Method and device for evaluating economic performance of carbon dioxide storage in saline aquifer

Abstract

The present disclosure relates to a method and device for evaluating the economic performance of saline aquifer carbon dioxide storage. The method comprises: inputting the current injection rate and static geological parameters into the trained proxy model to obtain the predicted values of pressure peak, leakage risk and operation and maintenance input; generating a plurality of time step candidate injection rate sequences through an optimization algorithm and inputting them into the proxy model to obtain pressure peak sequences and leakage risk sequences; calculating the net resource present value of the candidate injection rate sequence; determining whether the candidate injection rate sequence meets the safety and capacity constraints; selecting the candidate sequence with the maximum net resource present value as the optimal injection rate sequence and executing the injection plan of the first time step in the optimal injection rate sequence; and outputting the net resource present value and critical incentive unit price according to the actual injection rate at each decision time and the corresponding monitoring data. The present scheme improves the accuracy of the economic performance evaluation of carbon dioxide storage.

Description

Technical Field

[0001] This disclosure relates to the field of carbon dioxide sequestration technology, and in particular to a method and apparatus for evaluating the economic efficiency of carbon dioxide sequestration in saline aquifers. Background Technology

[0002] Among related technologies, saline aquifer carbon dioxide sequestration is an important technological pathway for carbon emission reduction. Currently, the techno-economic performance evaluation of its entire life cycle mostly adopts static engineering economic models, estimating resource input and carbon emission reduction benefits based on fixed injection plans and deterministic parameters (such as reservoir permeability, porosity, and injection rate). However, actual sequestration systems face significant uncertainties: geological parameters exhibit spatial variability and long-term evolution characteristics, carbon emission reduction incentive policies fluctuate, and the dynamic response of injection strategies to pressure fields and leakage risks is complex. Existing methods struggle to quantify the impact of these multi-source uncertainties on the overall project performance, and also cannot dynamically adjust the injection plan based on real-time monitoring data and market changes during the injection operation period. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides a method and apparatus for evaluating the economic efficiency of carbon dioxide sequestration in saline aquifers.

[0004] According to a first aspect of the present disclosure, a method for evaluating the economic efficiency of carbon dioxide sequestration in saline aquifers is provided, comprising:

[0005] At each preset decision point, obtain the current reservoir pressure, cumulative injection volume, current injection rate, and current carbon emission reduction incentive unit price of the target saline aquifer. The current injection rate and static geological parameters are input into the trained surrogate model to obtain predicted values ​​for peak pressure, leakage risk, and operation and maintenance input; the surrogate model is a Gaussian process regression model. Based on the current injection rate, multiple candidate injection rate sequences for the next N time steps are generated through an optimization algorithm, and each candidate injection rate sequence is input into the surrogate model to obtain the corresponding pressure peak sequence and leakage risk sequence. For each candidate injection rate sequence, the net present value of resources for the candidate injection rate sequence is calculated based on the injection amount of the candidate injection rate sequence, the unit price of carbon emission reduction incentive, the predicted value of operation and maintenance investment, and the risk adjustment investment mapped by the leakage risk; and based on the currently monitored reservoir pressure, the cumulative injection amount, and the pressure peak and total injection amount of the candidate injection rate sequence, it is determined whether the candidate injection rate sequence meets the safety and capacity constraints. From the candidate injection rate sequences that satisfy safety and capacity constraints, select the candidate sequence with the largest net resource present value as the optimal injection rate sequence, and execute the injection plan for the first time step in the optimal injection rate sequence. Return to the steps described at each preset decision time until the injection period ends. Based on the actual injection rate executed at each decision time and the corresponding monitoring data, output the final net resource present value and critical incentive unit price.

