Carbon dioxide geological sequestration leakage early warning method based on Bayesian estimation
By using a Bayesian estimation method and various time-series monitoring data to calculate the probability of carbon dioxide leakage, the accuracy of early warning of carbon dioxide geological storage leakage in existing technologies is insufficient, and effective prediction of leakage risk is achieved.
Patent Information
- Application Number
- CN202511281007.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-09
- Publication Date
- 2026-01-13
AI Technical Summary
There is a lack of effective early warning methods for carbon dioxide geological storage leaks in existing technologies, making it difficult to accurately predict the leakage risk during the carbon dioxide geological storage process.
A Bayesian estimation-based approach was adopted. By acquiring various time-series monitoring data (such as formation pressure, temperature, deformation, soil, carbon dioxide concentration, etc.), the data was cleaned and standardized to obtain likelihood statistics and prior distribution parameters, and the predicted probability of carbon dioxide leakage was calculated.
It enables effective prediction of the probability of carbon dioxide geological storage leakage, improving the accuracy and reliability of early warning.
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Figure CN121328798A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of carbon dioxide geological storage technology, and in particular to a method, device, equipment and storage medium for early warning of carbon dioxide geological storage leakage based on Bayesian estimation. Background Technology
[0002] As global warming becomes increasingly severe, carbon dioxide geological storage has gained widespread attention as an important means of mitigating greenhouse gas emissions. To ensure the safe implementation of carbon dioxide geological storage, effective early warning systems for carbon dioxide leaks are necessary. Summary of the Invention
[0003] This application aims to at least partially address one of the technical problems in the related art.
[0004] Firstly, this application proposes a Bayesian estimation-based method for early warning of carbon dioxide geological storage leaks. The method includes: acquiring multiple time-series monitoring data of a carbon dioxide geological storage area; wherein the multiple time-series monitoring data includes at least formation monitoring data and carbon dioxide concentration data; performing data cleaning and standardization on the multiple time-series monitoring data to obtain multiple standard time-series monitoring data; obtaining a likelihood statistic based on the multiple standard time-series monitoring data; obtaining a prior distribution parameter of carbon dioxide leakage risk; and obtaining a predicted leakage probability based on the prior distribution parameter and the likelihood statistic.
[0005] In one implementation, the formation monitoring data includes at least one of the following: formation pressure, formation temperature, and formation deformation; the carbon dioxide concentration data includes at least one of the following: soil carbon dioxide concentration, groundwater carbon dioxide concentration, and atmospheric carbon dioxide concentration.
[0006] In one implementation, obtaining the likelihood statistic based on the multiple standard time-series monitoring data includes: performing time-series alignment on the multiple standard time-series monitoring data; based on the time-series aligned multiple standard time-series monitoring data, determining the number of times each of the standard time-series monitoring data simultaneously exhibits anomalies; obtaining the total number of monitoring times for the time-series aligned multiple standard time-series monitoring data; obtaining the number of normal monitoring times based on the total number of monitoring times and the number of anomalies; and using the number of anomalies and the number of normal monitoring times as the likelihood statistic.
[0007] In one optional implementation, obtaining the prior distribution parameters of carbon dioxide leakage risk includes: obtaining leakage probability characteristic values of similar carbon dioxide storage projects; obtaining Beta distribution shape parameters based on the leakage probability characteristic values; and obtaining prior normal monitoring counts and prior abnormal monitoring counts based on the Beta distribution shape parameters as the prior distribution parameters.
[0008] In one optional implementation, obtaining the prior distribution parameters of carbon dioxide leakage risk includes: obtaining the number of historical normal monitoring events within a preset historical period as the prior normal monitoring events; obtaining the number of historical abnormal monitoring events within the preset historical period as the prior abnormal monitoring events; and using the prior normal monitoring events and the prior abnormal monitoring events as the prior distribution parameters.
[0009] Optionally, obtaining the predicted leakage probability based on the prior distribution parameters and the likelihood statistics includes: obtaining the posterior normal monitoring count based on the prior normal monitoring count and the normal monitoring count; obtaining the posterior abnormal monitoring count based on the prior abnormal monitoring count and the abnormal monitoring count; and obtaining the predicted leakage probability based on the posterior normal monitoring count and the posterior abnormal monitoring count.
