Porosity and permeability reduction and enhancement in nature are ultimately the cumulative result of physical and chemical processes acting on a given volume of rock. These processes are constrained by the fundamental physical-chemical variables of Pressure (P), Temperature (T), Time (t), and Composition (X). Composition is taken to include not only the chemical composition of the rock expressed as mineralogy but also fluid chemistry and such textural variables as size, sorting, roundness, sphericity, orientation, and packing. Porosity, at any given time for a given unit of rock at depth, is controlled by the complex interaction of these variables.
Predictive models range between theoretical chemical models and purely empirical models. Since chemical reactions alone do not describe all of the porosity-controlling processes (e.g., mechanical compaction), predictive equations or models can be considered to lie along a spectrum which can be considered as process-oriented as one end member and effect-oriented as the other. Theoretical chemical and mechanical equations seek to describe and quantify the processes or the individual stages and reactions, the sum total of which result in a given porosity and permeability state. The current chemical models of specific diagenetic reactions are examples of this approach. At the other extreme, most empirical effect-oriented equations seek to predict porosity or permeability directly and are not concerned with the processes which produced the predicted state. Prediction of porosity using depth (a spatial coordinate) is a good example of this approach. Published models represent both ends of the spectrum and a full gamut of
Figures & Tables
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.