Carbon dioxide geological storage project full life cycle cost accounting method and device

Abstract

The invention provides a carbon dioxide geological sequestration project full life cycle cost accounting method and device, and the method comprises the steps: obtaining the cost fluctuation data of each cost type in a carbon dioxide sequestration project; wherein the cost type comprises at least one of the following items: engineering geological exploration, engineering construction, engineering purchase, injection and monitoring operation, and emergency maintenance; for each cost type, estimation is carried out based on the corresponding probability distribution model, and cost probability distribution parameters are obtained; and performing Monte Carlo simulation based on the cost probability distribution parameter of each cost type to obtain a total engineering cost parameter. Through the technical scheme of the invention, the full-life-cycle cost accounting of the carbon dioxide geological sequestration project can be realized, and data support is provided for the economic feasibility verification of an engineering project.

Description

Technical Field

[0001] This application relates to the field of carbon dioxide geological storage technology, and in particular to a method, apparatus, equipment and storage medium for calculating the full life cycle cost of a carbon dioxide geological storage project. Background Technology

[0002] Carbon dioxide geological storage is an important technology for mitigating greenhouse gas emissions. However, because storage projects involve multiple complex stages including geology, construction, and operation, cost estimation is subject to significant uncertainties. Traditional cost estimation methods often use a single cost value, ignoring the impact of these uncertainties on the cost. 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 method for calculating the full life-cycle cost of a carbon dioxide geological storage project. The method includes: acquiring cost fluctuation data for each cost type in the carbon dioxide storage project; wherein the cost type includes at least one of the following: engineering geological exploration, engineering construction, engineering procurement, injection and monitoring operation, and emergency maintenance; for each cost type, estimating the cost probability distribution parameters based on the corresponding probability distribution model; and performing Monte Carlo simulation based on the cost probability distribution parameters of each cost type to obtain the total project cost parameters.

[0005] In one implementation, the cost type is the engineering geological exploration, and the cost data of the engineering geological exploration includes geological exploration cost data and core testing cost data corresponding to different geological parameters. The step of estimating the cost based on the corresponding probability distribution model to obtain the cost probability distribution parameters includes: combining pre-acquired expert knowledge and using a triangular distribution model to process the geological exploration cost data and core testing cost data to obtain the cost probability distribution parameters.

[0006] In one implementation, the cost type is the engineering construction, and the cost data of the engineering construction includes well site engineering construction cost fluctuation data and surface facility construction cost fluctuation data. The step of estimating the cost based on the corresponding probability distribution model to obtain cost probability distribution parameters includes: processing the well site engineering construction cost fluctuation data using a normal distribution probability model to obtain well site engineering cost probability distribution parameters; and processing the surface facility engineering construction cost fluctuation data using a normal distribution probability model to obtain surface facility engineering cost probability distribution parameters.

[0007] In one implementation, the cost type is the engineering procurement, and the cost data of the engineering procurement includes equipment procurement cost fluctuation data and raw material procurement cost fluctuation data. The step of estimating the cost based on the corresponding probability distribution model to obtain cost probability distribution parameters includes: processing the equipment procurement cost fluctuation data using a log-normal distribution model to obtain equipment procurement cost probability distribution parameters; and processing the raw material procurement cost fluctuation data using a normal distribution model to obtain raw material procurement cost probability distribution parameters.

[0008] In one implementation, the cost type is the monitoring operation, and the cost data of the monitoring operation cost includes electricity cost fluctuation data, sensor maintenance cost fluctuation data, and satellite data subscription cost fluctuation data. The step of estimating the cost based on the corresponding probability distribution model to obtain cost probability distribution parameters includes: processing the electricity cost fluctuation data using a random walk model to obtain electricity cost probability distribution parameters; processing the sensor maintenance cost fluctuation data using a normal distribution model to obtain sensor maintenance cost distribution parameters; and processing the satellite data subscription cost fluctuation data using a normal distribution model to obtain satellite data subscription cost distribution parameters.

[0009] In one implementation, the cost type is emergency maintenance, and the cost data for emergency maintenance includes emergency maintenance costs corresponding to different carbon dioxide geological storage projects. The step of estimating the cost based on the corresponding probability distribution model to obtain cost probability distribution parameters includes: combining pre-acquired expert knowledge and using a triangular distribution model to process the emergency maintenance cost data to obtain cost probability distribution parameters.

