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Permeability is a key parameter in determining the economic value of a hydrocarbon accumulation; however, our ability to predict the magnitude and range of permeability in undrilled areas is poor. Traditional methods of permeability prediction are empirical and rely on developing relationships between permeability and other parameters that may be predicted with greater confidence, such as porosity or lithology. These empirical methods may work well in areas where there is sufficient calibration data, but extrapolation away from well data is prone to large errors (often by orders of magnitude).
An alternative approach to permeability prediction is to model the effect of geological processes such as burial and cementation on the pore structure of the rock and, hence, calculate the change in permeability. Through understanding the effect of various geological processes on permeability, it is then possible to predict permeability from geological models. This approach has applications in both data-rich and undrilled areas.
The quantitative insight into which factors affect the permeability has been provided by computer modeling, which allows us to focus in on the most important controls, such as grain size and the amount of cement or ductile grains. Our ability to predict permeability in undrilled areas is now more often hampered by our inability to predict the variations in these controlling factors rather than by any lack of understanding of permeability itself.
1 Present address: Center for Subsurface Modeling, Texas Institute for Computational and Applied Mathematics, University of Texas at Austin, U.S.A.
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