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Permeability estimation conditioned to geophysical downhole log data in sandstones of the northern Galilee Basin,Queensland: Methods and application
Institution:1. School of Earth, Environmental & Biological Sciences, Queensland University of Technology, 4001, QLD, Brisbane, Australia;2. School of Earth and Environment, The University of Western Australia, 6009, Crawley, WA, Australia;3. School of Civil and Environmental Engineering, University of New South Wales, UNSW, 2052, Sydney, Australia;1. Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China;2. College of Computer Science and Technology, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Abstract:This study uses borehole geophysical log data of sonic velocity and electrical resistivity to estimate permeability in sandstones in the northern Galilee Basin, Queensland. The prior estimates of permeability are calculated according to the deterministic log–log linear empirical correlations between electrical resistivity and measured permeability. Both negative and positive relationships are influenced by the clay content. The prior estimates of permeability are updated in a Bayesian framework for three boreholes using both the cokriging (CK) method and a normal linear regression (NLR) approach to infer the likelihood function. The results show that the mean permeability estimated from the CK-based Bayesian method is in better agreement with the measured permeability when a fairly apparent linear relationship exists between the logarithm of permeability and sonic velocity. In contrast, the NLR-based Bayesian approach gives better estimates of permeability for boreholes where no linear relationship exists between logarithm permeability and sonic velocity.
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