A method, system and storage medium for economic evaluation of oil and gas field development based on Monte Carlo simulation

Abstract

The application relates to a Monte Carlo simulation-based oil and gas field development economic evaluation method, which comprises the following steps: (1) determining key uncertainty parameters in oil and gas field development, including oil and gas reservoir reserves, initial productivity, decline rate, engineering investment, oil and gas sales price and production operation cost; (2) giving each key parameter a probability distribution model, wherein the oil and gas reservoir reserves are based on a reserve evaluation model established by a volumetric method; (3) based on the probability distribution model, an economic evaluation model is constructed by combining a cash flow method; (4) the model is randomly sampled by Monte Carlo simulation to generate probability distribution results of internal rate of return (IRR) and net present value (NPV); and (5) according to the probability distribution results, the economic risks and benefits of the oil and gas field development project are quantified.

Description

Technical Field

[0001] This application relates to the fields of oil and gas field development and economic assessment, and in particular to a method, system and storage medium for economic assessment of oil and gas field development based on Monte Carlo simulation. Background Technology

[0002] Oil and gas field development is a complex system engineering project involving multiple stages such as geological exploration, engineering design, production operation, and market forecasting. Its core objective is to achieve efficient resource extraction and maximize economic benefits. However, due to the complexity of oil and gas resource occurrence conditions and the variability of the market environment, the development process faces multiple uncertainties and risks, which places extremely high demands on the accuracy and reliability of economic evaluation models.

[0003] Traditional economic assessment methods are primarily based on the discounted cash flow (DCF) approach, which assesses a project's economic feasibility by forecasting future cash flows and discounting them to their present value. However, traditional methods have significant limitations in practical applications, such as strong subjectivity in parameter estimation, insufficient risk quantification, and poor dynamic adaptability, making them unsuitable for the complex needs of long-term development projects.

[0004] Therefore, there is an urgent need for a refined economic assessment model tailored to the characteristics of oil and gas field development. This model should integrate multi-source data, optimize parameter probability distribution modeling, and introduce dynamic Monte Carlo simulation methods to quantify economic risks and returns throughout the entire life cycle, providing a scientific basis for investment decisions. Summary of the Invention

[0005] To address the aforementioned problems, this application provides an economic evaluation method for oil and gas field development based on Monte Carlo simulation, comprising the following steps:

[0006] (1) Determine the key uncertainty parameters in oil and gas field development, including oil and gas reservoir reserves, initial production capacity, deceleration rate, engineering investment, oil and gas sales price and production operation cost;

[0007] (2) Assign probability distribution models to each key parameter, and base the oil and gas reservoir reserves on the volumetric method. A reserve assessment model is established, where Ao represents the oil-bearing area and H represents the effective thickness. Let Ao be porosity, So be oil saturation, Bo be the original crude oil volume factor, and Ao, H be... So and Bo all conform to a normal distribution N(μ,σ2), where μ is the mean and σ2 is the variance.

[0008] The initial capacity and the rate of decrease are set as a triangular distribution based on historical experience data;

[0009] The project investment is characterized by a uniform distribution.

[0010] The oil and gas sales prices are predicted based on a geometric Brownian motion model to predict the probability distribution of future prices.

[0011] The production operation costs are characterized based on a triangular distribution.

[0012] (3) Based on the probability distribution model, an economic evaluation model is constructed by combining the cash flow method;

[0013] (4) Random sampling is performed on the model using Monte Carlo simulation to generate the probability distribution results of the internal rate of return (IRR) and net present value (NPV);

[0014] (5) Based on the probability distribution results, quantify the economic risks and benefits of oil and gas field development projects.

[0015] Optionally, the triangular distribution of the initial capacity and the rate of deceleration is defined by the minimum, maximum and most likely values, and the production profile is generated by Monte Carlo simulation.

[0016] Optionally, the geometric Brownian motion model of the oil and gas sales price satisfies the formula ln(P) i+1 / P i )~N(μ2,σ2 2 ), where Pi is the oil and gas price in the i-th period, and the mean μ2 and variance σ2 of the normal distribution are determined by fitting historical crude oil sales price data.

[0017] Optionally, the Monte Carlo simulation involves at least 10,000 samplings to ensure the statistical significance of the probability distribution.

