21st EANN 2020, 5 -7 June 2020, Greece

Predicting Permeability Based On Core Analysis

Harry Kontopoulos, Hatem Ahriz, Eyad Elyan, Richard Arnold


  Knowledge of permeability, a measure of the ability of rocks to allow fluids to flow through them, is essential for building accurate models of oil and gas reservoirs. Permeability is best measured in the laboratory using special core analysis (SCAL), but this is expensive and time-consuming. This is the first major work on predicting permeability in the in the UK Continental Shelf (UKCS) based only on routine core analysis (RCA) data and a machine-learning approach. We present a comparative analysis of the various machine learning algorithms and validate the results, using permeability measured on 273 core samples from 104 wells. Results suggest that machine learning can predict permeability with relatively high accuracy. This opens new research directions in particular in the oil and gas sector.  

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