Hydrocarbons are being recovered from ever greater depth and offshore in deeper voters. More marginal (offshore) projects are often only viable due to the use of new and innovative engineering solutions. Furthermore, these technically more advanced projects are being planned in today's volatile. projects are being planned in today's volatile. economic climate where oil prices are relatively low and uncertain. It is therefore not surprising that project feasibility and economic analysis methods project feasibility and economic analysis methods are becoming more elaborate, methods that take proper account of uncertainties and risks in all proper account of uncertainties and risks in all aspects of a project.
Statistical distribution methods have been in use since the early 1900's, but Monte Carlo methods were generally not applied until the 1940's, methods that were based on game theory of von Neumann and Ulam. While those early applications were mainly in science and some selective engineering areas as for example defence, wider technological user and business applications were not prevalent until the fifty's and early sixty's. Simple statistical methods used in petroleum engineering, in common use since the early sixty's, were generally replaced by full Monte Carlo methods in the seventy's, with recent, more elaborate calculation methods requiring considerable computer power, as for example in reserves calculations of the North West Shelf Gas Developments.
The expectation curve, also known as cumulative frequency distribution, S-curve or exceedance chart, way be the result of simple statistical frequency analysis or more comprehensive probabilistic (Monte Carlo) modelling. As will be described, the use of the expectation curve is a universal and powerful technique which allows for a quantitative appreciation of data in all areas of exploration and production, situations where variation and production, situations where variation and uncertainties in variables are of importance.
Risk can be considered to fall into two categories: situations which give rise to equipment failure or human error leading to a major accident, as for example the recent Piper Alpha platform disaster, and those situations which are related to uncertainty, the subject of this paper. A number of Australian examples are presented.
Petroleum exploration and production is a high risk Petroleum exploration and production is a high risk business venture due to the inherent degree of uncertainty. As part of the business cycle every manager needs a clear understanding of the risks he is taking; the expectation curve method will allow for such appreciation.
Risk can be viewed as the product of chance and (negative) result, the chance of failure (inadequacy) when compared to all likely events (occurrences). To determine risk quantitatively a systematic analysis of the situation is required, the determination of the range of possibilities and the likelihood of individual outcomes. In other words, probability theory governs the rules of risk distribution.
Risk appreciation may simply be based on the result of a single frequency distribution as shown in subsequent examples, or they way be the compound result of several distributions whose variables describe the situation, including possibly dependencies among variables. Such analysis is most often carried out by a Monte Carlo simulation process as shown in later sections. Relatively process as shown in later sections. Relatively simple cases can also be treated by using an analytical approach involving moment theory as related to convolution integral theory.