Quantitative Interpretation (QI) of seismic amplitude responses, and ultimately performing seismic reservoir characterisation has become a key aspect of the de-risking workflow. A significant part of this analysis is the interpretation of amplitude variation with offset (AVO). This type of interpretation makes demands on the seismic processing beyond those made by accurate imaging alone.
Obviously preserving the relative amplitudes across the offsets or angles is key and therefore amplitude, and AVO, friendly processing of seismic data is a must. For example this processing should include true amplitude preservation, spherical divergence correction, Q-compensation, velocity analysis and normal moveout (NMO).
Post-processing, some seemingly trivial issues may persist. These might include residual NMO, NMO stretch and inconsistencies with relative scaling. For the purposes of QI these issues are often in fact non-trivial, in that they have the potential to alter the interpretation of amplitude responses for a given reflector. In this situation the observed AVO behaviour is no longer solely related to the geology we are interested in, and is inconsistent with our learnings from well-based analyses. This can severely restrict our ability to make geological and petrophysical property predictions from seismic amplitudes – the aim of seismic reservoir characterisation.
A number seismic data conditioning (SDC) techniques have been developed over recent years that aim to address these residual issues, and to optimise the seismic data for seismic reservoir characterisation work (for example see Whitcombe and Hodgson, 2007, Whitcombe et. al, 2004, Zabihi Naeini, et al, 2009).
Ikon Science have recently implemented an interactive, recipe-based SDC workflow function into the QI module within Schlumberger’s Petrel software. The function allows rapid testing and application of a suite of processing options that can be used to refine seismic amplitudes for use in seismic reservoir characterisation, and couples nicely with the other functions within the QI module. The function allows the generalist user access to seismic data conditioning techniques within a user-friendly workflow. Furthermore the toolkit introduces pre-stack seismic conditioning workflows to Petrel for the first time.
In this presentation an example is shown where these new tools are used to optimise a seismic dataset for quantitative amplitude work, with a review of the impact and implications for seismic reservoir characterisation.