and improves the safety of gas storage.

[0004] The patent with publication number CN202310492352.2, Method and system for evaluating the sealing performance of caprock of aquifer reservoir using reservoir-cap assembly, discloses a method and system for evaluating the sealing performance of caprock of aquifer reservoir using reservoir-cap assembly. Using foamed cement to adjust the ratio to obtain a transition layer with similar density and porosity to the caprock can effectively evaluate the failure of caprock sealing and leakage caused by different types of caprocks and caprocks with different fracture characteristics during the injection and production process of the underlying reservoir, thereby providing a reference for establishing the upper limit operating pressure of aquifer energy storage reservoirs with different types of caprocks.

[0005] Publication No. CN202211625973.5 discloses a gas-stopping belt suitable for sealing layer expansion joints, comprising a belt body and a first protrusion and a second protrusion arranged on the belt body, wherein the first protrusion and the second protrusion are both hollow structures, thereby ensuring the gas-stopping effect, improving the working reliability of the gas-stopping belt, and thus improving the sealing of the energy storage tank.

[0006] There is currently a lack of methods for comprehensive evaluation and tightness monitoring of energy storage reservoir cap layers and cavities, and existing technologies may face challenges in terms of accuracy and sensitivity in monitoring the tightness of energy storage reservoirs. In particular, small leaks or changes may not be accurately detected and quantified, which may lead to safety risks and performance degradation. Existing technologies may face difficulties in real-time monitoring, may not be sufficient to meet the needs of such non-destructive monitoring, may be unstable or unreliable under certain environmental conditions, and may have difficulties in long-term monitoring, especially in maintaining accuracy and stability. In response to these issues, new energy storage reservoir cavity tightness monitoring methods may attempt to address the challenges of accuracy, real-time monitoring, cost-effectiveness, non-destructive monitoring, and adaptability to different environmental conditions.

[0007] Energy storage reservoirs are typically used to store high-pressure gases (such as natural gas or compressed air), so ensuring the sealing of the reservoir is critical to preventing gas leaks and safety accidents.

[0008] Therefore, there is an urgent need for a compressed air energy storage reservoir cavity sealing monitoring and evaluation method to monitor the energy storage reservoir in real time and ensure the safety of the gas storage reservoir. Summary of the invention

[0009] In order to solve the above problems, the present application provides a method for monitoring and evaluating the sealing performance of a compressed air energy storage cavity.

[0010] The main purpose of the present invention is to provide a scientific comprehensive evaluation of the pipe string, cap layer and cavity of the energy storage reservoir, which is used to evaluate the pipe string, cap layer and cavity of each energy storage reservoir block and give a certain grade classification; at the same time, a method for monitoring the sealing of the cap layer and cavity of the energy storage reservoir is proposed to ensure that the sealing during the operation of the energy storage reservoir can meet the safety and performance requirements. The comprehensive evaluation and monitoring method of the cap layer and cavity proposed in the present invention can scientifically evaluate and monitor the sealing problems of the cap layer and cavity, and make timely reminders when leakage occurs to reduce potential safety risks.

[0011] The method for monitoring and evaluating the sealing performance of a compressed air energy storage cavity mentioned in the present invention comprises: S1. Collect the cap rock properties and cap rock parameters of the block, analyze and evaluate the collected cap rock properties and cap rock parameters, and evaluate the cap rock sealing level of the block; S2. Real-time monitoring of the energy storage cap layer and the energy storage cavity to obtain real-time monitoring data; S3. Collect the cavity properties and cavity parameters of the cavity, use the XGBoost machine learning algorithm to analyze and evaluate the cavity properties, cavity parameters and caprock properties and caprock parameters, and evaluate the sealing level of the energy storage reservoir in the block through the classification model of the XGBoost machine learning algorithm.

[0012] Preferably, the formation properties include rock formation properties, continuity, fracture development, dynamic sealing, formation water properties, tectonic stress, and brittleness; Relevant parameters include mud content, thinnest caprock thickness, permeability, and displacement pressure.

[0013] Preferably, the cavity characteristics include cavity size, cavity shape, earthquake and disaster prevention capabilities, pressure and corrosion resistance capabilities, electrophoresis mechanism, emergency shutdown device information or whether the equipment is available and qualified; Relevant parameters include maximum tensile stress, maximum shear stress, maximum distortion energy, expansion strength, creep strength and other parameters.