[0018] Optionally, the economic assessment results include: a probability distribution diagram of the internal rate of return (IRR); a probability distribution diagram of the net present value (NPV); the P50 value under risk-neutral conditions and the range of returns within a pre-set confidence interval.

[0019] On the other hand, this application provides an economic evaluation system for oil and gas field development based on Monte Carlo simulation, including:

[0020] The parameter input module is used to receive key uncertainty parameters for oil and gas field development, including oil and gas reservoir reserves, initial production capacity, deceleration rate, engineering investment, oil and gas sales price, and production operation cost.

[0021] The probability distribution modeling module is used to assign probability distribution models to various parameters, including: the calculation of oil and gas reservoir reserves is based on the volumetric method formula. Where Ao is the oil-bearing area and H is the effective thickness. Let Ao be porosity, So be oil saturation, Bo be the original crude oil volume factor, and Ao, H be... All three gas types (So, Bo, and O) follow a normal distribution N(μ,σ²); initial production capacity and deceleration rate are distributed triangularly; engineering investment is uniformly distributed; oil and gas sales prices are predicted based on a geometric Brownian motion model; and production operating costs are distributed triangularly.

[0022] The economic evaluation module constructs an economic evaluation model based on the probability distribution model and the cash flow method.

[0023] The Monte Carlo simulation module is used to randomly sample the model and generate probability distributions for the internal rate of return (IRR) and net present value (NPV).

[0024] The results output module is used to display the probability distribution results and quantify the economic risks and returns of the project.

[0025] Optionally, the Monte Carlo simulation module performs no fewer than 10,000 samplings to ensure statistical significance.

[0026] On the other hand, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the Monte Carlo simulation-based economic assessment method for oil and gas field development as described in any of the foregoing embodiments.

[0027] The technical solutions provided in this application embodiment may include the following beneficial effects:

[0028] This invention uses Monte Carlo simulation to quantify and analyze key risks in oil and gas field development (such as geological resource risk, production risk, engineering investment risk, and market risk), providing a clear and intuitive display of the probability distribution of project returns and net present value. This offers a more scientific and reliable basis for oil and gas field development decisions, helping investors and managers make more informed decisions in high-risk environments.

[0029] This invention, by assigning probability distributions to the evaluation parameters and combining them with Monte Carlo random simulation, can more accurately reflect the uncertainty of each parameter, thereby improving the accuracy and reliability of economic evaluation.

[0030] This invention comprehensively considers various uncertainties in the oil and gas field development process by establishing multiple sub-modules such as reserve assessment, production forecasting, investment estimation, price forecasting, and cost modeling, making the assessment results more comprehensive and objective.

[0031] This invention can not only assess the expected returns of a project, but also reveal the potential risks and return range of the project through probability distribution analysis. For example, by using the probability distributions of the internal rate of return (IRR) and net present value (NPV), the economic viability of the project under different risk levels can be clearly demonstrated, providing richer information for the economic analysis of the project. Attached Figure Description

[0032] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0033] Figure 1 This is a schematic diagram of an economic evaluation method for oil and gas field development based on Monte Carlo simulation, provided in an exemplary embodiment of this application.

[0034] Figure 2 This is a schematic diagram of an economic evaluation model for oil and gas field development based on Monte Carlo stochastic simulation provided in an exemplary embodiment of this application;

[0035] Figure 3 is a probability distribution diagram of IRR and NPV of an oil field provided in an exemplary embodiment of this application. Detailed Implementation

[0036] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of systems and methods consistent with some aspects of this application as detailed in the appended claims.

[0037] Please see Figure 1 This is a schematic diagram of an economic assessment method for oil and gas field development based on Monte Carlo simulation, provided in an exemplary embodiment of this application. The method includes the following steps:

[0038] (1) Determine the key uncertainty parameters in oil and gas field development, including oil and gas reservoir reserves, initial production capacity, deceleration rate, engineering investment, oil and gas sales price and production operation cost;

[0039] (2) Assign probability distribution models to each key parameter, and base the oil and gas reservoir reserves on the volumetric method. A reserve assessment model is established, where Ao represents the oil-bearing area and H represents the effective thickness. Let Ao be porosity, So be oil saturation, Bo be the original crude oil volume factor, and Ao, H be... So and Bo all conform to a normal distribution N(μ,σ2).

