Semester of Graduation

Fall

Degree

Master of Science in Engineering Science (MSES)

Department

Engineering Science

Document Type

Thesis

Abstract

Remote monitoring of hydrocarbon wells is a tedious and meticulously thought out task performed to create a cyber-physical bridge between the asset and the owner. There are many systems and techniques on the market that offer this solution but due to their lack of interoperability and/or decentralized architecture they begin to fall apart when remote assets become farther away from the client. This results in extreme latency and thus poor decision making. Microsoft's Azure IoT Edge was the focus of this writing. Coupled with off-the-shelf hardware, Azure's IoT Edge services were integrated with an existing unit simulating a remote hydrocarbon well. This combination successfully established a semi-autonomous IIoT Edge device that can monitor, process, store, and transfer data locally on the remote device itself. These capabilities were performed utilizing an edge computing architecture that drastically reduced infrastructure and pushed intelligence and responsibility to the source of the data. This application of Azure IoT Edge laid a foundation from which a plethora of solutions can be built, enhancing the intelligence capability of this asset. This study demonstrates edge computing's ability to mitigate latency loops, reduce network stress, and handle intermittent connectivity. Further experimentation and analysis will have to be performed at a larger scale to determine if the resources implemented will suffice for production level operations.

Committee Chair

Tyagi, Mayank

DOI

10.31390/gradschool_theses.5237

Download

Share

COinS