Data Analytics in Oil and Gas

Aasim Waheed has spent 23 years in oil and gas process automation. He started off as an electrical and controls engineer, moved into HCI design, commissioning, project management and finally into business management. He has been running businesses or business units in various companies since 2007. Currently, he is with a large service provider in the oil and gas industry, overseeing two business units and a new product development team.

Aasim is an electrical engineer, and during his career progression, he's done various business and technical courses from MIT Sloan, Chicago Booth, UT Austin and University of Houston. He is also a PMI certified PMP, a TUV certified Functional Safety Engineer, and an ISA Certified Automation Professional and Cyber Security Fundamental Specialist. Currently, he is doing a masters in data analytics from Georgia Tech.

Abstract:
Oil and Gas industry forms the backbone of the energy infrastructure in the US, just like in any other country. It is a highly interconnected system, and due to its criticality and process infrastructure, it needs to be very reliable and run continuously with minimal disruptions. Keeping that operation going uninterrupted is not easy. Even though we have come a long way in the past many years, there are still plenty of opportunities for further improvement of that process. Data analytics and machine learning are excellent tools to make it happen.

This presentation describes the oil and gas value stream at a high level, and touch on some use cases where analytics can help improve the whole process.