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7th Middle East Geosciences Conference and Exhibition
Manama, Bahrain
March 27-29, 2006
Reservoir
1 New England Research, Inc, 331 Olcott Drive
, Ste L1, White River Junction, VT 05001-9263, phone:
802-296-2401 ext 118, fax: 802-296-8333, [email protected]
2 New England Research, Inc.
3 ABQ Reservoir
Management, Saudi Aramco, Dhahran, 31311, Saudi Arabia
4 Reservoir
Characterization Department, Saudi Aramco, P.O. Box 10607, Dhahran, 31311, Saudi Arabia
5 Saudi Aramco Lab Research & Development Department, Dhahran, 31311, Saudi Arabia
Carbonate reservoir
characterization is commonly hampered by difficulty in relating dynamic
reservoir
properties to a
geologically consistent rock type classification system. Traditional approaches of log and core analysis do not produce
satisfactory definition of rock type or flow performance. Furthermore, the geological models of carbonate reservoirs are
typically not well linked to the
reservoir
flow units. We present a case study from a complex carbonate
reservoir
with large
vertical variability in production. By combining routine laboratory measurements with an integrated pore space inversion
analysis, we constructed detailed pore structure models and identified rock types from the calibration suite of plugs. The
analysis led to a fundamentally different rock type classification scheme, yielding meaningful correlation with the geologic
model of the
reservoir
and allowing identification of dual pore system samples and composite samples. The dual porosity
samples themselves were divided into multiple rock types based on crossplots of inferred pore structure parameters. We
show that systematic use of a pore structure based approach leads to a classification which is fundamentally different from
traditional schemes using permeability, porosity, and capillary pressure alone. Owing to the broad based petrophysical and
data-driven nature of the approach, the resulting classification system automatically inherits direct ties to a wide range of
petrophysical properties. The pore structure inversion method thus satisfies requirements of linking the classification
scheme to static and dynamic
reservoir
properties, as well as to the geophysically measurable properties used in log based
characterization.