CURE Lab — Publications

Peer-reviewed research in subsurface engineering, geological carbon storage, unconventional energy, and AI-driven geoscience.

Hyundon Shin PI · Inha University
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Honggeun Jo Co-PI · Inha University
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2026
14 journal · 5 conference
Journal Articles
  1. Jo, H., Park, E., & Ahn, S. (2026). From a Single Geological Interpretation to History Matching: A SinGAN–ES-MDA Framework for CO₂ Storage in Channelized Aquifers. Geoenergy Science and Engineering, 214709.
  2. 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.
  3. Merzoug, A., Jo, H., & Pyrcz, M. J. (2026). Generalized Conditioning of Generative Artificial Intelligence for History Matching Subsurface Models. Mathematical Geosciences, 58(2), 313–346.
  4. Chacon-Buitrago, N., Laugier, F. J., Jo, H., & Pyrcz, M. J. (2026). GeoRulesLobePy: A Markov chain-based approach for rule-based deepwater lobe training images in subsurface modeling. AAPG Bulletin.
  5. Choi, S., Chae, M., Yoon, S., Kim, T. W., Jo, S., Choi, B. I., Jo, H., & Min, B. (2026). Integrated design and optimization for a unified carbon capture and storage system using a machine-learning-assisted multi-objective optimization framework. Journal of CO₂ Utilization, 106, 103402.
  6. Yingsirisit, N., Ramadhan, R., Hosseini, S. A., Jo, H., Vo Thanh, H., Tippayawong, N., & Tangparitkul, S. (2026). Seasonal and Long-Term Underground Hydrogen Storage in Tight Reservoir: Hysteresis Effects and Scaling Assessment for Energy Transition at the Mae Moh Coal-Fired Power Plant. ACS Omega, 11(36), 54322–54331.
  7. Kim, N., & Shin, H. (2026). Optimization of SAGD well elevation utilizing characteristics of vertical permeability distribution in multi-realized 3-D reservoir models. Fuel, 420, 139018.
  8. Cho, S., Kim, H., Park, E., Kim, J., Jo, H., Byun, J., & Pyun, S. (2026). Assessment of Facies and Porosity Uncertainty in a West Sea CO₂ Storage Reservoir Using 3D Seismic-Driven Geostatistical Ensemble Modeling Techniques. Geophysics and Geophysical Exploration, 29(1), 76–86.
  9. Gong, H. J., Liu, Z. S., Gao, F., Bao, Q. Z., Jo, H., & Zhang, J. H. (2026). AVO impedance three-parameter non-linear inversion method based on gradient calculation of the adjoint state equation. Chinese Journal of Geophysics, 69(2), 857–872.
  10. Seol, J., Kim, N., Afrireksa, B. D., Jo, H., & Shin, H. (2026). Effect of Permeability on CO₂ Storage and Injectivity in Low-Permeability Saline Aquifers. Journal of the Korean Society of Mineral and Energy Resources Engineers, 63.
  11. Kim, D., & Jo, H. (2026). Sensitivity Analysis of Maximum Choke Pressure and Maximum Kick Volume under Drilling Operation Conditions. Journal of the Korean Society of Mineral and Energy Resources Engineers, 63.
  12. Kim, D., Eo, J., Park, E., Lee, M., Keehm, Y., Lee, K., & Jo, H. (2026). Generative Artificial Intelligence-Based Carbon Capture and Storage Caprock Shale Digital Rock Data Augmentation. Journal of the Korean Society of Mineral and Energy Resources Engineers, 63.
  13. Kim, S., Kim, N., Shin, H., Luo, X., & Lee, K. (2026). Impact of Non-Condensable Gas Selection on CO₂ Mitigation and Economic Viability in Steam and Gas Push. International Journal of Energy Research, 2026, 9930010.
  14. Kim, S., Kim, N., Shin, H., Kim, K., Park, C., & Lee, K. (2026). Economic Evaluation of CO₂-using Steam and Gas Push for Eco-friendly Oil Sands Production. Journal of the Korean Society of Mineral and Energy Resources Engineers, 63.
