Schlumberger

Technical Paper: Innovative Approach to Characterizing a Heavy Oil North Sea Reservoir Drilled with OBM, using Multi-Depth of Investigation NMR, Dielectric Dispersion and Sonic Anisotropy

Date: 9/10/2012

Abstract

Heavy oil reserves are often found at relatively shallow depths in unconsolidated environments and are associated with significant drilling and logging challenges, especially bad hole condition and hole stability. In these heavy oil reservoirs, unknown or varying formation water salinity renders standard resistivity saturation analysis unreliable, and needs to be calibrated with Dean Stark core results, which are only available months after data acquisition. Accurate understanding of reservoir properties like oil saturation, viscosity, relative permeabilities and free water volume is essential for the efficient and economic development of heavy oil fields.

In this paper, we present an innovative approach to heavy oil characterization using novel multi-depth of investigation (nuclear) magnetic resonance (MR), dielectric permittivity and sonic shear dispersion principles. This paper demonstrates how the MR reliably estimated heavy oil viscosity and also identified varying invasion profiles across the different hydrocarbon bearing sands in the reservoir, reflecting subtle changes in reservoir quality. The three independent radial measurements made by the MR tool allowed an estimate of the heavy oil mobility, by measuring the extent to which mud filtrate is able to move the reservoir fluids away from the wellbore.

We discuss how dielectric permittivity is used to provide formation water salinity (in the absence of a water leg) in the reservoir and also establish flushed zone resistivity in OBM. In addition, we demonstrate how this method directly provides irreducible water saturation along with heavy oil saturation without the need for resistivity data. Finally we review how sonic shear dispersion and azimuthal data can reliably measure formation slowness in challenging unconsolidated sands and identify fractures generated during a leak-off test. We show how the horizontal stress estimation from advanced sonic processing can be used to reproduce data normally acquired only by such leak-off tests.

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