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High risk is one of the most distinguishing features of international oil and gas (OG) projects. Suslick and Schiozer (2004) and Welkenhuysen et al. (2017) suggested that geological risk, economic risk and engineering risk should all be considered because they influence the exploration and development of OG projects. As both geological risk and engineering risk affect the uncertainty of volumes and production plans of oil and gas projects, we propose to use resource risks to represent geological risk and engineering risk. Besides, Liu et al. (2012) and Zhu et al. (2015) mentioned that policy risk, especially fiscal risk, has important impacts on international OG projects. Moreover, Zhang et al. (2012) and Wang and Zhang (2012) proposed that resource risks, economic risks and policy risks should be considered in the evaluation process of international OG projects. In summary, there are three types of risks for an operating international OG project: (1) resource risks. They mainly influence the production of the projects, and include geological structure, recovery ratio, resource quality, production planning and so on. They can be classified into geological risk, resource acquisition risk, and engineering risk. The geological risk is related to underground structures; resource acquisition risk is related to OG quality and difficulties of exploitation, both of them have impacts on the volumes of OG resources. Meanwhile, engineering risk is related to engineering technology, production planning and other factors; they have impacts on the volumes of produced OG. (2) Economic risks. They affect the costs and prices of the product, and contain production costs, operating costs, OG prices, exchange rates and so on. They influence the net revenues of OG projects. (3) Policy risks. They impact the projects from different aspects-policy risks may influence the project revenues or the project costs, and may even affect the demand and supply of the product. They consist of fiscal policies, nationalization policies and so forth; among which fiscal policies affect the revenue distribution of OG projects between resource countries and OG companies, and nationalization policies affect the ownership of OG projects. These risk factors exist in the whole project life cycle and always affect the project values.
International Valuation, Modelling and Project ...
Apart from high risk, international OG projects have other features: (1) international OG projects are featured by high investment and high returns. Investment is enormous for international OG projects, frequently more than 10 million US dollars. Meanwhile, the potential returns are also substantial for international OG projects, usually as high as 100 million dollars. Besides, there are over a dozen factors that have impacts on the project returns, among which several are risk factors. Therefore, the impacts of risk factors on project values is relatively significant for international OG projects, which make it urgent to measure the risks of international OG projects. (2) The revenue distribution of international OG projects depends on contract types. The revenue distribution is different for royalty contracts, production share contracts, and service contracts. Therefore, contract diversity complicates the risk measurement of international OG projects. What is more, it makes this study more valuable. In summary, it is essential to study the impacts of risk factors on the values of international OG projects, aiming to help decision makers make more reasonable decisions given the uncertainties.
Studies of the policy evaluation mainly evaluate the policies which influence the net present value (\(NPV\)) of OG projects. Liu et al. (2012) applied a probabilistic model to thoroughly study the impacts of key fiscal terms of a production share contract on the values of OG projects under oil price uncertainty. Wang et al. (2012) applied a probabilistic model to analyze the impacts of the key terms in a royalty contract on the \(NPV\) of an international OG projects. Besides, other scholars applied a probabilistic model to production prediction (such as Rivera et al. (2007)), to well construction (such as McIntosh (2004) and Adams et al. (2010)) and so on.
In summary, although the probabilistic model is widely applied in OG studies, most of the previous studies focus on resources indicators, especially the papers published by SPE, and aim to estimate the technically recoverable resources. Other studies applied the probabilistic model to investigate the impacts of risk factors on the \(NPV\) of the OG projects. However, they lack a standard research framework. Liu et al. (2012) and Wang et al. (2012) conducted similar studies, however, they confined their studies to a certain fiscal system, and they mainly studied the impacts of oil uncertainty. In order to propose a research framework and to comprehensively measure the risks of international OG projects, this paper chooses risk factors from resource uncertainties, economic uncertainties, and policy uncertainties, then simulates the risk factors based on a traditional \(NPV\) model and compares the impacts of different risk factors on the \(NPV\) based on the proposed research framework.
