Probability distribution assessment for classifying subterranean formations using machine learning

Abstract

According to some aspects, machine-learning models can be executed to classify a subsurface rock. Examples include training numerous machine-learning models using training data sets with different probability distributions, and then selecting a model to execute on a test data set. The selection of the model may be based on the similarity of each data point of the test data set and the probability distribution of each training class. Examples include detecting and recommending a pre-trained model to generate outputs predicting a classification, such as a lithology, of a test data set. Recommending the trained model may be based on calculated prior probabilities that measure the similarity between the training and test data sets. The model with a training data set that is most similar to the test data set can be recommended for classifying a physical property of the subsurface rock for hydrocarbon formation.