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Publications

2021

Almajid, M. M., Wong, A. Y., et al. (2021). Mechanistic foam flow model with variable flowing foam fraction and its implementation using automatic differentiation. Advances in Water Resources, 150, 103877. doi: 10.1016/j.advwatres.2021.103877

Nasir, Y., Volkov, O., et al. (2021). A two-stage optimization strategy for large-scale oil field development. Optimization and Engineering, 105.doi: 10.1007/s11081-020-09591-y 

Yang, J., Tchelepi, H. A., et al. (2021). Phase‐field modeling of rate‐dependent fluid‐driven fracture initiation and propagationInternational Journal for Numerical and Analytical Methods in Geomechanics. doi: 10.1002/nag.3190

K. Li, G. Garrison, et al. (2021). Thermoelectric power generator: Field test at Bottle Rock geothermal power plant. Journal of Power Sources, 485, 229266. doi: 10.1016/j.jpowsour.2020.229266

H. Tang, O. Volkov, et al. (2020). Reduced-order modeling in a general reservoir simulation setting. SPE Western Regional Meeting, Bakersfield, California, USA, April 2021. Paper Number: SPE-200794-MS. doi: 10.2118/200794-MS

G. Wen, M. Tang, et at. (2021). Towards a predictor for CO2 plume migration using deep neural networks. International Journal of Greenhouse Gas Control, Volume 105, 103223. doi: 10.1016/j.ijggc.2020.103223

M. J. J. Aljubran & R. Horne, (2020). Surrogate-based prediction and optimization of multilateral ICV flow performance in a real field. SPE Western Regional Meeting, Bakersfield, California, USA, January 2021. Paper Number: SPE-200884-MS. doi: 10.2118/200884-MS

Connolly, M., Pan, H., et al. (2020). Reduced method for rapid multiphase isenthalpic flash in thermal simulationChemical Engineering Science, 116150 In Press Journal Preroof. doi: 10.1016/j.ces.2020.116150

2020

Esmaeilzadeh, S., Jiang, C. M., et al., (2020). A deep learning based physics informed continuous spatio temporal super-resolution framework. 73rd Annual Meeting of the APS Division of Fluid Dynamics, Nov 22–24, 2020, Virtual, Session S01: Focus Session: Deep Learning in Experimental and Computational Fluid Mechanics (Part II). Abstract: S01.00033

Mern, J., Yildiz, A., et al., (2020). Improved POMDP tree search planning with prioritized action branching. arXiv:2010.03599v1 [cs.LG]

Esmaeilzadeh, S., Qin, Z., et al., (2020). A numerical study of pore scale partially wet interfacial displacements. 73rd Annual Meeting of the APS Division of Fluid Dynamics, Nov 22–24, 2020, Virtual, Session U08: Microscale Flows: Interfaces and Wetting. Abstract: U08.00005

Qin, Z., Esmaeilzadeh, S., et al., (2020). A multiscale numerical model for the multiphase flow in porous media. 73rd Annual Meeting of the APS Division of Fluid Dynamics, Nov 22–24, 2020, Virtual, Session J12: Multiphase Flows: Computational Methods. Abstract: J12.00018

Mern, J., Yildiz, A., et al., (2020). Bayesian optimized monte carlo planning. arXiv:2010.03597v1 [cs.AI]

Wongpattananukul, K. & Horne, R. (2020). Revisiting machine learning approaches for pressure data deconvolution. Society of Petroleum Engineers, SPE Annual Technical Conference and Exhibition, 26-29 October, Virtual, SPE-201335-MS. doi: 10.2118/201335-MS

Alemán, D. O. & Horne, R. (2020). Improved robustness in long-term pressure data analysis using wavelets and deep learningSociety of Petroleum Engineers, SPE Annual Technical Conference and Exhibition, 26-29 October, Virtual, SPE-201597-MS. doi: 10.2118/201597-MS

Alakeely, A. A. & Horne, R. (2020). Application of deep learning methods in evaluating well production potential using surface measurementsSociety of Petroleum Engineer, SPE Annual Technical Conference and Exhibition, 26-29 October, Virtual, SPE-201785-MS. doi: 10.2118/201785-MS

Long, W. & Brandt, A. R. (2020). Large scale steam flood performance forecast using type curve analysis and multi temperature fractional flow. Society of Petroleum Engineers, SPE Annual Technical Conference and Exhibition, 26-29 October, Virtual, SPE-201708-MS.doi: 10.2118/201708-MS

Aljubran, M. J. & Horne, R. (2020). Surrogate-based prediction and optimization of multilateral inflow control valve flow performance with production data. Society of Petroleum Engineers, SPE Production & Operations, Preprint, SPE-200884-PA. doi: 10.2118/200884-PA

Liu, Y. & Durlofsky,L. J., (2020). 3D CNN-PCA: A deep-learning-based parameterization for complex geomodels. arXiv:2007.08478v1 [cs.CV]

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