原文传递 T-R Cycle Characterization and Imaging: Advanced Diagnostic Methodology for Petroleum Reservoir and Trap Detecion and Delineation. Final Technical Report September 1, 2003 - August 30, 2006.
题名: T-R Cycle Characterization and Imaging: Advanced Diagnostic Methodology for Petroleum Reservoir and Trap Detecion and Delineation. Final Technical Report September 1, 2003 - August 30, 2006.
作者: mancini, e. a. parcell, w. c. hart, b. s.
关键词: stratigraphy, petrography, research, geology, well logging, data integration
摘要: Characterization of stratigraphic sequences (T-R cycles or sequences) included outcrop studies, well log analysis and seismic reflection interpretation. These studies were performed by researchers at the University of Alabama, Wichita State University and McGill University. The outcrop, well log and seismic characterization studies were used to develop a depositional sequence model, a T-R cycle (sequence) model, and a sequence stratigraphy predictive model. The sequence stratigraphy predictive model developed in this study is based primarily on the modified T-R cycle (sequence) model. The T-R cycle (sequence) model using transgressive and regressive systems tracts and aggrading, backstepping, and infilling intervals or sections was found to be the most appropriate sequence stratigraphy model for the strata in the onshore interior salt basins of the Gulf of Mexico to improve petroleum stratigraphic trap and specific reservoir facies imaging, detection and delineation. / Supplementary Notes: Prepared in cooperation with Department of Geological Sciences, Tuscaloosa, AL., Wichita State Univ., KS. Dept. of Geology. and McGill Univ., Montreal (Quebec). Earth & Planetary Sciences. Sponsored by Department of Energy, Washington, DC. / Availability Note: Order this product from NTIS by: phone at 1-800-553-NTIS (U.S. customers); (703)605-6000 (other countries); fax at (703)605-6900; and email at orders@ntis.gov. NTIS is located at 5285 Port Royal Road, Springfield, VA, 22161, USA.
总页数: u0721;251p
报告类型: 科技报告
检索历史
应用推荐