Published in SPE Journal, 2025
Journal article on long-term CO₂ storage risk assessment using survival analysis-based surrogates.
Recommended citation: Gurwicz, Allan, Chen, Jungang, Gutman, David H., & Gildin, Eduardo. (2025). Assessing Risk in Long-Term CO₂ Storage Under Uncertainty via Survival Analysis-Based Surrogates. SPE Journal, 30(05), 2837–2854. Society of Petroleum Engineers.
-->Published in International Journal of Greenhouse Gas Control, 2024
Journal paper on optimizing CO₂ storage pressure management strategies with surrogate model-based reinforcement learning.
Recommended citation: Chen, Jungang, Gildin, Eduardo, & Kompantsev, Georgy. (2024). Optimization of pressure management strategies for geological CO₂ storage using surrogate model-based reinforcement learning. International Journal of Greenhouse Gas Control, 138, 104262. Elsevier.
-->Published in Mathematics, 2023
Journal paper on transfer learning-based physics-informed CNNs for porous media flow with dynamic controls.
Recommended citation: Chen, Jungang, Gildin, Eduardo, & Killough, John E. (2023). Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls. Mathematics, 12(20), 3281.
-->Published in Computational Geosciences, 2023
Journal paper on generating subsurface earth models with discrete representation learning and deep autoregressive networks.
Recommended citation: Chen, Jungang, Misra, Siddharth, Huang, Chung-Kan, & Delgado, Jose F. (2023). Generating subsurface earth models using discrete representation learning and deep autoregressive network. Computational Geosciences, 27, 955–974. Springer Nature.
-->Published in International Petroleum Technology Conference, 2024
Conference paper on generative AI for geomodeling.
Recommended citation: Misra, Siddharth, Chen, Jungang, Churilova, Polina, & Falola, Yusuf. (2024). Generative Artificial Intelligence for Geomodeling. International Petroleum Technology Conference.
-->Published in SPE Annual Technical Conference and Exhibition, 2024
Conference paper on assessing CO₂ storage risk using survival analysis-based surrogates.
Recommended citation: Gurwicz, A., Chen, J., Gutman, D. H., & Gildin, E. (2024). Assessing risk in long-term CO₂ storage under uncertainty via survival analysis-based surrogates. SPE Annual Technical Conference and Exhibition.
-->Published in SPE Annual Technical Conference and Exhibition, 2024
Conference paper on advancing proxy modeling in reservoir simulation with a multi-step embed to control approach.
Recommended citation: Chen, Jungang, Gildin, Eduardo, & Killough, John. (2024). Advancing Proxy Modeling in Reservoir Simulation: A Multi-Step Embed to Control Approach. SPE Annual Technical Conference and Exhibition.
-->Published in SPE Europec featured at EAGE Conference and Exhibition, 2023
Conference paper on geomodel compression and generation using autoencoders and autoregressive neural networks.
Recommended citation: Misra, Siddharth, Chen, Jungang, Falola, Yusuf, Churilova, Polina, Huang, Chung-Kan, & Delgado, Jose. (2023). Massive geomodel compression and rapid geomodel generation using advanced autoencoders and autoregressive neural networks. SPE Europec at EAGE Conference and Exhibition.
-->Published in SPE Annual Technical Conference and Exhibition, 2022
Conference paper on coupled flow and geomechanics in fractured reservoirs using embedded meshes.
Chen, Jungang, Wu, Kan, & Killough, John. (2022). Coupled Flow and Geomechanics in Reservoirs with Complex Fractures Using Embedded Meshes. SPE Annual Technical Conference and Exhibition.
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Published in arXiv preprint, 2025
Preprint introducing a game-theoretic perspective on optimal CO₂ storage management with safety constraints across multiple stakeholders.
Chen, Jungang, & Hosseini, Seyyed A. (2025). Optimal CO₂ storage management considering safety constraints in multi-stakeholder multi-site CCS projects: a game theoretic perspective. arXiv preprint arXiv:2508.11618.
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Published in arXiv preprint, 2024
Preprint on a deep learning-based approach for surrogate modelling in reservoir simulation.
Chen, Jungang, Gildin, Eduardo, & Killough, John. (2024). Multi-Step Embed to Control: A Novel Deep Learning-based Approach for Surrogate Modelling in Reservoir Simulation. arXiv preprint arXiv:2409.09920.
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Published in arXiv preprint, 2023
Preprint introducing a physics-informed convolutional recurrent surrogate model for reservoir simulation with well controls.
Chen, Jungang, Gildin, Eduardo, & Killough, John E. (2023). Physics-informed convolutional recurrent surrogate model for reservoir simulation with well controls. arXiv preprint arXiv:2305.09056.
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