--> Grosmont Prolific Carbonate Reservoir: Unravel Subtle Facies Variability Through Integrated Evaluation of High Resolution Seismic and Well Data

AAPG Annual Convention and Exhibition

Datapages, Inc.Print this page

Grosmont Prolific Carbonate Reservoir: Unravel Subtle Facies Variability Through Integrated Evaluation of High Resolution Seismic and Well Data

Abstract

This work is an integrated study using high resolution seismic data and well logs in order to understand rock type distribution in the Upper Ireton formation in the Shell Grosmont Lease, in Alberta, Canada. Upper Ireton is a Late Devonian, Frasnian intertidal platform dolomite at a depth less than 400 meters. It is a highly prolific heterogeneous, fractured reservoir with an angular unconformity and well developed sinkholes on the top. The Grosmont Platform in Alberta, Canada holds one of the largest heavy oil accumulations in the world. This study is part of Shell's efforts to unlock one of the largest bitumen resources trapped in Devonian fractured reservoirs. Understanding reservoir distribution and facies variability will have strong impact on recovery efficiency, heat conduction and hence future pilots and development locations. In order to increase seismic resolution and minimize artifacts 3D seismic data was reprocessed. Reprocessing was focused on refining seismic velocities using well data, statics correction and testing different migration algorithms. New seismic data enabled imaging facies changes within the Upper Ireton reservoir subunits which are less than 10 m thick. Three rock types were interpreted from core data and well logs: laminated dolomite, calcite and breccia. Calcite was identified using PEF logs, breccia and laminated dolomite by using BHI logs calibrated with core data. These rock types showed different seismic synthetic response at the well scale. Seismic attributes were used to interpret reservoir facies distribution away from the wells. In conclusion, improved seismic data (higher Freq. high S/N ratio, attenuated artifacts) and use of various attributes enabled better integration of well data and improved our ability to predict subtle facies variability away from the wells.