Conference Proceedings
  • Ismodes, A. V. S., Afrireksa, B. D., Shin, H., & Jo, H. (2026). Machine-learning assisted assessment of CO₂ leakage through adjacent wells in geological carbon storage. EGU General Assembly 2026, Vienna, Austria. Oral
  • Park, E., Lee, H., Yoon, J., & Jo, H. (2026). SinFusion-based Geological Model Augmentation and Well Data Integration. EGU General Assembly 2026, Vienna, Austria. Poster
  • Choi, S., Hernandez-Mejia, J. L., Jo, H., & Pyrcz, M. J. (2026). Spatiotemporal Shapley Values: A New Diagnostic Method for Quantifying Geological Uncertainty in Dynamic Reservoir Responses. IAMG 2026 Annual Conference, Montreal, Canada.
  • Park, E., Jo, H., Yoon, J., Min, B., & Pyrcz, M. J. (2026). Scalable 3D Patch-Based Diffusion Modeling for Field-Scale Channelized Reservoirs. IAMG 2026 Annual Conference, Montreal, Canada.
  • Min, B., Kim, T., Choi, S., Jo, S., Jo, H., & Chae, M. (2026). Machine-Learning-Assisted Multi-Objective Design of Integrated CO₂ Transport–Injection–Storage Systems for Geological Carbon Storage. IAMG 2026 Annual Conference, Montreal, Canada.
2025
10 journal · 1 conference
Journal Articles
  1. Liu, L., Maldonado-Cruz, E., Jo, H., Prodanović, M., & Pyrcz, M. J. (2025). Data Conditioning for Subsurface Models with Single-Image Generative Adversarial Network (SinGAN). Mathematical Geosciences, 57(8), 1451–1470.
  2. Liu, L., Salazar, J. J., Jo, H., Prodanović, M., & Pyrcz, M. J. (2025). Minimum acceptance criteria for subsurface scenario-based uncertainty models from single image generative adversarial networks (SinGAN). Computational Geosciences, 29(1), 6.
  3. 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.
  4. Kim, J., Kim, D., Jo, W., Kim, J., Jo, H., & Choe, J. (2025). Physics-Informed Sampling Scheme for Efficient Well Placement Optimization. Journal of Energy Resources Technology, Part B: Subsurface Energy and Carbon.
  5. Kim, D., King, M., Jo, H., & Choe, J. (2025). Fast and Reliable History Matching of Channel Reservoirs Using Initial Models Selected by Streamline and Deep Learning. Journal of Energy Resources Technology, Part B: Subsurface Energy and Carbon.
  6. Cuenca, Y. R., Nguyen, L. V., Yuan, W., Shin, H., & Chaianansutcharit, T. (2025). Development of prediction models for storage efficiency factor to estimate volumetric CO₂ storage capacity in saline aquifer. Geosystem Engineering, 28(6), 435–452.
  7. Baek, I., Kim, N., Shin, H., & Chaianansutcharit, T. (2025). Proxy Model-Driven Optimization of CO₂ Operating Condition and Hydraulic Fracturing Design for Maximizing EGR-CCS Performance in the Duvernay Shale Formation, Canada. Gas Science and Engineering, 205731.
  8. Gomes, A. F., Kim, N., & Shin, H. (2025). Cost Analysis of the Blue Hydrogen Supply from Canada to Korea. Journal of the Korean Society of Mineral and Energy Resources Engineers, 62.
  9. Kim, S., Shin, H., Park, C., Chen, Z., & Lee, K. (2025). A review of design factors in steam and gas push for eco-friendly oil sands production and its field application in Canada. Journal of Petroleum Exploration and Production Technology, 15(1), 8.
  10. Thanasaksukthawee, V., Patthanaporn, T., Bangpa, N., Suwannathong, A., Shin, H., et al. (2025). Assessing the geological storage capacity of CO₂ in Khorat Sandstone: Geochemistry and fluid flow examinations. International Journal of Greenhouse Gas Control, 141, 104322.
