Predicting permeability based on core analysis.

Kontopoulos, Harry; Ahriz, Hatem; Elyan, Eyad; Arnold, Richard

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.

Citation

KONTOPOULOS, H., AHRIZ, H., ELYAN, E. and ARNOLD, R. 2020. Predicting permeability based on core analysis. In Iliadis, L., Angelov, P.P., Jayne, C. and Pimenidis, E. (eds.) Proceedings of the 21st Engineering applications of neural networks conference 2020 (EANN 2020); proceedings of the EANN 2020, 5-7 June 2020, Halkidiki, Greece. Proceedings of the International Neural Networks Society, vol 2. Cham: Springer [online], pages 143-154. Available from: https://doi.org/10.1007/978-3-030-48791-1_10

• Conference Name: 21st Engineering applications of neural networks conference 2020 (EANN 2020)
• Conference Location: Halkidiki, Greece
• Start Date: Jun 5, 2020
• End Date: Jun 7, 2020
• Acceptance Date: Mar 29, 2020
• Online Publication Date: May 28, 2020
• Publication Date: Dec 31, 2020
• Deposit Date: Apr 7, 2020
• Publicly Available Date: May 29, 2021
• Publisher: Springer
• Pages: 143-154
• Series Title: Proceedings of the International Neural Networks Society
• Series Number: 2
• Series ISSN: 2661-8141
• Book Title: Proceedings of the 21st Engineering applications of neural networks conference 2020 (EANN 2020): proceedings of the EANN 2020
• ISBN: 9783030487904
• DOI: https://doi.org/10.1007/978-3-030-48791-1_10
• Keywords: Machine learning; Support vector regression; Core analysis; Permeability prediction
• Public URL: https://rgu-repository.worktribe.com/output/891680