Doveton, John H.1, W. Lynn Watney2, Geoffrey C. Bohling1
(1) Kansas Geological Survey, University of Kansas, Lawrence, KS
(2) Kansas Geological Survey, Lawrence, KS
ABSTRACT: Theory and Practice of a Web-based Intelligent Agent in the Location of Pay Zones on Digital Well-Log Files
Traditional log analysis methods locate pay in a deductive ("top-down") mode by applying the Archie equation in the calculation of water saturation. A computed saturation log is a serviceable reconnaissance procedure to locate potentially productive zones, although additional insight on pore size is needed to predict actual fluid production. In an alternative approach, pay zones may be located by an inductive ("bottom-up") mode in which the fluids produced from DST intervals and perforated zones are used in the categorization of associated well log patterns. In several exploratory case-studies, a Java applet was trained to distinguish fluid types by enumerating data-point densities of log measurements on a neural lattice framework and classification by Bayesian probability methods. Endmember categories of oil, water, and mud, were classified in terms of their gamma-ray, neutron and density porosities, photoelectric factor, and resistivity in Kansas Paleozoic carbonates and sandstones. Mappings of the separate fluid data clouds within this multivariate log space were examined, both as a means of quality control and in the pattern recognition of reservoir properties that control production. At the conclusion of learning and validation phases, the trained intelligent agent was applied to a database of digital LAS log files to assess potential pay within stratigraphic equivalents of new wildcats and bypassed pay on older wells.
AAPG Search and Discovery Article #90026©2004 AAPG Annual Meeting, Dallas, Texas, April 18-21, 2004.