[0006] According to a second aspect of the present disclosure, an economic efficiency assessment device for saline aquifer carbon dioxide sequestration is provided, comprising: The acquisition unit is used to acquire the reservoir pressure, cumulative injection volume, current injection rate, and current carbon emission reduction incentive unit price of the target saline aquifer at each preset decision time. The prediction unit is used to input the current injection rate and static geological parameters into the trained surrogate model to obtain predicted values ​​of pressure peak, leakage risk, and operation and maintenance input; the surrogate model is a Gaussian process regression model. The sequence generation unit is used to generate multiple candidate injection rate sequences for the next N time steps based on the current injection rate through an optimization algorithm, and input each candidate injection rate sequence into the surrogate model to obtain the corresponding pressure peak sequence and leakage risk sequence. The judgment unit is used to calculate the net present value of resources for each candidate injection rate sequence based on the injection amount of the candidate injection rate sequence, the unit price of carbon emission reduction incentive, the predicted value of operation and maintenance investment, and the risk adjustment investment mapped by the leakage risk; and to determine whether the candidate injection rate sequence meets the safety and capacity constraints based on the currently monitored reservoir pressure, the cumulative injection amount, and the pressure peak and total injection amount of the candidate injection rate sequence. The execution unit is used to select the candidate sequence with the largest net resource present value from the candidate injection rate sequences that meet the safety and capacity constraints as the optimal injection rate sequence, and execute the injection plan of the first time step in the optimal injection rate sequence. The output unit is used to return the steps executed at each preset decision time until the injection period ends. Based on the actual injection rate executed at each decision time and the corresponding monitoring data, it outputs the final net resource present value and critical incentive unit price.

[0007] According to a third aspect of the present disclosure, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of the first aspects.

[0008] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of the first aspects.

[0009] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in any one of the first aspects.

[0010] The technical solutions provided by the embodiments of this disclosure can include the following beneficial effects: By acquiring dynamic data such as reservoir pressure, cumulative injection volume, current injection rate, and carbon emission reduction incentive unit price in real time at each preset decision moment, and using a pre-trained Gaussian process regression surrogate model to quickly predict the pressure peak, leakage risk, and operation and maintenance input under different candidate injection strategies, the net resource present value of each candidate sequence is quantified and the optimal injection rate sequence that meets safety and capacity constraints is selected, realizing the rolling optimization and closed-loop execution of the injection plan; This method organically integrates geological safety constraints (the relationship between pressure peak and reservoir fracture pressure, and the relationship between cumulative injection volume and storage capacity) with economic efficiency indicators (net resource present value based on carbon emission reduction incentive unit price, operation and maintenance input, and risk adjustment input), avoiding economic losses or safety risks of fixed injection schemes under uncertain conditions; At the same time, by iteratively updating and finally outputting the critical incentive unit price at each decision moment, a quantifiable technical basis is provided for investment decisions, risk management, and policy subsidy thresholds throughout the entire life cycle of the project.

[0011] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0012] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0013] Figure 1 This is a flowchart illustrating an economic efficiency assessment method for saline aquifer carbon dioxide sequestration according to an exemplary embodiment.

[0014] Figure 2 This is a block diagram illustrating an economic efficiency assessment device for saline aquifer carbon dioxide sequestration according to an exemplary embodiment.

[0015] Figure 3 This is a block diagram illustrating an apparatus for evaluating the economic efficiency of carbon dioxide sequestration in a saline aquifer, according to an exemplary embodiment. Detailed Implementation

[0016] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0017] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. The singular forms “a” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0018] It should be understood that although the terms first, second, third, etc., may be used to describe various information in embodiments of this disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of embodiments of this disclosure, and similarly, second information may also be referred to as first information. Depending on the context, the words “if” and “suppose” as used herein may be interpreted as “when”, “when”, or “in response to a determination”.

[0019] Furthermore, various forms of processes shown in the embodiments of this disclosure can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and no limitation is imposed herein.

[0020] It should be noted that the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0021] Figure 1 This is a flowchart illustrating an economic efficiency assessment method for saline aquifer carbon dioxide sequestration according to an exemplary embodiment, such as... Figure 1 As shown, it should be noted that the economic efficiency evaluation method for saline aquifer carbon dioxide sequestration in this embodiment is applied to the economic efficiency evaluation device for saline aquifer carbon dioxide sequestration. For example... Figure 1 As shown, the method may include the following steps: Step 101: At each preset decision time, obtain the reservoir pressure, cumulative injection volume, current injection rate, and current carbon emission reduction incentive unit price of the target saline aquifer.

[0022] Specifically, the preset decision-making time can be a fixed time point every quarter or every half year, or it can be customized according to project needs.

[0023] The methods for obtaining the above parameters include, but are not limited to: real-time collection of reservoir pressure through downhole pressure sensors, recording of cumulative injection volume and current injection rate through flow meters, and obtaining the current carbon emission reduction incentive unit price through external data interfaces (such as carbon trading market information interfaces).

