A method for quantifying geological uncertainties in assessing remaining oil targets:: a case study from the Glitne Field, North Sea
Published:January 01, 2008
K. J. Keogh, F. K. Berg, Glitne Petek, 2008. "A method for quantifying geological uncertainties in assessing remaining oil targets:: a case study from the Glitne Field, North Sea", The Future of Geological Modelling in Hydrocarbon Development, A. Robinson, P. Griffiths, J. Price, J. Hegre, A. Muggeridge
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Evaluating the static volume potential of a field from a single geological reservoir model can be a risky business. Each piece of input data used to build the model carries an uncertainty that is not expressed in a single deterministic realization. In evaluating the technical and economic feasibility of drilling a new production well on the StatoilHydro operated Glitne Field, a quantified assessment of the range in expected volumes was undertaken. A geological uncertainty study was initiated to identify and quantify the input parameters of greatest impact on static volumetric uncertainty in the reservoir model and to identify potential...
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The Future of Geological Modelling in Hydrocarbon Development
The 3D geological model is still regarded as one of the newest and most innovative tools for reservoir management purposes. The computer modelling of structures, rock properties and fluid flow in hydrocarbon reservoirs has evolved from a specialist activity to part of the standard desktop toolkit. The application of these techniques has allowed all disciplines of the subsurface team to collaborate in a common workspace. In today’s asset teams, the role of the geological model in hydrocarbon development planning is key and will be for some time ahead.
The challenges that face the geologists and engineers will be to provide more seamless interaction between static and dynamic models. This interaction requires the development of conventional and unconventional modelling algorithms and methodologies in order to provide more risk-assessed scenarios, thus enabling geologists and engineers to better understand and capture inherent uncertainties at each aspect of the geological model’s life.