Measurement of Independent Variables – Open-Closed System Variables
Peter T. Stanton, Alan P. Byrnes, 1994. "Measurement of Independent Variables – Open-Closed System Variables", Reservoir Quality Assessment and Prediction in Clastic Rocks, Michael D. Wilson
Download citation file:
Porosity prediction in an open system is extremely difficult because sources of pore-filling cements and grain- and cement-dissolving solutions are not readily delineated, nor are the volumes of these solutions easily quantifiable. A good example of the problems encountered is the case of creating secondary porosity by dissolution of carbonate cement. In this case, porosity prediction depends upon the ability to predict the conditions conducive to both the development of the porefilling cement and its subsequent dissolution.
The occurrence of carbonate cement must be considered in terms of timing with respect to other porosity reduction mechanisms, abundance and distribution within the prospective reservoir sandstone, and the various chemical and biological controls on precipitation. Similarly, late-stage dissolution must be evaluated in terms of timing with respect to both structuring and porosity preservation mechanisms, extent and distribution of secondary pores, and the chemical controls on dissolution. In light of these difficulties, prediction of porosity in a reservoir formed by decementation is highly problematic. A prediction of maximum porosity may be a more easily attained, although highly speculative and less meaningful, value for the explorationist.
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.