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Mudstone (Shale!) Permeability: A Key Unknown in Basin Modelling

APLIN, ANDREW O., YUNLAI YANG, STEVE R. LARTER, DAVID N. DEWHURST, and JEAN-PAUL SARDA

The utility of fluid flow modules within basin models are currently severely limited by a lack of basic data for shale permeability. Porosity:permeability functions for "standard shales" within basin models give a permeability range of four orders of Previous HitmagnitudeNext Hit at a porosity of 10 percent; published-porosity:permeability data for shales define a permeability range of 3 orders of Previous HitmagnitudeNext Hit at a single porosity. One way forward is to recognise the immense variability of shales and to realise that the rates at which shales lose porosity and permeability during burial are strongly influenced by shale lithology, defined for example by grain size (e.g. percent particles <2 microns). Our work has shown that both measured and modelled permeabilities of shales with 30-40 percent <2 microns particles are up to 3 orders of Previous HitmagnitudeTop greater than those of shales with 60-75 percent <2 micron particles. Since shale permeability is strongly influenced by porosity and lithology, we have been looking at ways of rapidly determining shale lithology from wireline logs, rather than just assuming shale properties! We have used data from shales deposited on the Norwegian Margin to train Artificial Neural Networks (ANN) to determine some physical properties of shales, including grain size. The ANN can determine the percent <2 micron particles to within +/-5 percent of the true value and should in the future be used in conjunction with better defined porosity:permeability equations for individual shale lithologies to place much better constraints on the shale-porosity:permeability:effective-stress/depth relationships used in basin models. 

AAPG Search and Discovery Article #91021©1997 AAPG Annual Convention, Dallas, Texas.