Quick Search:    advanced search

GSW Home   GeoRef Home   My GSW Alerts   Contact GSW   About GSW   Journals List   Help

This Article Figures Only Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Kurtzman, D. Articles by Janson, X. Search for Related Content GeoRef GeoRef Citation

Improving fractured carbonate-reservoir characterization with remote sensing of beds, fractures, and vugs

¹ Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Volcani Center, Bet Dagan 50250, Israel
² Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, 10100 Burnet Road, Austin, Texas 78758, USA

Many key aquifers and oil reservoirs are in carbonate rocks. Understanding the flow behavior within this commonly complex pore space requires new perspectives and technology in order to improve carbonate aquifer and reservoir characterization. Dissolution of carbonates is related to flow; hence, quantifying the size of dissolution vugs on carbonate outcrops can help characterize controls on flow, namely matrix permeability and fracture connectivity. LIDAR (light detection and ranging) scans, combined with high-resolution photography, enable us to both measure vugs' areas and assess spatial relationships between vugs, beds, and fractures. We developed a method of obtaining and interpreting necessary vug, bed, and fracture data on the basis of these technologies. Application of this method on a Cretaceous Edwards Group outcrop in Texas (United States) revealed a significant correlation between the relative vug area of beds obtained remotely and air permeability measured in plugs extracted from these beds (R² = 0.94, P = 0.001). The total area of vugs intersected by fractures was used to establish a probability density function of fracture lengths that can improve flow modeling of the reservoir. These findings show the potential of using LIDAR and photo images in reservoir characterization by data analysis of geological features, in addition to their use for accurate mapping.