[0010] Secondly, this application proposes a life-cycle cost accounting device for carbon dioxide geological storage projects. The device includes: an acquisition module for acquiring cost fluctuation data for various cost types in the carbon dioxide storage project; wherein the cost types include at least one of the following: engineering geological exploration, engineering construction, engineering procurement, injection and monitoring operation, and emergency maintenance; a first processing module for estimating the cost probability distribution parameters for each cost type based on a corresponding probability distribution model; and a second processing module for performing Monte Carlo simulations based on the cost probability distribution parameters for each cost type to obtain the total project cost parameters.

[0011] In one implementation, the cost type is the engineering geological exploration, and the cost data of the engineering geological exploration includes geological exploration cost data and core testing cost data corresponding to different geological parameters. The first processing module can be used to: combine pre-acquired expert knowledge and use a triangular distribution model to process the geological exploration cost data and core testing cost data to obtain the cost probability distribution parameters.

[0012] In one implementation, the cost type is the engineering construction, and the cost data of the engineering construction includes well site engineering construction cost fluctuation data and surface facility construction cost fluctuation data. The first processing module can be used to: process the well site engineering construction cost fluctuation data using a normal distribution probability model to obtain well site engineering cost probability distribution parameters; and process the surface facility engineering construction cost fluctuation data using a normal distribution probability model to obtain surface facility engineering cost probability distribution parameters.

[0013] In one implementation, the cost type is the engineering procurement, and the cost data of the engineering procurement includes equipment procurement cost fluctuation data and raw material procurement cost fluctuation data. The first processing module can be used to: process the equipment procurement cost fluctuation data using a log-normal distribution model to obtain equipment procurement cost probability distribution parameters; and process the raw material procurement cost fluctuation data using a normal distribution model to obtain raw material procurement cost probability distribution parameters.

[0014] In one implementation, the cost type is the monitoring operation, and the cost data of the monitoring operation cost includes electricity cost fluctuation data, sensor maintenance cost fluctuation data, and satellite data subscription cost fluctuation data. The first processing module can be used to: process the electricity cost fluctuation data using a random walk model to obtain electricity cost probability distribution parameters; process the sensor maintenance cost fluctuation data using a normal distribution model to obtain sensor maintenance cost distribution parameters; and process the satellite data subscription cost fluctuation data using a normal distribution model to obtain satellite data subscription cost distribution parameters.

[0015] In one implementation, the cost type is emergency maintenance, and the cost data for emergency maintenance includes emergency maintenance costs corresponding to different carbon dioxide geological storage projects. The first processing module can be used to: combine pre-acquired expert knowledge and use a triangular distribution model to process the emergency maintenance cost data to obtain cost probability distribution parameters.

[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 carbon dioxide geological storage project lifecycle cost accounting method 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 carbon dioxide geological storage project lifecycle cost accounting method as described in the first aspect.

[0019] The method, apparatus, equipment, and storage medium for calculating the full life-cycle cost of carbon dioxide geological storage projects provided in this application can process cost fluctuation data of different cost types based on corresponding probability distribution models to obtain cost probability distribution parameters. Monte Carlo simulations can then be performed based on these cost probability distribution parameters for each cost type to obtain the total project cost parameters. This enables full life-cycle cost accounting for carbon dioxide geological storage projects, providing data support for verifying the economic feasibility of such projects.

[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 calculating the full life-cycle cost of a carbon dioxide geological storage project, as provided in an embodiment of this application.

[0023] Figure 2 This is a schematic diagram of the structure of a carbon dioxide geological storage project life cycle cost accounting device provided in the embodiments 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 calculating the full life-cycle cost of a carbon dioxide geological storage project according to embodiments of this application.

[0027] Figure 1 This is a flowchart illustrating a method for calculating the full life-cycle cost of a carbon dioxide geological storage project, as provided in an embodiment of this application. Figure 1As shown, the method may include, but is not limited to, the following steps:

[0028] S101: Obtain cost fluctuation data for each cost type in the carbon dioxide storage project.

[0029] The cost types include at least one of the following: engineering geological exploration, engineering construction, engineering procurement, injection and monitoring operation, and emergency maintenance.

[0030] S102: For each cost type, estimate the cost probability distribution parameters based on the corresponding probability distribution model.

