PREDICTIVE PERMEABILITY CHARACTERIZATION

MACHINE LEARNING–BASED INTEGRATED CORE–LOG MODELING FOR PREDICTIVE PERMEABILITY CHARACTERIZATION IN CLASTIC RESERVOIRS

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Abstract
Permeability is one of the most important properties in reservoir engineering because it controls how fluids move through rocks and strongly influences production forecasting, recovery efficiency, and field development planning. Conventional methods for estimating permeability depend on core measurements and empirical correlations with porosity and water saturation. While core analysis provides accurate results, it is expensive, time-consuming, and limited to specific depths. Empirical models such as those proposed by Timur, Coates and Dumanoir, and Tixier often fail to capture the complexity of heterogeneous formations like the Niger Delta. This
study develops an integrated framework that combines core
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