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A reliable regression-based approach for seismic reservoir characterization

ABSTRACT Seismic reservoir characterisation includes seismic inversion and property prediction which provides valuable information from seismic data. A good reservoir characterisation workflow requires a reliable seismic inversion method. The most common and reliable approach for reservoir property prediction from surface seismic data is rock physics in which a physical or empirical model is used to describe the properties. However, these models require a huge number of predefined constant parameters, which makes their use complicated in practice. To overcome this, we propose a simple regression-based methodology to estimate reservoir properties (including porosity, water saturation and shale volume) from the elastic properties. The proposed methodology is a modified version of Lambda-Mu-Rho analysis for regression of water saturation and shale volume estimations. To find the elastic properties, we use Bayesian linearised amplitude versus offset inversion. Both the inversion method and the proposed methodology for seismic reservoir characterisation provide reliable information that correlates very well with the well data. Compared with the rock physics method, which is complicated and requires a number of parameters, the proposed methodology is straightforward and easy to apply.