Comparison of machine learning techniques for predicting porosity of chalk

Meysam Nourani, Najeh Alali, Saeed Samadianfard, Shahab S. Band, Kwok wing Chau, Chi Min Shu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Article number109853
JournalJournal of Petroleum Science and Engineering
Publication statusPublished - Feb 2022


  • Chalk
  • Hand-held X-ray fluorescence
  • Multilayer perceptron
  • Multilayer perceptron optimized by genetic algorithm
  • Porosity
  • Random forest
  • Random forest optimized by genetic algorithm

ASJC Scopus subject areas

  • Fuel Technology
  • Geotechnical Engineering and Engineering Geology

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