--> --> Abstract: Quantifying the Architecture of Carbonate Reservoirs to Set up a Simulation Model, by Dan J. Hartmann, Stephen T. Solomon, and Alden J. Martin; #90914(2000)

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Dan J. Hartmann1, Stephen T. Solomon2, Alden J. Martin2
(1) D J H Energy Consulting, Fredericksburg, TX
(2) Conoco Inc, Houston, TX

Abstract: Quantifying the architecture of carbonate reservoirs to set up a simulation model

Quantifying the architecture of a reservoir to perform valid simulation is a special challenge for carbonates. The several fields of data create an integration management problem, and the differing attitudes about data processing result in sub-par models for simulation.

A procedure for identifying and characterizing petrophysical flow units helps resolve some of the challenges encountered in simulation of carbonate reservoirs. Application of the procedure, and the models it produces, reveals that one key to understanding and predicting performance of carbonates is to represent them as combinations of differing flow units. A flow unit is an interval of rock with similar porosity, permeability and Sw attributes, i.e. a unique pore type. Since each pore type is defined by it's mean pore throat radius (port size), it represents unit intervals of specific flow potential.

When a relationship exists, and can be documented, between depositional facies and flow units, one can develop a common geological and engineering zonation. Parasequences can then be characterized in terms of petrophysical flow unit types. Combining water saturation, hydrocarbon column height and relationship of the flow units with the interpreted sequence stratigraphy of the area, provides a useful tool for mapping resevoir performance cells. A field with multiple performance cells reflects the lateral complexity of the bundles of flow units, as identified in the wells. This assists in selecting the geostistical constraints to apply to the simulation model. The approach can also be instrumental in managing producing reservoirs to develop bypassed pay, and to establish presimulation performance predictions. By fine tuning the reservoir models with presimulation iterations, the final simulation becomes much more realistic.

AAPG Search and Discovery Article #90914©2000 AAPG Annual Convention, New Orleans, Louisiana