Prediction of well production event using machine learning algorithms

In this paper, a new approach was identified and tested to detect abnormal events in producing wells when a labeled dataset is unavailable or the number of instances are below 10% and are insufficient for conventional modelling methods. Autoencoders (AE), a type of unsupervised learning, are trained to learn normal behavior by trying to...

An intelligent operator support system for dynamic positioning

This paper proposes a human-centered approach to Dynamic Position-ing systems which combines multiple technologies in an intelligent operator sup-port system (IOSS). IOSS allows the operator to be roaming and do other tasks in quiet conditions. When conditions become more demanding, the IOSS calls the operator to return to his bridge position....

Arriving on time using uncertainty aware deep learning

The Port of Rotterdam is an important hub in international maritime supply chains. Efficient functioning of the port requires accurate planning of the processes, thus reducing waiting times and costs for all parties. Therefore, predictions should be incorporated in the planning of all logistic processes. In particular, uncertainty information is...

An intelligent operator support system for Dynamic positioning

This paper proposes a human-centered approach to Dynamic Positioning systems which combines multiple technologies in an intelligent operator support system (IOSS). IOSS allows the operator to be roaming and do other tasks in quiet conditions. When conditions become more demanding, the IOSS calls the operator to return to his bridge position. In...

Getting logistics closer to end users

Nowadays, logistics could make you purchase a book in Amazon and delivering it in 24-hours using a service from a logistics operator or a transport company. In a more complex scenario, an order could be traveling around the globe, passing from hand to hand between unknown actors for months. In most cases the client would not be notified in a...