[0014] Preferably, the operation of real-time monitoring of the energy storage cap layer and the energy storage reservoir cavity in S2 includes: An ultrasonic monitoring instrument is installed at the wellhead of the energy storage reservoir to monitor the energy storage cap layer and obtain real-time monitoring data of the cap layer; Install distributed optical fiber on the outside of the oil pipe to monitor the pressure, temperature, stress, vibration, and fluid flow parameters around the wellbore in real time over a long period of time to ensure the integrity of the wellbore; Cement plugging of the oil pipes and casings of the energy storage reservoir, installation of downhole pressure gauges and thermometers, and mutual verification with optical fiber monitoring; Inject water into the blocked tubing and casing until they are full, and inject nitrogen or air between the unblocked casing and tubing to make the depth of the air-water interface at the neck of the cavity below the production overshoe; Combining the change in the depth of the gas-water interface in the wellbore and the change trend of the gas leakage rate over time, a comprehensive sealing evaluation standard is formed; Carry out data collection and real-time monitoring of the wellbore, cavity and caprock of the energy storage reservoir; The formation properties and formation parameters collected by the current sensor, as well as the cavity properties and cavity parameters, are used as the constraints of the XGBoost regression model. Through the analysis of historical data, the relationship between the data is found, and these relationships are used to predict the sealing of the energy storage reservoir cap layer cavity; Establish a cloud monitoring system, upload the data of energy storage wells to the cloud database, and establish a complete cloud monitoring system; Use the XGBoost algorithm to perform data analysis and anomaly evaluation on cloud data. The XGBoost algorithm has an anomaly threshold set; If the abnormal evaluation is lower than the abnormal threshold, no abnormal situation occurs and the system returns to continuous monitoring to ensure the safe operation of the energy storage facility; If the abnormal evaluation is higher than the abnormal threshold, the sealing is damaged and leakage occurs, an alarm is issued and relevant personnel are notified to handle it. If the leakage is serious, the emergency shutdown device is activated to stop working; Record the exact location of the energy storage reservoir leak and automatically generate a report; Repair or maintain the leaking area and improve the sealing between the cover layer and the cavity.

[0015] Preferably, the specific contents of data collection and real-time monitoring of the energy storage wellbore, cavity and cap layer are as follows: Use non-destructive sound waves to monitor the sound wave reflection and echo of the cover layer and cavity, and further judge whether there is sound of gas leakage and material damage; Use 3D imaging combined with lightweight target monitoring algorithms to monitor the cover layer and cavity surface for cracks, deformation or damage, and determine whether abnormal phenomena such as gas leakage occur; A standard pressure difference threshold is preset. After waiting for a preset time, if the difference in the pressure monitoring results of the casing and the tubing does not exceed the standard pressure difference threshold, it proves that the sealing of the wellbore, caprock and cavity is reliable.

[0016] In summary, the present invention is a method for evaluating and monitoring the sealing performance of the cavity of a compressed air energy storage reservoir. Compared with traditional technologies, the main purpose of the present invention is to provide a scientific comprehensive evaluation of the cover layer and cavity of the energy storage reservoir, which is used to evaluate the cover layer and cavity conditions of each energy storage reservoir block and give a certain level of classification; at the same time, a method for monitoring the sealing performance of the cover layer and cavity of the energy storage reservoir is proposed to ensure that the sealing performance of the energy storage reservoir during operation can meet the safety and performance requirements. It is possible to detect small leaks or changes in the sealing performance of the energy storage reservoir to ensure the sealing level of the energy storage reservoir. This method advocates real-time monitoring, and sealing problems can be detected at any time, so that timely measures can be taken to reduce downtime and potential safety risks. The present invention provides a more cost-effective solution, reducing the cost of equipment purchase, maintenance and operation. The monitoring method adopts non-destructive technology, which will not cause damage to the structure and equipment of the energy storage reservoir, ensuring long-term stability. By analyzing the monitoring data, potential sealing problems can be predicted in advance, further improving the performance and safety of the energy storage reservoir. It has obvious advantages in the field of energy storage.

[0017] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flow chart of a method for evaluating and monitoring the sealing performance of a compressed air energy storage cavity. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is further described below by means of the accompanying drawings and embodiments. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​described in these embodiments do not limit the scope of the present application.

[0020] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present application, its application, or uses.

[0021] Technologies, systems, and devices known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, systems, and devices should be considered part of the specification.

