--> ABSTRACT: Wireline Log Modeling of Geologic Data: Application to Automated Lithology Identification, by David E. King and John A. Quirein; #91043 (2011)

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Wireline Log Modeling of Geologic Data: Application to Automated Lithology Identification

David E. King, John A. Quirein

A method has been developed to quantitatively translate geologic data into modeled wireline log data. The modeled log data have been used to construct a data base for a computer program that performs automatic lithology identification. This technique provides accuracy previously unattainable using empirically constructed data bases.

The local rock types of interest are translated into statistical mineral models; each mineral in a modeled rock is assigned a mean volume and a volumetric range. Ideally, these values are derived from quantitative core analysis. Several porosity ranges are combined with each model, which allows rock types to be differentiated by porosity range as well as by mineralogy. Additional textural information such as secondary porosity may also be added. The statistical rock models are then entered in a program containing wireline-tool response equations, mineral response parameters, and tool measurement uncertainties to produce "synthetic" wireline data. These data provide a range of simulated log readings for the modeled rock, including modeled variations in both the mineralogy of the rock a d the tool measurements.

Electrofacies--n-dimensional ellipsoids defined by the n log measurements of each rock model--are constructed from the synthetic data and entered in the data base. The electrofacies are defined by minimum and maximum log values for the eight possible log measurements (density, neutron porosity, gamma ray, sonic transit time, photoelectric cross section, potassium, thorium, and uranium) and correlation coefficients for each possible pair of logs.

A data base containing approximately 130 electrofacies has been constructed for the Permian basin region of west Texas and has been tested on a variety of lithologies. Comparison of results using the modeled data base and sidewall core analyses demonstrates the accuracy of the data base for automatic lithology identification.

AAPG Search and Discovery Article #91043©1986 AAPG Annual Convention, Atlanta, Georgia, June 15-18, 1986.