[0010] Secondly, this application proposes a Bayesian estimation-based early warning device for carbon dioxide geological storage leaks. The device includes: a first acquisition module for acquiring various time-series monitoring data of a carbon dioxide geological storage area; wherein the various time-series monitoring data includes at least formation monitoring data and carbon dioxide concentration data; a first processing module for cleaning and standardizing the various time-series monitoring data to obtain various standard time-series monitoring data; a second processing module for obtaining likelihood statistics based on the various standard time-series monitoring data; a second acquisition module for acquiring prior distribution parameters of carbon dioxide leak risk; and a third processing module for obtaining the predicted leak probability based on the prior distribution parameters and the likelihood statistics.
[0011] In one implementation, the formation monitoring data includes at least one of the following: formation pressure, formation temperature, and formation deformation; the carbon dioxide concentration data includes at least one of the following: soil carbon dioxide concentration, groundwater carbon dioxide concentration, and atmospheric carbon dioxide concentration.
[0012] In one implementation, the second processing module can be used to: perform time-series alignment on the multiple standard time-series monitoring data; based on the time-series aligned multiple standard time-series monitoring data, determine the number of times each of the standard time-series monitoring data simultaneously exhibits anomalies; obtain the total number of monitoring times for the time-series aligned multiple standard time-series monitoring data; obtain the number of normal monitoring times based on the total number of monitoring times and the number of anomalies; and use the number of anomalies and the number of normal monitoring times as the likelihood statistic.
[0013] In one optional implementation, the second acquisition module can be used to: acquire leakage probability characteristic values of similar carbon dioxide storage projects; acquire Beta distribution shape parameters based on the leakage probability characteristic values; and acquire prior normal monitoring counts and prior abnormal monitoring counts as prior distribution parameters based on the Beta distribution shape parameters.
[0014] In one optional implementation, the second acquisition module can be used to: acquire the number of historical normal monitoring events within a preset historical period as the prior normal monitoring events; acquire the number of historical abnormal monitoring events within the preset historical period as the prior abnormal monitoring events; and use the prior normal monitoring events and the prior abnormal monitoring events as the prior distribution parameters.
[0015] Optionally, the third processing module can be used to: obtain the posterior normal monitoring count based on the prior normal monitoring count and the normal monitoring count; obtain the posterior abnormal monitoring count based on the prior abnormal monitoring count and the abnormal monitoring count; and obtain the predicted leakage probability based on the posterior normal monitoring count and the posterior abnormal monitoring count.
[0016] Thirdly, this application proposes an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the Bayesian estimation-based early warning method for carbon dioxide geological sequestration leaks as described in the first aspect.
[0017] Fourthly, this application proposes a computer-readable storage medium for storing instructions that, when executed, cause the method described in the first aspect to be implemented.
[0018] Fifthly, this application proposes a computer program product, including a computer program that, when executed by a processor, implements the steps of the Bayesian estimation-based early warning method for carbon dioxide geological sequestration leaks as described in the first aspect.
[0019] The Bayesian estimation-based method, apparatus, equipment, and storage medium for early warning of carbon dioxide geological storage leaks provided in this application can obtain likelihood statistics based on various time-series monitoring data of the carbon dioxide geological storage area, and then obtain the predicted leakage probability based on the prior distribution parameters and the likelihood statistics. This enables effective prediction of the leakage probability of carbon dioxide geological storage.
[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description
[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:
[0022] Figure 1 This is a flowchart illustrating a method for early warning of carbon dioxide geological storage leaks based on Bayesian estimation, as provided in an embodiment of this application.
[0023] Figure 2 This is a schematic diagram of a carbon dioxide geological storage leakage early warning device based on Bayesian estimation provided in an embodiment of this application;
[0024] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0025] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.
[0026] The following describes, with reference to the accompanying drawings, a method and apparatus for early warning of carbon dioxide geological storage leakage based on Bayesian estimation, according to embodiments of this application.
[0027] Figure 1 This is a flowchart illustrating a method for early warning of carbon dioxide geological storage leaks based on Bayesian estimation, as provided in an embodiment of this application. Figure 1 As shown, the method may include, but is not limited to, the following steps:
[0028] S101: Acquire various time-series monitoring data of the carbon dioxide geological storage area.
[0029] In the embodiments of this application, the various time-series monitoring data include at least formation monitoring data and carbon dioxide concentration data.
[0030] In the embodiments of this application, the formation monitoring data includes at least one of the following: formation pressure, formation temperature, and formation deformation.