It is necessary and meaningful to quantitatively estimate the risks of international OG projects and to study the impacts of different risk factors. Therefore, in order to address these issues, this paper proposes a research framework (the probabilistic model), in which a Monte Carlo Simulation method and VaR are applied. The probabilistic model provides the distribution of \(NPV\) and VaR, therefore it is helpful for decision makers to acknowledge the risks of international OG projects and make decisions based on this information. The contribution of this study is that it considers the features of the appraisal of international OG projects, and introduces VaR to the research framework of the probabilistic model, which provides a new method for the risk measurement of international OG projects.
There are many factors which influence the values of international OG projects, such as resource volume, production curve, investment, operation expenditure, product prices, and taxes. These factors can be divided into two categories, namely the fixed variables and stochastic variables. Fixed variables refer to the certain factors, whose values do not change in the future. Stochastic variables refer to the uncertain factors, whose values are not fixed and may change in the future. Distributions (like the normal distribution, triangle distribution, and uniform distribution) and corresponding parameters (the mean, the standard deviation (Std)) can be used to describe stochastic variables, and historical data or available data can be used to fit the distributions for stochastic variables.
Parameters in the traditional \(NPV\) model are assumed to be fixed variables. Therefore, the assumptions in the traditional \(NPV\) model are rigid, and they cannot reflect the uncertain factors embedded in the projects. However, parameters in the probabilistic models are divided into fixed variables and stochastic variables, and the Monte Carlo Simulation method is applied to simulate the stochastic parameters. The impacts of stochastic variables on the values of international OG projects are studied, therefore the probabilistic model can overcome the inherent defects of the traditional \(NPV\) model. The Monte Carlo Simulation method can comprehensively measure and analyze the stochastic characters of risk factors of international OG projects, therefore it is proper to apply the Monte Carlo Simulation method to measure the impacts of uncertain factors (Falconett and Nagasaka 2010; Montes et al. 2011; Welkenhuysen et al. 2017).
In order to analyze the impacts of risk factors on the values of international OG projects, this paper proposes the analytical framework of the probabilistic model, which is shown in Fig. 1. @RISK software from Palisade (USA) is applied to conduct the Monte Carlo Simulation. After 10,000 iterations, the distribution histogram of the \(NPV\) is obtained, and the probability density curve is acquired. VaR can be applied to measure the probability that the \(NPV\) is higher than a certain threshold. Besides, different confidence levels can be selected to measure the thresholds of different risk levels, which can comprehensively measure the risks of international OG projects.
A probabilistic model is based on the \(NPV\) model, while economic evaluation of international OG projects is influenced by many factors, such as production scheme, oil prices, costs, fiscal terms, project revenue, and taxes. Therefore, we set up different models to depict the above-mentioned factors to construct the economic evaluation model of international OG projects. Then, we divide the factors into stochastic variables and fixed variables, and we finally employ @RISK to simulate the stochastic variables.
In order to conduct the empirical analysis, an international oil project is used as an example in this paper. Three economic evaluation models and corresponding probabilistic models of the different fiscal systems are established in Excel. @RISK is applied to conduct Monte Carlo Simulation, and \(NPV\) frequency histograms of the OG company in different fiscal systems are obtained, and then project risks are measured.
Suppose that the international oil project is a new project for the OG company, its life cycle is 11 years.Footnote 1 Given that international OG projects face resource risks and economic risks, oil originally in place (\(OOIP\)), variable \(Opex\) (\(Opex\) per barrel), and oil price are assumed to be stochastic variables. As for the policy risks, royalty rate, cost recovery rate, and compensation rate are selected as stochastic variables in the royalty contract, production share contract, and service contract, respectively, because these three variables are the core fiscal terms in the three fiscal systems. As for \(OOIP\), with reference of Jakobsson et al. (2012), we assume it follows a lognormal distribution with a range between 0 and positive infinity. As for \(Opex\) per barrel, with reference of Falconett and Nagasaka (2010), we assume it follows a triangular distribution. As for royalty rate, cost recovery rate, and compensation rate, we assume they follow uniform distributions. As for oil price, we assume it is a Mean-Reverting Process. Stochastic variables and their assumptions are listed in Table 1. 041b061a72