Porosity data are an integral part of all databases used to develop predictive models of reservoir quality. In most instances, such data are obtained from core analysis and/or log analysis (discussed in the preceding chapter). However, useful porosity data also can be generated from petrographic point counts. Point counts (modal analyses) provide quantitative data on both total thin-section porosity and specific types of porosity.
Four types of porosity occur in sandstones: intergranular, dissolution, micro, and fracture (Pittman, 1978). Intergranular pores can be identified in thin section by their location between detrital grains. Identification of dissolution pores was discussed in detail in the chapter dealing with secondary porosity (Chapter 7). Microporosity is best recognized by using fluorescent microscopy on samples containing fluorescent epoxy injected into the rock's pore system (Yurewicz and Dravis, 1984; Yanguas and Dravis, 1985). Fracture porosity is generally less than 1 to 2% and does not contribute significantly to total porosity. Fracture porosity cannot be estimated reliably from thin sections.
Modal analysis of porosity is usually part of a general procedure to quantify the volumes of sandstone elements (grains, cements, and pores). Unlike other techniques, petrographic analysis can also provide information on the processes involved in porosity reduction or enhancement (or both) and thus help in the choice of parameters for predictive equations. Most importantly, petrographic observations provide clues as to the best approach to be used in reservoir quality prediction for a given target population (see Chapters 20 through 22 involving case histories).
Other than measurements on unconsolidated
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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.