Indian Journal of Science and Technology
Year: 2017, Volume: 10, Issue: 3, Pages: 1-9
Tareq Moqbel Qaid Alghuribi1 , Mohd Shahir Liew2 , Noor Amila Zawawi3 and Mohammed Abdalla Ayoub1
1Department of Petroleum Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia; [email protected], [email protected] 2Faculty of Petroleum Engineering and Geoscience, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia; [email protected] 3Department of Civil Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia; [email protected]
Background/Objectives: Offshore oil and gas platform are aging in Asian Pacific Region and requiring a transparent decommissioning framework to address the major environmental impacts and cost pertaining the removal of offshore structures. This paper endeavors to establish a benchmark model to update platform decommissioning cost for four decommissioning options. Methods/Statistical Analysis: This paper provides a benchmark methodology adopting a case study for a previous decommissioning project to estimate and update future decommissioning cost based on Net Present Value (NPV) approach. Linear regression was established to predict cost inflation in decommissioning projects to be put to use in NPV method. Monte Carlo Simulations were employed to assess and evaluate uncertainty, and variability of each decommissioning alternative cost model to validate their exemplary implementation. Findings: After implementing Net Present Value methodology to attain platform decommissioning cost, it was found that platform decommissioning costs were USD91,997,398.97 USD29,312,019.08 USD36,913,049.82, and USD21,185,843.13 for complete removal, partial removal, conversion to reef, and re-using platform for wind turbine power generation respectively. These cost data reveal the tremendous cost incurred by platform’s owners due to decommissioning. Uncertainty and variability of cost update estimation were demonstrated through Monte Carlo Simulations. After running 100,000 simulations, the results showed insignificant discrepancy with uncertainty ratio varies between 0.023% and 0.10% for the four decommissioning alternatives. Therefore, Monte Carlo Simulation exhibits a very good agreement with the cost estimate using NPV which just confirms the viability and applicability of utilizing NPV method in updating decommissioning cost for offshore installations. Hence, the contribution of this study is significant and timely efficient, allowing for cost update through systematic method instead of embracing regression analysis which is tedious and time-consuming. Application/Improvements: Net Present Value methodology may be useful and newly tool in updating platform decommissioning cost, and can strengthen its applicability in this field by incorporating probabilistic method as such Monte Carlo Simulation.
Keywords: Net Present Value, Measuring Uncertainty, Monte Carlo Method, Platform Decommissioning Cost
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