Talk · International Conference on Electronics, Computer and Computation · November 1, 2018

Machine Learning Algorithms for Classification Geology Data from Well Logging

Timur Merembayev, Rassul Yunussov, Yedilkhan N. Amirgaliyev

Abstract

Machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words - it is a great tool to describe nonlinear phenomena. We tried to use this technique to improve existing process of stratigraphy and lithology interpretation and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article describes usage of machine learning algorithms for several geology data stratigraphy and lithology boundaries classification based on geophysics logging data for deposits in Kazakhstan. Correct marking of stratigraphy and lithology from geophysics logging data is complex non-linear task. To solve this task we applied several algorithms of machine learning: random forest, logistic regression, gradient boosting (scikit-learn library), k - nearest neighbour (KNN) and XGBoost.

Geochemistry and Geologic MappingHydrocarbon exploration and reservoir analysisData Mining and Machine Learning Applicationswell logslithology interpretationmachine learning

Machine Learning Algorithms for Classification Geology Data from Well Logging

Abstract

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