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Journal papers using MRST
The following is a list of journal papers in which MRST is used as a key research tool. All papers are written (primarily) by authors who are not part of the MRST development team at SINTEF.
Preprints
  1. R. Rizzo, N. Forbes Inskip, H. Fazeli, P. Betlem, K. Bisdom, N. Kampman, J. Snippe, K. Senger, F. Doster, and A. Busch. Modelling geological CO2 leakage: Integrating fracture permeability and fault zone outcrop analysis. Available at SSRN: ssrn.com/abstract=4571419 or DOI 10.2139/ssrn.4571419.
  2. O. Chaabi and M.A. Kobaisi. Algorithmic monotone multiscale finite volume methods for porous media flow. arXiv:2308.06579, 2023.
  3. C.S. Lee, F.P. Hamon, N. Castelletto, P.S. Vassilevski, and J. A. White. Multilevel well modeling in aggregation-based nonlinear multigrid for multiphase flow in porous media. arXiv:2308.00125.
  4. C. M. O'Sullivan, P. Rodriguez-Salgado, C. Childs, and P. M. Shannon. Subsurface storage capacity in underexplored sedimentary basins: Hydrogen and carbon dioxide storage on the Irish Atlantic margin. ResearchGate, May 2023.
  5. X. Ju, F.P. Hamon, G. Wen, R. Kanfar, M. Araya-Polo, and H.A. Tchelepi. Learning CO2 plume migration in faulted reservoirs with graph neural networks. arXiv:2306.09648.
  6. Y. Wang, M. Zechner, G. Wen, A. L. Corso, J. M. Mern, M. J. Kochenderfer, and J. K. Caers. Optimizing carbon storage operations for long-term safety. arXiv:2304.09352v1.
  7. M. Haugen, L. Salo-Salgado, K. Eikehaug, B. Benali, J. W. Both, E. Storvik, O. Folkvord, R. Juanes, J. M. Nordbotten, and M. A. Ferno. Physical variability in meter-scale laboratory CO2 injections in faulted geometries. arXiv:2301.07347.
  8. F. V. Donze, L. Bourdet, L. Truche, C. Dusséaux, and P. Huyghe. Modeling deep control pulsing flux of native H2 throughout tectonic fault-valve systems. Authorea. November 30, 2022. DOI: 10.1002/essoar.10512945.1.
  9. O. Olorode and H. Rashid. Analytical modification of EDFM for transient flow in tight rocks. Research Square. DOI: 10.21203/rs.3.rs-1971564/v1.
  10. D. Losapio and A. Scotti. Local embedded discrete fracture model (LEDFM). arXiv:2207.14009, 2022.
  11. N. Wang, Q. Liao, H. Chang, and D. Zhang. Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network. arXiv:2201.00698 [cs.LG], 2022.
  12. C.S. Lee, F.P. Hamon, N. Castelletto, P.S. Vassilevski, and J.A. White. An aggregation-based nonlinear multigrid solver for two-phase flow and transport in porous media. arXiv:2109.07546.
  13. A. R. Shojaee, S. Kord, and R. Miri, Rohaldin. Reactive transport modelling of scale precipitation and deposition during water injection - an MRST study. Available at SSRN: https://ssrn.com/abstract=4017212 or DOI: 10.2139/ssrn.4017212.
  14. D. Landa-Marbán, K. Kumar, S. Tveit, and S.E. Gasda. Numerical studies of CO2 leakage remediation by MICP-based plugging technology. arXiv preprint arXiv:2105.04382, 2021.
  15. J.J. Hu, C. Siefert, and R.S. Tuminaro. Smooth aggregation for difficult stretched mesh and coefficient variation problems. arXiv preprint arXiv:2103.10476, 2021.
  16. S. Dana, X. Zhao, and B. Jha. Two-grid method on unstructured tetrahedra: Applying computational geometry to staggered solution of coupled flow and mechanics problems. arXiv preprint arXiv:2102.04455, 2021
  17. D. Illiano, J.W. Both, I.S. Pop, and F.A. Radu. Efficient solvers for nonstandard models for flow and transport in unsaturated porous media. arXiv preprint arXiv:2012.14773, 2020.
  18. R. March, D. Egya, C. Maier, A. Busch, and F. Doster. Numerical computation of stress-permeability relationships of fracture networks in a shale rock. arXiv preprint arXiv:2012.02080, 2020.
  19. M. Elizarev, A. Mukhin, and A. Khlyupin. Objective-sensitive principal component analysis for high-dimensional inverse problems. arXiv preprint arXiv:2006.04527, 2020.
  20. B. Wang. MRST-Shale: An open-source framework for generic numerical modeling of unconventional shale and tight gas reservoirs. Preprints 2020, 2020010080. DOI: 10.20944/preprints202001.0080.v1.
  21. D. Illiano, I. S. Pop, and F. A. Radu. An efficient numerical scheme for fully coupled flow and reactive transport in variably saturated porous media including dynamic capillary effects. arXiv preprint arXiv:1912.06731, 2019
  22. L. Mosser, O. Dubrule, and M. J. Blunt. DeepFlow: History matching in the space of deep generative models. arXiv preprint arXiv:1905.05749, 2019.
  23. C. Xiao, O. Leeuwenburgh, H.X. Lin, A. Heemink. Subdomain POD-TPWL with local parameterization for large-scale reservoir history matching problems. arXiv preprint arXiv:1901.08059, 2019
  24. A. Capolei, L.H. Christiansen, and J.B. Jørgensen. Risk minimization in life-cycle oil production optimization. arXiv preprint arXiv:1801.00684, 2018
  25. M.M. Siraj, P.M.J. Van den Hof and J.D. Jansen. Asymmetric risk measures for optimizing economic performance of oil reservoirs. Submitted for publication in Journal of Process Control.
  26. M.M. Siraj, P.M.J. Van den Hof, and J.D. Jansen. Robust closed-loop reservoir management using residual analysis, Submitted for publication in Journal of Petroleum Science & Engineering, 2017.
  27. D. Stone, G. Lord. A positivity preserving convergent event based asynchronous PDE solver. arXiv preprint:1610.06800, 2016.
2023
  1. S. M. Mousavi, P. Bakhtiarimanesh, F. Enzmann, M. Kersten, and S. Sadeghnejad. Machine-learned surrogate models for efficient oil well placement under operational reservoir constraints. SPE Journal. DOI: 10.2118/217467-PA.
  2. Z. Xu, J.Chen, and J. Y. Leung. An improved dual-porosity dual-permeability modeling workflow for representing nonplanar hydraulic fractures. Gas Science and Engineering Volume 118, October 2023, 205108. DOI: 10.1016/j.jgsce.2023.205108.
  3. Z. Xiang, R. Zhen, Y. Xu, S. Wang, X. Ao, Z. Chen, and J. Hu. A numerical pressure transient model of fractured well with complex fractures of tight gas reservoirs considering gas-water two phase by EDFM. Geoenergy Science and Engineering. DOI: 10.1016/j.geoen.2023.212286.
