Using relative seismic impedance to predict porosity in the Eagle Ford shale

Abstract

Summary Acoustic impedance (AI) is often inversely proportional to porosity. This has led to a seismic reservoir characterization technology where the AI from seismic inversion is used to predict porosity away from well locations. The porosity is typically predicted from the absolute seismic AI derived from a model based inversion (MBI). There is an inherent error in AI derived from MBI due to the accuracy of the low frequency model (LFM) provided to the inversion engine, introducing an additional error in the predicted porosity. Here we show an use of relative AI after seismic Coloured Inversion to predict porosity in a target zone. This is possible if the seismic data have good low frequency content and if the target zone is small and appropriate for the field development. Within a zone of interest the sensitivity to the effects of the LFM is minimum and therefore relative AI can be sufficient to predict porosity. An Eagle Ford Shale example from the South Texas is used in this study.