Although porosity and fluid saturations are important in determining total oil production, permeability controls the rate of oil production and is thus important to much of the economics. Permeability prediction, like porosity prediction, has traditionally involved univariate relationships of permeability with a single independent variable. Possibly as many as 90% of the published predictive relationships for permeability involve porosity, usually presented for a specific formation, lithology, field, or well. As with porosity (see Chapter 2), these correlations are accurate for the specific population they represent because other key independent variables (e.g., pore radius, surface area, tortuosity) are either held constant, or because porosity is collinear with these other independent variables and accounts for some of the variance which is actually controlled by them. Similar to the problems encountered when porosity-depth trends are applied to different lithologies or basins, a different porosity-permeability relationship is generated whenever the correlation between porosity and other independent variables changes. Since these relationships change frequently, a multitude of different porosity-permeability trends are reported in the literature.
Many ofthe lithologic controls on permeability have been discussed in Chapters 8 and 9. However, before discussing permeability prediction modeling, it is important to review key variables which control permeability, those variables which have been used in predictive models, and reasons why these variables have been selected.
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Reservoir Quality Assessment and Prediction in Clastic Rocks
This course is designed to emphasize the following topics: (1) Historical perspective on previous and current empirical, and geochemical methods of reservoir quality prediction; (2) Overview of diagenetic processes which significantly impact reservoir quality and those factors which act as major controls on those processes; (3) Proper design of a comprehensive or limited-focus predictive analysis of reservoir quality; (4) Methodologies for the accurate measurement of all major dependent and independent variables; (5) Data analysis techniques involved in quality control and the assessment of variability prior to performing multivariate regression; (6) Steps involved in the generation of a multivariate regression to insure that the model developed provides maximum accuracy using a minimum number of independent variables; (7) Case histories from a variety of settings illustrating application of the recommended approach to reservoir quality prediction.