[0031] In the embodiments of this application, the carbon dioxide concentration data includes at least one of the following: soil carbon dioxide concentration, groundwater carbon dioxide concentration, and atmospheric carbon dioxide concentration.
[0032] S102: Perform data cleaning and standardization on various time-series monitoring data to obtain various standard time-series monitoring data.
[0033] For example, the Grubbs test is used to remove outliers in the monitoring data, and Z-standardization is performed on different types of monitoring data to obtain various standard time series monitoring data.
[0034] S103: Obtain likelihood statistics based on multiple standard time series monitoring data.
[0035] In one implementation, likelihood statistics are obtained based on multiple standard time-series monitoring data, including: time-series alignment of the multiple standard time-series monitoring data; the number of abnormal monitoring events occurring simultaneously in each standard time-series monitoring data after time-series alignment; obtaining the total number of monitoring events for the multiple standard time-series monitoring data after time-series alignment; obtaining the number of normal monitoring events based on the total number of monitoring events and the number of abnormal monitoring events; and using the number of abnormal monitoring events and the number of normal monitoring events as likelihood statistics.
[0036] For example, when aligning multiple standard time-series monitoring data using a preset duration (e.g., 1 hour) as the smallest time unit, interpolation or resampling is used based on the original sampling interval of each standard time-series monitoring data to ensure that each standard time-series monitoring data has corresponding monitoring values at the same time node, forming a multi-source data matrix with consistent time dimension. Based on the aligned data, each time node is checked to see if all indicators simultaneously trigger the corresponding preset anomaly rules, and the total number of time nodes that meet the conditions is counted as the anomaly monitoring count; the total number of all valid time nodes in the aligned data matrix is counted as the total monitoring count; the total monitoring count is subtracted from the anomaly monitoring count to obtain the normal monitoring count, and finally, the anomaly monitoring count and the normal monitoring count are used as the likelihood statistic.
[0037] S104: Obtain the prior distribution parameters of carbon dioxide leakage risk.
[0038] In one implementation, for a newly constructed carbon dioxide geological storage project, the prior distribution parameters of carbon dioxide leakage risk can be obtained by following these steps: obtaining leakage probability characteristic values of similar carbon dioxide storage projects; obtaining Beta distribution shape parameters based on leakage probability characteristic values; and obtaining prior normal monitoring counts and prior abnormal monitoring counts as prior distribution parameters based on the Beta distribution shape parameters.
[0039] For example, monitoring data of similar carbon dioxide sequestration projects with similar geological conditions and design parameters are obtained. Based on this monitoring data, the mean and variance of the leakage probability of similar carbon dioxide sequestration projects are obtained. These mean and variance are used as the mean and variance of a Beta distribution to calculate the prior distribution parameters of the Beta distribution. These prior distribution parameters include the prior number of normal monitoring sessions and the prior number of abnormal monitoring sessions. The above calculation process can be expressed as follows:
[0040]
[0041] α+β=N
[0042] Where μ is the mean leakage probability of similar carbon dioxide storage projects, σ 2 Let α be the variance of the leakage probability of similar carbon dioxide storage projects, β be the number of prior normal monitoring sessions, β be the number of prior abnormal monitoring sessions, and N be the total number of monitoring sessions.
[0043] In one implementation, after a period of operation of carbon dioxide geological storage, the prior distribution parameters of carbon dioxide leakage risk can be obtained by following these steps: obtaining the number of historical normal monitoring times within a preset historical period as the prior normal monitoring times; obtaining the number of historical abnormal monitoring times within a preset historical period as the prior abnormal monitoring times; and using the prior normal monitoring times and the prior abnormal monitoring times as the prior distribution parameters.
[0044] For example, the number of historical normal monitoring times within a historical period before acquiring various time-series monitoring data in S101 is obtained as the prior normal monitoring times; the number of historical abnormal monitoring times within a preset historical period is obtained as the prior abnormal monitoring times; and the prior normal monitoring times and the prior abnormal monitoring times are used as prior distribution parameters.
[0045] S105: Obtain the predicted leakage probability based on prior distribution parameters and likelihood statistics.
[0046] In one implementation, the predicted leakage probability is obtained based on prior distribution parameters and likelihood statistics, including: obtaining the posterior normal monitoring count based on the prior normal monitoring count and the normal monitoring count; obtaining the posterior abnormal monitoring count based on the prior abnormal monitoring count and the abnormal monitoring count; and obtaining the predicted leakage probability based on the posterior normal monitoring count and the posterior abnormal monitoring count.
