
Michael Connolly
Michael Connolly joined DeGolyer and MacNaughton in 2025 with a background spanning reservoir engineering, strategy consulting, and investment banking. His technical expertise includes compositional simulation, thermal EOR, and phase behavior modeling. Prior to joining D&M, he worked at Evercore, McKinsey & Company, and BP. He has over 10 years of experience in the industry.
Connolly earned a doctorate in petroleum engineering from Stanford University in 2018. He also holds a master’s degree in petroleum and natural gas engineering from Penn State University and bachelor’s degrees in petroleum engineering (with honors) and finance from the University of New South Wales.
Geographical Experience
- Australia
- Colombia
- Papua New Guinea
- Peru
- Thailand
- United Kingdom
- United States
Topical Areas of Expertise
- Phase behavior
- Compositional and thermal reservoir simulation
- Miscible and immiscible gas flooding
- Corporate finance and asset evaluations
- Technical due diligence and transaction support
- Performance turnarounds
Major Projects
Prior to joining D&M, Connolly was a Senior Associate with Evercore in Houston, where he advised on upstream and LNG transactions valued at more than $3 billion. He has worked on transactions for Ecopetrol, PetroTal, Mubadala, New Fortress Energy, and various private equity firms. He has extensive experience in financial modeling, asset valuations, the execution of A&D and M&A transactions, and project finance advisory across a range of fiscal regimes in North and South America.
Earlier in his career, Connolly served as an Engagement Manager in the oil and gas practice at McKinsey & Company, where he led strategy, capital allocation, and performance improvement programs for oil and gas clients with operations in the United States, Latin America, and Southeast Asia. He began his career as a reservoir engineer with Oil Search and later worked with BP in Alaska, where he developed compositional models for Prudhoe Bay and supported carbonate reservoir recharacterization using pressure transient analysis and discrete fracture network models.