[0031] In one implementation, the cost type is engineering geological exploration. The cost data for engineering geological exploration includes geological exploration cost data and core testing cost data corresponding to different geological parameters. The cost is estimated based on the corresponding probability distribution model to obtain cost probability distribution parameters. This includes: combining pre-acquired expert knowledge and using a triangular distribution model to process the geological exploration cost data and core testing cost data to obtain cost probability distribution parameters.

[0032] For example, based on pre-acquired expert knowledge combined with geological exploration cost data and core testing cost data corresponding to different geological parameters, the minimum, most likely, and maximum values ​​of geological exploration cost and core testing cost under the current geological conditions are determined, thereby obtaining cost probability distribution parameters.

[0033] In one implementation, the cost type is engineering construction. The cost data for engineering construction includes fluctuation data of well site engineering construction costs and fluctuation data of surface facility construction costs. The cost is estimated based on the corresponding probability distribution model to obtain cost probability distribution parameters. This includes: processing the well site engineering construction cost fluctuation data using a normal distribution probability model to obtain well site engineering cost probability distribution parameters; and processing the surface facility engineering construction cost fluctuation data using a normal distribution probability model to obtain surface facility engineering cost probability distribution parameters.

[0034] For example, engineering construction cost data and ground facility construction cost data of several other carbon dioxide geological storage projects with similar geological conditions and engineering design parameters are obtained. The mean and standard deviation of the well site engineering construction cost fluctuation data are used as engineering construction cost fluctuation data and ground facility construction cost fluctuation data to calculate the probability distribution parameters of the normal distribution of well site engineering costs. The mean and standard deviation of the ground facility construction cost fluctuation data are also calculated as the probability distribution parameters of the normal distribution of ground facility engineering costs.

[0035] In one implementation, the cost type is engineering procurement. The cost data for engineering procurement includes equipment procurement cost fluctuation data and raw material procurement cost fluctuation data. The cost is estimated based on the corresponding probability distribution model to obtain cost probability distribution parameters, including: processing the equipment procurement cost fluctuation data using a log-normal distribution model to obtain equipment procurement cost probability distribution parameters; and processing the raw material procurement cost fluctuation data using a normal distribution model to obtain raw material procurement cost probability distribution parameters.

[0036] For example, the equipment procurement costs at different times over a period of time are obtained as equipment procurement cost fluctuation data. After verifying that the data has positive skewness and meets the fitting conditions of the log-normal distribution, the logarithmic operation is performed on the equipment procurement cost fluctuation data, and the mean and standard deviation of the data after the logarithmic operation are calculated and used as the probability distribution parameters of the log-normal distribution of equipment procurement costs.

[0037] For example, the raw material procurement costs at different times over a period of time are obtained as raw material procurement cost fluctuation data. After confirming that the data shows regular fluctuations and meets the requirements of normal distribution, the mean and standard deviation of the raw material procurement cost fluctuation data are calculated as probability distribution parameters of the normal distribution of raw material procurement costs.

[0038] In one implementation, the cost type is monitoring operation. The cost data for monitoring operation includes electricity cost fluctuation data, sensor maintenance cost fluctuation data, and satellite data subscription cost fluctuation data. The cost is estimated based on the corresponding probability distribution model to obtain cost probability distribution parameters, including: processing the electricity cost fluctuation data using a random walk model to obtain electricity cost probability distribution parameters; processing the sensor maintenance cost fluctuation data using a normal distribution model to obtain sensor maintenance cost distribution parameters; and processing the satellite data subscription cost fluctuation data using a normal distribution model to obtain satellite data subscription cost distribution parameters.

[0039] For example, historical monthly electricity price time series data within a preset historical period is obtained, monthly rate of return is calculated based on the historical electricity price data, and the mean and standard deviation of the monthly rate of return are obtained as probability distribution parameters.

[0040] For example, historical sensor maintenance data (e.g., single maintenance cost, total cost corresponding to maintenance frequency, etc.) of several other carbon dioxide geological storage projects with similar geological conditions and engineering design parameters are obtained as sensor maintenance cost fluctuation data, and the mean and standard deviation of sensor maintenance cost fluctuation data are calculated as distribution parameters of the normal distribution of sensor maintenance cost.

[0041] For example, firstly, annual satellite data subscription fee data within a preset historical period (e.g., the past three years) is collected as satellite data subscription cost fluctuation data. The mean and standard deviation of the satellite data subscription cost fluctuation data are calculated, and these two indicators are used as distribution parameters of the normal distribution of satellite data subscription costs.

