Water saturation is an important petrophysical property for formation evaluation, defining
the final wellbore destination. The Archie’s equation calculates the water saturation for clean
formations in function of rock resistivity, from a deep resistivity log and porosity, from one
porosity log. The Archie’s equation, still involves the knowledge of formation water resistivity,
which requires local determination and appropriated Archie’s coefficients. Hingle plot is
traditional method in well logging for water saturation calculus, specially when the water
resistivity is unknown. This method promotes a linearization of Archie’s equation from resistivity
and porosity logs as the water line in the Hingle plot. The water resistivity is obtained from water
line inclination. Independent of logging tools and digital computers development, the log analyst
still handles with visual data interpretation and as all visual data interpretation, the Hingle plots
interpretation is subject of sharpness errors. The objective of this dissertation is to simulate the
visual interpretation of Hingle plot by a angular competitive neural network to mitigate the
occurrence of sharpness errors and produces a real time first approach of water saturation, based
on angular pattern identification in the raw well logging data.. The evaluation of this
methodology is accomplished on synthetic data that satisfies the Archie’s equation and on actual
well logging data.
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