Carbon dioxide geological sequestration leakage monitoring and early warning system

Abstract

The invention discloses a carbon dioxide geological sequestration leakage monitoring and early warning system. The system comprises a sensor arrangement module, a data acquisition and processing module, a prediction and early warning module and a linkage control module. The sensor laying module is used for laying pressure sensors, temperature sensors and microseism monitoring equipment on an injection well, a monitoring well and the ground surface, and real-time collection of pressure and temperature of a reservoir and a shaft is achieved by combining distributed optical fiber temperature measurement. And the data acquisition and processing module performs dynamic fitting and trend analysis on the multi-source monitoring data, and calculates to obtain a pressure evolution function. The prediction and early warning module predicts a future pressure state through a threshold value judgment and prediction model and automatically sends out an early warning signal when the future pressure state is close to a critical value, the linkage control module is combined with the injection control unit, the injection rate can be automatically adjusted or adjusted in an auxiliary mode according to an early warning result, risks are reduced, and safety is improved. The system can significantly improve the safety and reliability of carbon dioxide geological sequestration.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and more specifically, to a carbon dioxide geological storage leakage monitoring and early warning system. Background Technology

[0002] With increasing global pressure to reduce emissions, carbon dioxide capture and storage (CCS) technology is considered an important pathway to achieving carbon neutrality. The basic idea is to capture carbon dioxide from industrial emission sources and transport it via pipelines to deep geological structures (such as depleted oil and gas reservoirs, deep saline aquifers, or unused coal seams) for long-term storage. To ensure the safety and stability of the storage process, continuous monitoring of pressure changes in the storage area is essential. If the reservoir pressure rises too rapidly or exceeds the pressure limit of the caprock, it may lead to caprock failure, carbon dioxide leakage, or even induce microseismic events, posing significant risks to engineering safety and the environment.

[0003] Existing methods for monitoring reservoir pressure mainly fall into the following categories: First, direct measurement using downhole pressure gauges, where pressure sensors are installed in injection or monitoring wells to acquire point pressure data; second, distributed fiber optic sensing technology, which indirectly reflects pressure changes by deploying fiber optic cables to measure temperature and stress changes around the wellbore; third, seismic exploration and microseismic monitoring, which uses artificial or natural microseismic data to invert the reservoir stress field and pressure evolution; and fourth, numerical simulation methods, which combine geological parameters and injection data to predict the reservoir pressure field.

[0004] However, these methods have certain limitations in practical applications. While downhole pressure gauges can provide relatively accurate point pressure data, their spatial coverage is limited and cannot reflect the three-dimensional pressure distribution of the entire reservoir; fiber optic sensing is sensitive to temperature and stress changes, but requires extensive deployment and has complex signal interpretation; seismic surveys and microseismic monitoring are costly and are usually used for phased observations, making continuous monitoring difficult; numerical simulations rely on geological model parameters, and the results often deviate significantly from reality, lacking a real-time update mechanism.

[0005] The following problems are common in existing monitoring systems: First, data acquisition is isolated and lacks multi-source fusion, making it difficult to analyze data collected by different sensors in a unified manner, which limits the accuracy of monitoring results; Second, the early warning mechanism is lagging behind, and in most cases it is only discovered after pressure anomalies or even leaks have occurred, lacking foresight; Third, the control link is missing, and most existing systems can only achieve "monitoring" but cannot link the early warning results with the injection process in real time, making it difficult to take timely control measures to reduce risks.

[0006] To address the aforementioned technological gap, this invention proposes a carbon dioxide geological storage leakage monitoring and early warning system. Summary of the Invention

[0007] The purpose of this invention is to solve the technical problems mentioned in the background section and to provide a carbon dioxide geological storage leakage monitoring and early warning system, comprising:

[0008] Sensor deployment module: used to deploy pressure sensors at the bottom of the injection well in the sealing area to obtain the current pressure P(t), deploy temperature sensors inside the wellbore to obtain the current temperature T(t), deploy micro-vibration sensors around the caprock to monitor the expansion of formation fractures, and deploy seepage sensors around the sealing area to obtain the seepage coefficient S(t).

