The aim of the course is to introduce how Machine Learning (ML) is used in predicting fluids and lithology. It will give an understanding of the "workflows" used in ML. The used algorithms can be studied separately using references. Power-point presentations and videos will introduce various aspects of ML, but the emphasis is on computer-based exercises using open-source software.
All those interested in understanding the impact Machine Learning will have on the Geosciences and then as an example the impact on lithology and pore-fluid prediction. Hence, geologists, geophysicists, petrophysicists and engineers, involved in exploration and development of hydrocarbon or mineral resources.
A basic understanding of Geophysics and Statistics. A Pre-requirement quiz can be taken by participants to check whether their knowledge of Geophysics and Statistics is sufficient to follow the course.
The lectures and exercises deal with pre-conditioning the datasets (balancing the input classes, standardization & normalization of data) and applying several methods to classify the data: Bayes, Logistic, Multilayer Perceptron, Support Vector, Nearest Neighbour, AdaBoost, Trees. Non-linear Regression is used to predict porosity.
At the end of the course participants will have a clear idea how Machine learning, being part of Artificial Intelligence will impact the future of Geosciences. This will be evident from the examples of Machine Learning discussed and applied to the case of predicting lithology and pore fluids.