Conference Proceedings
  • Jo, H., Park, E., Kim, D., Eo, J., Lee, K., & Shin, H. (2025). Generative AI-assisted Digital Rock Augmentation for Uncertainty Assessment. Geological Society of Korea Annual Conference.
2024
13 journal
Journal Articles
  1. Kim, H., Shin, H., & Jo, H. (2024). Uncertainty Quantification Based on Deep-Learning Approach Integrating Time-Lapse Seismic Data for Geological Carbon Storage. Lithosphere, 2024(4).
  2. Kim, D., Kim, D., Jo, W., Choe, J., & Jo, H. (2024). Improved Injection Schedules of CO₂ for Pohang Basin, Yeongil Bay, South Korea: Regarding Field Security and Injection Effectiveness. Lithosphere, 2024(4).
  3. Kim, M. J., Jo, H., Park, H., & Cho, Y. (2024). Sequential binary classification of lithofacies from well-log data and their uncertainty quantification. Interpretation, 12(4), T573–T584.
  4. Lee, Y., Kim, D., Jo, H., & Choe, J. (2024). Application of latent variable evolution for channel reservoir characterization using generative adversarial networks and particle swarm optimization. Geoenergy Science and Engineering, 240, 213016.
  5. Kim, N., Jo, H., & Shin, H. (2024). Field-scale SAGD performance evaluation utilizing homogeneous reservoir model based on vertical wells. Journal of the Korean Society of Mineral and Energy Resources Engineers, 61.
  6. Kim, H., Kim, N., Shin, H., & Jo, H. (2024). Machine learning-based 4-D seismic data integration and characterization of channelized anticline aquifer for geological carbon sequestration. Journal of the Korean Society of Mineral and Energy Resources Engineers, 61.
  7. Choi, B., Kim, N., & Shin, H. (2024). Optimization of Well-Pair Spacing and Well Configuration for the SAGD Process in Oilsands Reservoirs. Journal of the Korean Society of Mineral and Energy Resources Engineers, 61.
  8. Baek, J., Kim, N., & Shin, H. (2024). Optimization of Toe-up SAGD Preheating in the Athabasca Oil Sands Reservoir. Journal of the Korean Society of Mineral and Energy Resources Engineers, 61.
  9. Lee, J., Baek, J., Kim, N., & Shin, H. (2024). Optimization of the SAGD preheating for the Athabasca oil sands reservoir with a water transition zone. Journal of the Korean Society of Mineral and Energy Resources Engineers, 61.
  10. Kim, S., Shin, H., Park, C., Min, B., Chung, S., & Lee, K. (2024). Analysis of Non-condensable Gas Injection Timing in eMSAGP Method for Oil Sands Reservoir with Thief Zone. Journal of the Korean Society of Mineral and Energy Resources Engineers, 61.
  11. Kim, M., Shin, H., & Kim, N. (2024). Correlations between Geomechanical Effects, SAGD Performance, and Reservoir Conditions in SAGD Operations in Alberta, Canada. Journal of the Korean Society of Mineral and Energy Resources Engineers, 61.
  12. Kong, H., Kim, N., & Shin, H. (2024). Economic Analysis of Canadian Oil Sands Projects at Different Participation Timings Considering the Oil Price Cycle. Journal of the Korean Society of Mineral and Energy Resources Engineers, 61.
  13. Cho, J., Kim, N., & Shin, H. (2024). Analyzing the hydrogen supply cost of various scenarios for a blue hydrogen supply chain between Korea and Australia. Journal of the Korean Society of Mineral and Energy Resources Engineers, 61.
2023
8 journal/proceedings
Journal Articles
  1. Jo, H., Pyrcz, M. J., Laugier, F., & Sullivan, M. (2023). Sensitivity analysis of geological rule-based subsurface model parameters on fluid flow. AAPG Bulletin, 107(6), 887–906.