[0024] It should be noted that this step provides real-time and accurate input data for subsequent surrogate model prediction and optimization decisions, ensuring that the evaluation method can respond to reservoir dynamics and market changes, and avoiding decision bias caused by parameter lag in traditional static evaluation.

[0025] Step 102: Input the current injection rate and static geological parameters into the trained surrogate model to obtain the predicted values ​​of pressure peak, leakage risk and operation and maintenance input.

[0026] The surrogate model is a Gaussian process regression model.

[0027] Specifically, static geological parameters include, but are not limited to, fixed properties such as permeability, porosity, effective reservoir thickness, formation fracture pressure, and total storage capacity.

[0028] The Gaussian process regression model is pre-trained based on multiple sets of numerical simulation samples. Its output includes not only the mean of each predicted quantity (i.e., the predicted values ​​of pressure peak, leakage risk, and operation and maintenance input), but also the variance of the prediction uncertainty.

[0029] It should be noted that using a Gaussian process regression model as a surrogate model can complete high-fidelity numerical simulation calculations that would otherwise take hours in milliseconds, thus supporting real-time decision-making; at the same time, the predictive uncertainty inherent in this model lays the foundation for subsequent risk quantification.

[0030] Step 103: Based on the current injection rate, generate multiple candidate injection rate sequences for the next N time steps using an optimization algorithm, and input each candidate injection rate sequence into the surrogate model to obtain the corresponding pressure peak sequence and leakage risk sequence.

[0031] In this embodiment, the current injection rate serves as a baseline value, determining the search center for candidate sequences. An optimization algorithm (e.g., random sampling, differential evolution, or Bayesian optimization) generates several injection rate change paths over the next N time steps near this baseline value; each path represents a candidate injection rate sequence. Each candidate sequence is fed step-by-step into a pre-trained Gaussian process regression surrogate model. The model rapidly outputs the pressure peak and leakage risk corresponding to each time step based on the injection rate and static geological parameters. The outputs of all time steps are combined sequentially to form the pressure peak sequence and leakage risk sequence of the candidate sequence.

[0032] It should be noted that this step, by generating multiple candidate sequences and quickly evaluating them on the surrogate model, can explore a large number of possible injection strategies in a short time, overcoming the bottleneck that traditional numerical simulators take several hours for each evaluation, and providing a sufficient set of candidate solutions for subsequent economic efficiency optimization and security constraint judgment.

[0033] In some embodiments of this disclosure, step 103 may specifically include the following sub-steps: Step a1: Using the current injection rate as the baseline, construct a preset number of candidate injection rate sequences by random generation between the preset upper limit and lower limit of rate change. Each candidate injection rate sequence includes the injection rate values ​​for the next N time steps.

[0034] Specifically, the upper and lower limits of the rate change can be set based on engineering experience, for example, the upper limit is 120% of the current injection rate and the lower limit is 80%. The preset number can be 50, 100 or more, depending on the balance between computing resources and optimization accuracy.

[0035] It should be noted that this generation method is simple and efficient, and can fully explore different rate change paths near the current injection strategy, avoiding the computational explosion problem caused by exhaustive search.

[0036] Step a2: For each candidate injection rate sequence, input the injection rate of each time step in the candidate injection rate sequence and the corresponding static geological parameters into the surrogate model, and output the pressure peak and leakage risk of the time step by the surrogate model.

[0037] Specifically, the surrogate model performs forward prediction independently for each time step. The inputs are the injection rate and constant static geological parameters for that time step, and the outputs are the pressure peak (i.e., the maximum formation pressure that may be reached within that time step) and leakage risk (i.e., the assessment value of the probability of carbon dioxide leakage within that time step). The time-step prediction can accurately depict the temporal impact of the injection process on reservoir pressure and safety risks, providing complete data support for subsequent net resource present value calculation.

[0038] Step a3: Combine the pressure peak values ​​of each time step into a pressure peak value sequence according to the time sequence, and combine the leakage risks output by each time step into a leakage risk sequence according to the time sequence.

[0039] It should be noted that by combining the sequences, the security risk trend of the injection strategy over time can be seen intuitively, and it is also convenient to calculate the maximum value (for security constraints) and the total injection amount (for capacity constraints) in the sequence.

