Continuing with the article on machine learning (ML), from the papers extracted in OnePetro, I have been able to detect the use of these ML algorithms:

Neural Network

Genetic Algorithm

Support Vector Machine

Principal Component Analysis

Linear Regression

Fuzzy Logic

Hierarchical

K-Means

Singular Value Decomposition

Decision Tree

Support Vector Regression

Deep Learning

Logistic Regression

Boosting

Random Forest

Nearest Neighbor

Discriminant Analysis

Gaussian Mixture

Gaussian Process Regression

Naive Bayes

Hierarchical Clustering

Reinforcement Learning

K-Nearest Neighbor

Hidden Markov

C-Means

Fuzzy C-Means

Gaussian Mixture Model

Kernel Density Estimation

Gradient Boosting Tree

Kernel Approximation

With a little bit of R magic:

From all these techniques, the top 20 most used are:

If you see I am forgetting a specific machine learning algorithm, please let me know.

Notes. (1) Fuzzy-Logic is not considered a ML technique; rather, belongs to control theory. But still, I am including it here because there is a considerable number of papers using the algorithm. (2) I grouped Convolutional Neural Networks with “Deep Learning”.

So, this is the state of use of machine learning in petroleum engineering.