Invasion-Based Method For Estimating Permeability From Logs

Abstract

Summary We have developed a new method of estimating permeability from logs. Our method, called PermLog, is mechanistic rather than empirical and does not require extensive (and expensive) core analysis for calibration. PermLog is based on modeling filtrate invasion from the time a zone is penetrated until it is logged. This method provides reasonably good estimates of permeability in moderate permeability formations. Similar methods applied to time-lapse logging have been presented by Tobola and Holditch1 and Yao and Holditch.2 Our method has been improved over these earlier works in two significant ways. First, our method is applied to the prediction of new generation array induction logs and docs not require multiple "time-lapse" logging runs. The development here is applied to the Schlumberger AIT log but our method is extensible to any tool which can provide a resistivity profile of the reservoir surrounding the wellbore. Second, our model includes an experimentally-verified model for predicting mudcake thickness and permeability during static and dynamic filtration conditions.3,4 PermLog is an important step in tying the static or volumetric petrophysical data normally associated with log analysis to the fluid flow petrophysical information required for reservoir performance prediction. Reservoir engineers, petrophysicists, geologists and geophysicists are challenged to bring their specialties to bear on the problem of creating a reservoir model which can be used to optimize the value of oil and gas reservoirs. Layered and otherwise complex models arc more often applied to interpret well tests and production data to reflect our increased understanding of the importance realistic models have in predicting and optimizing reservoir performance.5,6 Acquiring sufficient data to satisfy these analysis methods is often too time consuming and expensive. PermLog, in conjunction with new generation logging tools, provides a much-needed source of information on the distribution of permeability within reservoirs. This paper presents a description of the PermLog model, the underlying assumptions and the data requirements necessary to apply this technology. We also present comparisons between PermLog estimates and core measurements of permeability. The permeability estimates agree quite well with core data and the trends of permeability within an interval match up very well with the trends from cores.