Building more robust low-frequency models for seismic impedance inversion

Abstract

Seismic impedance inversion is an important tool for estimating rock and reservoir properties from the seismic data. Seismic data is band-limited in nature and lacks the low-frequency component. As the low-frequency component holds the basic information on geological structure, the lack of low-frequency information degrades the quantitative prediction based on seismic inversion. It is therefore essential to build an accurate low-frequency model to have confidence in seismic inversion and in turn on the quantitative predictions made therefrom. In this paper, we develop a novel workflow of predicting the low-frequency impedance model that uses a single-well lowfrequency model apart from other relevant seismic attributes in the multi-attribute regression analysis. The workflow was successfully applied to a number of impedance inversion exercises out of which two cases are discussed here. Our inversion exercises were carried out on datasets from northeastern British Columbia and Alberta, in Canada. The inversion results using this approach have been validated at blind well locations and an excellent match between well logs and inversion results has been observed.