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oa Rock types from cores and well-log electrofacies in support of regional reservoir fairways mapping: The Hanifa Formation, Saudi Arabia
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, GEO 2008, Jan 2008, cp-246-00337
Abstract
The Late Jurassic Hanifa Formation is one of the major oil-producing reservoirs in Saudi Arabia. Regional reservoir fairways have been recognized through an integrated approach of sequence stratigraphy, petrophysical characterization and seismic attributes. This study identified rock types from core descriptions, petrographic data, and well-log electrofacies, which supported the regional Hanifa reservoir fairways. The Hanifa Formation overlies the Tuwaiq Mountain/Hadriya sequence and is overlain by the Jubaila Formation. The lower Hanifa consists of predominantly lime mudstone and laminated organic-rich mudstones, which constitute one of the major Jurassic source rocks. The upper Hanifa Member is composed of mostly shallow-water, higher-energy grainstones and packstones, which represent an overall shoaling-upwards sequence in response to carbonate deposition and relative sea-level falls. Five rock types were identified using core descriptions, petrographic and capillary pressure data. Rock type I is dominated by well-sorted and cross-stratified oolitic, skeletal and intraclastic grainstones, and shows best reservoir quality. Rock type II is characterized by variably sorted fine-grained grainstones and weakly burrowed muddy skeletal grainstones and packstones with good reservoir quality. Rock type III consists of muddy, poorly sorted skeletal/peloidal packstones, and burrowed algal-rich packstones, which are moderate to poor quality reservoirs. Rock type IV is dominated by variably burrowed packstones, wackestones, and micritic limestones, which are very poor reservoir. Rock type V is predominantly argillaceous limestones and laminated organic-rich lime mudstones, which constitute the source rocks. The identified rock types were used to calibrate the electrofacies derived from well-logs using the neural-network technique.