A method and system for constructing an overseas LNG project investment and economic evaluation template
By acquiring input data, dividing the project into sections, and adapting to fiscal and tax policies, a cash flow model was constructed and risk analysis was conducted. This solved the closed-loop problem of the existing system in overseas LNG projects, and improved the accuracy and economic efficiency of investment decisions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CNOOC GAS & POWER GRP
- Filing Date
- 2026-03-02
- Publication Date
- 2026-06-05
AI Technical Summary
Existing cost and economic evaluation systems, when applied to overseas LNG projects, fail to form a complete closed loop from cost to benefit and from certainty to risk, thus failing to effectively support investment decisions.
By acquiring input data, dividing the project into unit projects and individual projects, determining the work breakdown structure framework, adapting to overseas fiscal and tax policies, constructing a cash flow model, conducting risk parameter analysis, generating risk warnings, and forming a complete investment and economic evaluation model.
It enables investment and economic evaluation that conforms to the characteristics of overseas investment, improves the accuracy and economic efficiency of investment decisions, and provides scientific pricing and bidding support.
Smart Images
Figure CN122155400A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of engineering cost technology, and in particular to a method and system for constructing an investment and economic evaluation template for overseas LNG projects. Background Technology
[0002] Against the backdrop of rapid development in overseas LNG infrastructure projects, the importance of preliminary research, commercial price negotiations, and international bidding is increasing daily. Among these, cost analysis and economic evaluation are core components of investment decisions. While large energy companies and engineering firms in the industry have developed and applied relatively mature cost and economic evaluation systems, their inherent limitations become apparent when these systems are directly applied to overseas projects, especially technology-intensive and internationally competitive liquefied natural gas (LNG) projects.
[0003] Chinese patent CN115222539A discloses a method and system for estimating engineering investment parameters for upstream overseas oil and gas field projects. Based on historical project data, it determines whether investment results can be directly applied; if not, it obtains the range of target investment parameters based on all technical solutions. Chinese patent CN117829771A discloses a method and system for optimizing digital cost and economic data analysis. It standardizes investment cost data and economic evaluation data, establishes standard evaluation templates and standard cost templates, and calculates evaluation data and standard costs. Both of these are independent or phased solutions, failing to form a complete closed loop for overseas projects, encompassing cost, benefit, certainty, and risk. Summary of the Invention
[0004] The purpose of this application is to provide a method and system for constructing an investment and economic evaluation template for overseas LNG projects.
[0005] The embodiments of this application adopt the following technical solution: a method for constructing an overseas LNG project investment and economic evaluation template, comprising:
[0006] Obtain input data from overseas LNG projects and determine multi-scheme engineering quantity templates based on the input data; Divide the project into unit projects and individual projects according to different technical solutions, and determine the work breakdown structure framework; Based on the input data, overseas tax policy data is obtained, tax rate and fee rate parameters are determined, and the tax rate and fee rate parameters are adapted to overseas market conditions to obtain cost combination data. Based on the construction cost adjustment coefficient and the work breakdown structure framework, the investment cost breakdown structure and cost combination are determined. Based on the aforementioned investment cost decomposition structure, a cash flow model is constructed; Based on the obtained gas price fluctuation parameters and exchange rate fluctuation parameters, risk parameters are determined, and the cash flow model is adjusted to obtain the adjusted cash flow model. The adjusted cash flow model is subjected to probabilistic simulation analysis. Based on the obtained probability distribution of internal rate of return, the sensitivity analysis results are determined, and risk warnings are generated based on preset risk thresholds.
[0007] In some embodiments, the input data includes basic project information, country identification code, and basic investment and financial data; Based on the basic information of the project, a quantity calculation template is matched, and the quantity calculation template is decomposed using a probabilistic simulation optimization method to determine multiple possible quantity templates.
[0008] In some embodiments, acquiring input data from overseas LNG projects and determining multi-scheme quantity templates based on the input data includes: Basic project information, country identification codes, and investment financial data were obtained from overseas LNG projects. Data was cleaned to remove null values and formatted into the target format to obtain a structured input dataset. Based on the basic project information in the structured input dataset, match the existing engineering quantity calculation template library to determine the appropriate engineering quantity calculation template; The established engineering quantity calculation template is decomposed to obtain multi-scheme engineering quantity simulation data.
[0009] In some embodiments, the step of dividing unit projects and individual projects according to different technical solutions and determining the work breakdown structure framework includes: Based on the technical solution, the basis for dividing the work breakdown structure is obtained, and the unit project and individual project are determined. Based on the classification criteria, the technical solutions are decomposed to obtain an initial list that matches the technical solutions; Extract unit projects and individual projects from the initial list, construct a work breakdown structure hierarchy, determine the structural framework, and form the investment pricing cost composition and logic.
[0010] In some embodiments, based on the input data, overseas tax policy data is obtained, tax rate and fee rate parameters are determined, and based on the tax rate and fee rate parameters, overseas market conditions are adapted to obtain cost combination data, including: Using the country identification code in the input data, overseas policy data is obtained from a preset fiscal and tax policy database to determine tax rate parameter values and fee rate parameter values; Based on the tax rate parameter value and the fee rate parameter value, determine the matching parameters that are appropriate for the overseas project; By integrating data, the adaptation parameters are combined with the cost calculation formula to generate initial cost combination data; Determine the degree of match between the initial cost mix data and the technical conditions of the overseas project; Based on the degree of matching, an optimization and adjustment scheme for the simplified cost combination data is determined, and the final cost combination data is generated. Based on the final cost combination data, a cost list adapted to the characteristics of the overseas solution is generated.
