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Facies Inversion with Plurigaussian Lithotype Rules

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Geostatistics Valencia 2016

Part of the book series: Quantitative Geology and Geostatistics ((QGAG,volume 19))

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Abstract

Accurate incorporation of geological concepts such as lithological facies distributions is an important aspect of building reservoir models. Consequently, accounting for facies in seismic inversion generates models conditioned to geological concepts and plays an important role in decision-making. Shell’s proprietary probabilistic model-based seismic inversion engine Promise is generally applied to invert for continuous variables, such as NTG, saturation, and layer thickness from seismic data. In some depositional environments, the spatial variability of reservoir properties is characterized at fine geological scale by facies and the corresponding petrophysical properties; hence, an implementation of facies in seismic inversion is desirable. In this study, we propose a novel methodology for lithological facies inversion utilizing Plurigaussian rock-type rules. Direct inversion of facies may result in unrealistic facies contacts; therefore, the proposed technique instead inverts for a pair of “guide” variables using Promise. The guide variables are then classified into facies using a methodology inspired by Plurigaussian simulations, where a defined lithofacies rule map is used to constrain facies proportions and contacts. The required inputs for the workflow are the lithofacies rules and variogram estimates of the guide variables. Both of these can be derived from a prior estimate of the facies distribution and can also take into account geological constraints from a human expert. We demonstrate the workflow for a three facies case with a synthetic wedge model and seismic data of a marine survey.

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Correspondence to Lewis Li .

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Li, L., Xu, S., Gelderblom, P. (2017). Facies Inversion with Plurigaussian Lithotype Rules. In: Gómez-Hernández, J., Rodrigo-Ilarri, J., Rodrigo-Clavero, M., Cassiraga, E., Vargas-Guzmán, J. (eds) Geostatistics Valencia 2016. Quantitative Geology and Geostatistics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-46819-8_42

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