Permeability evaluation in a glauconite‐rich formation in the Carnarvon Basin, Western Australia

It is usually difficult for petroleum engineers and geoscientists to obtain reliable estimates of permeability from geophysical logs, especially in lithologically complex formations such as the Mardie Greensand Formation in the Carnarvon Basin, Australia, which consists of lower Cretaceous glauconite‐rich sandstones. This paper presents an alternative petrophysical evaluation of permeability in this formation through the integration of the geological and petrophysical analyses. Neural network techniques were used to establish permeability prediction models in cored wells or sections and to predict permeability from well logs in uncored wells or sections. The permeabilities obtained from minipermeameter measurements were taken as the basis and reference for the petrophysical evaluation. Four log‐derived parameters, which best reflect the permeability in the Mardie Formation, were defined and extracted from the available conventional logs. These parameters (not original log responses) were taken as the log inputs to evaluate permeability. Through the training, testing, and validation of the networks using the log and core data in the cored intervals, a permeability prediction model/network was established. Further, the permeabilities in 15 wildcat wells were determined from conventional well logs. The results indicate that the petrophysical evaluation of permeability is valid and applicable in the Mardie Formation.