  4. J.S. Azevedo and J.A. Fernandes. The parameter inversion in coupled geomechanics and flow simulations using Bayesian inference. Journal of Computational Mathematics and Data Science. DOI: 10.1016/j.jcmds.2023.100083.
  5. L. Saló-Salgado, M. Haugen, K. Eikehaug, M. Fernø, J. M. Nordbotten, and R. Juanes. Direct comparison of numerical simulations and experiments of CO2 injection and migration in geologic media: Value of local data and forecasting capability. Transport in Porous Media. DOI: 10.1007/s11242-023-01972-y.
  6. B. Flemisch, J. M. Nordbotten, M. Fern&oslash et al. The FluidFlower validation benchmark study for the storage of CO2. Transport in Porous Media. DOI: 10.1007/s11242-023-01977-7.
  7. T. Alyousuf, Y. Li, R. Krahenbuhl, and D. Grana. Three-axis borehole gravity monitoring for CO2 storage using machine learning coupled to fluid flow simulator. Geophysical Prospecting. DOI: 10.1111/1365-2478.13413.
  8. D. Lauzon and D. Marcotte. Joint hydrofacies-hydraulic conductivity modeling based on a constructive spectral algorithm constrained by transient head data. Hydrogeology Journal, 2023. DOI: 10.1007/s10040-023-02638-1.
  9. Y Yao, L Wang, K Wang, CD Adenutsi, Y Wang, and D Feng. A novel high-dimension shale gas reservoir hydraulic fracture network parameters optimization framework. Geoenergy Science and Engineering, Volume 229, October 2023, 212155. DOI: 10.1016/j.geoen.2023.212155.
  10. Q. Liao, G. Li, S. Tian, X. Song, G. Lei, X. Liu, W. Chen, and S. Patil. An efficient analytical approach for steady-state upscaling of relative permeability and capillary pressure. Energy, Volume 282, 1 November 2023, 128426. DOI: 10.1016/j.energy.2023.128426.
  11. H.M. Naghneh, M. Amani, A. Farhadi, and M.T. Isaai. Application of the closed loop industrial internet of things (IIoT)‐based control system in enhancing the oil recovery factor and the oil production. IET Cyber‐Physical Systems: Theory & Applications, 2023. DOI: 10.1049/cps2.12068.
  12. L. Xie, G. Li, Z. Wang, L. Cui, and M. Gong. Surrogate-assisted evolutionary algorithm with model and onfill criterion auto-configuration. IEEE Transactions on Evolutionary Computation, 2023. DOI: 10.1109/TEVC.2023.3291614.
  13. A. Kubeyev. Enhancing multi-physics modelling with deep learning: Predicting permeability through structural discontinuities. Engineering Applications of Artificial Intelligence, Volume 124, September 2023, 106562. DOI: 10.1016/j.engappai.2023.106562.
  14. W. Zhang, D. Han, B. Wang, Y. Chen, K. Jiao, L. Gong, and B. Yu. Thermal-hydraulic-mechanical-chemical modeling and simulation of an enhanced geothermal system based on the framework of extended finite element methods - Embedded discrete fracture model. Journal of Cleaner Production, June 2023, 137630. DOI: 10.1016/j.jclepro.2023.137630.
  15. X. Zhao, Z. Chen, B. Zhou, X. Liao, H. Wang, and B. Wang. Multiple flow mechanism-based numerical model for CO2 huff-n-puff in shale gas reservoirs with complex fractures. Energy & Fuels, 2023. DOI: 10.1021/acs.energyfuels.3c00931.
  16. J.A. Silva, L. Saló-Salgado, J. Patterson, G.R. Dasari, and R. Juanes. Assessing the viability of CO2 storage in offshore formations of the Gulf of Mexico at a scale relevant for climate-change mitigation. International Journal of Greenhouse Gas Control, Volume 126, June 2023, 103884. DOI: 10.1016/j.ijggc.2023.103884.
  17. N. Wang, H. Chang, X. Kong, M.O. Saar, and D. Zhang. Deep learning based closed-loop optimization of geothermal reservoir production. Renewable Energy, 2023. DOI: 10.1016/j.renene.2023.04.088.
  18. Z. Chen, B. Zhou, S. Zhang, D. Li, and K. Sepehrnoori. Pressure transient behaviors for horizontal wells with well interferences, complex fractures and two-phase flow. Geoenergy Science and Engineering, 2023. DOI: 10.1016/j.geoen.2023.211845.
  19. J. Cornelio, S. M. Razak, Y. Cho, H.-H. Liu, R. Vaidya, and B. Jafarpour. Transfer learning with prior data-driven models from multiple unconventional fields. SPE Journal, 2023. DOI: 10.2118/214312-PA.
  20. P. Hu, S. Geng, X. Liu, C. Li, R. Zhu, and X. He. A three-dimensional numerical pressure transient analysis model for fractured horizontal wells in shale gas reservoirs. Journal of Hydrology, 2023. DOI: 10.1016/j.jhydrol.2023.129545
  21. J.G. Souza Debossam, G. de Souza, H.P. Amaral Souto, and A. P. Pires. Numerical simulation of single-phase two-component non-Darcy flow in naturally fractured reservoirs for enhanced gas recovery and carbon dioxide storage. Brazilian Journal of Chemical Engineering, 2023. DOI: 10.1007/s43153-023-00318-x.
  22. W. Xiong, L.H. Zhang, Y. Tian, L.X. Li, Y.L. Zhao, and Z.X. Chen. Phase equilibrium modeling for carbon dioxide capture and storage (CCS) fluids in brine using an electrolyte association equation of state. Chemical Engineering Science. April 2023, 118723. DOI: 10.1016/j.ces.2023.118723.
  23. G. Li, L. Xie, Z. Wang, H. Wang, and M. Gong. Evolutionary algorithm with individual-distribution search strategy and regression-classification surrogates for expensive optimization. Information Sciences, Volume 634, 2023. DOI: 10.1016/j.ins.2023.03.101.
  24. X. Zhao, Z. Chen, B. Wang, X. Liao, D. Li, and B. Zhou. A multi-medium and multi-mechanism model for CO2 injection and storage in fractured shale gas reservoirs. Fuel, Volume 345, 2023, 128167. DOI: 10.1016/j.fuel.2023.128167.
  25. L. Xin, X. Rao, X. Peng, Y. Xu, and J. Chen. Production dynamic prediction method of waterflooding reservoir based on deep convolution generative adversarial network (DC-GAN). Energy Engineering 2022, 119(5), 1905-1922. DOI: 10.32604/ee.2022.019556.
  26. C. Xiao, S. Zhang, X. Ma, T. Zhou, T. Hou, and F. Chen. Deep-learning-generalized data-space inversion and uncertainty quantification framework for accelerating geological CO2 plume migration monitoring, Geoenergy Science and Engineering, 2023, 211627. DOI: 10.1016/j.geoen.2023.211627.
