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Studies in Contemporary Sand Seas Inform Better Reservoir Models


Stratigraphic architecture is a first-order influence on fluid flow in hydrocarbon reservoirs. This is never truer than in aeolian systems, yet the interpretation of complex 3D aeolian architecture from 1D subsurface datasets is difficult and reservoir models often lack key elements of both spatial architecture and permeability contrasts. Consequently, aeolian reservoir models poorly represent production performance.

To improve the fidelity of aeolian reservoir models, we have undertaken a multi-year study of the contemporary Wahiba Sand Sea in Oman. Two field sites were selected that represent end members of the evolving architecture. To image subsurface architecture, we acquired 2D & 3D GPR at 250 & 30 MHz. To calibrate ground radar profiles and volumes, we augered and trenched to determine bedding, and measured relative permittivity and magnetic susceptibility. To constrain the age of events and determine mineralogy, we sampled for OSL and ESEM analyses. To map surficial bedforms we acquired hi-resolution aerial images, and to reconstruct the seasonal 4D movement vectors of these bedforms we used time-lapsed, high-resolution satellite imagery.

These data have enabled some insights into the evolution of the architecture and controls on accumulation for the Wahiba system.

Interpretation of the data enabled construction of 3D models where petrophysical properties were distributed according to architecture. These models facilitate quantification of fluid-flow behavior over a range of parameter uncertainty. Dip analysis provided a template through which subsurface reservoirs can be matched against known architectures and their associated production behavior.

The key learning is that the Wahiba Sand Sea and likely many, if not all, sand seas evolve sporadically with changes in boundary conditions. Rates and magnitude of change dictate the degree to which antecedent morphology influences further accumulation. Layers deposited by discretely different dune types contribute to a complex architecture. Careful capture of this architecture has the potential to improve subsurface field performance prediction.