Enhancing Decision Analysis During Project Concept Evaluation using Monte Carlo Simulation and Other Decision Support Tools: A Case Study on the Onshore/Swamp Oil & Gas Asset Development in Nigeria
Open AccessThis Praxis addressed the issues related to the poor economic evaluation of decision variables during project concept selection that leads to inappropriate decisions resulting in profitability issues due to cost overrun, schedule slippage, and poor revenue forecast. It utilized the Monte Carlo Simulation (MCS) to develop the Stochastic Discount Cashflow (DCF) model using 12 case study projects from the onshore/swamp terrain in Nigeria and the multiple linear regression technique to gain insights on the relationship between the input variables, such as costs and revenues, and the output variable (Net Present Value—NPV) for the case study projects.The application of the MCS determined the mean contingency of 9.5% required to mitigate the cost overruns with 95% confidence and within the confidence interval of 8.91% to 10%. This is lower than the 15% contingency based on the percentage approach in the Reference Case widely applicable in the oil and gas industry, and to which the company also applied. This led to savings of between $4M to $200M across the project(s), resulting in better NPV values. Also, the application of the Auto-Regressive Integrated Moving Average (ARIMA) time series model to forecast the Forcados oil price, taking into account the historical oil price trends and cycles, led to an optimum revenue estimate with ultimate gains in all the 12 projects under study, except for projects 5 and 6 where the produced gas is sold to the domestic gas market in Nigeria at a regulated gas price of $3/mmBTU.Projects 12, 1, 5, 3, 4, and 2 were determined, via the Stochastic DCF model, to have positive NPVs of $2M, 346M, $63M, $6M, $60M, $58M, and $7M, respectively, and less than 50% chance of reaching the value at risk at the 5th level (VAR 5%). The gas export supply project (12) was found to be the most profitable project despite having large capital expenditures (CAPEX) due to the gas pricing mechanism (indexed to LNG prices) while regulated gas prices impacted the profitability of all the other 11 projects (I-XI). Projects 7, 10, 8, and 11 with negative NPVs of -$358M, -$952M, -2,396M, and -$2,681M respectively and 95% chances of reaching a negative VAR 5%, were found to be very unprofitable with zero percent chance of being profitable (NPV greater than zero). Projects 9 and 6, though unprofitable with negative NPVs of -$12M and -$28M, have 36% and 2% of being profitable (NPV greater than zero) respectively. Using the multiple linear regression analysis for the Stochastic DCF model, it was determined that for every 1% increase in CAPEX and OPEX, the NPV decreased by 0.64% and 0.74% respectively, while for every 1% increase in revenue, the NPV increased by 0.15%. The multiple linear regression model performed better predictions on larger projects.
- All rights reserved
Notice to Authors
If you are the author of this work and you have any questions about the information on this page, please use the Contact form to get in touch with us.