[0040] Step 104: For each candidate injection rate sequence, calculate the net present value of resources of the candidate injection rate sequence based on the injection amount, carbon emission reduction incentive unit price, predicted value of operation and maintenance investment, and risk adjustment investment mapped by leakage risk; and determine whether the candidate injection rate sequence meets safety and capacity constraints based on the currently monitored reservoir pressure, cumulative injection amount, and pressure peak and total injection amount of the candidate injection rate sequence.

[0041] As an example, in some embodiments of this disclosure, the calculation of net resource present value in step 104 may specifically include the following sub-steps: Step b1: Multiply the injection amount at each time step in the candidate injection rate sequence by the carbon emission reduction incentive unit price to obtain the carbon emission reduction benefit at each time step.

[0042] It should be noted that carbon emission reduction benefits are a benefit of economic efficiency, reflecting the policy incentives or carbon credit value obtained from injecting carbon dioxide.

[0043] Step b2: Add the predicted value of operation and maintenance input to the risk adjustment input to obtain the total resource consumption at each time step.

[0044] The calculation formula for risk adjustment input is: Risk Adjustment Input = Leakage Probability × Unit Leakage Penalty Price. The leakage probability is output by the Gaussian process proxy model, and the unit leakage penalty price can be set with reference to carbon leakage regulatory standards. It should be noted that incorporating leakage risk into costs through monetization enables the optimization algorithm to automatically avoid high-risk strategies, achieving synergistic optimization of economy and safety.

[0045] Step b3: Subtract the total resource consumption of each time step from the carbon emission reduction benefit of that time step to obtain the net resource flow of each time step.

[0046] It should be noted that net resource flow is the net economic efficiency generated within that time step; a positive value indicates a surplus, and a negative value indicates a loss.

[0047] Step b4: Discount the net resource flow of each time step to the current decision time according to the preset discount rate, and sum the discounted net resource flows of each time step to obtain the net resource present value of the candidate injection rate sequence.

[0048] Specifically, the discount rate can be the social resource time preference rate, for example, with a value between 0.05 and 0.10. The discount formula is: Discount factor = 1 / (1+ρ) t Where ρ is the discount rate and t is the number of years from the current decision point. By discounting, resource flows occurring at different times in the future can be equated to the current moment for comparison, avoiding the valuation distortion caused by ignoring the time value of money.

[0049] In some embodiments of this disclosure, the determination of safety and capacity constraints in step 104 may specifically include the following sub-steps: Step b5: Add the currently monitored reservoir pressure to the maximum value in the pressure peak sequence of the candidate injection rate sequence to obtain the predicted peak pressure; determine whether the predicted peak pressure is less than the formation fracture pressure, otherwise determine that the safety and capacity constraints are not met.

[0050] It should be noted that formation fracture pressure is an inherent property of reservoirs. Once the predicted peak pressure exceeds this value, it may trigger safety accidents such as formation fracture and carbon dioxide leakage. Therefore, it needs to be used as a hard constraint.

[0051] Step b6: Add the cumulative injection amount to the total injection amount of the candidate injection rate sequence to obtain the predicted total injection amount; determine whether the predicted total injection amount is less than the total storage capacity, otherwise it is determined that the safety and capacity constraints are not met.

[0052] It should be noted that the total storage capacity is the physical upper limit of the saline aquifer. Exceeding this capacity may lead to pressure buildup or overflow risks, so it also needs to be strictly controlled.

[0053] Step b7: If the predicted peak pressure is less than the formation fracture pressure and the predicted total injection volume is less than the total storage capacity, then the candidate injection rate sequence is determined to meet the safety and capacity constraints.

[0054] It should be noted that only candidate sequences that simultaneously meet both pressure and capacity constraints will proceed to the subsequent selection process, ensuring the safety of the injection plan.

[0055] Step 105: From the candidate injection rate sequences that satisfy the safety and capacity constraints, select the candidate sequence with the largest net resource present value as the optimal injection rate sequence, and execute the injection plan for the first time step in the optimal injection rate sequence.

[0056] Specifically, after comparing the net resource present value of each candidate sequence, the one with the largest value is selected as the optimal solution, and then the injection rate command for the first time step (e.g., the next quarter) is sent to the field injection control system for execution.

[0057] It should be noted that this step only executes the first time step of the optimal sequence, not the entire sequence. This is because the next decision point will be re-optimized based on new monitoring data, thereby dynamically adapting to reservoir response and market changes and achieving rolling optimization.

