Grain size and bed thickness are important geological attributes for sedimentary facies analysis and reservoir quality ranking in deepwater turbidite reservoirs. Grain size controls reservoir quality and has causative or correlational effects on most well logs. Additionally, bed thickness affects well logs in various ways because of different vertical logging-tool resolutions. The objective of this paper is to quantitatively classify rock and bed types based on conventional well logs to assist facies interpretation and stratigraphic reservoir modeling.

We model physical properties and well-log responses originating from clastic reservoirs with different grain-size distributions in the context of deepwater turbidite systems. The model accounts for fluid effects introduced by capillary transitions and salty connate water. Petrophysical relationships are examined between grain sizes and pore geometry as inferred from the combined effects of initial connate-water saturation and reservoir capillary pressure. From modeling, we derive several quantitative petrophysical attributes which correlate significantly with grain size. A list of such attributes includes volumetric concentration of shale, total porosity, neutron-density porosity difference, and reservoir quality index (RQI). Rock classification is then performed based on a relevant set of attributes to detect and rank different rock types with specific grain-size distributions. Concomitantly, a quantitative rock classification scheme using three-dimensional Thomas-Stieber diagrams constructed with gamma ray, bulk density, and resistivity logs is used to classify petrophysical zones based on their average bed thicknesses. A dual-attribute rock classification scheme is proposed to better associate rock types with depositional facies.

The above-mentioned rock-classification and bed-typing methods are synthesized and applied to a turbidite field from the Gulf of Mexico. We perform facies analysis based on the vertical succession of rock types (grain sizes) and the inferred variations of bed thickness. The field case demonstrates that these two geologic attributes have great potential in reducing uncertainty and non-uniqueness in the construction of stratigraphic reservoir models. Results indicate that the classified rock types and their petrophysical ranking agree with core data while the interpreted facies fit into the depositional context as inferred from core descriptions and seismic horizons.

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