  27. P.K. Gupta, B. Gharedaghloo, and J.S. Price. Multiphase flow behavior of diesel in bog, fen, and swamp peats. Journal of Contaminant Hydrology, 2023, 104162. DOI: 10.1016/j.jconhyd.2023.104162.
  28. D. Wang, F. Liu, G. Li, S. He, K. Song, and J. Zhang. Characterization and dynamic adjustment of the flow field during the late stage of waterflooding in strongly heterogeneous reservoirs. Energies 2023, 16, 831. DOI: 10.3390/en16020831.
  29. F. Jiang, Y. Guo, T. Tsuji, Y. Kato, M. Shimokawara, L. Esteban, M. Seyyedi, M. Pervukhina, M. Lebedev, and R. Kitamura. Upscaling permeability using multiscale X‐ray‐CT images with digital rock modeling and deep learning techniques. Water Resources Research, 2023. DOI: 10.1029/2022WR033267-
  30. A. Alqahtani, X. He, B. Yan, and H. Hoteit. Uncertainty analysis of CO2 storage in deep saline aquifers using machine learning and bayesian optimization. Energies, 16(4), 1684, 2023. DOI: 10.3390/en16041684
  31. M. Zeynalli, E. W. Al-Shalabi, and W. AlAmeri. An extended unified viscoelastic model for predicting polymer apparent viscosity at different shear rates. SPE Reservoir Evaluation & Engineering, 26 (01): 99–121, 2023. DOI: 10.2118/206010-PA.
  32. Z. Guo, S. Sankaran, and W. Sun. Reservoir modeling, history matching, and characterization with a reservoir graph network model. SPE Reservoir Evaluation & Engineering, 2023. DOI: 10.2118/209337-PA.
  33. M. Ahmadinia, M. Sadri, B. Nobakht, and S.M. Shariatipour. Uncertainty quantification of the CO2 storage process in the Bunter Closure 36 model. Sustainability, 2023, , 15(3), 2004. DOI: 10.3390/su15032004.
  34. Y. Wang, D. Fernàndez-Garcia, and M. W.Saaltink. Modeling reactive multi-component multi-phase flow for Geological Carbon Sequestration (GCS) with Matlab. Computers & Geosciences, Volume 172, March 2023, 105300. DOI: 10.1016/j.cageo.2023.105300.
  35. J. Zhou, H. Wang, C. Xiao, and S. Zhang. Hierarchical surrogate-assisted evolutionary algorithm for integrated multi-objective optimization of well placement and hydraulic fracture parameters in unconventional shale gas reservoir. Energies 2023, 16(1), 303. DOI: 10.3390/en16010303.
  36. C. Xiao, S. Zhang, X. Ma, T. Zhou, T. Hou, and F. Chen. Data-driven model predictive control for closed-loop refracturing design and optimization in naturally fractured shale gas reservoir under geological uncertainty. Computers & Chemical Engineering, Volume 169, January 2023, 108096. DOI: 10.1016/j.compchemeng.2022.108096.
  37. A. Dell'Oca, A. Manzoni, M. Siena, N.G. Bona, L. Moghadasi, M. Miarelli, D. Renna, and A. Guadagnini. Stochastic inverse modeling of transient laboratory-scale three-dimensional two-phase core flooding scenarios. International Journal of Heat and Mass Transfer, vol. 202, 2023, 123716. DOI: 10.1016/j.ijheatmasstransfer.2022.123716.
2022
  1. L. Saló-Salgado, J.S. Davis, and R. Juanes. Fault permeability from stochastic modeling of clay smears. Geology, 2022. DOI: 10.1130/G50739.1.
  2. A. M. AlRassas, H. V. Thanh, S. Ren, R. Sun, N. L. N. Hai, and K.-K. Lee. Integrated static modelling and dynamic simulation framework for CO2 storage capacity in upper Qishn Clastics, S1A reservoir, Yemen. Geomech. Geophys. Geo-Energy Geo-Resour. 2022, 8 (1), 2, DOI: 10.1007/s40948-021-00305-x.
  3. Q. Hu, D. Grana, and KA Innanen. Feasibility of seismic time-lapse monitoring of CO2 with rock physics parameterized full waveform inversion. Geophysical Journal International, 2022. DOI: 10.1093/gji/ggac462.
  4. H. Xiao, S. Geng, H. Luo, L. Song, H. Wang, and X. He. Numerical simulation of fractured horizontal well considering threshold pressure gradient, non‐darcy flow, and stress sensitivity. Energy Science & Engineering, 2022. DOI: 10.1002/ese3.1365.
  5. R. J. N. C. Junior, J. D. L. Silva, G. E. S. D. Castro, A. C. M. Almeida. Análise da eficiência no uso de software de simulação de reservatórios para a determinação da permeabilidade na produção de petróleo. Open Science Research VIII, Chapter 82, pp. 1111-1127, 2022, Editora Científica Digital. DOI: 10.37885/221211198.
  6. Y. Wang, S.F. Estefen, and M.I. Lourenço. A fully integrated reservoir/pipeline network model and its application on the evaluation of subsea water separation performance. Journal of Petroleum Science and Engineering, 2022. DOI: 10.1016/j.petrol.2022.111140.
  7. E. M. Ramos, M. R. Borges, G. A. Giraldi, B. Schulze, and F. Bernardo. Prediction of permeability of porous media using optimized convolutional neural networks. Computational Geosciences, 2022. DOI: 10.1007/s10596-022-10177-z.
  8. E. Ranaee, F. Inzoli, M. Riva, and A. Guadagnini. Sensitivity-based parameter calibration of single- and dual-continuum coreflooding simulation models Transport in Porous Media, 2022. DOI: 10.1007/s11242-022-01854-9.
  9. J.Chen, Z. Xu, and J. Y. Leung. Analysis of fracture interference – Coupling of flow and geomechanical computations with discrete fracture modeling using MRST. Journal of Petroleum Science and Engineering, October 2022, 111134. DOI: 10.1016/j.petrol.2022.111134.
  10. O. Amrollahinasab, S. Azizmohammadi, and H. Ott. Simultaneous interpretation of SCAL data with different degrees of freedom and uncertainty analysis. Computers and Geotechnics, October 2022. DOI: 10.1016/j.compgeo.2022.105074.
  11. D. Yang and C. V. Deutsch. Managing risk in well placement optimization within an expected utility framework. SPE Reservoir Evaluation & Engineering, 2022. DOI: 10.2118/212305-PA.
  12. M. K. Marvin, A. B. Ngulde, and A. M. Abubakar. Pattern effect for oil reservoir waterflooding using smart well. Applied Engineering, pp 50-56, October 2022. DOI: 10.11648.j.ae.20220602.13.
  13. L. Gutierrez-Sosa, S. Geiger, and F. Doster. Poro-mechanical coupling for flow diagnostics. Transport in Porous Media, 2022. DOI: 10.1007/s11242-022-01857-6.
  14. X. Cui,C. Jiang, B. Nassichuk, and J. Wilson. Characteristics of canister core desorption gas from unconventional reservoirs and applications to improve assessment of hydrocarbons-in-place. Minerals 2022, 12(10), 1226. DOI: 10.3390/min12101226.
