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Petroleum Geoscience

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Uncertainty analysis of fluvial outcrop data for stochastic reservoir modelling

¹ Statoil Research Centre, Arkitekt Ebbellsvei 10, Rotvoll, N-7005 Trondheim, Norway
² Statoil Exploration & Production, Strandveien 4, Postboks 273, N-7501 Stjørdal, Norway

Uncertainty analysis and reduction is a crucial part of stochastic reservoir modelling and fluid flow simulation studies. Outcrop analogue studies are often employed to define reservoir model parameters but the analysis of uncertainties associated with sedimentological information is often neglected. In order to define uncertainty inherent in outcrop data more accurately, this paper presents geometrical and dimensional data from individual point bars and braid bars, from part of the low net:gross outcropping Tortóla fluvial system (Spain) that has been subjected to a quantitative and qualitative assessment. Four types of primary outcrop uncertainties are discussed: (1) the definition of the conceptual depositional model; (2) the number of observations on sandstone body dimensions; (3) the accuracy and representativeness of observed three-dimensional (3D) sandstone body size data; and (4) sandstone body orientation. Uncertainties related to the depositional model are the most difficult to quantify but can be appreciated qualitatively if processes of deposition related to scales of time and the general lack of information are considered. Application of the N₀ measure is suggested to assess quantitatively whether a statistically sufficient number of dimensional observations is obtained to reduce uncertainty to an acceptable level. The third type of uncertainty is evaluated in a qualitative sense and determined by accurate facies analysis. The orientation of sandstone bodies is shown to influence spatial connectivity. As a result, an insufficient number or quality of observations may have important consequences for estimated connected volumes. This study will give improved estimations for reservoir modelling.

KEYWORDS: uncertainty analysis, fluvial sedimentology, stochastic reservoir modelling