Specific computational tools assist geologists in identifying and sorting lithologies in well
surveys and reducing operational costs and practical working time. This allows for the …

In this paper, an integrated procedure was adopted to obtain accurate lithofacies
classification to be incorporated with well log interpretations for a precise core permeability …

This study aims to put a supervised learning method for automatically classifying lithofacies
in well-logging dataset, where several machine learning algorithms were compared in this …

An integrated multidisciplinary workflow of machine learning and data analytics was
conducted for the multivariate geostatistical characterization of clastic reservoirs. This …

The purpose of this study is to investigate the possibility of applying multilayer perceptron
neural network (MLPNN) and cluster analysis approaches for rebuilding non-cored …

The performance of a naïve Bayes classifier is compared with a well-established statistical
classification approach, linear discriminant analysis, by considering core and log data from …

Facies analysis is crucial for reservoir evaluation because the distribution of facies has
significant impact on reservoir properties. Artificial Neural Networks (ANN) are a powerful …

Geological processes such as weathering and metamorphism normally lead to the
destruction of primary physical features on rocks, making it difficult to accurately classify …

A novel lithofacies and stratigraphic, supervised machine-learning prediction methodology,
coupling a standardized well-log representation with an optimized nearest-neighbour …

Derivative and volatility attributes calculated for well-log versus depth sequences extract
characteristics that can be usefully exploited by automated machine-learning (ML) …