[0011] In some embodiments, the investment cost breakdown structure and cost combination are determined based on the construction cost adjustment factor and the work breakdown structure framework, including: Obtain the hierarchical data of each construction task from the work breakdown structure framework, parse the association between task nodes and child nodes, and determine the task hierarchical decomposition structure. Based on the task hierarchy decomposition structure, the national construction cost adjustment coefficients corresponding to each task are extracted to obtain the cost coefficient combination. For the aforementioned cost coefficient combination, the relationship between the proportions of labor costs, material costs, and machinery costs is analyzed to obtain cost proportion weights; wherein, the construction costs include labor costs, material costs, and machinery costs; Based on the cost ratio weights and task hierarchy decomposition structure, a model for the investment cost decomposition structure is generated. The cost allocation data for each task is extracted from the investment cost breakdown structure, and the proportions of labor costs, material costs, and machinery costs are integrated to generate the cost combination ratio.
[0012] In some embodiments, constructing a cash flow model based on the investment cost decomposition structure includes: By decomposing the investment cost structure and adjusting the personnel and machine costs, the cost of each unit project is determined, and then the unit project cost is calculated layer by layer to obtain the final project cost. Based on the project cost, the second category of costs and other costs are calculated to obtain the final construction investment. Based on the cost estimates and the project's overseas business model, conduct a cost and benefit analysis. Determine the cash flow value at each time point to establish the cash flow sequence.
[0013] In some embodiments, determining risk parameters based on the obtained gas price fluctuation parameters and exchange rate fluctuation parameters, and adjusting the cash flow model to obtain an adjusted cash flow model includes: Historical data on natural gas prices and foreign exchange rates are obtained from the database, and natural gas price fluctuation parameters and foreign exchange rate fluctuation parameters are extracted based on these data. Based on natural gas price fluctuation parameters and foreign exchange rate fluctuation parameters, multiple risk scenarios are generated to obtain risk assessment parameters. By using risk assessment parameters, sensitivity analysis is conducted in a pre-established cash flow forecasting model to identify key influencing factors. When the fluctuation range of key influencing factors exceeds the preset fluctuation threshold, the parameters of the pre-established cash flow forecasting model are adjusted by a linear regression algorithm to obtain a preliminary optimized cash flow forecasting model. Based on the preliminary optimized cash flow forecasting model, the mean square error is calculated, the forecasting accuracy of the model is evaluated, and the accuracy evaluation results are obtained. Based on the accuracy evaluation results, the weight parameters of the initially optimized cash flow forecasting model are adjusted to obtain the final optimized cash flow forecasting model. Based on the final optimized cash flow forecasting model, risk-adjusted cash flow forecast data is generated, and business decision support parameters are determined.
[0014] In some embodiments, determining the sensitivity analysis results based on the probability distribution of the obtained internal rate of return and forming a risk warning based on a preset risk threshold includes: Input data is obtained from the adjusted cash flow model, and the probability distribution of the internal rate of return is generated using Monte Carlo simulation. Based on the generated probability distribution, the sensitivity of key parameters is determined, and the sensitivity analysis results are obtained. If the sensitivity analysis results exceed the preset risk threshold, the parameter is marked as a high-risk parameter, and a list of risk parameters is generated. High-risk parameters are extracted from the list of risk parameters, and a decision tree algorithm is used to determine their potential impact on the internal rate of return. When the potential impact level corresponds to a high-risk level, an early warning signal is generated; By using early warning signals, the parameters of the cash flow model are adjusted to generate an optimized cash flow model.
[0015] This application also provides a template construction system for overseas LNG project investment and economic evaluation, including: The data acquisition module is configured to acquire input data from overseas LNG projects and determine multi-scheme engineering quantity templates based on the input data. The structure generation module is configured to divide unit projects and individual projects according to different technical solutions and determine the work breakdown structure framework. The tax and finance adaptation module is configured to obtain overseas tax and finance policy data based on the input data, determine tax rate and fee rate parameters, and adapt overseas market conditions based on the tax rate and fee rate parameters to obtain cost combination data. The cost generation module is configured to determine the investment cost breakdown structure and cost combination based on the construction cost adjustment coefficient and the work breakdown structure framework. The model building module is configured to build a cash flow model based on the investment cost decomposition structure. The risk adjustment module is configured to determine risk parameters based on the obtained gas price fluctuation parameters and exchange rate fluctuation parameters, and adjust the cash flow model to obtain the adjusted cash flow model. The analysis and early warning module determines the sensitivity analysis results based on the probability distribution of the obtained internal rate of return, and generates risk warnings based on preset risk thresholds.
[0016] The beneficial effects of this application's embodiments are as follows: This application, through work breakdown structure division, dynamic fiscal and tax country matching, the introduction of cost adjustment coefficients, and a pricing method combining cost breakdown structure divisions, simultaneously establishes an economic evaluation model for risk parameter analysis under different profit models. This achieves an investment and economic evaluation model that conforms to the characteristics of overseas investment, providing scientific support for the pricing and bidding of overseas LNG projects, and improving the accuracy and economic efficiency of investment decisions. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 A flowchart illustrating the method for constructing a template for investment and economic evaluation of overseas LNG projects in this application; Figure 2 A simplified flowchart illustrating the method for constructing the template for overseas LNG project investment and economic evaluation in this application; Figure 3 This is a structural diagram of the system for constructing the template for overseas LNG project investment and economic evaluation in this application. Detailed Implementation
[0019] Various embodiments and features of this application are described herein with reference to the accompanying drawings.
[0020] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.
[0021] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.
[0022] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.
[0023] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application.