[0058] Step 106: Return to the steps executed at each preset decision time until the injection period ends. Based on the actual injection rate executed at each decision time and the corresponding monitoring data, output the final net resource present value and critical incentive unit price.

[0059] Specifically, after the injection period ends, data such as the actual injection rate, monitored reservoir pressure, and cumulative injection volume at each decision point are summarized. The net present value of resources for the entire cycle is recalculated (actual data can be used instead of predicted values), and the critical incentive price is calculated, which is the carbon emission reduction incentive price when the net present value of resources is zero. The final net present value of resources reflects the actual economic efficiency of the project throughout its entire life cycle, while the critical incentive price can provide policymakers with a reference for subsidy thresholds.

[0060] In some embodiments of this disclosure, after executing the injection plan at each decision moment, actual reservoir pressure response data is acquired; the actual pressure response data is compared with the pressure peak prediction value of the surrogate model at the corresponding decision moment, and the prediction deviation is calculated; when the prediction deviation exceeds a preset threshold, the current injection rate and static geological parameters are added as incremental samples to the training dataset to incrementally update and train the surrogate model. Through incremental updates, the surrogate model can continuously learn new patterns in the actual reservoir response, improve prediction accuracy, and make subsequent decisions closer to physical reality.

[0061] Specifically, the preset threshold can be set according to engineering requirements, such as a relative error exceeding 10% or an absolute error exceeding 0.5 MPa.

[0062] The method for evaluating the economic efficiency of carbon dioxide sequestration in saline aquifers, as proposed in this disclosure, acquires dynamic data such as reservoir pressure, cumulative injection volume, current injection rate, and carbon emission reduction incentive price in real time at each preset decision point. It then utilizes a pre-trained Gaussian process regression surrogate model to quickly predict pressure peaks, leakage risks, and operation and maintenance costs under different candidate injection strategies. This quantifies the net present value of each candidate sequence and selects the optimal injection rate sequence that meets safety and capacity constraints, achieving rolling optimization and closed-loop execution of the injection plan. This method organically integrates geological safety constraints (the relationship between pressure peaks and reservoir fracture pressure, and the relationship between cumulative injection volume and sequestration capacity) with economic efficiency indicators (net present value of resources based on carbon emission reduction incentive price, operation and maintenance costs, and risk adjustment costs), avoiding economic losses or safety risks under uncertain conditions with fixed injection schemes. Furthermore, through iterative updates at each decision point and the final output of the critical incentive price, it provides quantifiable technical basis for investment decisions, risk management, and policy subsidy thresholds throughout the project's entire lifecycle.

[0063] Figure 2 This is a block diagram illustrating an economic efficiency assessment device for saline aquifer carbon dioxide sequestration according to an exemplary embodiment. (Refer to...) Figure 2 The device includes an acquisition unit 201, a prediction unit 202, a sequence generation unit 203, a judgment unit 204, an execution unit 205, and an output unit 206.

[0064] The acquisition unit 201 is used to acquire the reservoir pressure, cumulative injection volume, current injection rate and current carbon emission reduction incentive unit price of the target saline aquifer at each preset decision time. Prediction unit 202 is used to input the current injection rate and static geological parameters into the trained surrogate model to obtain predicted values ​​of pressure peak, leakage risk and operation and maintenance input; the surrogate model is a Gaussian process regression model. The sequence generation unit 203 is used to generate multiple candidate injection rate sequences for the next N time steps based on the current injection rate through an optimization algorithm, and input each candidate injection rate sequence into the surrogate model to obtain the corresponding pressure peak sequence and leakage risk sequence. The judgment unit 204 is used to calculate the net present value of resources of each candidate injection rate sequence based on the injection amount, carbon emission reduction incentive unit price, operation and maintenance investment forecast, and risk adjustment investment mapped by leakage risk; and to determine whether the candidate injection rate sequence meets the safety and capacity constraints based on the currently monitored reservoir pressure, cumulative injection amount, and pressure peak and total injection amount of the candidate injection rate sequence. The execution unit 205 is used to select the candidate sequence with the largest net resource present value from the candidate injection rate sequences that meet the safety and capacity constraints as the optimal injection rate sequence, and execute the injection plan of the first time step in the optimal injection rate sequence. Output unit 206 is used to return the steps executed at each preset decision time until the end of the injection period. Based on the actual injection rate executed at each decision time and the corresponding monitoring data, it outputs the final net resource present value and critical incentive unit price.