[0047] For example, the above calculation process can be represented as follows:
[0048] α 后验 =α 先验 +k
[0049] β 后验 =β 先验 +(nk)
[0050]
[0051] Where, μ 后验 To predict the leakage probability, α 先验 Let k be the number of prior normal monitoring cycles, and β be the number of normal monitoring cycles. 先验 denoted as the prior anomaly detection count, and (nk) represents the total number of anomaly detections.
[0052] Understandably, Bayes' theorem can be expressed as:
[0053]
[0054] Since P(p)=Beta(p;α,β) and P(D∣p)=Binomial(k;n,p) are conjugate pairs, the posterior distribution is still a Beta distribution.
[0055] By implementing the embodiments of this application, likelihood statistics can be obtained based on various time-series monitoring data of carbon dioxide geological storage areas. Based on prior distribution parameters and likelihood statistics, the predicted leakage probability can be obtained. This enables effective prediction of the leakage probability of carbon dioxide geological storage.
[0056] Please see Figure 2 , Figure 2 This is a schematic diagram of a carbon dioxide geological storage leakage early warning device based on Bayesian estimation, provided in an embodiment of this application. Figure 2 As shown, the device 200 includes: a first acquisition module 201, used to acquire multiple time-series monitoring data of a carbon dioxide geological storage area; wherein, the multiple time-series monitoring data includes at least formation monitoring data and carbon dioxide concentration data; a first processing module 202, used to perform data cleaning and standardization processing on the multiple time-series monitoring data to obtain multiple standard time-series monitoring data; a second processing module 203, used to obtain likelihood statistics based on the multiple standard time-series monitoring data; a second acquisition module 204, used to acquire prior distribution parameters of carbon dioxide leakage risk; and a third processing module 205, used to acquire the predicted leakage probability based on the prior distribution parameters and the likelihood statistics.
[0057] In one implementation, the formation monitoring data includes at least one of the following: formation pressure, formation temperature, and formation deformation; the carbon dioxide concentration data includes at least one of the following: soil carbon dioxide concentration, groundwater carbon dioxide concentration, and atmospheric carbon dioxide concentration.
[0058] In one implementation, the second processing module 203 can be used to: perform time-series alignment on multiple standard time-series monitoring data; based on the time-series aligned multiple standard time-series monitoring data, determine the number of times each standard time-series monitoring data simultaneously exhibits anomalies; obtain the total number of monitoring times for the time-series aligned multiple standard time-series monitoring data; obtain the number of normal monitoring times based on the total number of monitoring times and the number of anomalies; and use the number of anomalies and the number of normal monitoring times as a likelihood statistic.
[0059] In one optional implementation, the second acquisition module 204 can be used to: acquire leakage probability characteristic values of similar carbon dioxide storage projects; acquire Beta distribution shape parameters based on leakage probability characteristic values; and acquire prior normal monitoring counts and prior abnormal monitoring counts as prior distribution parameters based on the Beta distribution shape parameters.
[0060] In one optional implementation, the second acquisition module 204 can be used to: acquire the number of historical normal monitoring events within a preset historical period as the prior normal monitoring events; acquire the number of historical abnormal monitoring events within a preset historical period as the prior abnormal monitoring events; and use the prior normal monitoring events and the prior abnormal monitoring events as prior distribution parameters.
[0061] Optionally, the third processing module 205 can be used to: obtain the posterior normal monitoring count based on the prior normal monitoring count and the normal monitoring count; obtain the posterior abnormal monitoring count based on the prior abnormal monitoring count and the abnormal monitoring count; and obtain the predicted leakage probability based on the posterior normal monitoring count and the posterior abnormal monitoring count.
[0062] The apparatus described in this application can obtain likelihood statistics based on various time-series monitoring data of a carbon dioxide geological storage area, and then obtain the predicted leakage probability based on prior distribution parameters and likelihood statistics. This enables effective prediction of the leakage probability of carbon dioxide geological storage.
[0063] It should be noted that the foregoing explanation of the embodiment of the early warning method for carbon dioxide geological storage leakage based on Bayesian estimation also applies to the early warning device for carbon dioxide geological storage leakage based on Bayesian estimation in this embodiment, and will not be repeated here.