[0024] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.
[0025] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as the basis and representative basis for the claims to teach those skilled in the art to use this application in a variety of substantially any suitable detailed structures.
[0026] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.
[0027] To address the problems in the background art, this application provides a method for constructing an investment and economic evaluation template for overseas LNG projects, combined with... Figure 1 and Figure 2 The method includes the following steps: S1, Obtain input data for overseas LNG projects, and determine multi-scheme engineering quantity templates based on the input data.
[0028] For example, input data for overseas LNG projects can be obtained in various ways, such as by compiling and storing preliminary project data, which can then be retrieved directly from the storage location when needed. Based on this input data, it can be decomposed to determine multiple engineering quantity templates. In some embodiments, the input data includes basic project information, country identification codes, and basic investment and financial data. By matching the engineering quantity calculation template with the basic project information, a probabilistic simulation optimization method can be used to decompose the engineering quantity calculation template and determine multiple engineering quantity templates.
[0029] In one possible implementation, step S1 may include the following steps: S11: Obtain basic project information, country identification codes, and investment financial data from overseas LNG projects. Clean the data to remove null values and format it into the target format, such as a table, to obtain a structured input dataset.
[0030] S12, based on the basic project information in the structured input dataset, match the existing engineering quantity calculation template library to determine the appropriate engineering quantity calculation template.
[0031] S13 can use the NumPy library (a fundamental library for scientific computing) to decompose a given engineering quantity calculation template and obtain multi-scheme engineering quantity simulation data.
[0032] For example, when matching the quantity calculation template, a suitable template can be selected from the preset template library according to the project size and process type.
[0033] For example, an LNG project with a capacity of 5 million tons per year, located in an overseas country, has technical characteristics such as three 160,000 cubic meter LNG storage tanks and three liquefaction lines. The bill of quantities template includes components for storage tanks, main process systems, auxiliary systems, and utilities. This matching method quickly determines the applicable template by comparing project characteristics with template categories and parameters.
[0034] When generating engineering quantity simulation data using the NumPy library, the mean and standard deviation can be set for key variables in the template. For example, with a total pipeline length of 50 kilometers and a standard deviation of 10 kilometers, a certain set of simulation data can be generated by sampling using `random.normal` (a sampling function used to generate random numbers conforming to a normal distribution (also called a Gaussian distribution)). Each set of data represents a certain engineering quantity margin range. The generated multiple sets of data provide diverse scenario support for subsequent cost analysis.
[0035] In one possible implementation, the quantity template can serve as a basis for subsequent investment pricing.
[0036] The entire process forms a closed loop through basic project information, template matching, and simulation analysis, ensuring the integrity of the investment estimation template.
[0037] S2, divide the project into unit projects and individual projects according to different technical solutions, and determine the work breakdown structure framework.
[0038] For example, a work breakdown structure generation method can be used to divide the project into unit projects and individual projects according to different technical solutions, thereby obtaining a work breakdown structure framework and ensuring the integrity of the investment.
[0039] In one possible implementation, step S2 may include the following steps: S21. Based on the technical solution, obtain the basis for dividing the Work Breakdown Structure (WBS) and determine the unit project and individual project.
[0040] S21. Based on the classification criteria, the technical solutions are decomposed to obtain an initial list that matches the technical solutions.
[0041] S23. Extract unit projects and individual projects from the initial list. MindManager (a mind mapping and visual work management tool) can be used to construct the work breakdown structure hierarchy, determine the structural framework, and form the investment pricing cost composition and logic.
[0042] For example, when analyzing technical documents for overseas LNG projects, the criteria for dividing them into unit projects and individual projects can be determined by analyzing key information in the documents, such as project attributes, main schemes, and quantities of various professional works. The criteria for division are typically based on the functional modules and construction phases of the project.
[0043] For example, the storage system of an LNG project can be considered a single project, while the supporting specialties (such as pipelines) are unit projects. The technical plan might describe a storage tank capacity of 160,000 cubic meters and pipeline installation involving 2,000 meters of cryogenic pipeline. Based on this information, the supporting engineering for the storage tank can be divided into two unit projects: civil construction and pipeline material installation.
[0044] In one possible implementation, the technical solution is decomposed based on classification criteria to generate an initial cost item list. For example, tank civil engineering can be subdivided into two sub-items: pile foundation and outer tank. The initial cost list would list the name of each project, such as pile foundation structure. When using the MindManager tool to build the work breakdown structure, the tank civil engineering can be placed at the top level node according to the quota division, with pile foundation and outer tank as secondary nodes, intuitively displaying the hierarchical relationship. For example, based on the technical content, the tank body is the top level node, and secondary nodes are divided into unit projects such as pile foundation and outer tank. External supporting facilities are divided into unit projects such as process piping, instrumentation, and electrical systems. The tank body pile foundation includes: tank test piles and engineering piles. The outer tank includes: outer tank foundation, walls, dome concrete, prestressed steel strands, tank ceiling, outer tank steel structure, embedded parts, etc. Anti-corrosion painting of the tank exterior concrete surface, etc. The above forms the investment WBS decomposition and division.
[0045] It should be noted that the above method, by clearly breaking down the work content under each unit project, provides a basis for the pricing logic and content of overseas LNG project investment costs, thus ensuring the integrity of the investment.
[0046] S3. Based on the input data, obtain overseas tax policy data, determine tax rate and fee rate parameters, and adapt the tax rate and fee rate parameters to overseas market conditions to obtain cost combination data.
[0047] For example, based on the country identification code in the input data, overseas tax policy data can be obtained through the country parameter adaptation module to determine tax rate and fee rate parameters. Based on these tax rate and fee rate parameters, and adapted to overseas market conditions, cost combination data is obtained.