[0040] The initial capacity and the rate of decrease are set as a triangular distribution based on historical experience data;

[0041] The project investment is characterized by a uniform distribution.

[0042] The oil and gas sales prices are predicted based on a geometric Brownian motion model to predict the probability distribution of future prices.

[0043] The production operation costs are characterized based on a triangular distribution.

[0044] (3) Based on the probability distribution model, an economic evaluation model is constructed by combining the cash flow method;

[0045] (4) Random sampling is performed on the model using Monte Carlo simulation to generate the probability distribution results of the internal rate of return (IRR) and net present value (NPV);

[0046] (5) Based on the probability distribution results, quantify the economic risks and benefits of oil and gas field development projects.

[0047] like Figure 2 The present application discloses an exemplary embodiment of an economic evaluation model for oil and gas field development based on Monte Carlo stochastic simulation. The evaluation model consists of multiple sub-functional modules, including a reserves assessment module, a production forecasting module, an investment estimation module, a price forecasting module, a cost modeling module, and an economic evaluation module. These modules cooperate to jointly complete the economic evaluation of the project.

[0048] Specifically, the reserves assessment module is the foundational part of the model, used to evaluate the size and uncertainty of oil and gas reservoirs. This module establishes a reserves assessment model based on the volumetric method, with the following formula:

[0049] N = 100 × Ao × H × φ × So / Bo

[0050] in:

[0051] N represents oil and gas reserves;

[0052] Ao represents the oil-bearing area;

[0053] H is the effective thickness;

[0054] φ represents porosity;

[0055] So represents the oil saturation level;

[0056] Bo is the volume coefficient of the original crude oil.

[0057] These parameters all conform to a normal distribution, specifically N(μ,σ). 2 ), where μ is the mean and σ is the mean. 2 The variance is used. The distribution characteristics of these parameters can be determined using historical data and geological exploration data.

[0058] The production prediction module is used to establish production profiles for oil and gas reservoirs. The establishment of these profiles mainly involves two parameters: initial production capacity and decline rate. When data is abundant, the test recovery index method and reservoir simulation method are commonly used; however, when test data is limited, the relevant parameters have significant uncertainties. The model establishes the triangular probability distribution characteristics of the production rate [U(min,max,maxPro)], the initial decline rate [R1(min,max,maxPro)], and the mid-to-late stage decline rate [R2(min,max,maxPro)] based on oilfield empirical data, and then uses the Monte Carlo method to establish the oil and gas reservoir production profile.

[0059] By using Monte Carlo simulation and combining the probability distribution of the above parameters, a production profile of the oil and gas reservoir is generated, providing production data for subsequent economic assessment.

[0060] The investment estimation module is used to estimate the range of investment amounts for oil and gas field development. Based on historical experience and industry data, this module uses a uniform distribution CA(a,b) to characterize the uncertainty of the investment amount, where a and b are the minimum and maximum values ​​of the investment amount, respectively.

[0061] The price forecasting module is used to predict future trends in oil and gas sales prices. This module is based on a geometric Brownian motion model, assuming that the rate of price change follows a normal distribution, specifically in the form: ln(Pi+1 / Pi)~N(μ2,σ2). 2 ), where Pi is the current oil price, Pi+1 is the future oil price, and μ2 and σ2 2 These represent the mean and variance of the rate of price change, respectively. Monte Carlo simulation is used to generate a probability distribution of future oil prices, providing price inputs for economic assessment.

[0062] The cost modeling module is used to build a model of production operation costs. Based on empirical data, this module uses a triangular distribution Ap(min,max,most probable) to represent the uncertainty of production operation costs.

[0063] The economic evaluation module is the core of the model. It integrates the outputs of the modules mentioned above and estimates the project's rate of return and net present value through Monte Carlo random simulation. Based on the cash flow method, this module inputs the probability distributions of each parameter into the economic evaluation model to generate the probability distributions of the internal rate of return (IRR) and net present value (NPV).

[0064] Monte Carlo simulations can reveal the probability distribution characteristics of a project's rate of return and net present value (NPV), such as the range of the project's rate of return within a certain confidence interval and the distribution of NPV under different discount rates. These results provide a quantitative basis for the economic evaluation of a project.