[0064] To implement the above embodiments, this application also proposes an electronic device. Please see [link to relevant documentation]. Figure 3 , Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. For example... Figure 3 As shown, the electronic device 300 includes: a processor 301, and a memory 302 communicatively connected to the processor 301; the memory 302 stores computer execution instructions; the processor 301 executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.
[0065] To implement the above embodiments, this application also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.
[0066] To implement the above embodiments, this application also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.
[0067] It should be noted that the acquisition, transmission, storage, use, and processing of data in this application comply with the relevant provisions of national laws and regulations and do not violate public order and good morals.
[0068] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.
[0069] It is worth noting that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.
[0070] In the description of this application, unless otherwise stated, " / " means "or", for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone.
[0071] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0072] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.
[0073] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.
[0074] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.
[0075] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0076] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
[0077] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
[0078] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.
Claims
1. A method for early warning of carbon dioxide geological storage leaks based on Bayesian estimation, characterized in that, include: Acquire various time-series monitoring data of the carbon dioxide geological sequestration area; wherein, the various time-series monitoring data include at least stratigraphic monitoring data and carbon dioxide concentration data; Data cleaning and standardization processes are performed on the various time-series monitoring data to obtain various standard time-series monitoring data. Likelihood statistics are obtained based on the aforementioned multiple standard time-series monitoring data; Obtain the prior distribution parameters of carbon dioxide leakage risk; Based on the prior distribution parameters and the likelihood statistics, the predicted leakage probability is obtained.
2. The method according to claim 1, characterized in that, The formation monitoring data includes at least one of the following: formation pressure, formation temperature, and formation deformation; The carbon dioxide concentration data includes at least one of the following: soil carbon dioxide concentration, groundwater carbon dioxide concentration, and atmospheric carbon dioxide concentration.
3. The method according to claim 1, characterized in that, The acquisition of likelihood statistics based on the aforementioned multiple standard time-series monitoring data includes: Time-series alignment is performed on the various standard time-series monitoring data; Based on multiple standard time-series monitoring data after time-series alignment, the number of times each of the standard time-series monitoring data simultaneously exhibits anomalies; Obtain the total number of monitoring times for various standard time series monitoring data after time series alignment; The number of normal monitoring sessions is obtained based on the total number of monitoring sessions and the number of abnormal monitoring sessions. The number of abnormal monitoring events and the number of normal monitoring events are used as the likelihood statistic.
4. The method according to claim 3, characterized in that, The prior distribution parameters for obtaining the risk of carbon dioxide leakage include: Obtain the leakage probability characteristic value of similar carbon dioxide sequestration projects; The shape parameters of the Beta distribution are obtained based on the leakage probability feature value; The prior normal monitoring count and the prior abnormal monitoring count are obtained based on the shape parameters of the Beta distribution and used as the prior distribution parameters.
5. The method according to claim 3, characterized in that, The prior distribution parameters for obtaining the risk of carbon dioxide leakage include: Obtain the historical normal monitoring count within a preset historical period as the prior normal monitoring count; Obtain the historical anomaly monitoring count within the preset historical time period; The prior normal monitoring count and the prior abnormal monitoring count are used as the prior distribution parameters.
6. The method according to claim 4 or 5, characterized in that, The step of obtaining the predicted leakage probability based on the prior distribution parameters and the likelihood statistic includes: The posterior normal monitoring count is obtained based on the prior normal monitoring count and the normal monitoring count. The posterior anomaly detection count is obtained based on the prior anomaly detection count and the anomaly detection count. The predicted leakage probability is obtained based on the number of normal posterior monitoring events and the number of abnormal posterior monitoring events.
7. A carbon dioxide geological storage leakage early warning device based on Bayesian estimation, characterized in that, include: The first acquisition module is used to acquire various time-series monitoring data of the carbon dioxide geological storage area; wherein, the various time-series monitoring data include at least formation monitoring data and carbon dioxide concentration data; The first processing module is used to perform data cleaning and standardization on the various time-series monitoring data to obtain various standard time-series monitoring data. The second processing module is used to obtain likelihood statistics based on the various standard time-series monitoring data. The second acquisition module is used to acquire the prior distribution parameters of carbon dioxide leakage risk; The third processing module is used to obtain the predicted leakage probability based on the prior distribution parameters and the likelihood statistics.
8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 6.
10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.