Integrated scenario and probabilistic analysis for asset decision support

Abstract

In recent years, much effort has been spent in integration of the hydrocarbon E&P business processes. The new challenge lies in the use of the generated data for decision-making. In particular, in hydrocarbon assets, where large uncertainties occur, it is important to include the formal quantification of these uncertainties in the integrated workflow and allow for a decision framework based on a full characterization of these uncertainties. For quantification of uncertainties two classes of approach are currently in use –the probabilistic approach based on the description of a stochastic model for capturing all uncertainty and the scenario approach based on a definition of a number of conceptually different models expressing the uncertainty. Some hybrid versions of the approaches exist as well. The pros and cons of these approaches are discussed. Following this, an extended statistical framework is presented in which the previous approaches to subsurface uncertainty modelling have been combined. The framework is not limited to subsurface uncertainties, but can also be applied to the other uncertainties and decisions in the asset model. In the integrated framework several scenarios may be identified for the static earth model, the dynamic earth model, the drilling model, the surface facilities model and the economic model. A scenario tree can represent the combination of all these scenarios. Within each scenario a stochastic model can be applied to capture the within-scenario uncertainty. Calculation rules for the integration of the stochastic and scenario uncertainties are presented. Finally, decision rules are given that allow for decision-making based on the full uncertainty span in the integrated asset framework.

Advances and Challenges of Reservoir Characterization: A Review of the Current State-of-the-Art

“…Outcrop studies have developed from qualitative to quantitative. Traditional outcrop studies were focused on collecting outcrop data, such as sand width, thickness, to populate inter-well areas by stochastic or object-based methods (Dreyer et al, 1993;Bryant and Flint, 1993;Chapin et al, 1994;Clark and Pickering, 1996;Reynolds, 1999;Floris and Peersmann, 2002). However, traditional outcrop studies can hardly provide useful data especially when it needs to be integrated into reservoir engineering database or be visualized in 3D.…”

Economic Performance and Environmental Assessment

“…Based on these experiences, the oil and gas industry has developed quantitative frameworks for better decision making regarding the allocation of resources, when faced with uncertainty beyond human control (Floris and Peersmann, 2002;Bos, 2005). Evidence exists that adopting such frameworks has improved the economic performance of companies (Jonkman et al, 2000).…”

“…Decision Trees Knowing the key parameters to reduce financial risk, which have been derived from sensitivity analyses, for example, the decision framework of a project can be adapted (Floris and Peersmann, 2002). A main aspect in realizing projects with large capital investments at the beginning and high financial risk is to establish decision making, in regard to project continuation, modification, or abortion, depending on milestones.…”

3D high-resolution digital models of outcrop analogue study sites to constrain reservoir model uncertainty: an example from Alport Castles, Derbyshire, UK

“…Recent reservoir modelling practises quantify increasingly the reservoir uncertainty by adopting either a probabilistic approach based on a 'best-guess' 'deterministic' model incorporating all uncertainty, or the scenario approach, based on a suite of conceptually different models (Floris & Peersmann 2002). High-resolution modelling presented in this paper, therefore, has used both approaches to produce a suite of different scenario models and a best-guess 'deterministic' model.…”

“…Comparisons of resulting models with observed sedimentary horizons at the outcrop cliff face showed, surprisingly, little similarity; therefore, the facies were further subdivided into the appropriate interpreted elements 1-13 (Fig. 3) to create more realistic models; the input parameters are listed in Table 1. Multiple models, or realizations, were run using input parameters that followed standard practice (see Floris & Peersmann 2002), in order to create a range of models from which the reservoir modeller could pick the 'best-fit' model with the reservoir data. An example of a stochastic reservoir model is shown in Figure 9a.…”