Latin American Oil Export Destination Choice: A Machine Learning Approach We implement machine learning techniques to predict the destination for Latin American crude oil exports. Utilizing a unique dataset of microlevel crude oil shipment data, derived from the Automatic Identification System (AIS) for ship tracking, we investigate the micro and macrolevel determinants of the destination choice. We use decision tree, Random Forests and boosted trees techniques in training a model to predict the export destinations which can help to identify sellerbuyer groups with similar oil trade requirements. The results show that while macro data, such as regional oil price differences and crack spreads, impacts the crude oil flow, micro level information about the oil shipment are key attributes in the destination prediction. Our research has practical implications, particularly with regards to prediction of oil transportation demand, spatial price arbitrage and shortterm forecasting of regional crack spreads.

Machine Learning Based Approach to Predict ShortTerm Fuel Consumption on Mobile Offshore Drilling Units The application of machine learning models for optimization and improved decisionmaking has great potential in the drilling industry. This paper demonstrates a model for predicting fuel consumption on a Mobile Offshore Drilling Unit (MODU) with a Multilayer Perceptron (MLP) artificial neural network. The model is proposed as a tool for setting fuel consumption related performance goals for offshore personnel on a MODU. Operational and environmental data have been used as input variables for the model, with a dataset split into 80% training set and 20% test set. The highest performance is obtained with three hidden layers with 38 nodes each. The Adam solver performs better than the Stochastic Gradient Descent (SGD) solver for weight optimization, and the best  alpha  parameter for the L2 regularization term is 0.0001 with the Adam solver. The MLP regression model predicts fuel consumption for the test set with a Root Mean Squared Error (RMSE) of 0.0770. This result indicates that artificial neural networks and the MLP regressor is a suitable algorithm for predictive modelling of fuel consumption on a MODU.