[0065] In some embodiments of this disclosure, the sequence generation unit 203 may be specifically used to: construct a preset number of candidate injection rate sequences by random generation between the current injection rate as a reference value and the preset upper limit and lower limit of rate change, wherein each candidate injection rate sequence includes the injection rate value for the next N time steps.

[0066] In some embodiments of this disclosure, the sequence generation unit 203 may specifically be used for: For each candidate injection rate sequence, the injection rate of each time step in the candidate injection rate sequence and the corresponding static geological parameters are input into the surrogate model, and the surrogate model outputs the pressure peak and leakage risk of the time step. The pressure peak values ​​at each time step are combined into a pressure peak value sequence according to the time sequence, and the leakage risks output at each time step are combined into a leakage risk sequence according to the time sequence.

[0067] In some embodiments of this disclosure, the determination unit 204 may specifically be used for: Multiply the injection amount at each time step in the candidate injection rate sequence by the carbon emission reduction incentive unit price to obtain the carbon emission reduction benefit at each time step. The total resource consumption at each time step is obtained by adding the predicted value of operation and maintenance input to the risk adjustment input; Subtracting the total resource consumption at each time step from the carbon reduction benefit at that time step yields the net resource flow at each time step. The net resource flow at each time step is discounted to the current decision time according to a preset discount rate. The net resource flow at each time step after discounting is summed to obtain the net resource present value of the candidate injection rate sequence.

[0068] In some embodiments of this disclosure, the determination unit 204 may specifically be used for: The predicted peak pressure is obtained by adding the currently monitored reservoir pressure to the maximum value in the pressure peak sequence of the candidate injection rate sequence; Determine whether the predicted peak pressure is less than the formation fracture pressure; otherwise, it is determined that the safety and capacity constraints are not met. The cumulative injection amount is added to the total injection amount of the candidate injection rate sequence to obtain the predicted total injection amount; Determine whether the predicted total injection amount is less than the total storage capacity; otherwise, it is determined that the safety and capacity constraints are not met. If the predicted peak pressure is less than the formation fracture pressure and the predicted total injection volume is less than the total storage capacity, then the candidate injection rate sequence is determined to meet the safety and capacity constraints.

[0069] In some embodiments of this disclosure, the apparatus further includes an updating unit, which can be specifically used for: After executing the injection plan at each decision point, obtain the actual reservoir pressure response data; The actual pressure response data is compared with the pressure peak predictions of the proxy model at the corresponding decision time, and the prediction deviation is calculated. When the prediction deviation exceeds the preset threshold, the current injection rate and static geological parameters are added as incremental samples to the training dataset to incrementally update and train the surrogate model.

[0070] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0071] The saline aquifer carbon dioxide sequestration economic efficiency assessment device proposed in this disclosure acquires dynamic data such as reservoir pressure, cumulative injection volume, current injection rate, and carbon emission reduction incentive unit price in real time at each preset decision time. It then uses a pre-trained Gaussian process regression surrogate model to quickly predict pressure peaks, leakage risks, and operation and maintenance inputs under different candidate injection strategies. This quantifies the net present value of each candidate sequence and selects the optimal injection rate sequence that meets safety and capacity constraints, achieving rolling optimization and closed-loop execution of the injection plan. This method organically integrates geological safety constraints (the relationship between pressure peaks and reservoir fracture pressure, and the relationship between cumulative injection volume and sequestration capacity) with economic efficiency indicators (net present value of resources based on carbon emission reduction incentive unit price, operation and maintenance inputs, and risk adjustment inputs), avoiding economic losses or safety risks under uncertain conditions with fixed injection schemes. Simultaneously, through iterative updates at each decision time and the final output of the critical incentive unit price, it provides quantifiable technical basis for investment decisions, risk management, and policy subsidy thresholds throughout the project's entire lifecycle.

[0072] Figure 3 This is a block diagram illustrating an apparatus for evaluating the economic efficiency of carbon dioxide sequestration in saline aquifers, according to an exemplary embodiment. For example, apparatus 300 may be an electronic device, such as a mobile phone, computer, digital broadcasting terminal, messaging device, tablet device, personal digital assistant, etc.