[0009] Data acquisition and processing module: used to transmit and filter the current pressure P(t), current temperature T(t), injection rate Q(t), and seepage coefficient S(t);

[0010] Prediction and early warning module: used to construct a prediction function f(·) based on P(t), T(t), Q(t), and S(t).

[0011] P pred (t+Δt)=f(P(t),T(t),Q(t),S(t))

[0012] And calculate the pressure increase rate R. p It is then compared with threshold parameters R1, R2, and R3 to generate a graded early warning signal;

[0013] Visualization and control module: used to display the three-dimensional pressure field distribution P(x,y,z,t), and according to the R... p Adjusting the injection rate Q(t),

[0014] Where: P(t): pressure at the current moment; T(t): temperature at the current moment; Q(t): injection rate; S(t): permeability coefficient; P pred (t+Δt): Predicted pressure; Δt: Time step; R p : Pressure growth rate; R1, R2, R3: Early warning classification thresholds; P(x,y,z,t): Three-dimensional pressure field distribution.

[0015] As a preferred technical solution of the present invention, the prediction function f(·) is established and trained using a long short-term memory network to improve the accuracy of the prediction results and the ability to model long-term dependencies of time series. Its optimization objective function is:

[0016]

[0017] Where L is the loss function, the smaller the value, the smaller the error between the prediction result and the actual measurement value; This is the i-th predicted pressure value obtained according to the prediction function; The i-th measured pressure value collected by the sensor; n is the number of samples. By minimizing this loss function, the parameters of the prediction function are continuously optimized to make the predicted pressure closer to the measured value and improve the warning accuracy.

[0018] As a preferred technical solution of the present invention, when the data acquisition and processing module preprocesses the pressure P(t) signal at the current moment, the Kalman filtering method is used to remove noise and outliers to improve the reliability of the data. Its update formula is:

[0019] P est (t) = P est (t - Δt) + K·(P obs (t) - P est (t - Δt))

[0020] Among them, P pest (t) is the estimated pressure at time t; P obs (t) is the measured pressure collected by the sensor at this moment; Δt is the sampling time interval; K is the Kalman gain. This formula is updated by weighting by combining the historical estimated value and the current measured value, effectively suppressing the influence of sensor noise.

[0021] As a preferred technical solution of the present invention, the prediction and warning module calculates the pressure growth rate R pred using the difference between the predicted pressure P p (t + Δt) and the pressure P(t) at the current moment. Its formula is:

[0022]

[0023] Among them, R p is the pressure growth rate per unit time. When R p is relatively high, it means that the pressure in the sealed area increases rapidly in a short time, and there is an abnormal risk. Therefore, this parameter is the core basis for triggering the warning mechanism.

[0024] As a preferred technical solution of the present invention, the prediction and warning module divides the warning level by comparing the relationship between the pressure growth rate R p and different threshold parameters R1, R2, R3, specifically including:

[0025] When R p < R1, the system is in a normal state and no measures need to be taken.

[0026] ]>When R1 ≤ R p < R2, the system enters a state of concern, prompting the operator to strengthen monitoring;

[0027] When R2 ≤ R pWhen R < R3, the system enters the warning state, and injection rate adjustment or other interventions are required;

[0028] When R p ≥ R3, the system enters the emergency state, and injection should be stopped immediately to prevent leakage risk. Among them, R1, R2, and R3 respectively correspond to different levels of warning thresholds.

[0029] As a preferred technical solution of the present invention, the threshold parameters R1, R2, and R3 are calculated based on formation physical property parameters and storage conditions, and their calculation formula is:

[0030]

[0031] Among them, R j is the j - level threshold; α is an empirical coefficient; Q(t) is the injection rate; β c is the fluid compressibility; φ is the porosity; V is the effective volume of the reservoir. This formula reflects the relationship between the threshold and the formation permeability, reservoir capacity, and injection conditions, so as to achieve adaptation to different geological conditions.