  15. R. Alguliyev, R. Aliguliyev, Y. Imamverdiyev, L. Sukhostat. History matching of petroleum reservoirs using deep neural networks. Intelligent Systems with Applications, 2022, 200128. DOI: 10.1016/j.iswa.2022.200128.
  16. H. Lin, F. Zhou, C. Xiao, X. Yang, Y. Wang, Y. Zhang, and T. Hou. Data-driven inversion-free workflow of well performance forecast under uncertainty for fractured shale gas reservoirs. Journal of Energy Resource Technology, 2022. DOI: 10.1115/1.4055537.
  17. C. Xiao, G. Wang, Y. Zhang, and Y. Deng. Machine-learning-based well production prediction under geological and hydraulic fracture parameters uncertainty for unconventional shale gas reservoirs, Journal of Natural Gas Science and Engineering, Volume 106, 2022, 104762. DOI: 10.1016/j.jngse.2022.104762.
  18. P.R. Punnam, A. Dutta, B. Krishnamurthy, and V.K. Surasani . Study on utilization of machine learning techniques for geological CO2 sequestration simulations. Materials Today: Proceedings, 2022. DOI: 10.1016/j.matpr.2022.08.109.
  19. J. Zhao and H Rui. Weak Galerkin coupled with conforming Finite Element method for hybrid dimensional fracture model. Journal of Computational and Applied Mathematics, 2022. DOI: 10.1016/j.cam.2022.114732.
  20. J. Li, Z. Huang, G. Li, Z. Huang, J. Dai, K. Cheng. Field test of radial jet drilling technology in a surface formation. Journal of Petroleum Science and Engineering. 2022, 110928. DOI: 10.1016/j.petrol.2022.110928.
  21. S. Xu, J. Guo, Q. Feng, G. Ren, Y. Li, S. Wang. Optimization of hydraulic fracturing treatment parameters to maximize economic benefit in tight oil. Fuel, 2022. DOI: 10.1016/j.fuel.2022.125329.
  22. W. Pan, H. Jo, J.E. Santos, C. Torres-Verdín, M.J. Pyrcz. Hierarchical machine learning workflow for conditional and multiscale deep-water reservoir modeling. AAPG Bulletin, 2022. DOI: 10.1306/05162221022.
  23. L. Wang, Y. Yao, G. Zhao, C. D. Adenutsi, W. Wang, and F. Lai. A hybrid surrogate-assisted integrated optimization of horizontal well spacing and hydraulic fracture stage placement in naturally fractured shale gas reservoir. Journal of Petroleum Science and Engineering, Volume 216, 2022, 110842. DOI: 10.1016/j.petrol.2022.110842.
  24. M. Zeynalli, E. Walid Al-Shalabi, and W. AlAmeri. An extended unified viscoelastic model for predicting polymer apparent viscosity at different shear rates. SPE Reservoir Evaluation & Engineering, 2022. DOI: 10.2118/206010-PA.
  25. N. Andrianov. Upscaling of two-phase discrete fracture simulations using a convolutional neural network. Computational Geosciences, 2022. DOI: 10.1007/s10596-022-10149-3.
  26. M. Liu, D. Grana, and T. Mukerji. Randomized tensor decomposition for large-scale data assimilation problems for carbon dioxide sequestration. Mathematical Geosciences, 2022. DOI: 10.1007/s11004-022-10005-1.
  27. H. Li, H. Yu, H. Yang, T. Deng, and X. Li. Adaptive stress sensitivity analysis of fractured heterogeneous tight reservoir. Petroleum Drilling Techniques. DOI: 10.11911/syztjs.2022054.
  28. J. Cornelio, S. M. Razak, Y. Cho, H.-H. Liu, R. Vaidya, and B. Jafarpour. Residual learning to integrate neural network and physics-based models for improved production prediction in unconventional reservoirs. SPE Journal, 2022. DOI: 10.2118/210559-PA.
  29. D. Zeqiraj. Stochastic modeling of fluid flow in the simulation and quantification of fracture initiation and propagation in the oil carbonate reservoirs during water injection. Results in Control and Optimization, 2022. DOI: 10.1016/j.rico.2022.100131.
  30. M. Ashworth, A.H. Elsheik, and F. Doster. Machine learning-based multiscale constitutive modelling: Development and application to dual-porosity mass transfer. Advances in Water Resources. Volume 163, May 2022, 104166. DOI: 10.1016/j.advwatres.2022.104166.
  31. T. Chen. The impact of fracture geometries on heterogeneity and accuracy of upscaled equivalent fracture models . Lithosphere, 2022: 5070481. DOI: 10.2113/2022/5070481.
  32. Y. Wang, D. Fernàndez-Garcia, G. Sole-Mari, and P. Rodríguez-Escales. Enhanced NAPL removal and mixing with engineered injection and extraction. Water Resources Research, 2022. DOI: 10.1029/2021WR031114.
  33. S. Pannu, R. Phukan, and P. Tiwari . Experimental and simulation study of surfactant flooding using a combined surfactant system for enhanced oil recovery. Petroleum Science and Technology, 2022. DOI: 10.1080/10916466.2022.2052089.
  34. H. Amer and O. Olorode. Numerical evaluation of a novel slot-drill enhanced oil recovery technology for tight rocks. SPE Journal, 2022. DOI: 10.2118/209597-PA.
  35. J. Jiang and H. Pan. Dissipation-based nonlinear solver for fully implicit compositional simulation. SPE Journal, 2022. DOI: 10.2118/209233-PA.
  36. G. Zhao, Y. Yao, L. Wang, C. D. Adenutsi, D. Feng, and W. Wu. Optimization design of horizontal well fracture stage placement in shale gas reservoirs based on an efficient variable-fidelity surrogate model and intelligent algorithm. Energy Reports, Volume 8, pp. 3589-3599, November 2022. DOI: 10.1016/j.egyr.2022.02.228.
  37. H. Singh. Hydrogen storage in inactive horizontal shale gas wells: Techno-economic analysis for Haynesville shale. Applied Energy, Volume 313, 1 May 2022, 118862. DOI: 10.1016/j.apenergy.2022.118862.
  38. L. Zhu, G. Han, W. Ke, X. Liang, J. Tang, and J. Dai. A numerical multistage fractured horizontal well model concerning hilly-terrain well trajectory in shale reservoirs with natural fractures. Energies, Vol. 15, No. 5, 2022. DOI: 10.3390/en15051854
  39. S. Joon, I. Dawuda, E. Morgan, and S. Srinivasan. Rock physics-based data assimilation of integrated continuous active-source seismic and pressure monitoring data during geological carbon storage. SPE Journal, 2022. DOI: 10.2118/209585-PA.
  40. L. Gutierrez-Sosa, S. Geiger, and F. Doster. Fast screening tool for assessing the impact of poro-mechanics on fractured reservoirs using dual-porosity flow diagnostics. SPE Journal, 2022. DOI: 10.2118/203981-PA.
