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ASEG Extended Abstracts
RESEARCH ARTICLE

Estimation of Reservoir Fluid Saturation from 4D Seismic Data: Effects of Noise on Seismic Amplitude and Impedance Attributes

Rafael Souza, David Lumley and Jeffrey Shragge

ASEG Extended Abstracts 2016(1) 1 - 8
Published: 2016

Abstract

Time-lapse (4D) seismic data sets have proven to be extremely useful for reservoir monitoring. Seismic-derived impedance estimates are commonly used as a 4D attribute to constrain updates to reservoir fluid flow models. However, 4D seismic estimates of P-wave impedance can contain significant errors associated with the effects of seismic noise and the inherent instability of inverse methods. These errors may compromise the geological accuracy of the reservoir model leading to incorrect reservoir model property updates and reservoir fluid-flow predictions. To evaluate such errors and uncertainties we present a time-lapse study based on a 3D reservoir model example, thereby exploring a number of inverse theory concepts associated with the instability and error of coloured inversion operators and their dependence on seismic noise levels. In this example, we use an oilfield benchmark case based on the Namorado Field in Campos Basin, Brazil. We introduce a histogram similarity measure to quantify the impact of seismic noise on maps of 4D seismic amplitude and impedance changes as a function of S/N levels, which indicate that amplitudes are less sensitive to 4D seismic noise than inverted impedances. The root-mean-square errors in the estimates of water saturation changes derived from 4D seismic amplitudes are also smaller than for 4D seismic impedances, over a wide range of typical seismic noise levels. These results quantitatively demonstrate that seismic amplitudes can be more accurate and robust than inverted seismic impedances for quantifying water saturation changes from 4D seismic data, and emphasize that seismic amplitudes may be more reliable to update fluid-flow model properties in the presence of realistic 4D seismic noise.

https://doi.org/10.1071/ASEG2016ab159

© ASEG 2016

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