[0048] In one feasible embodiment, step S3 may include the following steps: S31, using the country identification code in the input data, obtain overseas policy data from the preset fiscal and tax policy database, and determine the tax rate parameter value and fee rate parameter value.
[0049] S32. Based on the tax rate parameter value and fee rate parameter value, and in combination with the characteristics of the overseas project, determine the matching parameters that match the overseas project.
[0050] S33, through data integration, combines the adaptation parameters with the cost calculation formula to generate initial cost combination data.
[0051] S34. Based on the generated initial cost combination data, determine the degree of matching between the initial cost combination data and the technical conditions of the overseas project.
[0052] S35, based on the matching degree, determine the optimization and adjustment scheme of the simplified cost combination data, and generate the final cost combination data.
[0053] S36. Based on the final cost combination data, generate a cost list adapted to the overseas solution characteristics.
[0054] For example, by parsing country identification codes, overseas policy data is retrieved from a pre-defined tax policy database to determine tax rate and fee rate parameter values. For instance, during parsing, pre-defined country identification codes such as CamT / CamD;VieT / VieD can be used to match countries in the tax table, revealing that overseas country A has a VAT rate of 10% with no tariffs, and overseas country B has a VAT rate of 9% with no tariffs. Specifically, the tax table contains foreign tax policies, including tax rates and surcharges. During parsing, relevant tax rate parameters can be extracted through code matching to ensure that the parameters accurately reflect the regulatory requirements of the target market. For example, in some Southeast Asian countries, considering a 10% VAT rate and a high proportion of logistics costs, appropriate parameters are generated, including a tax rate of 10% and a logistics surcharge rate of 5%.
[0055] In one possible implementation, the weights of the adaptation parameters can be adjusted based on fluctuations in currency exchange rates, freight costs, and tariffs to ensure that the adaptation parameters reflect the actual needs of the local country. This approach can flexibly address differences in the fiscal and tax environments of different countries. Through a data integration process, the market adaptation parameters are combined with the cost calculation formula to generate initial cost combination data.
[0056] For example, by substituting the appropriate parameters for country A into the formula, the calculation of a combination of costs including taxes, freight, and port charges is performed to generate an initial data table containing multiple cost dimensions such as basic taxes and transportation costs. This results in a CIF (Cost, Insurance and Freight) or FOB (Free On Board) equipment price that conforms to the local country's pricing, forming the original value of the equipment or materials.
[0057] Specifically, the data integration process can be achieved through WPS tools, ensuring seamless integration of adaptation parameters and formulas, thereby improving data processing efficiency.
[0058] If the number of feature dimensions in the initial cost combination data exceeds a preset dimension threshold, K-Means (an unsupervised clustering algorithm) is used for classification to obtain simplified cost combination data. For example, assuming the initial data contains 10 dimensions (such as taxes, logistics costs, and shipping costs), and the dimension threshold is set to 5, the K-Means algorithm can cluster the data into 3 groups, each representing a cost pattern, such as a low-cost pattern, a medium-cost pattern, etc.
[0059] The clustering process can be weighted according to the importance of cost dimensions, prioritizing the retention of key dimensions, reducing data complexity, and facilitating subsequent analysis. Based on the simplified cost combination data, the degree of matching between the data and overseas market conditions is determined, generating a matching score. For example, by comparing the simplified data with actual market needs (such as cost sensitivity and tax policies), a matching score is calculated. Market A might score 85 points, reflecting a high degree of suitability.
[0060] The scoring can be based on a weighted indicator system, comprehensively considering factors such as policy compliance and cost-effectiveness, to ensure that the score objectively reflects market fit. The final cost combination data is generated through the matching score.
[0061] The checklist can be broken down by market, making it easy for companies to quickly refer to and implement. This method effectively ensures that investments comply with local tax and financial requirements, guaranteeing tax compliance and cost control.
[0062] S4. Based on the construction cost adjustment coefficient and the work breakdown structure framework, determine the investment cost breakdown structure and cost combination.
[0063] For example, an investment cost breakdown structure and cost combination can be generated based on the work breakdown structure framework and construction cost adjustment coefficients, which include the proportions of installation costs for labor, materials, and machinery, as well as the multiplier of the comprehensive unit price for civil engineering.
[0064] In one embodiment, step S4 may include the following steps: S41, Obtain the hierarchical data of each construction task from the work breakdown structure framework, parse the association between task nodes and sub-nodes, and determine the task hierarchical decomposition structure.
[0065] S42, Based on the task hierarchy decomposition structure, extract the national construction cost adjustment coefficients corresponding to each task to obtain the cost coefficient combination.
[0066] S43, For the combination of cost coefficients, analyze the relationship between the proportion of labor cost, the proportion of material cost, and the proportion of machinery cost to obtain the cost proportion weights; wherein, the construction cost includes labor cost, material cost, and machinery cost.
[0067] S44. Based on the cost ratio weights and task hierarchy decomposition structure, generate a model of the investment cost decomposition structure, including the cost allocation of each task, and obtain the cost decomposition structure.
[0068] S45 extracts cost allocation data for each task from the investment cost breakdown structure, integrates the proportions of labor costs, material costs, and machinery costs, and generates cost combination proportions.