  2. Hernandez-Mejia, J. L., Pisel, J., Jo, H., & Pyrcz, M. J. (2023). Dynamic time warping for well injection and production history connectivity characterization. Computational Geosciences, 27(1), 159–178.
  3. Nguyen-Le, V., Shin, H., & Chen, Z. (2023). Deep neural network model for estimating Montney shale gas production using reservoir, geomechanics, and hydraulic fracture treatment parameters. Gas Science and Engineering, 120, 205161.
  4. Feng, Y., Zhang, S., Ma, C., Liu, F., Mosleh, M. H., & Shin, H. (2023). The role of geomechanics for geological carbon storage. Gondwana Research, 124, 100–123.
  5. Kim, N., Shin, H., & Lee, K. (2023). Feature engineering process on well log data for machine learning-based SAGD performance prediction. Geoenergy Science and Engineering, 229, 212057.
  6. Musayev, K., Shin, H., & Nguyen-Le, V. (2023). Optimization of CO₂ injection and brine production well placement using a genetic algorithm and ANN-based proxy model. International Journal of Greenhouse Gas Control, 127, 103915.
  7. Kim, M., Kwon, S., Ji, M., Shin, H., & Min, B. (2023). Multi-lateral horizontal well with dual-tubing system to improve CO₂ storage security and reduce CCS cost. Applied Energy, 330, 120368.
Conference Proceedings
  • Pan, W., Chen, J., Mohamed, S., Jo, H., Santos, J. E., & Pyrcz, M. J. (2023). Efficient subsurface modeling with sequential patch generative adversarial neural networks. SPE Annual Technical Conference and Exhibition.
2022
5 journal
Journal Articles
  1. Jo, H. & Pyrcz, M. J. (2022). Automatic semivariogram modeling by convolutional neural network. Mathematical Geosciences, 54(1), 177–205.
  2. Pan, W., Jo, H., Santos, J. E., Torres-Verdín, C., & Pyrcz, M. J. (2022). Hierarchical machine learning workflow for conditional and multiscale deep-water reservoir modeling. AAPG Bulletin, 106(11), 2163–2186.
  3. Tang, H., Fu, P., Jo, H., Jiang, S., Sherman, C. S., Hamon, F., Azzolina, N. A., et al. (2022). Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using InSAR. International Journal of Greenhouse Gas Control, 120, 103765.
  4. Jo, H., Cho, Y., Pyrcz, M., Tang, H., & Fu, P. (2022). Machine-learning-based porosity estimation from multifrequency poststack seismic data. Geophysics, 87(5), M217–M233.
  5. Nguyen-Le, V., & Shin, H. (2022). Artificial neural network prediction models for Montney shale gas production profile based on reservoir and fracture network parameters. Energy, 244, 123150.
2021 & Earlier
Selected major works
Journal Articles (Selected)
  • Jo, H., Pan, W., Santos, J. E., Jung, H., & Pyrcz, M. J. (2021). Machine learning assisted history matching for a deepwater lobe system. Journal of Petroleum Science and Engineering, 207, 109086.
  • Santos, J. E., Yin, Y., Jo, H., Pan, W., Kang, Q., Viswanathan, H. S., Prodanović, M., et al. (2021). Computationally efficient multiscale neural networks applied to fluid flow in complex 3D porous media. Transport in Porous Media, 140(1), 241–272.
  • Yu, Y., Xu, C., Misra, S., Li, W., Ashby, M., Pan, W., Deng, T., Jo, H., Santos, J. E., Fu, L., et al. (2021). Synthetic sonic log generation with machine learning: A contest summary from five methods. Petrophysics, 62(4), 393–406.
  • Nguyen-Le, V., Kim, M., Shin, H., & Little, E. (2021). Multivariate approach to gas production forecast using early production data for Barnett shale reservoir. Journal of Natural Gas Science and Engineering, 87, 103776.
  • Song, G., Lee, H. S., & Shin, H. (2021). Three-dimensional modelling of desorbed gas volume and comparison to gas production rate in the Montney plays, Western Canadian Sedimentary Basin. Geofluids, 2021, 6674183.