[0022] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0023] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0024] like Figure 1 As shown, the present invention provides a method for monitoring and evaluating the sealing performance of a compressed air energy storage cavity, which is characterized by comprising: S1. Collect the cap rock properties and cap rock parameters of the block, analyze and evaluate the collected cap rock properties and cap rock parameters, and evaluate the cap rock sealing level of the block; Furthermore, formation properties include rock formation properties, continuity, fracture development, dynamic sealing, formation water properties, tectonic stress, and brittleness; Relevant parameters include mud content, thinnest caprock thickness, permeability, and displacement pressure.

[0025] S2. Real-time monitoring of the energy storage cap layer and the energy storage cavity to obtain real-time monitoring data; S3. Collect the cavity properties and cavity parameters of the cavity, use the XGBoost machine learning algorithm to analyze and evaluate the cavity properties, cavity parameters and caprock properties and caprock parameters, and evaluate the sealing level of the energy storage reservoir in the block through the classification model of the XGBoost machine learning algorithm.

[0026] The sealing level of the energy storage reservoir in the target block is evaluated by setting a comprehensive evaluation standard table of caprock reservoir quality, as shown in Table 1.

[0027] Table 1

[0028] Furthermore, the cavity characteristics include cavity size, cavity shape, earthquake and disaster resistance, pressure and corrosion resistance, electrophoresis mechanism, emergency shutdown device information or whether the equipment is available and qualified; Relevant parameters include maximum tensile stress, maximum shear stress, maximum distortion energy, expansion strength, creep strength and other parameters.

[0029] Furthermore, the operation of real-time monitoring of the energy storage cap layer and the energy storage cavity in S2 includes: An ultrasonic monitoring instrument is installed at the wellhead of the energy storage reservoir to monitor the energy storage cap layer and obtain real-time monitoring data of the cap layer; Install distributed optical fiber on the outside of the oil pipe to monitor the pressure, temperature, stress, vibration, and fluid flow parameters around the wellbore in real time to ensure the integrity of the wellbore; Cement plugging of the oil pipes and casings of the energy storage reservoir, installation of downhole pressure gauges and thermometers, and mutual verification with optical fiber monitoring; Inject water into the blocked tubing and casing until they are full, and inject nitrogen or air between the unblocked casing and tubing to make the depth of the air-water interface at the neck of the cavity below the production overshoe; Combining the change in the depth of the gas-water interface in the wellbore and the change trend of the gas leakage rate over time, a comprehensive sealing evaluation standard is formed; Carry out data collection and real-time monitoring of the wellbore, cavity and caprock of the energy storage reservoir; The formation properties and formation parameters collected by the current sensor, as well as the cavity properties and cavity parameters, are used as the constraints of the XGBoost regression model. Through the analysis of historical data, the relationship between the data is found, and these relationships are used to predict the sealing of the energy storage reservoir cap layer cavity; Establish a cloud monitoring system, upload the data of energy storage wells to the cloud database, and establish a complete cloud monitoring system; Use the XGBoost algorithm to perform data analysis and anomaly evaluation on cloud data. The XGBoost algorithm has an anomaly threshold set; If the abnormal evaluation is lower than the abnormal threshold, no abnormal situation occurs and the system returns to continuous monitoring to ensure the safe operation of the energy storage facility; If the abnormal evaluation is higher than the abnormal threshold, the sealing is damaged and leakage occurs, an alarm is issued and relevant personnel are notified to handle it. If the leakage is serious, the emergency shutdown device is activated to stop working; Record the exact location of the energy storage reservoir leak and automatically generate a report; Repair or maintain the leaking area and improve the sealing between the cover layer and the cavity.

[0030] Furthermore, the specific contents of data collection and real-time monitoring of the energy storage reservoir wellbore, cavity and cap layer are as follows: Use non-destructive sound waves to monitor the sound wave reflection and echo of the cover layer and cavity, and further judge whether there is sound of gas leakage and material damage; Use 3D imaging combined with lightweight target monitoring algorithms to monitor the cover layer and cavity surface for cracks, deformation or damage, and determine whether abnormal phenomena such as gas leakage occur; A standard pressure difference threshold is preset. After waiting for a preset time, if the difference in the pressure monitoring results of the casing and the tubing does not exceed the standard pressure difference threshold, it proves that the sealing of the wellbore, caprock and cavity is reliable.

