|Fermi Dwi Wicaksono||Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, 76100, Malaysia|
|Yusri Bin Arshad||Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, 76100, Malaysia|
|Haeryip Sihombing||Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, 76100, Malaysia|
This paper presents a techno-economic analysis for oil and gas production sharing contract (PSC) which is subjected to uncertainty from fluctuation of natural gas prices and production reservoir capacity. Net present value (NPV) is calculated based on a 10-year contract duration considering capital-operational expenditure, production sharing contract bidding value, and salvage value. The Monte Carlo method is embedded in the NPV analysis to quantify the probability of the production sharing contract’s profit and loss. The result of this probability is utilized as input for determining the decision to acquire the PSC. This paper confirms that investment in the oil and gas industry is high risk. This type of investment is only suitable for companies with strong equity or financial power.
Monte Carlo method; Net present value; Production sharing contract
Techno-economic analysis is a decision process to determine the value of a long-term investment in a project. The decision making process should be based on maximum equity return from an investment. Oil and gas is a risky industry. This kind of business is subjected to major uncertainty, sophisticated technology, and high capital investment. Cheng et al. (2018) states that “oil and gas play a pivotal role in the modern industry, and oil and gas demand is closely related to economic development.” The demand for oil and gas production is in correlation with the growth of the transportation, residential, and industrial sectors (Silitonga et al., 2012). Atabani et al. (2012) states that “energy consumption has grown rapidly in the recent years”. Therefore, techno-economic analysis for an oil and gas project becomes crucial to determine whether it shall be undertaken or not.
Several studies have been conducted to analysis the technique for optimizing project investment portfolios (Arifin et al., 2015). Net present value is one of the most powerful tools used in techno-economic analysis (Shaffie & Jaaman, 2015). Net present value calculates the present value of future cash flows and, when compared with initial outflows, an investment project is seen as acceptable whenever a positive NPV is the outcome. The internal rate of return (IRR) is a percentage rate that equates the present value of future cash inflows with the present value (Bennouna et al., 2010). Both NPV and IRR are widely used to determine capital investment decisions. However, the usage of this technique has limitations in calculating high risks capital investment projects, such as those in the oil and gas industry. The NPV technique only focuses on current predictable cash flows and ignores the future risk of uncertainty, therefore, may undervalue the production sharing contract and mislead the decision makers (Ho & Liao, 2011). The improvement of conventional NPV techniques should be performed in order to quantify the capital investment risk (Arnold & Yildiz, 2015; Rout et al., 2018). A risk-quantified methodology is required to reduce probability of failure in both operational and financial aspects of the project (Hidayanto et al., 2015).
The Monte Carlo method was first used in 1960, and it was extended to simulate uncertainty in financial applications (Shaffie & Jaaman, 2015). The Monte Carlo method has been implemented in several techno-economic analyses for quantifying financial risks. Arnold and Yildiz (2015) present an analysis to determine the financial risk for investors of renewable bio-energy projects. Monte Carlo methods are also implemented for economic evaluation of a Photovoltaic/Thermal concentrator in Sweden (Gu et al., 2018) and an uncertainty analysis for hydrogen production from high pressure polymer electrolyte membrane water electrolysis in Korea (Lee et al., 2017). The Monte Carlo method is able to quantify risk analysis by adopting random numbers in the probability distribution to generate the possibility of uncertainty in net present value (Verbeteen, 2006; Huang, 2008; Nawrocki, 2001; Shaffie & Jaaman, 2015). The novelty of this paper is in its ability to quantify risk as an exact number. The obtained probability then results as a risk percentage of loss, which is embedded into the decision tree analysis. The comprehensive integration of Monte-Carlo NPV and decision tree analysis gives both scientific and practical guidance for the decision making process.
Based on the analysis that has been performed, it is confirmed that oil and gas industries are high-risk investments. As a basic economic principle, high risk investments yield high potential returns. Based on the NPV analysis, with 11% rate of return, the Constellation-x PSC shows a positive value. The internal rate of return calculated is at 15.6%, taking into account the salvage value, and 13.07% without considering the salvage value. This number is categorized as a high return investment. As a comparison, most bank obligations only promise to have returns of 3.35%-5.05%. This paper has also calculated the value of positive NPV over1,000 iterations using the Monte Carlo methodology. At this point, the probability of loss is quite high (82.15%). In conclusion, this type of investment is only suitable for a company with strong equity or financial power. This analysis also proves that the oil and gas industry provides high risk – high returns investments.
The analysis presented in this paper reveals that quantified risks and uncertainties come from two parameters: reservoir production and the US natural gas price. The analytical results are expected to lead to correct decision making. However, this paper has limitations in determining intangible factors, such as the natural gas commercial market, operational process efficiency, and government tax rates. This paper demonstrates those factors as negligible aspects. Further deep analysis on risk quantification may be performed by using more realistic statistics distribution. As this paper uses triangular distribution for the Monte Carlo simulation, normal distribution or binomial distribution may be more relevant to simulate the Monte Carlo simulation for the uncertainties parameters.
This research was conducted for the production sharing contract operated by the Petroleum Company. The author(s) would like to express gratitude to the Petroleum Company’s employee for the research data. High appreciation is also given to the anonymous reviewers for their comments as part of this paper’s improvement. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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