In order to develop meaningful multivariate predictive functions, it is necessary to correctly delineate those physical and compositional (including textural) variables that constitute major controls on reservoir properties and, therefore, warrant detailed measurement. Many of the pertinent controls may be known from previous studies in the general area of interest or from studies in similar sandstones elsewhere. Or, one could simply choose to input data for each of the variables listed by Scherer (1987) (Table 2-1), or include all variables used by various authors in their regression models (these have been reviewed in Chapter 2). A number of review papers discuss the more common diagenetic processes in sandstones of various types and controls on these processes (e.g., Nagtegaal, 1978; Burley and others, 1985). In a given sandstone unit, however, the more common mechanisms of porosity reduction may not be operative. Thus, it is unwise to assume a standard set of diagenetic mechanisms will dominate in a particular rock unit without conducting at least a preliminary examination. However, when the lithologies of interest have not been subjects of in-depth investigation, or where highly diverse opinions about the controls on reservoir quality exist, it may be necessary to rely on generalized lists such as that prepared by Scherer, and then be prepared to conduct a followup study should other controls emerge not originally incorporated in the analyses.
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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.