Permeability prediction from well logs using an improved windowing technique

Permeability prediction from well logs is of great importance in reservoir characterization and engineering. In this paper, a new method is proposed t o correlate conventional well logs and core permeability data. It uses an i mproved "windowing" technique to incorporate adjacent core data to the perm eability predictor in such a way that the scales of the well log and core m easurements are matched It also has the capability to evaluate the reliabil ity of each and every prediction. The method is implemented by the use of a neural network and is demonstrated by means of a case study. The study use s a set of well logs and limited core permeability data to produce continuo us permeability profiles. The results show that the permeability profiles a re consistent with the core permeability and the geological sequence of the reservoir The reliability indicator is particularly useful for examining r eservoir heterogeneity and sampling.