Two reservoir quality assessment case studies (Kekiktuk Formation sandstones and Yacheng field) have been discussed in preceding chapters (Chapters 4 and 8). The Yacheng reservoir quality prediction model was based on a calibration data set from wells with similar temperature and effective pressure histories. As a result of these similarities, reservoir quality was simply a function of depositionally-controlled differences in composition, sorting, and grain size. In contrast, reservoir quality in the Kekiktuk Formation sandstone was controlled primarily by texture (grain size) and burial history.
In this chapter, three more case studies are discussed (Middle Jurassic sandstones of the Fangst group, offshore mid-Norway; Late Cretaceous-Late Eocene sandstones in the Taranaki basin, New Zealand; and Middle Eocene-Late Oligocene sandstones of the San Emigdio area, California). The reservoir quality of compositionally- and texturally-similar sandstones from the Haltenbanken area (offshore mid-Norway) is controlled by their burial history. In sandstones of the Taranaki Basin (New Zealand), the key to successful porosity prediction is understanding burial diagenetic processes and, in particular, proper evaluation of the importance of secondary porosity. Finally, a case study from the southernmost San Joaquin basin illustrates an approach to predicting porosity in sandstones with a wide range of zeolite and clay cement abundances.
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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.