[0032] As a preferred technical solution of the present invention, the microseismic sensor is used to monitor the fracture expansion caused by abnormal pressure, and its triggering condition is expressed by an energy criterion as:

[0033]

[0034] Among them, E m is the microseismic energy; A is the source energy factor; d is the distance between the sensor and the source; β e is the energy attenuation coefficient. When the measured microseismic energy E m exceeds the background noise energy and shows a continuous increase, it indicates that the formation fracture may expand due to pressure. This module can be mutually verified with the R p criterion to improve the warning reliability.

[0035] As a preferred technical solution of the present invention, the seepage sensor calculates the breakthrough risk coefficient based on the injection rate Q(t) and the seepage rate Q obs (t) at the monitoring point:

[0036]

[0037] Among them, S risk (t) is the breakthrough risk coefficient; Q obs (t) is the measured seepage rate at the monitoring point; Q(t) is the injection rate. When S risk (t) is greater than the preset threshold (such as 0.05), it indicates that the external seepage is close to 5% of the injection rate, and there may be a risk of breakthrough of the stored gas. The system will automatically trigger a risk warning.

[0038] As a preferred technical solution of the present invention, the visualization and control module is based on the pressure P(t) and the permeability tensor k. t The formula for calculating the three-dimensional pressure field distribution is as follows:

[0039]

[0040] Where P(x,y,z,t) represents the pressure distribution at time t at spatial coordinates (x,y,z); P0 represents the initial pressure field; k t This is the permeability tensor. The calculation results are presented through three-dimensional visualization, allowing operators to intuitively view the pressure evolution within the storage area.

[0041] As a preferred technical solution of the present invention, the visualization and control module adjusts the pressure increase rate R according to the pressure increase rate R. p The injection rate Q(t) is dynamically adjusted using the following formula:

[0042]

[0043] Among them, Q adj (t) represents the adjusted injection rate; Q(t) represents the original injection rate; λ represents the control coefficient; R p R is the rate of pressure increase; R3 is the emergency threshold. When R p When the pressure approaches or exceeds R3, the module will automatically reduce the injection rate to prevent reservoir overpressure and leakage accidents.

[0044] Beneficial Effects: The carbon dioxide geological storage leakage monitoring and early warning system of this invention can achieve real-time monitoring of underground pressure status during the carbon dioxide storage process. By deploying pressure sensors, temperature sensors, and microseismic monitoring equipment in injection wells, monitoring wells, and on the surface, combined with distributed fiber optic temperature measurement technology, the system can continuously collect key parameters, avoiding the problems of traditional monitoring relying on single-point instruments and limited coverage. This allows pressure changes inside the reservoir and around the wellbore to be captured in a timely manner.

[0045] In the data processing stage, this invention incorporates trend analysis and prediction algorithms to dynamically fit the collected pressure, temperature, and injection rate data. This not only reflects the current pressure distribution but also predicts pressure changes over a future period. When the pressure approaches a critical value or abnormal fluctuations occur, the system issues an early warning signal, allowing managers sufficient time to respond and avoiding risks caused by monitoring delays.

[0046] Furthermore, this invention incorporates a linkage mechanism with the injection control unit. When the system detects a risk, it can automatically or assistedly adjust the injection rate, or even switch to a backup injection well, to reduce the pressure level in the storage area. This closed-loop control method integrates monitoring, early warning, and regulation, significantly improving the safety and stability of carbon dioxide storage. Attached Figure Description

[0047] Figure 1 This is a system block diagram of a carbon dioxide geological storage leakage monitoring and early warning system proposed in this invention;

[0048] Figure 2 This is a flowchart illustrating the operation of a carbon dioxide geological storage leakage monitoring and early warning system proposed in this invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0050] See Figure 1 and Figure 2 The implementation of the present invention will be described in detail below with reference to specific embodiments.

