Technical Paper
Technical Paper

Rock Physics, Broadband Seismic and Facies Based Seismic Inversion

Written by: Ehsan Naeini and Russell Exley

Rock Physics, Broadband Seismic and Facies Based Seismic Inversion: De-risking via objective quantitative interpretation (QI) is an essential part of successful hydrocarbon exploration and appraisal. Given suitable subsurface scenarios QI using AVO inversion can be used to identify lithologies, indicate pore fluid fill and determine net rock volume. However, the detail that can be extracted from a conventional AVO inversion workflow is limited by the averaging effects of not taking into account facies variations and adopting a simplified, rigid low frequency model input to supplement band limited seismic input. An alternative emerging technology, joint-impedance facies inversion, is presented here and provides rock physics models for each individual facies, whilst simultaneously updating the low frequency model; thereby removing one of the main sources of error in conventional AVO inversion routines.

This paper demonstrates a workflow using a novel facies based Bayesian seismic inversion technique to analyse the distribution of reservoir bodies through a range of facies based sensitivities. Facies based seismic inversion was introduced by Kemper and Gunning (2014) in which the low frequency model is a product of the inversion process itself, constrained by per-facies input trends, the resultant facies distribution and the match to the seismic. So the inversion benefits from a rock physics model (and therefore a low frequency model) per-facies to optimize the inversion. This new Bayesian inversion system simultaneously inverts for facies and elastic properties. QI workflows also often consist of rock physics analysis, fluid substitution, synthetic modeling, followed by well tying before inversion to elastic properties and facies. The problem of wavelet estimation for broadband seismic data, however, arises during the well tie process when the length (in time) of the well-logs is often seriously inadequate to provide sufficient constraints on the low frequency content of the resulting wavelet. In this study we use one of the three methods proposed in Zabihi Naeini et al. (2016), namely the “parametric constant phase” method to estimate the wavelet for inversion.

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