Deep Learning for Mud Pulser Communications

Mud pulse telemetry is a widely used technology for measurement-while-drilling (MWD) in the oil and gas sector. MPT signals are used in real-time drilling application to provide insight about the pressure, temperature, drill bit direction and deviation, and transmit logging data about the surroundings to improve the reservoir drilling economic efficiency. It transmits signals using the communication channel (drilling fluid column of mud) running between the drill pipe downhole and the receiver at the surface. As mud pulse signals are a physical phenomenon, there requires extensive research on the interference and channel modeling to achieve good communication performance. This project proposes a data driven approach for mud pulse communications to develop channel model and interference mitigation using deep learning. The developed techniques will be validated using the experimental facilities of the industrial partner.

Faculty Supervisor:

Henry Leung

Student:

Partner:

Quantum Energy Technologies

Discipline:

Engineering

Sector:

Mining

University:

University of Calgary

Program: