Prestack Bayesian lithofacies classification technology and application in oil-sand reservoir prediction

Abstract

The oil-sand reservoirs in the Athabasca region of Canada are estuarine deposits affected by tides. The strata are inclined, and the interlayers are well-developed. Accurate spatial characterization of reservoirs and interlayers is the key for efficient oil-sand development. In this paper, we have used prestack Bayesian lithofacies classification technology to predict the spatial distribution characteristics of reservoirs and interlayers of oil-sand reservoirs. We first use log lithofacies data as a label, select lithofacies sensitive elastic parameters to make a lithofacies classification probability distribution crossplot, and then project the lithofacies-sensitive elastic parameter volumes into the lithofacies classification probability distribution crossplot. Finally, we predict the spatial probability distribution of different lithofacies. Probabilistic characterization can enhance the recognition of transitional lithology and thin layers in the inversion results, reduce the uncertainty in the prediction of reservoirs and interlayers, and significantly improve the prediction accuracy of reservoirs and interlayers. The field application results in the Kinosis study area indicate that the probability volume predicted by this technology can distinguish interlayers greater than 1 m thick and identify interlayers greater than 2 m thick, which meets the technical requirements of oil-sand steam-assisted gravity drainage development.