Lithofacies Classification Using Supervised and Semi-Supervised Machine Learning Approach

Lilik T. Hardanto, Lili Ayu Wulandhari


The machine learning approach can help Geoscientists do their work in well log analysis to developing the oil and gas field. Prediction categorical or numerical response variable using a set of predictor variables supervises and semi-supervises learning is an important goal of the machine learning approach in classifying lithofacies using well log data. Semi-supervised classification offers the possibility of exploring the structure of the data without entirely external knowledge or guidance in the form of target or class information, and semi-supervised is very rarely research in the field of lithofacies classification.  Well log data in gamma-ray, resistivity, neutrality, and density logs are collected and selected for data processing and transformation. The use of machine learning algorithms such as Naïve Bayes, SVM, and Decision Tree is to find the log pattern or pattern classifications of lithofacies in supervised and semi-supervised to create a model with conditions requiring the change of data and the corresponding requirements. All supervised machine learning algorithms have the best accuracy because algorithms provide useful predictive in classifications based on the target but not if there are no targets given or semi-supervised. This paper compares some of the famous classification algorithms of machine learning, such as Decision tree, SVM, and Naïve Bayes, on classifying lithofacies with supervised and semi-supervised learning. This research found that the semi-supervised learning of Naïve Bayes has performed well in classified lithofacies. In contrast, in supervised learning, Decision Tree and SVM are superior in accuracy and visualization approach based on expert’s interpretation.


Machine learning; decision tree; SVM; naïve bayes; lithofacies; supervised; semi-supervise learning.

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P. Dell’Aversana, “Comparison of different machine learning algorithms for lithofacies classification from well logs,” Bollettino Di Geofisica Teorica Ed Applicata,, 2019

M.K.Dubois, A.P.Byrnes, G.C.Bohling, S.C.Seals and J.H.Doveton, “Statistically-based lithofacies predictions for 3-D reservoir modelling: Examples from the Panama (Council Grove) Field, Hugoton embayment, Southwest Kansas,” American Association of Petroleum Geologists, Annual Convention, Salt Lake City, Utah, 2003.

M.K.Dubois, A.P.Byrnes, G.C.Bohling, S.C.Seals and J.H.Doveton, “Multiscale geologic and petrophysical modelling of the giant Hugoton Gas Field (Permian),” Kansas and Oklahoma, U.S.A. In AAPG Memoir., 2006.

J. C.Ordóñez-Calderón and S. Gelcich, “Machine learning strategies for classification and prediction of alteration facies: Examples from the Rosemont Cu-Mo-Ag skarn deposit, SE Tucson Arizona,” Journal of Geochemical Exploration., 2018.

S.Bhattacharya and S.Mishra, “Applications of machine learning for facies and fracture prediction using Bayesian Network Theory and Random Forest: Case studies from the Appalachian Basin,” USA. Journal of Petroleum Science and Engineering., 2018.

T.Wrona, I.Pan, R.L.Gawthorpe and H.Fossen, “Seismic facies analysis using machine learning. Geophysics,”, 2018.

X.Wang, S.Yang, Y.Zhao and Y. Wang, “Lithology identification using an optimized KNN clustering method based on entropy-weighed cosine distance in Mesozoic strata of Gaoqing field,” Jiyang depression. Journal of Petroleum Science and Engineering., 2018.

P.Bestagini, V. Lipari and S. Tubaro, “A machine learning approach to facies classification using well logs,” 87th Annual International Meeting, SEG, Expanded Abstracts, 2137–2142, doi: 10.1190/segam2017-17729805.1. 2017.

Opitz, D., Maclin, R. Popular Ensemble Methods: An Empirical Study

Journal of Artificial Intelligence Research, 11, pp. 169-198, 1999.

Cardarilli, G.C., Di Nunzio, L., Fazzolari, R., Giardino, D., Matta, M., Re, M., Silvestri, F., Spanò, S. Efficient ensemble machine learning implementation on FPGA using partial reconfiguration Lecture Notes in Electrical Engineering, 550 (9783030119720), pp. 253-259, 2019.

O.Bello, C. Teodoriu, T.Yaqoob , J.Oppelt, J.Holzmann and A.Obiwanne, “Application of artificial intelligence techniques in drilling system design and operations: A state of the art review and future research pathways,” Society of Petroleum Engineers - SPE Nigeria Annual International Conference and Exhibition., 2016.

James, G., Witten, D., Hastie, T., & Tibshirani, R,” An Introduction to Statistical Learning with Applications in R,” In Springer., 2013.

K. M. Ting, “Confusion Matrix. In Encyclopedia of Machine Learning and Data Mining,”, 2017.

L.Hamel, “Knowledge Discovery with Support Vector Machines. In Knowledge Discovery with Support Vector Machines,”, 2009.

E. Alpaydin, “Introduction to Machine Learning Ethem Alpaydin. Introduction to Machine Learning, “Third Edition, 2014.

J.Han, M. Kamber and J. Pei, “Data Mining: Concepts and Techniques. In Data Mining: Concepts and Techniques,”, 2012.

I. Guyon, “A practical guide to model selection,” In: Marie, J. (Ed.), Proceedings of the Machine Learning Summer School. Canberra, Australia, January 26 - February 6, Springer Text in Statistics, Springer p.37, 2009.

M.A. Sebtosheikh and A.Salehi, Lithology prediction by support vector classifiers using inverted seismic attributes data and petrophysical logs as a new approach and investigation of training data set size effect on its performance in a heterogeneous carbonate reservoir, Journal of Petroleum Science and Engineering, 134 143–149, 2015.

Ray, S. “6 Easy Steps to Learn Naive Bayes Algorithm (with code in Python),” Analytics Vidhya, 2015.

P. A. Harrison, R.Dunford, D. N.Barton, E.Kelemen, B.Martín-López, L.Norton, M.Termansen, H. Saarikoski, K.Hendriks, E.Gómez-Baggethun, , B.Czúcz, M.García-Llorente, D.Howard, S. Jacobs, M.Karlsen, L.Kopperoinen, A.Madsen, G.Rusch, M.van Eupen, G. Zulian, “Selecting methods for ecosystem service assessment: A decision tree approach ecosystem services,”, 2018.

A. S.J.Iyer and R.Sumbaly, ”Diagnosis of diabetes using classification mining techniques,” International Journal of Data Mining & Knowledge Management Process,, 2015.



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