ADF: Drilling Data Analytics tool

Drilling data is usually very noisy and require filtering (de-noising) before applying advanced data analytics methods. Conventional filters often remove, in addition to the noise, valuable information from the signal. This hides important information on small events and onsetting drilling problems. Professor Alexey Pavlov and his team developed a method, called Adaptive Differentiating Filter, which solves this problem by automatically tuning filter parameters to the signal properties in real time. In addition to efficient noise filtering, the method automatically calculates trends in drilling data and highlights periods of suspicious changes in the measurements.  This enables early detection of onsetting drilling problems and identification of small drilling events.