[0073] Reference Figure 3The device 300 may include one or more of the following components: processing component 302, memory 304, power component 306, multimedia component 308, audio component 310, input / output (I / O) interface 312, sensor component 314, and communication component 316.

[0074] Processing component 302 typically controls the overall operation of device 300, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 302 may include one or more processors 320 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 302 may include one or more modules to facilitate interaction between processing component 302 and other components. For example, processing component 302 may include a multimedia module to facilitate interaction between multimedia component 308 and processing component 302.

[0075] Memory 304 is configured to store various types of data to support the operation of device 300. Examples of such data include instructions for any application or method operating on device 300, contact data, phonebook data, messages, pictures, videos, etc. Memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0076] The power supply component 306 provides power to the various components of the device 300. The power supply component 306 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device 300.

[0077] Multimedia component 308 includes a screen that provides an output interface between device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 308 includes a front-facing camera and / or a rear-facing camera. When device 300 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0078] Audio component 310 is configured to output and / or input audio signals. For example, audio component 310 includes a microphone (MIC) configured to receive external audio signals when device 300 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 304 or transmitted via communication component 316. In some embodiments, audio component 310 also includes a speaker for outputting audio signals.

[0079] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0080] Sensor assembly 314 includes one or more sensors for providing state assessments of various aspects of device 300. For example, sensor assembly 314 may detect the on / off state of device 300, the relative positioning of components such as the display and keypad of device 300, changes in the position of device 300 or a component of device 300, the presence or absence of user contact with device 300, the orientation or acceleration / deceleration of device 300, and temperature changes of device 300. Sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 314 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0081] Communication component 316 is configured to facilitate wired or wireless communication between device 300 and other devices. Device 300 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 316 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 316 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0082] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0083] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, which can be executed by a processor 320 of the device 300 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0084] In an exemplary embodiment, a computer program product is also provided, including a computer program that implements the above-described method when executed by the processor 320 of the device 300.

[0085] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0086] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for evaluating the economic efficiency of carbon dioxide sequestration in saline aquifers, characterized in that, include: At each preset decision point, obtain the current reservoir pressure, cumulative injection volume, current injection rate, and current carbon emission reduction incentive unit price of the target saline aquifer. The current injection rate and static geological parameters are input into the trained surrogate model to obtain predicted values ​​for peak pressure, leakage risk, and operation and maintenance input; the surrogate model is a Gaussian process regression model. Based on the current injection rate, multiple candidate injection rate sequences for the next N time steps are generated through an optimization algorithm, and each candidate injection rate sequence is input into the surrogate model to obtain the corresponding pressure peak sequence and leakage risk sequence. For each candidate injection rate sequence, the net present value of resources for the candidate injection rate sequence is calculated based on the injection amount of the candidate injection rate sequence, the unit price of the carbon emission reduction incentive, the predicted value of operation and maintenance investment, and the risk adjustment investment mapped by the leakage risk. Based on the currently monitored reservoir pressure, the cumulative injection volume, and the pressure peak and total injection volume of the candidate injection rate sequence, it is determined whether the candidate injection rate sequence meets the safety and capacity constraints. From the candidate injection rate sequences that satisfy safety and capacity constraints, select the candidate sequence with the largest net resource present value as the optimal injection rate sequence, and execute the injection plan for the first time step in the optimal injection rate sequence. Return to the steps described at each preset decision time until the injection period ends. Based on the actual injection rate executed at each decision time and the corresponding monitoring data, output the final net resource present value and critical incentive unit price.

2. The method for evaluating the economic efficiency of carbon dioxide sequestration in saline aquifers according to claim 1, characterized in that, The process of generating multiple candidate injection rate sequences for the next N time steps based on the current injection rate using an optimization algorithm includes: Using the current injection rate as a baseline, a preset number of candidate injection rate sequences are constructed randomly between the preset upper limit and lower limit of rate change. Each candidate injection rate sequence includes the injection rate values ​​for the next N time steps.

3. The method for evaluating the economic efficiency of carbon dioxide sequestration in saline aquifers according to claim 1, characterized in that, The step of inputting each candidate injection rate sequence into the surrogate model to obtain the corresponding pressure peak sequence and leakage risk sequence includes: For each candidate injection rate sequence, the injection rate of each time step in the candidate injection rate sequence and the corresponding static geological parameters are input into the surrogate model, and the surrogate model outputs the pressure peak and leakage risk of the time step; The pressure peak values ​​at each time step are combined in time sequence to form the pressure peak value sequence, and the leakage risks output at each time step are combined in time sequence to form the leakage risk sequence.