[0051] Example 1: This invention provides a carbon dioxide geological storage leakage monitoring and early warning system. This system combines sensor deployment, real-time data acquisition, signal processing, artificial intelligence prediction, threshold determination, micro-seismic and seepage monitoring, as well as three-dimensional visualization and dynamic control to achieve intelligent monitoring and safety early warning of the pressure field during the geological storage process.

[0052] During implementation, pressure sensors are first installed at the bottom of the injection well to collect the current pressure P(t) inside the reservoir in real time; temperature sensors are installed inside the wellbore to record the current temperature T(t) at different depths; microseismic sensors are installed around the caprock to capture small seismic signals that may be triggered by pressure changes; and seepage sensors are installed around the sealing zone to obtain seepage-related parameters and calculate the seepage coefficient S(t). At the same time, the system records the injection rate Q(t) at the injection site. These data constitute the basic input information for the system operation.

[0053] The signals acquired by the sensor undergo preprocessing by the data acquisition and processing module. This module not only handles wireless or wired data transmission but also filters the signals and removes outliers. For example, when noise appears in the pressure signal P(t), a Kalman filter is used for correction, and its update formula is as follows:

[0054] Pest P(t) = P est (t - Δt) + K · (P obs (t) - P est (t - Δt))

[0055] Where, P est (t) is the estimated pressure, P obs (t) is the measured pressure by the sensor, Δt is the sampling time interval, and K is the Kalman gain. Through this process, a pressure signal closer to the true value is obtained, which helps improve the accuracy of the subsequent prediction model.

[0056] Based on this, the prediction and early warning module predicts the future change of reservoir pressure. The long short-term memory network (LSTM) is selected as the prediction model to capture short-term and long-term dependencies in the time series. The basic form of the prediction function is:

[0057] P pred (t + Δt) = f(P(t), T(t), Q(t), S(t))

[0058] Where, P pred (t + Δt) is the pressure at the future moment obtained by prediction, and Δt is the prediction step. To optimize the performance of this prediction function, the mean squared error loss function is introduced as the objective:

[0059]

[0060] Where, is the predicted pressure, is the measured pressure, and n is the number of samples. Through continuous iterative training, the model gradually approaches the actual formation pressure evolution trend.

[0061] When the predicted pressure value is obtained, the system further calculates the pressure growth rate R p , which is defined as

[0062]

[0063] This parameter represents the pressure increase amplitude per unit time. If R p remains high continuously, it indicates that the injection process may cause reservoir overpressure, which is a sign of danger. To facilitate the determination of the risk level, the system sets three groups of threshold parameters R1, R2, and R3. The specific early warning logic is: when R p < R1, the system determines it as the normal state; when R1 ≤ R p < R2, it is the attention state; when R2 ≤ R p < R3, it is the warning state; when R p ≥ R3, it is determined as the emergency state and immediate measures need to be taken.

[0064] The selection of the threshold parameter depends not only on experience but also on calculations based on the formation's physical properties. The improved threshold formula is as follows:

[0065]

[0066] Among them, R j Let α be the threshold for level j, α be an empirical coefficient, Q(t) be the injection rate, and β be the threshold for level j. c Here, φ represents the fluid compressibility coefficient, φ represents porosity, and V represents the effective reservoir volume. By introducing reservoir volume and porosity, the threshold is directly related to the reservoir's pressure-bearing capacity, ensuring the scientific validity of the early warning mechanism. In addition to pressure changes themselves, the system also uses microseismic and seepage monitoring modules for auxiliary verification.

[0067] When reservoir pressure rises too rapidly, microseismic sensors can detect the propagation energy of tiny fractures, based on the following criteria:

[0068]

[0069] Among them, E m Let A be the energy of the microseismic event, d be the energy factor of the seismic source, and β be the distance between the sensor and the seismic source. e Let E be the energy decay coefficient, when the measured E m When the pressure continues to increase, it indicates that the caprock may crack due to overpressure. Comparing abnormal changes in the pressure growth rate can provide cross-validation. Simultaneously, seepage sensors monitor for potential gas breakthrough risks; the criterion formula is as follows:

[0070]

[0071] Among them, S risk (t) represents the risk coefficient, Q obs Q(t) represents the measured seepage rate at the peripheral monitoring point, and Q(t) represents the injection rate.

