Uncertainty of Complex Systems by Monte Carlo Simulation

Estimating the uncertainty of complex systems by analytical methods is difficult and often the results do not inspire confidence. Monte Carlo Simulation (MCS) is widely used in business by decision-makers to assess risk. Risk in this context is uncertainty and can equally apply to the uncertainty of a system. This approach has been used to assess the uncertainty of gas density, calorific value and Wobbe Index and fiscal allocation metering uncertainty. It has also been used to assess the uncertainty of test separators when regarded as multi-phase meters. The MCS method of estimating uncertainty is fully compatible with the “Guide to the expression of uncertainty in measurement [1], the uncertainty of a result is usually evaluated using a mathematical model of the and the law of propagation of uncertainty [1] (Para. 3.4.1). The advent of powerful desktop computers enables this technique to be applied to the propagation of uncertainties of most simple and complex systems including many that cannot be found readily by conventional analytical means. Results of these investigations will be presented to demonstrate the underlying simplicity and reliability of this approach to uncertainty assessment. The authors consider the universal nature of MCS is well suited to computer implementation with potential to replace conventional methods for determining uncertainty. We hope that the methods presented will be considered in the development of future uncertainty standards.