[0065] The following are specific examples of methods for conducting economic assessments of oil and gas field development using the model of this invention:

[0066] (1) Based on the specific conditions of the oil and gas field, collect relevant data and determine the probability distribution characteristics of each parameter in the reserve assessment module, production prediction module, investment estimation module, price prediction module, and cost modeling module. For example:

[0067] · Normal distribution parameters of oil-bearing area Ao, effective thickness H, porosity φ, oil saturation So, and original crude oil volume factor Bo;

[0068] · Triangular distribution parameters of oil production rate U, initial decline rate R1, and mid-to-late stage decline rate R2;

[0069] · Uniform distribution parameters of investment amount range;

[0070] · Geometric Brownian motion parameters of the rate of change in oil and gas sales prices;

[0071] · Triangular distribution parameters of production operation costs.

[0072] (2) Based on the principles of the cash flow method and combining the outputs of the above modules, an economic evaluation model is established. This model generates the probability distribution of the project's rate of return and net present value through Monte Carlo random simulation.

[0073] (3) Through Monte Carlo simulation, multiple random samples are taken from the project's rate of return and net present value to generate probability distribution curves. Based on the simulation results, the project's economic viability is analyzed, for example:

[0074] · The range of project rate of return within a certain confidence interval;

[0075] · Distribution of net present value at different discount rates;

[0076] · The project's risk level and potential returns.

[0077] (4) Based on the simulation results, provide a scientific basis for investment decisions in oil and gas field development projects. For example, by analyzing the probability distribution of rate of return and net present value, determine the feasibility and risk level of the project, and provide guidance for the implementation of the project.

[0078] Specifically, as shown in Figure 3, taking a marginal oilfield development project in the East China Sea as an example, the application process of the model of this invention is explained in detail. Figure 3A This represents the probability distribution of the internal rate of return. Figure 3B This represents the probability distribution of net present value.

[0079] (1) Parameter settings

[0080] Based on the historical data and geological exploration data of the oil field, the parameter distribution of each module is set:

[0081] · Oil-bearing area Ao (km) 2 ): Normal distribution N(0.79, 0.3) 2 );

[0082] · Effective thickness H (m): Normal distribution N (4.4m, 2.3m). 2 );

[0083] · Porosity φ: Normal distribution N(0.21, 0.033) 2 );

[0084] · Oil saturation So: Normal distribution N(0.494, 0.125) 2 );

[0085] · The volume factor Bo of the original crude oil: normally distributed N(1.27, 0.1). 2 );

[0086] · Oil production rate U: triangular distribution (3%, 10%, 7%);

[0087] · Initial decline rate R1: triangular distribution (10%, 30%, 25%);

[0088] · Mid-to-late stage decline rate R²: triangular distribution (3%, 10%, 5%);

[0089] · Investment amount range: uniformly distributed CA (150 million yuan, 200 million yuan);

[0090] · Rate of change in oil and gas sales prices: Geometric Brownian motion μ2=0.0377,σ2 2 =0.0472;

[0091] · Production operating costs: triangular distribution Ap ($20 / barrel, $35 / barrel, $25 / barrel).

[0092] (2) Simulation and Evaluation

[0093] Monte Carlo simulation was used to randomly sample the project's rate of return and net present value, generating probability distribution curves. The simulation results show:

[0094] · The project's rate of return ranges from 19% to 147% within an 80% confidence interval;

[0095] · The net present value ranges from -7.227 million yuan to 846.93 million yuan at a discount rate of 12%.

[0096] · Under risk-neutral conditions, the P50 value is IRR = 69%, and NPV@12% = 282.65 million yuan.

[0097] As can be seen from the above, the model is more effective than traditional assessments and more directly reflects the project's risks and potential.

[0098] This invention, based on extensive empirical data regression and combining the principles of the cash flow method with Monte Carlo simulation, makes the evaluation process more scientific and objective. Compared with traditional methods, this invention can better reflect the dynamic changes and uncertainties in the oil and gas field development process, providing a more scientific tool for project economic evaluation. Through the model of this invention, project managers can identify potential risks in advance and formulate corresponding risk response strategies. This helps optimize project management processes, reduce uncertainties during project implementation, and improve overall project management efficiency.

