At the Conceptual Estimate stage of any prospect, capital and operating cost estimates are usually compiled in a simple spreadsheet. Net present Value (NPV) and Internal Rate of Return (IRR) calculations are typically performed and the economic viability of the project, based on these, or similar criteria, is assessed. At this early stage, the simple spreadsheet may or may not contain, amongst others, project financing proposal, inflation, taxation, or currency variation.
Moreover, how much contingency should be applied and to which component or components of the estimate? By the very nature of the input cost data being budgetary, the plant location, soils conditions, infrastructure and the like, being oft- times unknown and the plant throughput being less than optimised, how useful are these simple evaluations? What is known is that the accuracy of any economic evaluation is questionable due to the inherent inaccuracies of the inputs. The simple spreadsheet approach may provide specific NPV and IRR values but it offers little or no confidence as to the likelihood of achieving these. Moreover, this approach does not provide any information regarding which parameters are important to the evaluation.
By using a Monte Carlo Simulation package, the forecasting capability of the simple spreadsheet model may be greatly enhanced. By modelling the individual variables (assumptions) and applying probability distributions to each assumption in the model, a range of outcomes is generated. The resulting forecasts provide, not only a range of possible outcomes but also the certainty of achieving the forecast NPV and IRR economic return. Further refinements, such a the establishment of a base-line model and the subsequent re-modelling of different scenarios allows a higher degree of confidence to be achieved for trade-off studies than are normally possible using simple spreadsheet techniques. The flexibility of this modelling technique allows a more efficient assessment of all the project risks. Additionally, sensitivity analysis is used to highlight the key success factors for each project scenario, allowing the client to focus on those elements which most significantly determine project success.
For a recent world-scale oil refinery study in Africa, a Risk Analysis Model was developed and trade-off studies performed for four different crude feeds. Additionally, two different refinery throughputs were modelled and their associated capital and operating cost estimates evaluated, in order to ascertain the anticipated project returns and the certainty of achieving those returns. The analyses yielded a total of 8 scenarios in order to try to "optimise" project return even at this early conceptual estimate stage of the project.
Sensitivity Analysis was run for refinery yield; product- crude price gap; sales volume; operating costs; capital costs, inflation rate and interest rate. For the particular circumstances and client requirements, the optimised economic plant throughput, feed and configuration was determined and the relevant revenue, capex, opex and hence IRR were determined. The Monte Carlo Simulation Model indicated a mean IRR of 11.5% but more importantly, a certainty of approximately 80% that the project would achieve an after tax return greater than 10% as illustrated in the chart below. It also identified which of the inputs to the model had the greatest impact on project return.
Based upon the analysis, the Client intends progressing to a pre-feasibility study where the estimation accuracy will be improved through more engineering and the project viability will be confirmed.