[0042] In one implementation, the cost type is emergency maintenance. The cost data for emergency maintenance includes the emergency maintenance costs corresponding to different carbon dioxide geological storage projects. The cost probability distribution parameters are estimated based on the corresponding probability distribution model. This includes: combining pre-acquired expert knowledge and using a triangular distribution model to process the emergency maintenance cost data to obtain the cost probability distribution parameters.

[0043] For example, based on pre-acquired expert knowledge, and combined with historical emergency cases and geological risk characteristics of other carbon dioxide geological storage projects with similar geological conditions and engineering design parameters, the minimum, most likely, and maximum values ​​of emergency maintenance costs are determined as probability distribution parameters for emergency maintenance costs.

[0044] S103: Perform Monte Carlo simulation based on the cost probability distribution parameters of each cost type to obtain the total project cost parameters.

[0045] For example, each cost type is mapped to a random variable and assigned a corresponding cost probability distribution parameter to perform a Monte Carlo simulation to obtain the probability distribution of the total project cost.

[0046] By implementing the embodiments of this application, cost fluctuation data of different cost types can be processed based on corresponding probability distribution models to obtain cost probability distribution parameters. Monte Carlo simulations can then be performed based on the cost probability distribution parameters of each cost type to obtain the total project cost parameters. This enables full life-cycle cost accounting for carbon dioxide geological storage projects, providing data support for verifying the economic feasibility of engineering projects.

[0047] Please see Figure 2 , Figure 2 This is a schematic diagram of a life-cycle cost accounting device for a carbon dioxide geological storage project provided in an embodiment of this application. Figure 2 As shown, the device 200 includes: an acquisition module 201, used to acquire cost fluctuation data for each cost type in the carbon dioxide sequestration project; wherein the cost type includes at least one of the following: engineering geological exploration, engineering construction, engineering procurement, injection and monitoring operation, and emergency maintenance; a first processing module 202, used to estimate the cost probability distribution parameters for each cost type based on the corresponding probability distribution model; and a second processing module 203, used to perform Monte Carlo simulation based on the cost probability distribution parameters of each cost type to obtain the total project cost parameters.

[0048] In one implementation, the cost type is engineering geological exploration. The cost data of engineering geological exploration includes geological exploration cost data and core testing cost data corresponding to different geological parameters. The first processing module 202 can be used to: combine pre-acquired expert knowledge and use a triangular distribution model to process the geological exploration cost data and core testing cost data to obtain cost probability distribution parameters.

[0049] In one implementation, the cost type is engineering construction, and the cost data for engineering construction includes well site engineering construction cost fluctuation data and surface facility construction cost fluctuation data. The first processing module 202 can be used to: process the well site engineering construction cost fluctuation data using a normal distribution probability model to obtain well site engineering cost probability distribution parameters; and process the surface facility engineering construction cost fluctuation data using a normal distribution probability model to obtain surface facility engineering cost probability distribution parameters.

[0050] In one implementation, the cost type is engineering procurement, and the cost data for engineering procurement includes equipment procurement cost fluctuation data and raw material procurement cost fluctuation data. The first processing module 202 can be used to: process the equipment procurement cost fluctuation data using a log-normal distribution model to obtain the probability distribution parameters of the equipment procurement cost; and process the raw material procurement cost fluctuation data using a normal distribution model to obtain the probability distribution parameters of the raw material procurement cost.

[0051] In one implementation, the cost type is monitoring operation, and the cost data for monitoring operation includes electricity cost fluctuation data, sensor maintenance cost fluctuation data, and satellite data subscription cost fluctuation data. The first processing module 202 can be used to: process the electricity cost fluctuation data using a random walk model to obtain the electricity cost probability distribution parameters; process the sensor maintenance cost fluctuation data using a normal distribution model to obtain the sensor maintenance cost distribution parameters; and process the satellite data subscription cost fluctuation data using a normal distribution model to obtain the satellite data subscription cost distribution parameters.

[0052] In one implementation, the cost type is emergency maintenance, and the cost data for emergency maintenance includes the emergency maintenance costs corresponding to different carbon dioxide geological storage projects. The first processing module 202 can be used to: combine pre-acquired expert knowledge and use a triangular distribution model to process the emergency maintenance cost data to obtain cost probability distribution parameters.

