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Title: Comparison of machine learning techniques for predicting porosity of chalk
Authors: Nourani, M
Alali, N
Samadianfard, S
Band, SS
Chau, KW 
Shu, CM
Issue Date: Feb-2022
Source: Journal of petroleum science and engineering, Feb. 2022, v. 209, 109853
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.
Keywords: Porosity
Hand-held X-ray fluorescence
Random forest
Multilayer perceptron
Random forest optimized by genetic algorithm
Multilayer perceptron optimized by genetic algorithm
Publisher: Elsevier BV
Journal: Journal of petroleum science and engineering 
ISSN: 0920-4105
EISSN: 1873-4715
DOI: 10.1016/j.petrol.2021.109853
Rights: © 2021 Elsevier B.V. All rights reserved.
© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
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
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