[0069] For example, in the cost breakdown of a construction project, the Work Breakdown Structure (WBS) is the core foundation, used to clarify task hierarchy and relationships. The WBS breaks down the project into manageable task units through a tree structure. Specifically, when introducing construction cost adjustment coefficients, the proportions of labor, material, and machinery costs under different quota numbers can be obtained from the CNOOC quota database. For example, the labor cost proportion for foundation treatment is 40%, the material cost proportion is 35%, and the machinery cost proportion is 25%. If the machinery cost proportion for a sub-task, such as foundation pouring, is missing, it can be supplemented based on the average value of similar tasks in historical data. For instance, assuming historical data shows that the machinery cost proportion is typically 20%, the missing value can be supplemented accordingly. This supplementation method relies on the regularity of historical data to ensure the completeness of the cost coefficient combination.
[0070] Excel's linear regression function can be used to analyze the relationships between cost proportions. For example, by collecting cost data from 10 similar projects and inputting the proportions of labor, materials, and machinery costs, a linear regression analysis reveals that labor costs have the greatest impact on the total cost, with a weight of 0.55, while materials and machinery have weights of 0.30 and 0.15, respectively. This weighting reflects the dominant role of labor costs in the project, providing a quantitative basis for cost allocation. For instance, based on cost proportion weights and task hierarchy decomposition structures, when generating a preliminary cost breakdown structure, a total investment of 10 million yuan can be allocated to each task according to its weight. The foundation treatment task might be allocated 3 million yuan, with labor costs accounting for 40% (1.2 million yuan), material costs accounting for 35% (1.05 million yuan), and machinery costs accounting for 25% (750,000 yuan). This allocation method clearly reflects the resource requirements of each task and effectively separates labor, material, and machinery costs; the detailed cost items can serve as a benchmark for project comparison.
[0071] Understandably, the advantages of the above method lie in ensuring the rationality and transparency of cost allocation through structured task decomposition and data-driven cost analysis, while improving analysis efficiency by utilizing WPS tools, thus providing reliable support for budget management of construction projects.
[0072] S5. Based on the aforementioned investment cost decomposition structure, construct a cash flow model.
[0073] For example, in some possible embodiments, step S5 may include the following steps: S51 determines the cost of each unit project by decomposing the investment cost structure and adjusting the personnel and machine costs, and then prices the unit project costs layer by layer to obtain the final project cost. Based on the project cost, the second category of costs and other costs are calculated to obtain the final construction investment.
[0074] S52, conduct cost and benefit analysis based on the cost results and the project's overseas business model.
[0075] S53. Determine the cash flow value at each time point to establish the cash flow series. Calculate the cash flow value at each time point using the WPS tool, applying the NPV formula to calculate the total value. NPV equals the sum of the cash flows for each period divided by the discount factor, generating the cash flow series.
[0076] For example, a cash flow model is built in WPS to calculate the cash flow value at each time point and apply the NPV (Net Present Value) formula to calculate the total value. Time point settings: The project cycle is 20 years (2 years of construction and 20 years of operation). The time series runs from year 0 (project start) to year 20 (operation end). Cash flow assumptions: Construction investment is evenly distributed during the construction period: 50% in year 1 and 50% in year 2. The operation period begins in year 3, using a processing trade model, generating a net income of $900,000 annually. The discount rate (r) is assumed to be 10% to reflect project risk and market conditions.
[0077] The discount factors are presented in the table below:
[0078] In Excel, use the NPV formula: =NPV(rate, value1, value2, ...), where rate = 10%, and value1 to value20 correspond to the cash flows for each year (from year 1 to 20).
[0079] The specific formula is: =NPV(10%, B2:B23), where B2:B23 is the cash flow sequence (cash flow values from year 1 to year 20).
[0080] The calculated total NPV is approximately -$105,000 (a negative value indicates that the project may incur a loss after discounting, but actual decisions need to take other factors into account).
[0081] Understandably, the above method involves analyzing the construction investment obtained through investment decomposition, pricing, and adjustments, combined with a business model analysis of costs and benefits, and finally calculating cash flow and NPV in WPS. This method ensures the accuracy and practicality of the cash flow model, providing a reliable basis for project investment decisions.
[0082] S6. Based on the obtained gas price fluctuation parameters and exchange rate fluctuation parameters, determine the risk parameters, adjust the cash flow model, and obtain the adjusted cash flow model.
[0083] For example, gas price fluctuation parameters, exchange rate fluctuation parameters, and operating cost growth rate are obtained; risk parameters are determined based on the gas price fluctuation parameters and the exchange rate fluctuation parameters; and the cash flow model is adjusted based on the risk parameters to obtain the adjusted cash flow model.
[0084] In some possible implementations, step S6 may include the following steps: S61. Obtain historical data on natural gas prices and foreign exchange rates from the database, and extract natural gas price fluctuation parameters and foreign exchange rate fluctuation parameters based on this. Time series decomposition can be performed using the pandas library (a core Python library specifically for handling structured data, especially tables and time series data) to extract natural gas price fluctuation parameters and foreign exchange rate fluctuation parameters.
[0085] S62. Based on natural gas price fluctuation parameters and foreign exchange rate fluctuation parameters, generate multiple risk scenarios to obtain risk assessment parameters. Multiple risk scenarios can be generated using, but are not limited to, Monte Carlo simulation algorithms to obtain risk assessment parameters.
[0086] S63 uses risk assessment parameters to perform sensitivity analysis in a pre-established cash flow forecasting model to identify key influencing factors.
[0087] S64. When the fluctuation range of key influencing factors exceeds the preset fluctuation threshold, the parameters of the pre-established cash flow forecasting model are adjusted by a linear regression algorithm to obtain a preliminary optimized cash flow forecasting model.
[0088] S65. Based on the preliminary optimized cash flow forecasting model, calculate the mean square error, evaluate the model's forecasting accuracy, and obtain the accuracy evaluation result.
