Reservoir prediction of deep-water turbidite sandstones with seismic lithofacies control —A case study in the C block of lower Congo basin. (March 2016)

• Record Type: Journal Article
• Authors: Liu, Ling; Tang, Dazhen; Xu, Hao; Liu, Lihui
• Is Part Of: Marine and petroleum geology. Volume 71(2016:Mar.)
• Page Start: 1
• Page End: 11
• Publication Date: 2016-03
• Subjects: Deep-water deposits -- Turbidite sandstones -- Seismic lithofacies -- Double control -- Reservoir prediction; Submarine geology -- Periodicals; Petroleum -- Geology -- Periodicals; Géologie sous-marine -- Périodiques; Pétrole -- Géologie -- Périodiques; Petroleum -- Geology; Submarine geology; Periodicals; Electronic journals; 551.468
• DOI: 10.1016/j.marpetgeo.2015.12.004
• Languages: English
• ISSNs: 0264-8172

Abstract

Abstract: Currently, conventional forecasting methods of well-to-seismic integration are unable to identify turbidite channel sandstones due to scarcity of well data in deepwater areas, small geophysical differences between sandstones and mudstones of turbidite channels and strong sandstones heterogeneity. The reservoir prediction of deep-water turbidite channels is still a difficult issue in deep-water research. On the basis of previous studies, we propose a new technology named "reservoir prediction of deep-water turbidite sandstones with seismic lithofacies control" in view of the characteristics of deep-water turbidite sandstones. This new technology improves the reservoir prediction of complex sedimentary systems after classifying seismic lithofacies and connecting lithofacies with rock-physics. Furthermore, it can accomplish the genetic classification statistics of rock-physics, improve conversion accuracy of seismic elastic parameters/reservoir parameters and achieve the quantitative reservoir prediction under the double control of seismic geomorphology and seismic lithofacies. The C block of Lower Congo Basin is characterized by few well data, complex lithology but high resolution-seismic. We use the technology to predict the reservoirs of this area and have achieved excellent results. This has great significance for the later exploration. Highlights: Propose a new method to predict deep-water turbidite sandstones effectively. Accomplish the genetic classification statistics of rock-physics. Improve the success rate of deep-water turbidite channels' prediction. Achieves the quantitative prediction of reservoir thickness and porosity.