Working with Schlumberger on drill system parameter estimation and hazardous event detection.

Dan graduated with an MMath degree from the University of Sussex in 2016 and an MSc in Applied Mathematics at the University of Bath in 2017. He has undertaken various summer projects within the fields of harmonic analysis and wavelet theory, while his Master’s thesis focused on a numerical spectral approximation of a class of oscillatory integral operators. Dan’s exposure to Python via this project led him to become interested in machine learning and Bayesian programming, and he particularly enjoys how the rigorous results of probability can be used to powerful effect in everyday life. Away from mathematics, he enjoys learning and listening to music, football and (occasionally) cooking.

Research project title:
On-line drill system parameter estimation and hazardous event detection

Supervisor(s):
Kari Heine, Mark Opmeer, Inês Cecilio

Project description:
Dan’s research, in collaboration with Schlumberger, develops statistical methods for automatic detection of hazardous events in oil and gas drilling operations. Initially, only two particular hazardous events are considered. The first is called washout and it means the appearance of a hole in the drill pipe which may compromise the safety and efficiency of the operation as well as equipment integrity. The second event is called mud loss and it means the loss of drill  due to a leakage in the drill system to the surrounding rock formation. As the project progresses, more complex scenarios will be considered, involving multiphase flow, influx of gas from the formation, accumulation of rock cuttings around the drill pipe, wear of the drill bit, plugged bit nozzles, or the degradation of the motor. The initially one dimensional model could also be extended to two or three dimensions for increased accuracy.