3D Seismic Mapping with Attribute Analysis and Core Data to Detect Paleokarst Signatures in the Loppa High, Barents Sea

Abstract

Deeply buried paleokarst features have been recognized by several seismic studies in the Loppa High, Barents Sea. In this paper we present an integrated approach, to map and develop a criteria for the recognition of this features, consisting of: 1) a manually mapping of unconformities to develop a sequence stratigraphic framework of reference in the region, 2) a volume based multi-attribute seismic facies classification using Artificial Neural Networks and 3) a core analysis for calibration of the critical intervals related to the paleokarst. Data from previous studies in the Loppa High have recognized a major hiatus in the sedimentary units and presence of paleokarst features based on 3D seismic; however the results from the previous seismic studies have not been calibrated with the core data from well 7220/6-1 in the northern part of the area. We show that using this integrated approach we are able to map the subsurface heterogeneities and separate the response of the paleokarst surfaces and surrounding seismic facies. Our results combined with earlier published interpretations have provided us with new geological information on paleokarst distribution and recognition in the Loppa High.