Nima Daneshvarnejad is a Ph.D. candidate in Petroleum Engineering at the University of Southern California, where he is completing his dissertation on methane emission monitoring and environmental impact assessment of legacy oil and gas assets.
Nima earned his M.S. in Petroleum Engineering from USC in 2022, focusing on machine learning applications, and completed his B.S. in Petroleum Engineering at Sharif University of Technology in Iran in 2018, where he specialized in reservoir engineering and production optimization.
His doctoral research addresses a critical environmental challenge: monitoring low-emitting abandoned and orphaned wells that are often overlooked while industry attention focuses on "super emitters." He has developed an IoT-enabled canopy monitoring system capable of detecting methane emissions as low as 1 gram per hour. His work integrates experimental field research, computational fluid dynamics simulation, and machine learning, particularly liquid time-constant neural networks for emission prediction.
With four published SPE conference papers and a comprehensive journal manuscript in preparation, Nima is actively pursuing to bridge petroleum engineering, environmental science, and data science. His research supports broader sustainability goals by enabling scalable monitoring solutions for abandoned wells across the United States, helping prioritize remediation resources and reduce greenhouse gas emissions from legacy oil and gas infrastructure.