--> Rock Evolution on the Permeability-Porosity Plane: Data Sets and Models, by Philip H. Nelson, #90027 (2004)
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Rock Evolution on the Previous HitPermeabilityNext Hit-Previous HitPorosityNext Hit Plane: Data Sets and Models

Philip H. Nelson
U.S. Geological Survey, Denver, Colorado

The study of flow and storage in reservoir rocks (“reservoir quality”), has progressed from (1) description and classification to (2) petrophysical Previous HitpredictionNext Hit to (3) Previous HitpredictionNext Hit of reservoir quality before drilling by modeling the evolutionary (diagenetic) path. This paper reviews some results of (1) description and classification and (2) petrophysical Previous HitpredictionNext Hit

Classification is furthered by a recently developed catalog of siliciclastic Previous HitpermeabilityNext Hit and Previous HitporosityNext Hit data sets, supplemented with descriptors such as grain size or depositional environment, that provides examples of familiar controls on reservoir quality. The effect of grain size, so prominent in unconsolidated and poorly consolidated samples, becomes blurred as diagenesis progresses, although a high-Previous HitpermeabilityNext Hit signature is commonly retained in conglomerates and coarse-grained Previous HitsandstoneNext Hit samples. Quartz arenites are the most efficient in terms of fluid flow, maintaining high Previous HitpermeabilityNext Hit at lower total Previous HitporosityNext Hit than rocks with lower quartz content. The presence of grain-rimming secondary minerals such as chlorite can preserve Previous HitporosityNext Hit while the presence of pore-blocking clays can greatly reduce Previous HitpermeabilityNext Hit and Previous HitporosityNext Hit. These and other factors combine to determine the position and shape of core plug data on Previous HitpermeabilityNext Hit-Previous HitporosityNext Hit plots. The patterns vary from one formation to another. 

Petrophysical Previous HitpredictionNext Hit refers to the estimation of Previous HitpermeabilityNext Hit from Previous HitporosityNext Hit and other measurements. Most predictive models use some form of the Kozeny-Carman equation as a starting point and may emphasize either grain size, mineralogy, surface area, or pore size as the key predictive parameter. For example, the nuclear magnetic resonance method can be classed as a surface-area method. Most satisfying are the pore size approaches, which incorporate the square of the product of pore-throat size and Previous HitporosityNext Hit as a predictor of Previous HitpermeabilityNext Hit. Despite the widely varying diagenetic histories in siliciclastics, a simple relationship among Previous HitpermeabilityNext Hit, pore-throat size and Previous HitporosityNext Hit does exist. Data sets plotted on the Previous HitpermeabilityNext Hit-Previous HitporosityTop plane are a projection of data clouds in three-dimensional space, with pore-throat size comprising the missing third dimension.

These results highlight issues that must be considered in the third stage of study (not reviewed herein): modeling the evolutionary (diagenetic) path from initial deposition to present-day conditions. 

 

Copyright © 2004. The American Association of Petroleum Geologists. All Rights Reserved.