4. The method for evaluating the economic efficiency of carbon dioxide sequestration in saline aquifers according to claim 1, characterized in that, The calculation of the net present value of resources for the candidate injection rate sequence based on the injection amount of the candidate injection rate sequence, the unit price of carbon emission reduction incentives, the predicted value of operation and maintenance investment, and the risk adjustment investment mapped by the leakage risk includes: Multiply the injection amount at each time step in the candidate injection rate sequence by the carbon emission reduction incentive unit price to obtain the carbon emission reduction benefit at each time step. The total resource consumption at each time step is obtained by adding the predicted value of operation and maintenance input to the risk adjustment input; Subtract the total resource consumption at each time step from the carbon reduction benefit at that time step to obtain the net resource flow at each time step. The net resource flow at each time step is discounted to the current decision time according to a preset discount rate. The net resource flow at each time step after discounting is summed to obtain the net resource present value of the candidate injection rate sequence.

5. The method for evaluating the economic efficiency of carbon dioxide sequestration in saline aquifers according to claim 1, characterized in that, The step of determining whether the candidate injection rate sequence meets safety and capacity constraints based on the currently monitored reservoir pressure, the cumulative injection volume, and the pressure peak and total injection volume of the candidate injection rate sequence includes: The predicted peak pressure is obtained by adding the currently monitored reservoir pressure to the maximum value in the pressure peak sequence of the candidate injection rate sequence; Determine whether the predicted peak pressure is less than the formation fracture pressure; otherwise, it is determined that the safety and capacity constraints are not met. The cumulative injection amount is added to the total injection amount of the candidate injection rate sequence to obtain the predicted total injection amount; Determine whether the predicted total injection amount is less than the total storage capacity; otherwise, it is determined that the safety and capacity constraints are not met. If the predicted peak pressure is less than the formation fracture pressure and the predicted total injection volume is less than the total storage capacity, then the candidate injection rate sequence is determined to meet the safety and capacity constraints.

6. The method for evaluating the economic efficiency of carbon dioxide sequestration in saline aquifers according to claim 1, characterized in that, Also includes: After executing the injection plan at each decision point, obtain the actual reservoir pressure response data; The actual pressure response data is compared with the pressure peak prediction value of the proxy model at the corresponding decision time, and the prediction deviation is calculated. When the prediction deviation exceeds a preset threshold, the current injection rate and static geological parameters are added as incremental samples to the training dataset to perform incremental update training on the surrogate model.

7. A device for evaluating the economic efficiency of carbon dioxide sequestration in saline aquifers, characterized in that, include: The acquisition unit is used to acquire the reservoir pressure, cumulative injection volume, current injection rate, and current carbon emission reduction incentive unit price of the target saline aquifer at each preset decision time. The prediction unit is used to input the current injection rate and static geological parameters into the trained surrogate model to obtain predicted values ​​of pressure peak, leakage risk, and operation and maintenance input; the surrogate model is a Gaussian process regression model. The sequence generation unit is used to generate multiple candidate injection rate sequences for the next N time steps based on the current injection rate through an optimization algorithm, and input each candidate injection rate sequence into the surrogate model to obtain the corresponding pressure peak sequence and leakage risk sequence. The judgment unit is used to calculate the net present value of resources for each candidate injection rate sequence based on the injection amount of the candidate injection rate sequence, the unit price of carbon emission reduction incentive, the predicted value of operation and maintenance investment, and the risk adjustment investment mapped by the leakage risk. Based on the currently monitored reservoir pressure, the cumulative injection volume, and the pressure peak and total injection volume of the candidate injection rate sequence, it is determined whether the candidate injection rate sequence meets the safety and capacity constraints. The execution unit is used to select the candidate sequence with the largest net resource present value from the candidate injection rate sequences that meet the safety and capacity constraints as the optimal injection rate sequence, and execute the injection plan of the first time step in the optimal injection rate sequence. The output unit is used to return the steps executed at each preset decision time until the injection period ends. Based on the actual injection rate executed at each decision time and the corresponding monitoring data, it outputs the final net resource present value and critical incentive unit price.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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