  41. J. Yu, A. Jahandideh, and B. Jafarpour. Efficient robust production optimization with reduced sampling. SPE Journal, 2022. DOI: 10.2118/209226-PA.
  42. J. Liu, L. Jiang, T. Liu, and D. Yang. Modeling tracer flowback behaviour for a fractured horizontal well in a tight oil reservoir using the embedded discrete fracture model. Journal of Petroleum Science and Engineering, 2022, 110347. DOI: 10.1016/j.petrol.2022.110347.
  43. M. Dugstad and K. Kumar. Dimensional reduction of a fractured medium for a two-phase flow. Advances in Water Resources, 2022, 104140. DOI: 10.1016/j.advwatres.2022.104140.
  44. A.A. Kazakov, P.V. Lomovitskiy, and A.N. Khlyupin. A hybrid algorithm for automated adaptation of hydrodynamic models using tracer research. Mathematical Models and Computer Simulations, Vol. 14, pp. 47–55, 2022. DOI: 10.1134/S207004822201015X
  45. Y. Wang, D. Ye, X. Zhu, Y. Yang, C. Qin, R. Chen, and Q. Liao. Spontaneous imbibition in paper-based microfluidic devices: experiments and numerical simulations. Langmuir, 2022. DOI: 10.1021/acs.langmuir.1c03403.
  46. Q. Z. Liao, L. Xue, G. Lei, X. Liu, S.Y. Sun, and S. Patil. Statistical prediction of waterflooding performance by K-means clustering and empirical modeling. Petroleum Science, 2022. DOI: 10.1016/j.petsci.2021.12.032.
  47. D. Lauzon and D. Marcotte, Statistical comparison of variogram-based inversion methods for conditioning to indirect data. Computers and Geosciences, 2022. DOI: 10.1016/j.cageo.2022.105032.
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2021
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2020
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  24. G.B. Diaz Cortes, C.Vuik, and J.D.Jansen. On POD-based deflation vectors for DPCG applied to porous media problems. Journal of Computational and Applied Mathematics. Volume 330, pp. 193-213, 2018. DOI: 10.1016/j.cam.2017.06.032
  25. H Singh, J Cai. Screening improved recovery methods in tight-oil formations by injecting and producing through fractures. International Journal of Heat and Mass Transfer, Vol. 116, pp. 977-993, 2018. DOI: 10.1016/j.ijheatmasstransfer.2017.09.071
  26. J. Abbasi, M. Ghaedi, and M. Riazi. A new numerical approach for investigation of the effects of dynamic capillary pressure in imbibition process. Journal of Petroleum Science and Engineering, Volume 162, Pages 44-54, March 2018. DOI: 10.1016/j.petrol.2017.12.035
2017
  1. A.D. Obembe, M.E. Hossain, K. Mustapha, and S.A. Abu-Khamsin. A modified memory-based mathematical model describing fluid flow in porous media. Computers & Mathematics with Applications, Volume 73, Number 6, pp. 1385-1402, 2017. DOI: 10.1016/j.camwa.2016.11.022
  2. A.D. Obembe, M.E. Hossain, and S.A. Abu-Khamsin. Variable-order derivative time fractional diffusion model for heterogeneous porous media. Journal of Petroleum Science and Engineering 152, 391-405, 2017. DOI: 10.1016/j.petrol.2017.03.015
  3. E. Ucar, I. Berre, and E. Keilegavlen. Post-injection normal closure of fractures as a mechanism for induced seismicity. Geophysical Research Letters, Volume 44, Issue 19, pp. 9598-9606, 2017. DOI: 10.1002/2017GL074282
  4. A. Capolei, L. H. Christiansen, and J. B. Jørgensen. A novel approach for risk minimization in life-cycle oil production optimization. Computer Aided Chemical Engineering. Volume 40, pp. 157-162, 2017. DOI: 10.1016/B978-0-444-63965-3.50028-3
  5. A. S. Grema, D. Baba, U. H. Taura, M. B. Grema, and L. T. Popoola. Optimization and non-linear identification of reservoir water flooding process. Arid Zone Journal of Engineering, Technology and Environment, [S.l.], Volume 13, Number 5, pp. 610-619, 2017.
  6. H. Singh and N. J. Huerta. Detecting subsurface fluid leaks in real-time using injection and production rates. Advances in Water Resources, Volume 110, pp. 147-165, 2017. DOI: 10.1016/j.advwatres.2017.10.012
  7. L Agélas, G Enchéry, B Flemisch, M Schneider. Convergence of nonlinear finite volume schemes for heterogeneous anisotropic diffusion on general meshes. Journal of Computational Physics, Volume 351, pp. 80-107, December 2017. DOI: 10.1016/j.jcp.2017.09.003
  8. E. Insuasty, P. M. J. Van den Hof, S. Weiland, and J.-D. Jansen. Flow-based dissimilarity measures for reservoir models: a spatial-temporal tensor approach. Computational Geosciences, Volume 21, Issue 4, pp 645 -663, 2017. DOI: 10.1007/s10596-017-9641-4
  9. K. Zhang, X. Zhang, L. Zhang, L. Li, H. Sun, Z. Huang, and J Yao. Assisted history matching for the inversion of fractures based on discrete fracture-matrix model with different combinations of inversion parameters. Computational Geosciences, Volume 21, Issue 5 -6, pp 1365 -1383, December 2017. DOI: 10.1007/s10596-017-9690-8
  10. L. Li, H. Jiang, J. Li, K. Wu, F. Meng, and Z. Chen, Modeling tracer flowback in tight oil reservoirs with complex fracture networks, Journal of Petroleum Science and Engineering, Volume 157, pp. 1007-1020, 2017. DOI: 10.1016/j.petrol.2017.08.022.
  11. R. V. Ponce, F.V. Alvarado, and M.S. Carvalho. Water-alternating-macroemulsion reservoir simulation through capillary number-dependent modeling. Journal of the Brazilian Society of Mechanical Sciences and Engineering, Volume 39, Issue 10, pp. 4135 -4145, October 2017. DOI: 10.1007/s40430-017-0885-7
  12. M. M. Siraj, P.M.J Van den Hof, and J.-D. Jansen. Handling geological and economic uncertainties in balancing short-term and long-term objectives in waterflooding optimization. SPE Journal, Vol. 22, No. 4, pp. 1313-1325, 2017. DOI: 10.2118/185954-PA
  13. W. Zhang and M. Al Kobaisi. A two-step finite volume method for the simulation of multiphase fluid flow in heterogeneous and anisotropic reservoirs. Journal of Petroleum Science and Engineering, Volume 156, pp. 282-298, 2017 DOI: 10.1016/j.petrol.2017.06.003
  14. D. Stone, S. Geiger, G. Lord. Asynchronous discrete event schemes for PDEs. Journal of Computational Physics, Volume 342, pp. 161-176, August 2017,. DOI: 10.1016/j.jcp.2017.04.026
  15. L. Zhang, X. Zhang, K. Zhang, H. Zhang, J. Yao. Inversion of fractures with combination of production performance and in-situ stress analysis data. Journal of Natural Gas Science and Engineering, Volume 42, pp. 232-242, 2017. DOI: 10.1016/j.jngse.2017.03.002
  16. E. Ahmed, J. Jaffré, J.E. Roberts, A reduced fracture model for two-phase flow with different rock types, Mathematics and Computers in Simulation, Volume 137, pp. 49-70, 2017. DOI: 10.1016/j.matcom.2016.10.005.