[0089] S66. Based on the accuracy evaluation results, adjust the weight parameters of the initially optimized cash flow forecasting model to obtain the final optimized cash flow forecasting model.
[0090] S67. Based on the final optimized cash flow forecasting model, generate risk-adjusted cash flow forecasting data and determine business decision support parameters.
[0091] For example, in one possible implementation, when retrieving historical data on natural gas prices and foreign exchange rates from a database, one can use SQL (Structured Query Language) to query and extract the past five years' worth of natural gas price and USD / CNY exchange rate data from the company's internal energy trading database.
[0092] For example, assuming a database records daily natural gas prices (yuan / cubic meter) and exchange rates (USD / CNY), data from 2020 to 2025 can be filtered out, containing data points from approximately several trading days. For instance, natural gas prices might show seasonal fluctuations due to peak winter demand, the trend part reflecting long-term price increases, while the residual captures short-term market disturbances. Similarly, exchange rate data decomposition might reveal trend changes and short-term fluctuations caused by policy adjustments.
[0093] When generating risk scenarios using Monte Carlo simulation algorithms, but not limited to this, the volatility parameters obtained from decomposition can be used. For example, assuming the standard deviation of natural gas price volatility is 0.5 yuan / cubic meter and the standard deviation of exchange rate volatility is 0.02 yuan / dollar, several sets of random scenarios can be generated through Monte Carlo simulation. Each scenario includes a combination of natural gas prices and exchange rates for the next 12 months. Risk assessment parameters may include the 95% confidence interval for price volatility, such as natural gas prices potentially fluctuating between 8 and 12 yuan / cubic meter. This method can quantify potential risks and help companies predict funding needs. In sensitivity analysis, assuming the cash flow forecasting model uses natural gas prices and exchange rates as input variables, changes in net present value are observed by adjusting the variable values one by one. The results may show that for every 1 yuan / cubic meter increase in natural gas prices, cash flow decreases by 5%, while exchange rate fluctuations have a smaller impact, only 2%. If natural gas price volatility exceeds a preset impact threshold (e.g., 10%), the model parameters are adjusted using linear regression. Regression analysis may indicate that the weight of the impact of natural gas prices on cash flow needs to be increased from 0.6 to 0.7 to more accurately reflect market changes.
[0094] In one embodiment, when calculating the mean squared error using the sklearn library, it is assumed that the deviation between the initially optimized model's predicted cash flow and the actual value is approximately 5 million yuan. Error analysis reveals that the model is insufficient in capturing seasonal fluctuations. After adjusting the weight parameters, for example, increasing the weight of the seasonality factor from 0.3 to 0.4, the mean squared error may decrease to 3 million yuan, improving prediction accuracy. The final optimized model generates risk-adjusted cash flow forecast data, for example, predicting a positive cash flow of 100 million yuan for the next 12 months, with a confidence interval of 80 million to 120 million yuan. This data can support corporate decision-making, such as optimizing or adjusting financing strategies.
[0095] It should be noted that the generation of business decision support parameters depends on the robustness of the model.
[0096] For example, companies might decide to increase inventory when natural gas prices are low or lock in foreign exchange contracts in advance when exchange rates are favorable, based on forecast data. This approach enhances a company's ability to manage funds in complex market environments by integrating historical data analysis and simulation forecasts.
[0097] S7. Perform probability simulation analysis on the adjusted cash flow model to obtain the probability distribution of the internal rate of return, generate sensitivity analysis results based on the probability distribution, and form risk warnings based on preset risk thresholds.
[0098] In some embodiments, step S7 may include the following steps: S71: Obtain input data from the adjusted cash flow model and use Monte Carlo simulation to generate the probability distribution of the internal rate of return.
[0099] S72. Based on the generated probability distribution, determine the sensitivity of key parameters and obtain the sensitivity analysis results.
[0100] S73: If the sensitivity analysis results exceed the preset risk threshold, the parameter is marked as a high-risk parameter and a list of risk parameters is generated.
[0101] S74. Extract high-risk parameters from the list of risk parameters and use a decision tree algorithm to determine their potential impact on the internal rate of return.
[0102] S75 generates an early warning signal when the potential impact level corresponds to a high-risk level.
[0103] S76 uses early warning signals to adjust the parameters of the cash flow model and generate an optimized cash flow model.
[0104] For example, when obtaining input data from an adjusted cash flow model, core variables typically include natural gas prices, foreign exchange rates, and projected cash flow values. Suppose an energy company, when formulating its annual budget, extracts natural gas price data from the past five years from a database. The average price is $3.5 per million British thermal units (MMBtu), fluctuating between $2.8 and $4.2. The foreign exchange rate, using the USD / CNY exchange rate as an example, has an average of 6.8, fluctuating between 6.5 and 7.1. This data serves as input to a Monte Carlo simulation to generate the probability distribution of the internal rate of return (IRR). The Monte Carlo simulation generates several scenarios through random sampling, simulating combinations of natural gas price and exchange rate fluctuations, calculating the IRR for each scenario, and ultimately obtaining the probability distribution curve. For example, the IRR might be concentrated between 8% and 12%, with a probability of 70%, but in extreme cases it could be as low as 5% or as high as 15%.
[0105] In one possible implementation, when analyzing sensitivity coefficients using WPS tools, variables such as natural gas prices, exchange rates, and operating costs are varied one by one to observe their impact on the internal rate of return (IRR). It is assumed that a 10% change in natural gas prices results in a 2% change in IRR, and a 10% change in exchange rates results in a 1.5% change in IRR. A slope graph visually demonstrates this, identifying natural gas prices as the most sensitive parameter. If the sensitivity coefficient exceeds a preset risk threshold, such as 0.5, it is marked as a high-risk parameter, generating a list of risk parameters. This list may include natural gas prices and operating costs, while exchange rates are not marked due to their lower sensitivity.