  • Shin, H., Nguyen-Le, V., Kim, M., Shin, H., & Little, E. (2021). Development of production-forecasting model based on the characteristics of production decline analysis using the reservoir and hydraulic fracture parameters in Montney shale. Geofluids, 2021, 6613410.
  • Jo, H., Santos, J. E., & Pyrcz, M. J. (2020). Conditioning stratigraphic, rule-based models with generative adversarial networks: a deepwater lobe example. Energy Exploration & Exploitation, 38(6), 2558–2578.
  • Jo, H. & Pyrcz, M. J. (2020). Robust rule-based aggradational lobe reservoir models. Natural Resources Research, 29(2), 1193–1213.
  • Santos, J. E., Xu, D., Jo, H., Landry, C. J., Prodanović, M., & Pyrcz, M. J. (2020). PoreFlow-Net: A 3D convolutional neural network to predict fluid flow through porous media. Advances in Water Resources, 138, 103539.
  • Jung, H., Jo, H., Kim, S., Kang, B., Jeong, H., & Choe, J. (2020). Use of channel information update and discrete cosine transform in ensemble smoother for channel reservoir characterization. Journal of Energy Resources Technology, 142(1), 012901.
  • Kim, M., & Shin, H. (2020). Machine learning-based prediction of the shale barrier size and spatial location using key features of SAGD production curves. Journal of Petroleum Science and Engineering, 191, 107205.
  • Kim, M., & Shin, H. (2020). Numerical simulation of undulating shale breaking with SAGD (UB-SAGD) for oil sands reservoir with a shale barrier. Journal of Petroleum Science and Engineering, 195, 107604.
  • Nguyen-Le, V., Shin, H., & Little, E. (2020). Development of shale gas prediction models for long-term production and economics based on early production data in Barnett reservoir. Energies, 13(2), 424.
  • Nguyen-Le, V., & Shin, H. (2019). Development of reservoir economic indicator for Barnett Shale gas potential evaluation based on the reservoir and hydraulic fracturing parameters. Journal of Natural Gas Science and Engineering, 66, 159–167.
  • Jun, C., Kim, M., & Shin, H. (2019). Optimization of well placement and operating conditions for various well patterns in CO₂ sequestration in the Pohang Basin, Korea. International Journal of Greenhouse Gas Control, 90, 102810.
  • Kim, M., & Shin, H. (2019). Application of a dual tubing CO₂ injection–water production horizontal well pattern for improving the CO₂ storage capacity and reducing the CAPEX: A case study in Pohang basin. International Journal of Greenhouse Gas Control, 90, 102813.
  • Kim, G., Lee, H., Chen, Z., Athichanagorn, S., & Shin, H. (2019). Effect of reservoir characteristics on the productivity and production forecasting of the Montney shale gas in Canada. Journal of Petroleum Science and Engineering, 182, 106276.
  • Jung, H., Jo, H., Kim, S., Lee, K., & Choe, J. (2018). Geological model sampling using PCA-assisted support vector machine for reliable channel reservoir characterization. Journal of Petroleum Science and Engineering, 167, 396–405.
  • Kim, M., & Shin, H. (2018). Development and application of proxy models for predicting the shale barrier size using reservoir parameters and SAGD production data. Journal of Petroleum Science and Engineering, 170, 331–344.
  • Kim, K. H., Lee, K., Lee, H. S., Rhee, C. W., & Shin, H. (2018). Lithofacies modeling by multipoint statistics and economic evaluation by NPV volume for the early Cretaceous Wabiskaw Member in Athabasca oilsands area, Canada. Geoscience Frontiers, 9(2), 441–451.
  • Jung, H., Jo, H., Kim, S., Lee, K., & Choe, J. (2017). Recursive update of channel information for reliable history matching of channel reservoirs using EnKF with DCT. Journal of Petroleum Science and Engineering, 154, 19–37.