  17. A. Benali, A. Bacetti, A. Belkherroubi, H. Harhad, and S. Fasla-Louhibi, S. Fluid flow simulation through a naturally fractured reservoir with Matlab & Eclipse software. Nature & Technology, Vol. 16, pp. 21-29, 2017.
  18. K.G. Hanssen, A. Codas, and B. Foss. Closed-loop predictions in reservoir management under uncertainty. SPE Journal, Volume 22, Issue 05, pp. 1585-1595, 2017. DOI: 10.2118/185956-PA
  19. A. Singh, N.M. Reddy, and P. Tiwari. Petroleum Reservoir Simulation of Two-Phase Flow. In: Saha A., Das D., Srivastava R., Panigrahi P., Muralidhar K. (eds) Fluid Mechanics and Fluid Power - Contemporary Research. Lecture Notes in Mechanical Engineering. Springer, New Delhi. DOI: 10.1007/978-81-322-2743-4_89
  20. F. Hourfar, B. Moshiri, K. Salahshoor, A. Elkamel, Real-time management of the waterflooding process using proxy reservoir modeling and data fusion theory. Computers & Chemical Engineering, Volume 106, pp. 339-354, 2017, DOI: 10.1016/j.compchemeng.2017.06.018.
  21. G.J. Lord and D. Stone. New efficient substepping methods for exponential timestepping. Applied Mathematics and Computation, Volume 307, pp. 342 -365, August 2017. DOI: 10.1016/j.amc.2017.02.052
  22. W. Zhang, M. Al Kobaisi. A simplified enhanced MPFA formulation for the elliptic equation on general grids. Computational Geosciences. Volume 21, Issue 4, pp 621 -643, 2017. DOI: 10.1007/s10596-017-9638-z
  23. A. A. Shamsuddeen, H. Ismail, and Z. Z. Ibrahim. Well trajectory optimization of homogeneous and heterogeneous reservoirs by the use of adjoint-based optimization technique. International Research Journal of Advanced Engineering and Science, Volume 2, Issue 2, pp. 36-50, 2017.
  24. A. Codas, K.G. Hanssen, B. Foss, A. Capolei, J. B. Jørgensen. Multiple shooting applied to robust reservoir control optimization including output constraints on coherent risk measures. Computational Geosciences, Volume 21, Issue 3, pp 479 -497, June 2017. DOI: 10.1007/s10596-017-9625-4
  25. L. H. Christiansen, A. Capolei, J. B. Jørgensen. A least squares approach for efficient and reliable short-term versus long-term optimization. Computational Geosciences, Volume 21, Issue 3, pp. 411 -426, June 2017. DOI: 10.1007/s10596-017-9620-9.
  26. W. Zhang, M. Al Kobaisi. A globally coupled pressure method for the discretization of the tensor-pressure equation on non-K-orthogonal grids. SPE Journal, Vol. 22, Issue 2, pp. 679 - 698, 2017. DOI: 10.2118/184405-PA.
  27. N. Sorek, E. Gildin, F. Boukouvala, B. Beykal, C. A. Floudas. Dimensionality reduction for production optimization using polynomial approximations. Computational Geosciences. Volume 21, Issue 2, pp. 247 -266, 2017. DOI: 10.1007/s10596-016-9610-3
  28. L. Perozzi, B. Giroux, D.R. Schmitt, and E. Gloaguen. Sensitivity of seismic response for monitoring CO2 storage in a low porosity reservoir of the St Lawrence Lowlands, Québec, Canada: Part 2 - Synthetic modeling. Greenhouse Gases Science and Technology, Volume 7, pp. 613 -623, 2017. DOI: 10.1002/ghg.1670
  29. K. Katterbauer, S. Arango, S. Sun, I. Hoteit. Integrating gravimetric and interferometric synthetic aperture radar data for enhancing reservoir history matching of carbonate gas and volatile oil reservoirs. Geophysical Prospecting. Volume 65, No 1, January 2017 pp. 337 - 364. DOI: 10.1111/1365-2478.12371.
  30. S. Le Clainche, F. Varas, J.M. Vega. Accelerating oil reservoir simulations using POD on the fly. International Journal for Numerical Methods in Engineering, Volume 110, Issue 1, pp. 79-100, April 2017. DOI: 10.1002/nme.5356
  31. S.Navabi, R. Khaninezhad, B. Jafarpour. A unified formulation for generalized oilfield development optimization. Computational Geosciences, Volume 21, Issue 1, pp. 47 -74, 2017. DOI: 10.1007/s10596-016-9594-z
2016
  1. L. Perozzi, E. Gloaguen, B. Giroux, K. Holliger. A stochastic inversion workflow for monitoring the distribution of CO2 injected into deep saline aquifers. Computational Geosciences, Volume 20, Issue 6, pp 1287 -1300, December 2016. DOI: 10.1007/s10596-016-9590-3
  2. A. Alyoubi, M. Ganesh. Parallel mixed FEM simulation of a class of single-phase models with non-local operators, Journal of Computational and Applied Mathematics, Volume 307, pp.106-118, December 2016. DOI: 10.1016/j.cam.2016.03.007.
  3. M. Jesmani, M.C. Bellout, R. Hanea, B. Foss. Well placement optimization subject to realistic field development constraints. Computational Geosciences. Volume 20, Issue 6, pp. 1185 -1209, December 2016. DOI: 10.1007/s10596-016-9584-1
  4. K.G. Hanssen, B. Foss. On selection of controlled variables for robust reservoir management. Journal of Petroleum Science and Engineering, Volume 147, pp. 504 -514, 2016. DOI: 10.1016/j.petrol.2016.08.027
  5. H. Yang, C. Yang, and S. Sun. Active-set reduced-space methods with nonlinear elimination for two-phase flow problems in porous media. SIAM Journal on Scientific Computing, Vol. 38, Number 4, pp. B593-B618, 2016. DOI: 10.1137/15M1041882
  6. J. S. Pau, W. Pao, and S.P. Yong. CO2 flow in saline aquifer with salt precipitation. International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 26, Issue 1, pp. 122-145, 2016. DOI: 10.1108/HFF-02-2015-0051
  7. H. Sharma and M. Pandey. Two phase flow in three dimensional multigrid model. International Journal of Innovative Research in Science, Engineering and Technology, Vol. 5, Issue 10, October 2016. DOI: 10.15680/IJIRSET.2016.0510039
  8. A. Codas, B. Foss, E. Camponogara, S. Krogstad, Black-oil minimal fluid state parametrization for constrained reservoir control optimization, Journal of Petroleum Science and Engineering, Volume 143, July 2016, Pages 35-43. DOI: 10.1016/j.petrol.2016.01.034
  9. E. G. D. Barros, P. M. J. Van den Hof, J. D. Jansen. Value of information in closed-loop reservoir management. Computational Geosciences. Volume 20, Issue 3, pp. 737 -749, 2016. DOI: 10.1007/s10596-015-9509-4
  10. M. Hosseini and M. A. Riahi. Sparsity-based compressive reservoir characterization and modeling by applying ILS-DLA sparse approximation with LARS on DisPat-generated MPS models using seismic, well log, and reservoir data. Nonlinear Processes in Geophysics Discuss., 2016. DOI: 10.5194/npg-2016-46.