[0106] Specifically, when using decision tree algorithms to determine the impact of high-risk parameters on the internal rate of return (IRR), a tree structure is constructed based on historical data. For example, the decision tree might show that when the natural gas price exceeds $4.0 and the exchange rate is below 6.6, the probability of the IRR being below 8% is 60%, which is classified as a high-risk level.
[0107] In one embodiment, adjusting the cash flow model parameters using early warning signals can optimize the weights of key variables. For example, the assumed range for natural gas price fluctuations can be reduced from ±20% to ±15% to decrease the model's sensitivity to extreme scenarios. Simultaneously, the weight of exchange rate hedging strategies can be increased, assuming the exchange rate is locked in at around 6.8 through forward contracts. The optimized cash flow model, in simulations, shows that the internal rate of return (IRR) fluctuation range is reduced to 7% to 13%, significantly improving stability. This adjustment ensures the model more closely reflects the actual business environment, providing more reliable support for decision-making.
[0108] It should be noted that the advantage of the above method lies in its multi-level analysis, from probability distribution to sensitivity analysis, then to risk analysis and model optimization, forming a closed-loop management system. The implementation of each step is closely aligned with the cash flow forecasting needs of the energy industry, combined with specific business scenarios, ensuring that the analysis results directly serve investment decisions or risk management.
[0109] For example, risk analysis can help companies proactively address risks, such as securing low-priced natural gas contracts, reducing cost fluctuation risks, and controlling the rate of increase in operating costs in advance. This approach enhances a company's adaptability in complex market environments through data-driven decision support.
[0110] This application also provides a template construction system for overseas LNG project investment and economic evaluation, combined with... Figure 3 The system includes the following modules: The data acquisition module is configured to acquire input data from overseas LNG projects and determine multi-scheme engineering quantity templates based on the input data.
[0111] The structure generation module is configured to divide unit projects and individual projects according to different technical solutions and determine the work breakdown structure framework.
[0112] The tax and finance adaptation module is configured to obtain overseas tax and finance policy data based on the input data, determine tax rate and fee rate parameters, and adapt the overseas market conditions based on the tax rate and fee rate parameters to obtain cost combination data.
[0113] The cost generation module is configured to determine the investment cost breakdown structure and cost combination based on the construction cost adjustment coefficient and the work breakdown structure framework.
[0114] The model building module is configured to build a cash flow model based on the investment cost decomposition structure.
[0115] The risk adjustment module is configured to determine risk parameters based on the obtained gas price fluctuation parameters and exchange rate fluctuation parameters, and adjust the cash flow model to obtain the adjusted cash flow model.
[0116] The analysis and early warning module determines the sensitivity analysis results based on the probability distribution of the obtained internal rate of return, and generates risk warnings based on preset risk thresholds.
[0117] The foregoing has described in detail several embodiments of this application, but this application is not limited to these specific embodiments. Those skilled in the art can make various variations and modifications based on the concept of this application, and all such variations and modifications should fall within the scope of protection claimed in this application.
Claims
1. A method for constructing an investment and economic evaluation template for overseas LNG projects, characterized in that, include: Obtain input data from overseas LNG projects and determine multi-scheme engineering quantity templates based on the input data; Divide the project into unit projects and individual projects according to different technical solutions, and determine the work breakdown structure framework; Based on the input data, overseas tax policy data is obtained, tax rate and fee rate parameters are determined, and the tax rate and fee rate parameters are adapted to overseas market conditions to obtain cost combination data. Based on the construction cost adjustment coefficient and the work breakdown structure framework, the investment cost breakdown structure and cost combination are determined. Based on the aforementioned investment cost decomposition structure, a cash flow model is constructed; Based on the obtained gas price fluctuation parameters and exchange rate fluctuation parameters, risk parameters are determined, and the cash flow model is adjusted to obtain the adjusted cash flow model. The adjusted cash flow model is subjected to probabilistic simulation analysis. Based on the obtained probability distribution of internal rate of return, the sensitivity analysis results are determined, and risk warnings are generated based on preset risk thresholds.
2. The method according to claim 1, characterized in that, The input data includes basic project information, country identification code, and basic investment and financial data. Based on the basic information of the project, a quantity calculation template is matched, and the quantity calculation template is decomposed using a probabilistic simulation optimization method to determine multiple possible quantity templates.
3. The method according to claim 1 or 2, characterized in that, The process of acquiring input data from overseas LNG projects and determining multi-scheme engineering quantity templates based on the input data includes: Basic project information, country identification codes, and investment financial data were obtained from overseas LNG projects. Data was cleaned to remove null values and formatted into the target format to obtain a structured input dataset. Based on the basic project information in the structured input dataset, match the existing engineering quantity calculation template library to determine the appropriate engineering quantity calculation template; The established engineering quantity calculation template is decomposed to obtain multi-scheme engineering quantity simulation data.
4. The method according to claim 1, characterized in that, The process of dividing unit projects and individual projects according to different technical solutions and determining the work breakdown structure framework includes: Based on the technical solution, the basis for dividing the work breakdown structure is obtained, and the unit project and individual project are determined. Based on the classification criteria, the technical solutions are decomposed to obtain an initial list that matches the technical solutions; Extract unit projects and individual projects from the initial list, construct a work breakdown structure hierarchy, determine the structural framework, and form the investment pricing cost composition and logic.
