Abstract
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.
This work is part of a Knowledge Transfer Partnership (KTP) programme, funded by Corex UK Ltd. and Innovate UK.
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Kontopoulos, H., Ahriz, H., Elyan, E., Arnold, R. (2020). Predicting Permeability Based on Core Analysis. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_10
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