[0053] The apparatus described in this application can process cost fluctuation data of different cost types based on corresponding probability distribution models to obtain cost probability distribution parameters. Monte Carlo simulations can then be performed based on these cost probability distribution parameters for each cost type to obtain the total project cost parameters. This enables full life-cycle cost accounting for carbon dioxide geological storage projects, providing data support for verifying the economic feasibility of such projects.

[0054] It should be noted that the foregoing explanation of the embodiment of the life cycle cost accounting method for carbon dioxide geological storage projects also applies to the life cycle cost accounting device for carbon dioxide geological storage projects in this embodiment, and will not be repeated here.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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 calculating the full life-cycle cost of a carbon dioxide geological storage project, characterized in that, include: Obtain cost fluctuation data for each cost type in the carbon dioxide sequestration project; wherein, the cost type includes at least one of the following: engineering geological exploration, engineering construction, engineering procurement, injection and monitoring operation, and emergency maintenance; For each of the aforementioned cost types, the cost probability distribution parameters are estimated based on the corresponding probability distribution model. Monte Carlo simulations are performed based on the cost probability distribution parameters of each cost type to obtain the total project cost parameters.

2. The method according to claim 1, characterized in that, The cost type is the engineering geological exploration, and the cost data for the engineering geological exploration includes geological exploration cost data corresponding to different geological parameters and core testing cost data. The cost probability distribution parameters are obtained by estimating based on the corresponding probability distribution model, including: By combining the previously acquired expert knowledge, the geological exploration cost data and core testing cost data are processed using a triangular distribution model to obtain the cost probability distribution parameters.

3. The method according to claim 1, characterized in that, The cost type is the engineering construction, and the cost data for the engineering construction includes fluctuation data of well site engineering construction costs and fluctuation data of surface facility construction costs. The estimation based on the corresponding probability distribution model to obtain cost probability distribution parameters includes: The fluctuation data of the well site construction cost were processed using a normal distribution probability model to obtain the probability distribution parameters of the well site construction cost. The data on the fluctuation of construction costs of ground facilities are processed using a normal distribution probability model to obtain the probability distribution parameters of the engineering costs of ground facilities.

4. The method according to claim 1, characterized in that, The cost type is the engineering procurement, and the cost data for the engineering procurement includes equipment procurement cost fluctuation data and raw material procurement cost fluctuation data. The estimation based on the corresponding probability distribution model to obtain cost probability distribution parameters includes: The equipment procurement cost fluctuation data are processed using a log-normal distribution model to obtain the probability distribution parameters of the equipment procurement cost; The raw material procurement cost fluctuation data are processed using a normal distribution model to obtain the probability distribution parameters of raw material procurement costs.

5. The method according to claim 1, characterized in that, The cost type is the monitoring operation, and the cost data for the monitoring operation includes electricity cost fluctuation data, sensor maintenance cost fluctuation data, and satellite data subscription cost fluctuation data. The cost probability distribution parameters are obtained by estimating based on the corresponding probability distribution model, including: The electricity cost fluctuation data is processed using a random walk model to obtain the probability distribution parameters of electricity costs; The sensor maintenance cost fluctuation data are processed using a normal distribution model to obtain sensor maintenance cost distribution parameters; The satellite data subscription cost fluctuation data is processed using a normal distribution model to obtain satellite data subscription cost distribution parameters.

6. The method according to claim 1, characterized in that, The cost type is emergency maintenance, and the cost data for emergency maintenance includes emergency maintenance costs corresponding to different carbon dioxide geological storage projects. The cost probability distribution parameters are obtained by estimating based on the corresponding probability distribution model, including: By combining the pre-acquired expert knowledge, the emergency maintenance cost data is processed using a triangular distribution model to obtain cost probability distribution parameters.

7. A device for calculating the full life-cycle cost of a carbon dioxide geological storage project, characterized in that, include: The acquisition module is used to acquire cost fluctuation data for various cost types in the carbon dioxide sequestration project; wherein, the cost types include at least one of the following: engineering geological exploration, engineering construction, engineering procurement, injection and monitoring operation, and emergency maintenance; The first processing module is used to estimate the cost probability distribution parameters for each cost type based on the corresponding probability distribution model. The second processing module is used to perform Monte Carlo simulation based on the cost probability distribution parameters of each cost type to obtain the total project cost parameters.

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.

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