The usefulness of geochemical modeling in diagenesis lies in the ability to analyze processes that cannot be measured directly in the laboratory, either due to the time element (slow kinetics) or, more typically, metastable equilibria. Important diagenetic process include: albitization (Morad and others, 1990), the illite/smectite transition (Perry and Hower, 1970; Bjørlykke and Aagaard, 1992), zeolitization, development of quartz overgrowths, carbonate cementation (Boles, 1979; Bjørlykke, 1983; Bjørlykke and others, 1989), the role of calcium-plagioclase and other unstable minerals in driving diagenetic reactions (Boles, 1991; Ram-seyer and others, 1992), and the effects of fluid composition on the final diagenetic state. Computer modeling of these processes has reached the stage that it can perform fairly realistic "what if” experiments in which the investigator has control of all pertinent variables and can examine the results from a number of perspectives.
All of the reactions mentioned above can be occurring separately or simultaneously, and the models have now reached a state of sophistication that they will accurately keep a mass balance and insure that chemical equilibrium is maintained at all points along the reaction path. At present, there is no other way to dissect these complex and effect relationships other than by invoking the so-called “PATH” models or their variants. They are powerful tools, difficult to master and prone to abuse, but overall constitute a tool for the study of diagenesis that rivals X-rays diffraction analysis and petrographic analysis.
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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.