  11. L. H. Christiansen, A. Capolei, J. Bagterp Jørgensen, Time-explicit methods for joint economical and geological risk mitigation in production optimization. Journal of Petroleum Science and Engineering, Volume 146, October 2016, pp. 158-169. DOI: 10.1016/j.petrol.2016.04.018.
  12. F. Hourfar, B. Moshiri, K. Salahshoor, M. Zaare-Mehrjerdi, and P. Pourafshary. Adaptive modeling of waterflooding process in oil reservoirs. Journal of Petroleum Science and Engineering, Vol. 146, pp. 702-713, 2016. DOI: 10.1016/j.petrol.2016.06.038
  13. F. Lindner, M. Pfitzner, C. Mundt. Multiphase, multicomponent flow in porous media with local thermal non-equilibrium in a multiphase mixture model. Transport in Porous Media, March 2016, Volume 112, Issue 2, pp 313 -332. DOI: 10.1007/s11242-016-0646-6
  14. M. Babaei and I. Pan. Performance comparison of several response surface surrogate models and ensemble methods for water injection optimization under uncertainty, Computers & Geosciences, Vol. 91, pp. 9-32, 2016. DOI: 10.1016/j.cageo.2016.02.022.
  15. M.N. Najafi, M. Ghaedi, and S. Moghimi-Araghi. Water propagation in two-dimensional petroleum reservoirs. Physica A: Statistical Mechanics and its Applications, Vol. 445, pp. 102-111, 2016. DOI: 10.1016/j.physa.2015.10.100
  16. R. March, F. Doster and S. Geiger. Accurate early-time and late-time modeling of countercurrent spontaneous imbibition. Water Resources Research, Vol. 52, Issue 8, pp. 6263 -6276, August 2016. DOI: 10.1002/2015WR018456.
  17. F. Sana, K. Katterbauer, T. Y. Al-Naffouri, and I. Hoteit. Orthogonal matching pursuit for enhanced recovery of sparse geological structures with the Ensemble Kalman filter. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Volume 9, Issue 4, pp. 1710-1724, 2016. DOI: 10.1109/JSTARS.2016.2518119
  18. A. S. Grema, Y. Cao. Optimal feedback control of oil reservoir waterflooding processes. International Journal of Automation and Computing, February 2016, Volume 13, Issue 1, pp. 73 -80. DOI: 10.1007/s11633-015-0909-7
  19. A. Alkhatib and M. Babaei. Applying the multilevel Monte Carlo method for heterogeneity-induced uncertainty quantification of surfactant/polymer flooding. SPE Journal, Volume 21, Issue 04, pp. 1192-1203, 2016. DOI: 10.2118/172635-PA
  20. H. Jeong and S. Srinivasan. Fast assessment of CO2 plume characteristics using a connectivity based proxy. International Journal of Greenhouse Gas Control, Volume 49, 387-412, 2016. DOI: 10.1016/j.ijggc.2016.03.001.
  21. F. Sana, F. Ravanelli, T. Y. Al-Naffouri and I. Hoteit. A sparse Bayesian imaging technique for efficient recovery of reservoir channels with time-lapse seismic measurements. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 6, pp. 2242-2254, June 2016. DOI: 10.1109/JSTARS.2016.2563163.
  22. H. Shahandeh, S. Rahim, and Z. Li. Strategic optimization of the oil sands development with SAGD: Drainage area arrangement and development planning. Journal of Petroleum Science and Engineering, Volume 137, 172 -184, 2016. DOI: 10.1016/j.petrol.2015.11.023
2015
  1. J. Zhang, and A. Revil. Cross-well 4-D resistivity tomography localizes the oil -water encroachment front during water flooding. Geophysical Journal International, Volume 201, Issue 1, pp. 343-354, 2015. DOI: 10.1093/gji/ggv028
  2. A. Codas, B. Foss, E. Camponogara. Output-constraint handling and parallelization for oil-reservoir control optimization by means of multiple shooting. SPE Journal, Volume 20, Issue 4, pp. 856-871, 2015. DOI: 10.2118/174094-PA
  3. K. Fossum and T. Mannseth. Assessment of ordered sequential data assimilation. Computational Geosciences, Volume 19, Issue 4, pp. 821-844, 2015. DOI: 10.1007/s10596-015-9492-9
  4. S. Rahim, Z. Li, J. Trivedi. Reservoir geological uncertainty reduction: an optimization-based method using multiple static measures. Mathematical Geosciences, May 2015, Volume 47, Issue 4, pp 373 -396. DOI: 10.1007/s11004-014-9575-5
  5. K. Katterbauer, S. Arango, S. Sun, S., and I. Hoteit. Multi-data reservoir history matching for enhanced reservoir forecasting and uncertainty quantification. Journal of Petroleum Science and Engineering, Volume 128, 160-176, 2015. DOI: 10.1016/j.petrol.2015.02.016
  6. M.N. Najafi, M. Ghaedi, Geometrical clusters of Darcy's reservoir model and Ising universality class, Physica A: Statistical Mechanics and its Applications, Volume 427, pp. 82-91, 2015. DOI: 10.1016/j.physa.2015.01.061.
  7. A. Hasan and B. Foss. Optimal switching time control of petroleum reservoirs. Journal of Petroleum Science and Engineering, Volume 131, pp. 131-137, 2015. DOI: 10.1016/j.petrol.2015.04.027
  8. J. Li, Z. Lei, G. Qin, B. Gong. Effective local-global upscaling of fractured reservoirs under discrete fractured discretization. Energies, Volume 8, number 9, pp. 10178-10197, 2015. DOI:10.3390/en80910178
  9. M. Babaei , A, Alkhatib, I. Pan. Robust optimization of subsurface flow using polynomial chaos and response surface surrogates. Computational Geosciences, Volume 19, Number 5, pp. 979 -998, 2015. DOI: 10.1007/s10596-015-9516-5
  10. T. Chen, C. Clauser, G. Marquart, K. Willbrand, and D. Mottaghy. A new upscaling method for fractured porous media. Advances in Water Resources, Vol. 80, pp. 60-68, 2015. DOI: 10.1016/j.advwatres.2015.03.009
  11. X. Lu, H. Jiang, J. Li, L. Zhao, Y. Pei, Y. Zhao, G. Liu, and W. Fang. Polymer thermal degradation in high-temperature reservoirs. Petroleum Science and Technology, Volume 33, Issue 17-18, pp. 1571-1579, 2015. DOI: 10.1080/10916466.2015.1072561
  12. K. Katterbauer, I. Hoteit, S. Sun. History matching of electromagnetically heated reservoirs incorporating full-wavefield seismic and electromagnetic imaging. SPE Journal, Volume 20, No 5, pp. 923 - 941, 2015. DOI: 10.2118/173896-PA
  13. F. Lindner, C. Munz, and M. Pfitzner. Fluid flow and heat transfer with phase change and local thermal non-equilibrium in vertical porous channels. Transport in Porous Media, Volume 106, Issue 1, pp 201 -220, 2015. DOI: 10.1007/s11242-014-0396-2.
