The following case history involves Jurassic reservoir sands in the Viking Graben of the North Sea and is based primarily on a study conducted while the author was with 17on Production Research (Wilson, 1977). It documents the relative importance of many of the major geologic controls on reservoir quality in clastic sandstones, and reviews methods used for prediction of porosity and permeability. The interpretations presented here are those of the author and do not necessarily reflect the opinions of Esso Exploration and Production, U.K. Ltd., or Shell Exploration, U.K. Ltd.
During the early stages of drilling in the Viking Graben, unexpectedly low permeability was encountered in the Middle Jurassic Brent Group in well 211/26-4 (Figure 21-1). In order to delineate factors causing this anomaly, a pilot study was first conducted on five wells. The pilot study consisted primarily of petrographic analysis along with limited amounts of scanning electron microscopy (SEM) and X-ray diffraction analysis. The following conclusions were reached in that study:
Porosity reduction was controlled primarily by quartz overgrowth development.
Anomalously low permeabilities were produced primarily by transformation of potassium feldspar and kaolinite/dickite to highsurface-area illitic clay.
Following completion of the pilot study, a detailed study was initiated involving 19 additional wells. Locations of all wells analyzed are shown in Figure 21-1. The wells are distributed stratigraphically as follows:
Brent Group (Middle Jurassic) - 17 wells
Magnus Sandstone (Upper Jurassic) - 2 wells
Statfjord Formation (Lower Jurassic) - 1 well
Sleipner Sandstone (MidldleJurassic) - 3 wells
Triassic Sandstone - 1 well
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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.