[0072] When this coefficient exceeds a set threshold (e.g., 0.05), it indicates that the sealed gas may be breaching the sealing zone, necessitating a risk warning. At the data fusion level, the system also incorporates a geological model to invert and visualize the three-dimensional pressure field. The calculation formula is:

[0073]

[0074] Where P(x,y,z,t) represents the pressure distribution in spatial coordinates, P0 represents the initial pressure field, and k t This is the permeability tensor. Through a 3D visualization platform, researchers and operators can intuitively observe the pressure distribution evolution in and around the injection zone, providing direct evidence for judging pressure anomalies. In the final control stage of the system, the early warning signal can directly affect the injection process control, dynamically adjusting the injection rate. Its control formula is:

[0075]

[0076] Among them, Q adj (t) represents the adjusted injection rate, and λ is the control coefficient. When R p When the system approaches or exceeds the emergency threshold R3, it automatically reduces the injection rate to avoid further exacerbating reservoir pressure.

[0077] Through the above steps, the system of this invention forms a complete closed loop: basic physical parameters are collected by sensors, data is filtered and fused, pressure prediction is achieved using prediction functions and machine learning models, risk classification is performed by determining the pressure growth rate and threshold, cross-validation is conducted using microseismic and seepage monitoring, and finally, real-time monitoring, risk warning, and safety control of geological storage pressure are achieved by combining three-dimensional visualization and dynamic control mechanisms. This system can not only promptly detect potential risks but also automatically control and prevent reservoir overpressure, thereby ensuring the long-term safety and stability of the carbon dioxide storage process.

[0078] Example 2: Taking a carbon dioxide geological sequestration project in a decommissioned oil and gas field in western China as an example, the reservoir depth is approximately 1800m, the reservoir thickness is 60m, the porosity φ = 0.18, and the effective reservoir volume V = 1.2 × 10⁻⁶. 8 m 3 The ultimate bearing capacity of the cap layer is approximately 35 MPa.

[0079] During implementation, a pressure sensor was first installed at the bottom of the injection well to collect the reservoir pressure P(t) in real time; distributed temperature sensors were deployed in the wellbore to acquire the temperature T(t) in real time; 12 microseismic monitoring points were deployed around the caprock to monitor fracture propagation caused by pressure anomalies; and 6 seepage monitoring points were deployed around the perimeter of the sealing zone to observe the seepage coefficient S(t) of the sealed gas. Simultaneously, the injection pump station recorded the carbon dioxide injection rate Q(t).

[0080] For example, at a certain moment, the pressure P(t) is monitored to be 18.5 MPa, the temperature T(t) is 345 K, and the injection rate Q(t) is 12.0 m. 3 / s, seepage coefficient S(t)=0.07. The data first enters the data acquisition and processing module, and then undergoes transmission and filtering. Taking the pressure signal as an example, when the sensor signal is disturbed by the wellbore, the system uses Kalman filtering for correction:

[0081] P est (t)=P est (t-Δt)+K·(P obs (t)-P est (t-Δt))

[0082] If we set Δt = 1s and K = 0.7, then for a certain moment, the observed pressure P obs (t) = 18.7 MPa, the estimated pressure at the previous moment.

[0083] P est (t-Δt)=18.4MPa, then the calculation yields:

[0084] P est (t)=18.4+0.7·(18.7-18.4)=18.61MPa

[0085] This result is used as a smoothed stress input into the subsequent prediction model.

[0086] In the prediction and early warning module, the system constructs a prediction function based on an LSTM network:

[0087] P pred (t+Δt)=f(P(t),T(t),Q(t),S(t))

[0088] After model training and validation, the predicted pressure P for the next hour was obtained. pred (t+Δt)=19.1MPa. The pressure increase rate can then be calculated:

[0089]

[0090] The system also calculates thresholds based on reservoir parameters.

