A framework for uncertainty quantification and technical-to-business integration for improved investment decision-making

To support the E&P investment decision-making process we use computer models extensively. This paper discusses in a conceptual way the potential merits of moving much more than what is currently being practised into the direction of "fully probabilistic" and "fully holistic" modeling, initially at the cost of model precision. In our opinion, the currently prevailing paradigm of maximum model precision (i.e. more physics, more grid blocks, more detail) may severely limit the optimization of the E&P decision-making process. Evidence for this statement is obtained when analyzing why the average of production and cashflow forecasts generally fails to coincide with the truth, as revealed in time: high-precision models on average result in biased forecasts and, hence, often lead to sub-optimal decisions or missed opportunities. To explain our approach, we introduce two postulates, discuss three modeling dimensions and link this to the business process of decision-making. The first postulate, when modeling the E&P value chain for investment decision-making, is that all uncertainties having a potential material effect on the model outcomes must be quantified within a comprehensive, internally consistent framework, and be taken into account when making E&P investment decisions. The second postulate is that, when trading-off the degree of "model precision" vs. the degree of uncertainty modeling and/or the degree of holistic value chain modeling, the latter two are more important than the former, especially when uncertainties are large. Initially therefore, reduced-physics or "approximate" models may be used, enabling a more comprehensive decision analysis. After having thus optimized the decisions, the sensivity (or "robustness") of the optimal decision to model precision should be tested. Multi-tiered decision-making, real options and an extended definition of "value" are also discussed within the framework's context. Copyright 2005, Society of Petroleum Engineers.