[0099] This invention can more accurately assess the economic benefits and risks of oil and gas field development projects, help to rationally plan oil and gas field development projects, avoid blind investment and waste of resources, and thus promote the sustainable development of oil and gas field development.

[0100] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered illustrative only, and the true scope and spirit of this application are indicated by the following claims.

[0101] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. An economic evaluation method for oil and gas field development based on Monte Carlo simulation, characterized in that, Includes the following steps: (1) Determine the key uncertainty parameters in oil and gas field development, including oil and gas reservoir reserves, initial production capacity, deceleration rate, engineering investment, oil and gas sales price and production operation cost; (2) Assign probability distribution models to each key parameter, and base the oil and gas reservoir reserves on the volumetric method. A reserve assessment model is established, where Ao represents the oil-bearing area and H represents the effective thickness. Let Ao be porosity, So be oil saturation, Bo be the original crude oil volume factor, and Ao, H be... So and Bo both conform to a normal distribution N(μ,σ2), where μ is the mean and σ2 is the variance; The initial capacity and the rate of decrease are set as a triangular distribution based on historical experience data; The project investment is characterized by a uniform distribution. The oil and gas sales prices are predicted based on a geometric Brownian motion model to predict the probability distribution of future prices. The production operation costs are characterized based on a triangular distribution. (3) Based on the probability distribution model, an economic evaluation model is constructed by combining the cash flow method; (4) Random sampling is performed on the model through Monte Carlo simulation to generate the probability distribution results of internal rate of return (IRR) and net present value (NPV); (5) Based on the probability distribution results, quantify the economic risks and benefits of oil and gas field development projects.

2. The method according to claim 1, characterized in that, The triangular distribution of the initial capacity and the rate of decrease is defined by the minimum, maximum and most likely values, and the production profile is generated by Monte Carlo simulation.

3. The method according to claim 1, characterized in that, The geometric Brownian motion model of the oil and gas sales price satisfies the formula ln(P) i+1 / P i )~N(μ2,σ2 2 ), where Pi is the oil and gas price in the i-th period, and the mean μ2 and variance σ2 of the normal distribution are determined by fitting historical crude oil sales price data.

4. The method according to claim 1, characterized in that, The Monte Carlo simulation involves at least 10,000 samplings to ensure the statistical significance of the probability distribution.

5. The method according to claim 1, characterized in that, The economic assessment results include: Probability distribution diagram of internal rate of return (IRR); Probability distribution diagram of Net Present Value (NPV); The P50 value and the range of returns within the pre-set confidence interval under risk-neutral conditions.

6. An economic evaluation system for oil and gas field development based on Monte Carlo simulation, characterized in that, include: The parameter input module is used to receive key uncertainty parameters for oil and gas field development, including oil and gas reservoir reserves, initial production capacity, deceleration rate, engineering investment, oil and gas sales price, and production operation cost. The probability distribution modeling module is used to assign probability distribution models to various parameters, including: the calculation of oil and gas reservoir reserves is based on the volumetric method formula. Where Ao is the oil-bearing area and H is the effective thickness. Let Ao be porosity, So be oil saturation, Bo be the original crude oil volume factor, and Ao, H be... Both So and Bo follow a normal distribution N(μ,σ²), where μ is the mean and σ² is the mean. 2 Variance is used; initial production capacity and deceleration rate are triangularly distributed; engineering investment is uniformly distributed; oil and gas sales prices are predicted based on a geometric Brownian motion model; production operating costs are triangularly distributed. The economic evaluation module constructs an economic evaluation model based on the probability distribution model and the cash flow method. The Monte Carlo simulation module is used to randomly sample the model and generate probability distribution results for the internal rate of return (IRR) and net present value (NPV). The results output module is used to display the probability distribution results and quantify the economic risks and returns of the project.

7. The system according to claim 6, characterized in that, The Monte Carlo simulation module performs no fewer than 10,000 samplings to ensure statistical significance.

8. A computer-readable storage medium storing a computer program, characterized in that, When the program is executed by the processor, it implements the economic assessment method for oil and gas field development based on Monte Carlo simulation as described in any one of claims 1-5.

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