[0091] Take the compressibility factor β c =4.2×10 -10 Pa -1 Porosity φ = 0.18, injection rate Q(t) = 12.0 m / s² 3 / s, effective volume V=1.2×10 8 m 3 Given an empirical coefficient α = 0.85, the third-level threshold R3 is calculated as follows:

[0092]

[0093] To further verify this, the system read signals from the microseismic sensor. At a certain monitoring point, the distance from the seismic source is d = 120m, and the measured energy E... m =8.2J, substitute into the formula: If we assume A = 30 J, β e =0.01m -1 Then, the calculation yields: E m =30·e -0.01·120 The value is approximately 9.0 J, which is close to the measured value, indicating that the microseismic activity has increased, which is consistent with the trend of pressure growth.

[0094] Meanwhile, the measured seepage rate at the peripheral seepage monitoring point is Q. obs (t)=0.65m 3 / s, then the risk coefficient for breaking through is:

[0095]

[0096] This indicates a risk of gas breakthrough.

[0097] The visualization module calculates and displays the three-dimensional pressure field:

[0098]

[0099] The 3D model shows that the pressure at the center of the injection zone has increased significantly, and a local high-pressure anomaly has formed near the caprock.

[0100] Finally, the system automatically performs injection rate regulation. The original injection rate is Q(t) = 12.0 m. 3 / s, the control formula is: If the control coefficient λ = 0.6 is set, substituting the data yields: The system automatically reduced the injection rate to approximately 8.0 m. 3 / s, to mitigate the risk of excessively rapid increase in reservoir pressure.

[0101] As can be seen from this embodiment, the system of the present invention can not only realize multi-point real-time monitoring of parameters such as pressure, temperature, and seepage, but also correct data and predict future trends through filtering and prediction models. When approaching a dangerous state, it can also perform cross-verification by combining microseismic and seepage signals. Finally, it can avoid reservoir overpressure through dynamic control, thus realizing intelligent and full-cycle safety assurance for the geological sealing process.

[0102] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A carbon dioxide geological storage leakage monitoring and early warning system, characterized in that, include: Sensor deployment module: used to deploy pressure sensors at the bottom of the injection well in the sealing area to obtain the current pressure P(t), deploy temperature sensors inside the wellbore to obtain the current temperature T(t), deploy micro-vibration sensors around the caprock to monitor the expansion of formation fractures, and deploy seepage sensors around the sealing area to obtain the seepage coefficient S(t). Data acquisition and processing module: used to transmit and filter the current pressure P(t), current temperature T(t), injection rate Q(t), and seepage coefficient S(t); Prediction and early warning module: used to construct a prediction function f(·) based on P(t), T(t), Q(t), and S(t): P pred (t+Δt)=f(P(t),T(t),Q(t),S(t)), and calculate the pressure increase rate R. p It is then compared with threshold parameters R1, R2, and R3 to generate a graded early warning signal; Visualization and control module: used to display the three-dimensional pressure field distribution P(x,y,z,t), and according to the R... p Adjusting the injection rate Q(t), Where: P(t): pressure at the current moment; T(t): temperature at the current moment; Q(t): injection rate; S(t): permeability coefficient; P pred (t+Δt): Predicted pressure; Δt: Time step; R p : Pressure growth rate; R1, R2, R3: Early warning classification thresholds; P(x,y,z,t): Three-dimensional pressure field distribution.

2. The carbon dioxide geological storage leakage monitoring and early warning system according to claim 1, characterized in that, The prediction function f(·) is built and trained using a long short-term memory network, and its optimization objective function is: Where L is the loss function, the smaller the value, the smaller the error between the prediction result and the actual measurement value; This is the i-th predicted pressure value obtained according to the prediction function; Let be the i-th measured pressure value collected by the sensor; n is the number of samples. By minimizing this loss function, the parameters of the prediction function are continuously optimized, making the predicted pressure closer to the measured value and improving the accuracy of the early warning.