5. The method according to claim 1, characterized in that, Based on the input data, overseas tax policy data is obtained, tax rate and fee rate parameters are determined, and based on the tax rate and fee rate parameters, overseas market conditions are adapted to obtain cost combination data, including: Using the country identification code in the input data, overseas policy data is obtained from a preset fiscal and tax policy database to determine tax rate parameter values and fee rate parameter values; Based on the tax rate parameter value and the fee rate parameter value, determine the matching parameters that are appropriate for the overseas project; By integrating data, the adaptation parameters are combined with the cost calculation formula to generate initial cost combination data; Determine the degree of match between the initial cost mix data and the technical conditions of the overseas project; Based on the degree of matching, an optimization and adjustment scheme for the simplified cost combination data is determined, and the final cost combination data is generated. Based on the final cost combination data, a cost list adapted to the characteristics of the overseas solution is generated.
6. The method according to claim 1, characterized in that, Based on the construction cost adjustment coefficient and the aforementioned work breakdown structure framework, the investment cost breakdown structure and cost combination are determined, including: Obtain the hierarchical data of each construction task from the work breakdown structure framework, parse the association between task nodes and child nodes, and determine the task hierarchical decomposition structure. Based on the task hierarchy decomposition structure, the national construction cost adjustment coefficients corresponding to each task are extracted to obtain the cost coefficient combination. For the aforementioned cost coefficient combination, the relationship between the proportions of labor costs, material costs, and machinery costs is analyzed to obtain cost proportion weights; wherein, the construction costs include labor costs, material costs, and machinery costs; Based on the cost ratio weights and task hierarchy decomposition structure, a model for the investment cost decomposition structure is generated. The cost allocation data for each task is extracted from the investment cost breakdown structure, and the proportions of labor costs, material costs, and machinery costs are integrated to generate the cost combination ratio.
7. The method according to claim 1, characterized in that, The construction of the cash flow model based on the aforementioned investment cost decomposition structure includes: By decomposing the investment cost structure and adjusting the personnel and machine costs, the cost of each unit project is determined, and then the unit project cost is calculated layer by layer to obtain the final project cost. Based on the project cost, the second category of costs and other costs are calculated to obtain the final construction investment. Based on the cost estimates and the project's overseas business model, conduct a cost and benefit analysis. Determine the cash flow value at each time point to establish the cash flow sequence.
8. The method according to claim 1, characterized in that, The process of determining risk parameters based on the obtained gas price fluctuation parameters and exchange rate fluctuation parameters, and adjusting the cash flow model to obtain the adjusted cash flow model includes: Historical data on natural gas prices and foreign exchange rates are obtained from the database, and natural gas price fluctuation parameters and foreign exchange rate fluctuation parameters are extracted based on these data. Based on natural gas price fluctuation parameters and foreign exchange rate fluctuation parameters, multiple risk scenarios are generated to obtain risk assessment parameters. By using risk assessment parameters, sensitivity analysis is conducted in a pre-established cash flow forecasting model to identify key influencing factors. When the fluctuation range of key influencing factors exceeds the preset fluctuation threshold, the parameters of the pre-established cash flow forecasting model are adjusted by a linear regression algorithm to obtain a preliminary optimized cash flow forecasting model. Based on the preliminary optimized cash flow forecasting model, the mean square error is calculated, the forecasting accuracy of the model is evaluated, and the accuracy evaluation results are obtained. Based on the accuracy evaluation results, the weight parameters of the initially optimized cash flow forecasting model are adjusted to obtain the final optimized cash flow forecasting model. Based on the final optimized cash flow forecasting model, risk-adjusted cash flow forecast data is generated, and business decision support parameters are determined.
9. The method according to claim 8, characterized in that, The process involves determining the sensitivity analysis results based on the obtained internal rate of return probability distribution, and forming risk warnings based on preset risk thresholds, including: Input data is obtained from the adjusted cash flow model, and the probability distribution of the internal rate of return is generated using Monte Carlo simulation. Based on the generated probability distribution, the sensitivity of key parameters is determined, and the sensitivity analysis results are obtained. If the sensitivity analysis results exceed the preset risk threshold, the parameter is marked as a high-risk parameter, and a list of risk parameters is generated. High-risk parameters are extracted from the list of risk parameters, and a decision tree algorithm is used to determine their potential impact on the internal rate of return. When the potential impact level corresponds to a high-risk level, an early warning signal is generated; By using early warning signals, the parameters of the cash flow model are adjusted to generate an optimized cash flow model.
10. A template construction system for overseas LNG project investment and economic evaluation, characterized in that, include: The data acquisition module is configured to acquire input data from overseas LNG projects and determine multi-scheme engineering quantity templates based on the input data. The structure generation module is configured to divide unit projects and individual projects according to different technical solutions and determine the work breakdown structure framework. The tax and finance adaptation module is configured to obtain overseas tax and finance policy data based on the input data, determine tax rate and fee rate parameters, and adapt overseas market conditions based on the tax rate and fee rate parameters to obtain cost combination data. The cost generation module is configured to determine the investment cost breakdown structure and cost combination based on the construction cost adjustment coefficient and the work breakdown structure framework. The model building module is configured to build a cash flow model based on the investment cost decomposition structure. The risk adjustment module is configured to determine risk parameters based on the obtained gas price fluctuation parameters and exchange rate fluctuation parameters, and adjust the cash flow model to obtain the adjusted cash flow model. The analysis and early warning module determines the sensitivity analysis results based on the probability distribution of the obtained internal rate of return, and generates risk warnings based on preset risk thresholds.