  • Jo, H., Jung, H., Ahn, J., Lee, K., & Choe, J. (2017). History matching of channel reservoirs using ensemble Kalman filter with continuous update of channel information. Energy Exploration & Exploitation, 35(1), 3–23.
  • Jung, H., Jo, H., Lee, K., & Choe, J. (2017). Characterization of various channel fields using an initial ensemble selection scheme and covariance localization. Journal of Energy Resources Technology, 139(6), 062906.
  • Lee, H., Jin, J., Shin, H., & Choe, J. (2015). Efficient prediction of SAGD productions using static factor clustering. Journal of Energy Resources Technology, 137(3), 032907.
  • Lee, K., Jung, S. P., Shin, H., & Choe, J. (2014). Uncertainty quantification of channelized reservoir using ensemble smoother with selective measurement data. Energy Exploration & Exploitation, 32(5), 805–816.
  • Shin, H. (2012). A simple parameter to optimize SAGD operating conditions. Geosystem Engineering, 15(2), 85–89.
  • Shin, H. & Polikar, M. (2007). Review of reservoir parameters to optimize SAGD and Fast-SAGD operating conditions. Journal of Canadian Petroleum Technology, 46(01).
  • Shin, H. & Polikar, M. (2006). Fast-SAGD application in the Alberta oil sands areas. Journal of Canadian Petroleum Technology, 45(09).
Conference Proceedings (Selected)
  • Santos, J. E., Prodanović, M., Landry, C. J., & Jo, H. (2018). Determining the impact of mineralogy composition for multiphase flow through hydraulically induced fractures. SPE/AAPG/SEG Unconventional Resources Technology Conference, D023S038R009.
  • Jo, H., An, J., Lee, K., & Choe, J. (2015). Crossover use of ensemble Kalman filter and ensemble smoother for efficient history matching. 77th EAGE Conference and Exhibition, 2015(1), 1–5.
  • Shin, H., Hwang, T., & Chon, B. H. (2012). Optimal grid system design for SAGD simulation. SPE Canada Heavy Oil Conference, SPE-157900-MS.
  • Shin, H. & Choe, J. (2009). Shale barrier effects on the SAGD performance. SPE/EAGE Reservoir Characterization & Simulation Conference. (101 citations)
  • Shin, H. & Polikar, M. (2007). Evaluation of SAGD projects using a dynamic economic indicator. PETSOC Canadian International Petroleum Conference, PETSOC-2007-173.
  • Shin, H. & Polikar, M. (2006). Experimental investigation of the Fast-SAGD process. PETSOC Canadian International Petroleum Conference, PETSOC-2006-097.
  • Shin, H. & Polikar, M. (2005). Optimizing the SAGD process in three major Canadian oil-sands areas. SPE Annual Technical Conference and Exhibition, SPE-95754-MS. (80 citations)
  • Shin, H. & Polikar, M. (2005). New economic indicator to evaluate SAGD performance. SPE Western Regional Meeting, SPE-94024-MS.
Patents
  • Chaki, S. C., Jo, H., Wong, T., & Zagayevskiy, Y. (2022). Estimating Reservoir Production Rates Using Machine Learning Models for Wellbore Operation Control. US Patent App. 17/136,895.
Software & Open Source
  • Pyrcz, M. J., Jo, H., Kupenko, A., Liu, W., Gigliotti, A. E., Salomaki, T., & Santos, J. (2021). GeostatsPy Python Package. Zenodo / PyPI / GitHub. Open-source spatial data analytics and geostatistics library.
Preprints / Under Review
  • Choi, S., Hernandez-Mejia, J. L., Jo, H., & Pyrcz, M. J. A Diagnostic Method for Spatiotemporal Analysis of the Impact of Subsurface Reservoir Uncertainty on Dynamic Response Using Shapley Values. SSRN Preprint. Under Review
  • Jiang, S., Tang, H., Fu, P., & Jo, H. A Transfer Learning-Based Surrogate Model for Geological Carbon Storage with Multi-Fidelity Training Data. In Preparation