Taha Yehia

Taha Yehia is pursuing his PhD in petroleum engineering at Texas A&M University, where his work brings together reservoir engineering and emerging energy technologies. With a background that spans both conventional oil and gas development and the energy transition, he focuses on advancing CO₂ storage, hydrogen storage, and geothermal applications through computational modeling and artificial intelligence.

His research combines machine learning and reinforcement learning to address challenges in subsurface systems. Projects have ranged from optimizing CO₂ injection strategies and geothermal reservoir design to improving shale gas reserve estimation, decline curve analysis, and drilling efficiency. A central theme in his work is the use of reduced-order modeling and AI-based optimization to shorten evaluation cycles and deliver practical insights for field operations.

In addition to research, he has held leadership roles including co-chairing the inaugural Aggie–Longhorn Leading Innovation in Energy Symposium (ALLIES 2026), led the
Technical Exhibition and Networking Event (TENE 2025) at Texas A&M’s SPE chapter, and has been an active contributor to the SPE Gulf Coast Section Data Science Study Group as well as SPE Young Professionals in Egypt. Through these roles he has organized technical programs, expanded industry–student engagement, and strengthened professional networks.

His work has resulted in more than 20 technical papers and numerous conference presentations, reflecting his commitment to advancing energy engineering through innovation, collaboration, and digital transformation.