A Comparative Study of Using Risk Adjusted Discount Rate and Historical-Based Monte Carlo Simulation to Evaluate Risk/Uncertainty in Oil and Gas Investment

2021 ◽  
Author(s):  
D. I. Lidyanto

This paper presents a comparative analysis of the use of two methods, Risk Adjusted Discount Rate (RADR) and Monte Carlo Simulation, in evaluating the risks and uncertainties in an oil and gas investment proposal. Basically, RADR method is the same as the usual discounted cash flow. But the discount rate already considers any risk/uncertainty that a project will face. Thus, some percentage, based on trusted publisher, will be added to the discount rate. While using monte carlo simulation, an economic model, with base discount rate, will be evaluated by creating hundreds of possible iterations that continually change the major economic assumption based on historical data such as production, capital expenditure, operating expenditure, oil and gas price. The purpose of this paper is to compare the use of two methods, RADR and Historical-Based Monte Carlo Simulation in evaluating risk/uncertainty in oil and gas investment proposal. There are four real oil and gas projects which will be evaluated: Project 1 (Gas Development Project), Project 2 (Shallow Water Development Project), Project 3 (Offshore Development Project), and Project 4 (EOR Development Project). The Net Present Value (NPV) of each project with those two methods will be evaluated and analyzed. The comparison study shows that NPV Calculation with Historical-Based Monte Carlo Simulation tend to have higher NPV. This is important to maintain the level of project attractiveness. Historical Based Monte Carlo Simulation method also shows the real risks and uncertainties because it is based on the historical data. Besides, this method gives real picture of what the project might face in the future instead of allowing static variables to be introduced into potential dynamic model. However, to make Historical-Based Monte Carlo Simulation robust, complete historical database is needed. While, Risk Adjusted Discount Rate method can simply be used by trusted publication.

Author(s):  
C. E. Ubani ◽  
A. O. Oluobaju

The exploration and production (E&P) operations of oil and gas project in deep waters, is associated with risks. These risks affects return on investment if they are not identified and analyzed to reduce their impact on the project. This study seek to apply Discounted Cash Flow (DCF) analysis, Monte Carlo Simulation and Sensitivity analysis, to an existing field in the Niger Delta region in Nigeria, to ascertain the viability of deepwater project when it is affected by government fiscal terms and technical terms. Economic and risk models were developed to determine profitability indicators and risk associated with the project. Risk simulator software was used to carry out Monte Carlo simulation and the sensitivity analysis. Results obtained showed that the project was economically viable with a Net Present Value (NPV) of $1,621.8 million and Internal Rate of Return (IRR) of 34%. The Monte Carlo Simulation and the sensitivity analysis showed that the Contractor’s NPV and percentage take were most sensitive to tax (under the fiscal terms) with an range of $639.27 million for a variation of by +/- 10% and crude price (under the technical term). The model developed can easily be applied in investment selections and decision makers should make decision based on the outcome of both economic model (cash flow analysis) and the risk model (Monte Carlo Simulation).


Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2885
Author(s):  
Daniel Losada ◽  
Ameena Al-Sumaiti ◽  
Sergio Rivera

This article presents the development, simulation and validation of the uncertainty cost functions for a commercial building with climate-dependent controllable loads, located in Florida, USA. For its development, statistical data on the energy consumption of the building in 2016 were used, along with the deployment of kernel density estimator to characterize its probabilistic behavior. For validation of the uncertainty cost functions, the Monte-Carlo simulation method was used to make comparisons between the analytical results and the results obtained by the method. The cost functions found differential errors of less than 1%, compared to the Monte-Carlo simulation method. With this, there is an analytical approach to the uncertainty costs of the building that can be used in the development of optimal energy dispatches, as well as a complementary method for the probabilistic characterization of the stochastic behavior of agents in the electricity sector.


Author(s):  
محمد الأمين ◽  
بن حامد عبد الغني ◽  
مراس محمد

Our research aims to try to present the modeling mechanisms in the field of simulation and quantitative methods. The research is a presentation of the role of quantitative methods in making investment project evaluation decisions, more than that and is the use of the Monte Carlo simulation model in evaluation and multi-period analysis of investment projects under conditions Risk and uncertainty. And highlighting the theoretical, scientific and practical importance of the Monte Carlo simulation method in particular, and the importance of using quantitative methods in helping to make decisions in general


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