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Predicting permeability based on core analysis.

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

Authors

Harry Kontopoulos

Richard Arnold



Contributors

Lazaros Iliadis
Editor

Plamen Parvanov Angelov
Editor

Chrisina Jayne
Editor

Elias Pimenidis
Editor

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

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