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.