Abstract Reservoir modelling tools can be invaluable for integrating knowledge and for supporting strategic oil field decisions. The pertinent issue is the capability of the modelling toolbox to achieve the required support: does modelling generate insights into the characterization of the subsurface, does it increase or decrease our working efficiency and does it help or hinder us in decision-making? In this respect, we see two directions emerging in reservoir modelling and simulation. One surrounds software technology development and a move towards a grid-independent world. This is a current research issue but some of the components required to complete a new workflow are already in place and tools for certain specific applications may not be far away. The other involves a change in approach to model design. This involves a move away from big, detailed ‘life-cycle’ models to more nimble workflows involving multi-models (either multi-scale or multi-concept) which may or not include full-field modelling exercises. A distinction between ‘resource models’ and ‘decision models’ helps crystallize this, is a positive step towards achieving ‘fit-for-purpose’ models, and is a change of model design strategy which can be achieved immediately.

Abstract The scenario-based reservoir modelling method places a strong emphasis on the deterministic control of the model design, contrasting with strongly probabilistic approaches in which effort is focused on the ‘richness’ of a geostatistical algorithm to derive multiple stochastic realizations. Scenario-based approaches also differ from traditional ‘rationalist’ modelling, which often involves the construction of only a single, best-guess or base-case model. The advantage of scenario modelling is that there is no requirement to anchor on a preferred, base-case model, and it is argued here that selection of a base case is detrimental to achieving appropriately wide uncertainty ranges. Multiple-deterministic scenario modelling also carries the advantage of maintaining explicit dependency between model parameters and the ultimate model outcome, such as a development plan. The approach has been applied widely to new fields, where multiple deterministic reservoir simulations of a suite of static models can be easily handled. The approach has also been extended to mature fields, in which practical approaches to multiple-history matching are required. Mature field scenario modelling, in particular, illustrates the weaknesses of base-case modelling, and delivers a strong statement on the non-uniqueness of modelling in general. Current issues are the need to develop better methodologies for multiple-history matching, and for linking discrete, deterministic, scenario-based outcomes to probabilistic reporting. Experimental design methods offer a solution to the latter issue, and a simple, practical workflow for its application is described.