3. The carbon dioxide geological storage leakage monitoring and early warning system according to claim 1, characterized in that, When the data acquisition and processing module preprocesses the pressure P(t) signal at the current moment, it uses the Kalman filter method to remove noise and outliers. The update formula is as follows: P est (t)=P est (t-Δt)+K·(P obs (t)-P est (t-Δt)) Among them, P est (t) represents the estimated pressure at time t; P obs (t) represents the measured pressure collected by the sensor at that moment; Δt represents the sampling time interval; K represents the Kalman gain, and this formula is updated by combining historical estimates and current measured values.

4. The carbon dioxide geological storage leakage monitoring and early warning system according to claim 1, characterized in that, The prediction and early warning module utilizes the predicted pressure P pred The pressure increase rate R is calculated by taking the difference between (t+Δt) and the current pressure P(t). p Its formula is: Among them, R p The rate of pressure increase per unit time, when R p A higher level indicates a rapid increase in pressure in the storage area within a short period of time, posing an abnormal risk.

5. A carbon dioxide geological storage leakage monitoring and early warning system according to claim 4, characterized in that, The prediction and early warning module compares the pressure growth rate R... p The warning levels are determined by their relationship with different threshold parameters R1, R2, and R3, specifically including: When R p <When R is less than R1, the system is in a normal state and no measures need to be taken. When R1 ≤ R p <When R2, the system enters the attention state, prompting the operator to strengthen monitoring; When R2 ≤ R p <When R3, the system enters the warning state and injection rate adjustment or other interventions are required; When R p When R3 is ≥, the system enters an emergency state and injection should be stopped immediately to prevent leakage risks. R1, R2, and R3 correspond to different levels of warning thresholds.

6. A carbon dioxide geological storage leakage monitoring and early warning system according to claim 5, characterized in that, The threshold parameters R1, R2, and R3 are calculated based on formation physical properties and storage conditions, and their calculation formulas are as follows: Among them, R j The threshold value is denoted as α; α is an empirical coefficient; Q(t) is the injection rate; β is the threshold value of level j. c φ is the fluid compressibility coefficient; φ is the porosity; V is the effective reservoir volume.

7. The carbon dioxide geological storage leakage monitoring and early warning system according to claim 1, characterized in that, The microseismic sensor is used to monitor crack propagation caused by abnormal pressure, and its triggering condition is expressed by an energy criterion as follows: Among them, E m β is the microseismic energy; A is the source energy factor; d is the distance between the sensor and the source; e Let E be the energy attenuation coefficient, when the measured microseismic energy E m When the energy exceeds the background noise level and continues to increase, it indicates that the pressure is causing the formation fractures to expand.

8. A carbon dioxide geological storage leakage monitoring and early warning system according to claim 1, characterized in that, The seepage sensor is based on the injection rate Q(t) and the seepage rate Q at the monitoring point. obs (t) Calculate the breakout risk coefficient: Among them, S risk (t) represents the risk coefficient for the breakthrough; Q obs (t) represents the measured seepage rate at the monitoring point; Q(t) represents the injection rate, when S risk When (t) is greater than the preset threshold, it indicates that there is a risk of gas leakage from the sealed container, and the system will automatically trigger a risk warning.

9. A carbon dioxide geological storage leakage monitoring and early warning system according to claim 1, characterized in that, The visualization and control module is based on the pressure P(t) and the permeability tensor k. t The formula for calculating the three-dimensional pressure field distribution is as follows: Where P(x,y,z,t) represents the pressure distribution at time t at spatial coordinates (x,y,z); P0 represents the initial pressure field; k t Let be the permeability tensor.

10. A carbon dioxide geological storage leakage monitoring and early warning system according to claim 1, characterized in that, The visualization and control module is based on the pressure growth rate R. p The injection rate Q(t) is dynamically adjusted using the following formula: Among them, Q adj (t) represents the adjusted injection rate; Q(t) represents the original injection rate; λ represents the control coefficient; R p R is the rate of pressure increase; R3 is the emergency threshold, when R p When the injection rate approaches or exceeds R3, the module will automatically reduce the injection rate.

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