Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/91953
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Civil and Environmental Engineering | en_US |
dc.creator | Nourani, M | en_US |
dc.creator | Alali, N | en_US |
dc.creator | Samadianfard, S | en_US |
dc.creator | Band, SS | en_US |
dc.creator | Chau, KW | en_US |
dc.creator | Shu, CM | en_US |
dc.date.accessioned | 2022-02-04T08:03:02Z | - |
dc.date.available | 2022-02-04T08:03:02Z | - |
dc.identifier.issn | 0920-4105 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/91953 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier BV | en_US |
dc.rights | © 2021 Elsevier B.V. All rights reserved. | en_US |
dc.rights | © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. | en_US |
dc.rights | The following publication Nourani, M., Alali, N., Samadianfard, S., Band, S. S., Chau, K.-w., & Shu, C.-M. (2022). Comparison of machine learning techniques for predicting porosity of chalk. Journal of Petroleum Science and Engineering, 209, 109853 is available at https://dx.doi.org/10.1016/j.petrol.2021.109853. | en_US |
dc.subject | Porosity | en_US |
dc.subject | Chalk | en_US |
dc.subject | Hand-held X-ray fluorescence | en_US |
dc.subject | Random forest | en_US |
dc.subject | Multilayer perceptron | en_US |
dc.subject | Random forest optimized by genetic algorithm | en_US |
dc.subject | Multilayer perceptron optimized by genetic algorithm | en_US |
dc.title | Comparison of machine learning techniques for predicting porosity of chalk | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 209 | en_US |
dc.identifier.doi | 10.1016/j.petrol.2021.109853 | en_US |
dcterms.abstract | Precise and fast estimation of porosity is a vital element of reservoir characterization. A new technology for fast and reliable porosity prediction of chalk samples is presented by applying machine learning methods and X-ray fluorescence (XRF) elemental analysis. Input parameters of prediction models are based on rapid and accurate elemental analysis of chalk samples obtained from Hand-held X-ray fluorescence (HH-XRF) measurements. The intelligent models, including Random Forest (RF), Multilayer perceptron (MLP), Random Forest integrated by Genetic Algorithm (GA-RF) and Multilayer Perceptron integrated by Genetic Algorithm (GA-MLP), are trained and tested based on samples consisting of outcrop chalk samples from Rørdal and Stevns Klint (ST) and core samples from Ekofisk Formation in the North Sea. Results are evaluated by sustainability index (SI), determination coefficient (R2), correlation coefficient (CC), and Willmott's Index of agreement (WI). Results indicate that the combination of GA-RF intelligent method with XRF elemental analysis successfully provides an accurate model by 0.99, 0.02, 0.995 and 0.99 respectively for CC, SI, WI and R2, respectively. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of petroleum science and engineering, Feb. 2022, v. 209, 109853 | en_US |
dcterms.isPartOf | Journal of petroleum science and engineering | en_US |
dcterms.issued | 2022-02 | - |
dc.identifier.isi | WOS:000741667900002 | - |
dc.identifier.scopus | 2-s2.0-85120879743 | - |
dc.identifier.eissn | 1873-4715 | en_US |
dc.identifier.artn | 109853 | en_US |
dc.description.validate | 202202 bcvc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a1153-n01 | - |
dc.identifier.SubFormID | 44017 | - |
dc.description.fundingSource | Self-funded | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Nourani_Comparison_Machine_Learning.pdf | Pre-Published version | 1.52 MB | Adobe PDF | View/Open |
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