Abstract
The aim of quantitative interpretation (QI) is to predict lithology and fluid content away from the well bore. This process should make use of all available data, not well and seismic data in isolation. Geological insight contributes to the selection of meaningful seismic attributes and the derivation of valid inversion products. Uncertainty must be taken into account at all stages to permit risk assessment and foster confidence in the predictions. The use of the Bayesian framework enables prior knowledge, such as a geological model, to be incorporated into a probabilistic prediction, which captures uncertainty and quantifies risk. Nostradamus is a fluid and lithology prediction toolkit that forms part of a comprehensive QI workflow. It utilises a Bayesian classification scheme to make quantitative predictions based upon inverted seismic data and depth-dependent, stochastic rock physics models. The process generates lithology and fluid probability volumes. All available information is combined using geological knowledge to create a realistic pre-drill model. Separately, stochastically modelled multidimensional crossplots, which account for the uncertainty in the rock and fluid properties (based on petrophysical analyses of well data), are used to build probability density functions such as acoustic impedance (AI) vs Vp/Vs and LambdaRho vs MuRho. These are then compared to crossplots of equivalent inverted data to make predictions and quantitatively update the geological model. Individual probability volumes as well as a most-likely lithology and fluid volume are generated. This paper presents a case study in the Carnarvon Basin that successfully predicts fluids and lithologies away from well control in a way that effectively quantifies risk and reserves. Two of the three successful gas exploration wells were drilled close to dry holes.
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