Celebrating Our Summer 2026 M.S. Graduates

Congratulations to Our Summer 2026 Graduates 🎓

Two of our M.S. students received their degrees this August — 박은실 (Eunsil Park), who now begins her career at SLB (Schlumberger), and 이영진 (Yongjin Lee), who continues into the Ph.D. program under Prof. Hyundon Shin. Between them they carried CO₂ storage monitoring and eco-friendly oil sands recovery forward, and both leave CURE stronger than they found it.

🎓 M.S. · August 2026 📍 Inha University 💼 SLB (Schlumberger) 🔬 Ph.D. Program · CURE

박은실 (Eunsil Park) — M.S., Summer 2026

이영진 (Yongjin Lee) — M.S., Summer 2026

박은실 · Eunsil Park

Joining SLB

Research Focus

InSAR · THM Simulation · CCS Monitoring

Linking satellite-observed surface deformation to what is actually happening in the reservoir — coupling geomechanics, flow, and data assimilation into one workflow for CO₂ storage characterization.

Next Chapter

SLB (Schlumberger)

Taking her subsurface modeling expertise into industry, where the same questions about uncertainty, monitoring, and reservoir performance are asked at full field scale.

Eunsil leaves behind a research record any graduate would be proud of — first-author papers in two leading journals and a poster at Europe’s largest geosciences meeting.

• IEEE TGRS · 2026
  Park, E., Kim, H., Jo, H., & Pyrcz, M. J. (2026). Multi-Scale Joint Deformation–BHP Data Assimilation for CO₂ Storage Reservoir Characterization Using ES-MDA. IEEE Transactions on Geoscience and Remote Sensing.
• Int. J. Greenhouse Gas Control · 2025
  Park, E., Kim, H., Shin, H., & Jo, H. (2025). Deep learning-assisted THM-integrated InSAR modeling for CO₂ storage characterization and surface deformation forecasting. International Journal of Greenhouse Gas Control, 147, 104461.
• EGU General Assembly · 2026 · Poster
  Park, E., Lee, H., Yoon, J., & Jo, H. (2026). SinFusion-based Geological Model Augmentation and Well Data Integration. EGU General Assembly 2026, Vienna, Austria.

She also contributed to collaborative work on SinGAN–ES-MDA history matching, seismic-driven geostatistical ensemble modeling of the West Sea CO₂ storage reservoir, and generative-AI digital rock augmentation — a reminder of how much a single dedicated student can lift an entire lab.

이영진 · Yongjin Lee

Continuing to Ph.D.

Research Focus

SAGD · Reinforcement Learning · ES-SAGD

Treating thermal recovery as a sequential decision problem — using reinforcement learning to search for steam and solvent injection strategies that a fixed operating rule would never find.

Next Chapter

Ph.D. Program · Prof. Hyundon Shin

Staying with CURE to take his master's work further, deepening the link between experimental ES-SAGD evidence and intelligent operation control for lower-carbon oil sands development.

🏅 A standout contribution to the ES-SAGD experiment. Yongjin played a central role in our expansion-solvent SAGD experimental campaign, where laboratory work is unforgiving and every result is hard won. What he achieved there gave the group experimental grounding for the solvent co-injection behavior we simulate — exactly the kind of bridge between bench and simulator that heavy-oil research needs.

Pairing hands-on experimental work with reinforcement learning is an unusual combination, and a powerful one: the experiments tell you what the physics really does, while the learning agent explores an operating space far larger than anything that can be tested by hand. We are delighted he is continuing that line of work with us.

Wishing You Both the Very Best! 🚀

Thank you for your curiosity, your persistence through every failed simulation run and every long day in the lab, and the generosity you both showed to the junior students who came to you for help. CURE is a better lab because you were part of it.

Eunsil — we could not be prouder to see you join SLB, and we know you will do remarkable things there. Yongjin — welcome to the next stage; we cannot wait to see where your Ph.D. takes you.

축하합니다, 은실 & 영진! 두 분의 앞날에 늘 행복과 좋은 일만 가득하기를 바랍니다. 🎓