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Introduction

Acoustic impedance inversion involves conversion of seismic traces to a reflection coefficient time series, and then into acoustic impedance (Lindseth, 1979; Lavergne and Willim, 1977). These impedance traces can enhance accuracy of interpretation and correlation with properties measured in well logs. Duboz et al. (1998) and Latimer and Riel (2000), among others, point out the advantages of impedance data over conventional seismic: acoustic impedance is a rock property and a product of velocity and density, both of which can be measured at well locations. Seismic reflection, in contrast, is an interface property and a relative measurement of changes in acoustic impedance between layers. Therefore having the data in layers, rather than at interfaces, improves visualization, including both layering and vertical resolution. In addition, the elimination of wavelet side-lobes and false stratigraphic-like effects makes sequence-stratigraphic analysis easier. Acoustic impedance has been shown to be correlated with lithology (Pendrel and van Riel, 1997), porosity (Brown, 1996; Burge and Neff, 1998), and other fundamental rock properties.

Although seismic-derived acoustic impedance is a powerful tool in many aspects mentioned above, it is trace-based, which can cause errors in the presence of dipping layers. According to the convolutional model, seismic traces are considered normal-incidence 1D seismograms, which is strictly true only in the case of horizontal layers. When the subsurface exhibits dipping layers, the convolutional model will no longer hold true, because the seismic waveform will be sampled vertically instead of normally to the reflector (Guo and Marfurt, 2010), introducing a possible bias in the acoustic-impedance result.

In this paper, I propose to approach this problem and improve the accuracy of impedance estimates by employing the stratigraphic coordinate system (Karimi and Fomel, 2014,2011) for impedance inversion. In stratigraphic coordinates, the vertical direction stays normal to reflectors (Mallet, 2014), conforming to the assumption of the convolutional model.

In the following sections, I start by briefly reviewing the algorithm used to generate stratigraphic coordinates. Then, I explain the proposed methodology for impedance inversion. I use a field-data example to test the proposed approach and to verify that, in the presence of dipping layers, seismic-derived impedance becomes biased and can be improved significantly by the use of stratigraphic coordinates.


next up previous [pdf]

Next: Method Up: Karimi: Structure-constrained acoustic impedance Previous: Karimi: Structure-constrained acoustic impedance

2015-05-06