[0031] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for monitoring and evaluating the sealing performance of a compressed air energy storage cavity, characterized in that: include: S1. Collect the cap rock properties and cap rock parameters of the block, analyze and evaluate the collected cap rock properties and cap rock parameters, and evaluate the cap rock sealing level of the block; S2. Real-time monitoring of the energy storage cap layer and the energy storage cavity to obtain real-time monitoring data; S3. Collect the cavity properties and cavity parameters of the cavity, use the XGBoost machine learning algorithm to analyze and evaluate the cavity properties, cavity parameters and caprock properties and caprock parameters, and evaluate the sealing level of the energy storage reservoir in the block through the classification model of the XGBoost machine learning algorithm.

2. A method for monitoring and evaluating the sealing performance of a compressed air energy storage cavity according to claim 1, characterized in that: Formation properties include rock formation properties, continuity, fracture development, dynamic sealing, formation water properties, tectonic stress, and brittleness; Relevant parameters include mud content, thinnest caprock thickness, permeability, and displacement pressure.

3. A method for monitoring and evaluating the sealing performance of a compressed air energy storage reservoir cavity according to claim 2, characterized in that: Cavity characteristics include cavity size, cavity shape, earthquake and disaster resistance, pressure and corrosion resistance, electrophoresis mechanism, emergency shutdown device information or equipment availability and qualification status; Relevant parameters include maximum tensile stress, maximum shear stress, maximum distortion energy, expansion strength, and creep strength.

4. A method for monitoring and evaluating the sealing performance of a compressed air energy storage cavity according to claim 3, characterized in that: The operations of real-time monitoring of the energy storage cap layer and the energy storage cavity in S2 include: An ultrasonic monitoring instrument is installed at the wellhead of the energy storage reservoir to monitor the energy storage cap layer; Install distributed optical fiber on the outside of the oil pipe to monitor the pressure, temperature, stress, vibration, and fluid flow parameters around the wellbore in real time to ensure the integrity of the wellbore; Cement plugging of the oil pipes and casings of the energy storage reservoir, installation of downhole pressure gauges and thermometers, and mutual verification with optical fiber monitoring; Inject water into the blocked tubing and casing until they are full, and inject nitrogen or air between the unblocked casing and tubing to make the depth of the air-water interface at the neck of the cavity below the production overshoe; Combining the change in the depth of the gas-water interface in the wellbore and the change trend of the gas leakage rate over time, a comprehensive sealing evaluation standard is formed; Carry out data collection and real-time monitoring of the wellbore, cavity and caprock of the energy storage reservoir; The formation properties and formation parameters collected by the current sensor, as well as the cavity properties and cavity parameters, are used as the constraints of the XGBoost regression model. Through the analysis of historical data, the relationship between the data is found, and these relationships are used to predict the sealing of the energy storage reservoir cap layer cavity; Establish a cloud monitoring system, upload the data of energy storage wells to the cloud database, and establish a complete cloud monitoring system; Use the XGBoost algorithm to perform data analysis and anomaly evaluation on cloud data. The XGBoost algorithm has an anomaly threshold set; If the abnormal evaluation is lower than the abnormal threshold, no abnormal situation occurs and the system returns to continuous monitoring to ensure the safe operation of the energy storage facility; If the abnormal evaluation is higher than the abnormal threshold, the sealing is damaged and leakage occurs, an alarm is issued and relevant personnel are notified to handle it. If the leakage is serious, the emergency shutdown device is activated to stop working; Record the exact location of the energy storage reservoir leak and automatically generate a report; Repair or maintain the leaking area and improve the sealing between the cover layer and the cavity.

5. A method for monitoring and evaluating the sealing performance of a compressed air energy storage cavity according to claim 4, characterized in that: The specific contents of data collection and real-time monitoring of the wellbore, cavity and cap layer of the energy storage reservoir are as follows: Use non-destructive sound waves to monitor the sound wave reflection and echo of the cover layer and cavity, and further judge whether there is sound of gas leakage and material damage; Use 3D imaging combined with lightweight target monitoring algorithms to monitor the cover layer and cavity surface for cracks, deformation or damage, and determine whether abnormal phenomena such as gas leakage occur; A standard pressure difference threshold is preset. After waiting for a preset time, if the difference between the pressure monitoring results of the casing and the tubing does not exceed the standard pressure difference threshold, it proves that the sealing of the wellbore, caprock and cavity is reliable.

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