Machine learning-based classification of petrofacies in fine laminated limestones

Characterization and development of hydrocarbon reservoirs depends on the classification of lithological patterns from well log data. In thin reservoir units, limited vertical data impedes the efficient classification of lithologies. We present a test case of petrofacies classification using machine...

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Bibliographic Details
Published in:	Anais da Academia Brasileira de Ciências Vol. 96; no. 1; p. e20230041
Main Authors:	Genesis, Gallileu, Gomes, Igor F, Barbosa, José Antonio, Araújo, Araly Fabiana L DE, Ramos, Germano Mário S
Format:	Journal Article
Language:	English
Published:	Brazil Academia Brasileira de Ciências 01-01-2024
Subjects:	Bayes Theorem Geologic Sediments laminated limestones Machine Learning MULTIDISCIPLINARY SCIENCES Oil and Gas Fields petrofacies classification reservoir modeling Support Vector Machine thin reservoir unit petrofacies classification thin reservoir unit machine learning reservoir modeling laminated limestones
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Summary:	Characterization and development of hydrocarbon reservoirs depends on the classification of lithological patterns from well log data. In thin reservoir units, limited vertical data impedes the efficient classification of lithologies. We present a test case of petrofacies classification using machine learning models in a thin interval of finely laminated limestones using pseudo-well data created over outcrops (radiometric and unconfined compressive strength logs). We tested Gaussian naïve Bayes (GNB) and support vector machine (SVM) techniques to classify eight petrofacies types, divided into two groups. The objective was to observe the capacity of some well-known models to classify petrofacies with a high-frequency vertical variation of diagenetic heterogeneities in an extreme scenario within a thin sedimentary interval. The GNB was less effective (F 1 score of 0.29), and the SVM achieved the best results in classifying the main facies patterns (F 1 = 0.47). However, the GNB performed better when the analysis was focused on distinguishing the two main groups of petrofacies. The results demonstrate that high-frequency facies variations present a challenge to the automatic identification of lithofacies, mainly due to local variations in horizontal heterogeneities (on the mm- to cm-scale) created by depositional and diagenetic processes, which impact the flow in porous media.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0001-3765 1678-2690 1678-2690
DOI:	10.1590/0001-3765202420230041