@inproceedings { , title = {Predicting permeability based on core analysis.}, 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.}, conference = {21st Engineering applications of neural networks conference 2020 (EANN 2020)}, doi = {10.1007/978-3-030-48791-1\_10}, isbn = {9783030487904}, note = {INFO COMPLETE (Published 17.06.2020 GB -- Still not on website 19/5/2020 LM; Info via contact 6/4/2020 LM) PERMISSION GRANTED (version = AAM; embargo = 12 months; licence = publisher's own; POLICY = https://www.springer.com/gp/open-access/publication-policies/self-archiving-policy 17.06.2020 GB) DOCUMENT READY (AAM rec'd 6/4/2020 LM) ADDITIONAL INFO - Contacts: Harry Kontopoulos ; Hatem Ahriz ; Eyad Elyan}, pages = {143-154}, publicationstatus = {Published}, publisher = {Springer}, url = {https://rgu-repository.worktribe.com/output/891680}, keyword = {Interactive Machine Vision, Machine learning, Support vector regression, Core analysis, Permeability prediction}, year = {2020}, author = {Kontopoulos, Harry and Ahriz, Hatem and Elyan, Eyad and Arnold, Richard} editor = {Iliadis, Lazaros and Angelov, Plamen Parvanov and Jayne, Chrisina and Pimenidis, Elias} }