首页|Comparison of machine learning techniques for predicting porosity of chalk
Comparison of machine learning techniques for predicting porosity of chalk
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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 Rordal 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.
PorosityChalkHand-held X-ray fluorescenceRandom forestMultilayer perceptronRandom forest optimized by genetic algorithmMultilayer perceptron optimized by genetic algorithm
Meysam Nourani、Najeh Mali、Saeed Samadianfard
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Reservoir Geology Department, Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark
College of Petroleum Engineering, Al-Ayen University, Thi-Gar, 64001, Iraq
Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
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