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    Oil and Gas Training Courses SLB NEXT

    Machine Learning for Facies Prediction

    The aim of the course is to introduce how Machine Learning (ML) can be used in predicting facies along a borehole. It will give an understanding of the “workflows” used in ML. Power-point presentations and videos will introduce various aspects of ML, but the emphasis is on computer-based exercises using an open-source software package. In the course a Well Facies data set is used for applying a range of ML prediction methods.

    All those interested in understanding the impact Machine Learning will have on the Geosciences. Hence, geologists, geophysicists and engineers, involved in exploration and development of hydrocarbon, mineral resources and investigation of the shallow subsurface.

    The lectures and exercises deal with pre-conditioning the datasets (balancing, standardization, normalization) and applying several methods to classify data: Bayes, Logistic, Multilayer Perceptron, Support Vector, Nearest Neighbour, AdaBoost, Trees. The algorithms can be studied separately using the provided references. From a so-called labelled data set a prediction “model” is derived, which is then used to predict the facies of unlabeled data. Non-linear Regression is applied to predict porosity.

    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.

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