Abstract

Pay volume (PV) is a measure of the amount of hydrocarbon volume or pay in the overall reservoir and is used to appraise reservoir quality and the economics associated with reservoir development. As seismic-based reservoir characterization technology is advancing, lithology and porosity information derived from seismic inversion is often used to derive an estimate of PV. In general, PV estimation from seismic data is based on some sort of rock physics transformation and seismic inversion algorithm, and both may be nonunique. As PV estimates are often used for economic decision making, it is important to associate expected risk or confidence associated with the prediction. This article presents a workflow to compute quantitative estimates of PV, along with associated uncertainties, from well-log calibrated prestack seismic inversion attributes. The main tools used in this workflow are seismic (AVO) inversion, rock physics, and Bayesian statistics. The PV is estimated from the seismically derived rock properties. The PV uncertainty is determined from three components: structural (or interpretation) uncertainty, fluid saturation uncertainty, and porosity uncertainty. Each component contributes to the estimated uncertainty.

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