  14. A. Capolei, E. Suwartadi, B. Foss, J. B. Jørgensen. A mean-variance objective for robust production optimization in uncertain geological scenarios. J. Petroleum Science and Engineering, Volume 125, pp. 23-37 2014, DOI: 10.1016/j.petrol.2014.11.015.
2014
  1. J. D. Jansen, R.M. Fonseca, S. Kahrobaei, M.M. Siraj, G.M. Van Essen, and P.M.J. Van den Hof. The egg model - A geological ensemble for reservoir simulation. Geoscience Data Journal, Volume 1 (2), pp. 192-195, 2014. DOI: 10.1002/gdj3.21
  2. O. Leeuwenburgh and R. Arts. Distance parameterization for efficient seismic history matching with the ensemble Kalman Filter. Computational Geosciences. Volume, 18, Issue: 3-4, pp. 535-548. 2014. DOI: 10.1007/s10596-014-9434-y
  3. K. Fossum and T. Mannseth. Parameter sampling capabilities of sequential and simultaneous data assimilation: II. Statistical analysis of numerical results. Inverse Problems, Volume 30, Number 11, 114002, 2014. DOI: 10.1088/0266-5611/30/11/114003
  4. T. D. Humphries, R. D. Haynes, and L. A. James. Simultaneous and sequential approaches to joint optimization of well placement and control. Computational Geosciences, Volume 18, Issue 3-4, pp 433-448, 2014. DOI: 10.1007/s10596-013-9375-x
  5. J. Rezaie and J. Eidsvik. Kalman Filter variants in the closed skew normal setting. Computational Statistics & Data Analysis, Volume 75, pp. 1 -14, 2014. DOI: 10.1016/j.csda.2014.01.014
  6. J. Rezaie, J. Eidsvig, and T. Mukerji. Value of information analysis and Bayesian inversion for closed skew-normal distributions: Applications to seismic amplitude variation with offset data. Geophysics, Volume 79, Issue 4, pp. R151-R163, 2014. DOI: 10.1190/geo2013-0048.1
  7. K. Katterbauer, I. Hoteit, and S. Sun. EMSE: Synergizing EM and seismic data attributes for enhanced forecasts of reservoirs. J. Petrol. Sci. Eng., 2014, DOI: 10.1016/j.petrol.2014.07.039.
  8. C. Lieberman and K. Willcox. Nonlinear goal-oriented Bayesian inference: Application to carbon capture and storage. SIAM Journal on Scientific Computing 36, no. 3, B427 -B449, 2014. DOI: 10.1137/130928315.
  9. A. Butler, R.D. Haynes, T.D. Humphries, and P. Ranjan. Efficient optimization of the likelihood function in Gaussian process modelling, Computational Statistics & Data Analysis, Volume 73, May 2014, Pages 40-52, ISSN 0167-9473, Doi: 10.1016/j.csda.2013.11.017.
  10. T. H. Sandve, E. Keilegavlen and J. M. Nordbotten. Physics-based preconditioners for flow in fractured porous media. Water Resources Research, Volume 50, Issue 2, pages 1357 -1373, February 2014. DOI: 10.1002/2012WR013034
2013
  1. Y. Efendiev, O. Iliev, and C. Kronsbein. Multilevel Monte Carlo methods using ensemble level mixed MsFEM for two-phase flow and transport simulations. Computational Geosciences, Volume 17, Issue 5, pp 833-850, 2013. DOI: 10.1007/s10596-013-9358-y
  2. A. Rotevatn, T. H. Sandve, E. Keilegavlen, D. Kolyukhin and H. Fossen. Deformation bands and their impact on fluid flow in sandstone reservoirs: the role of natural thickness variations. Geofluids, Volume 13, Issue 3, pages 359 -371, 2013. DOI: 10.1111/gfl.12030
  3. A. Capolei, E. Suwartadi, B. Foss, J. B. Jørgensen. Waterflooding optimization in uncertain geological scenarios. Computational Geosciences, Volume 17, Issue 6, pp 991-1013, 2013. DOI: 10.1007/s10596-013-9371-1
  4. A. Tambue. Efficient numerical simulation of incompressible two-phase flow in heterogeneous porous media based on exponential Rosenbrock−Euler method and lower-order Rosenbrock-type method. Journal of Porous Media, Volume 16, Issue 5, pp. 381-393, 2013. DOI: 10.1615/JPorMedia.v16.i5.10
  5. A. Tambue, I. Berre, J.M. Nordbotten. Efficient simulation of geothermal processes in heterogeneous porous media based on the exponential Rosenbrock-Euler and Rosenbrock-type methods. Advances in Water Resources, Volume 53, pp. 250 -262, 2013. DOI: 10.1016/j.advwatres.2012.12.004
2012
  1. Lijian Jiang, J. David Moulton, Daniil Svyatskiy, Analysis of stochastic mimetic finite difference methods and their applications in single-phase stochastic flows, Computer Methods in Applied Mechanics and Engineering, Volumes 217 -220, 1 April 2012, Pages 58-76, ISSN 0045-7825, doi: 10.1016/j.cma.2011.12.007.
  2. J. Rezaie and J. Eidsvig. Shrinked (1 − α) ensemble Kalman filter and α Gaussian mixture filter. Comput. Geosci., Vol. 16, No.3, pp. 837-852, 2012. DOI: 10.1007/s10596-012-9291-5.
  3. T. H. Sandve, I. Berre, J. M. Nordbotten. An efficient multi-point flux approximation method for Discrete Fracture-Matrix simulations. J. Comp. Phys., Vol. 231, Issue 9, pp. 3784 -3800, 2012. DOI: 10.1016/j.jcp.2012.01.023
  4. E. Keilegavlen, J. M. Nordbotten, A. F. Stephansen. Tensor relative permeabilities: origins, modeling and numerical discretization. Int. J Numer. Anal. Mod. (Special issue in memory of Magne Espedal), Vol. 9, No. 3, pp. 701-724, 2012.
  5. E. Suwartadi, S. Krogstad, and B. Foss. Nonlinear output constraints handling for production optimization of oil reservoirs. Comput. Geosci., Vol. No. 2, pp. 499-517, 2012. DOI: 10.1007/s10596-011-9253-3.
2011
  1. E. W. Bhark, B. Jafarpour, and A. Datta-Gupta. A generalized grid connectivity -based parameterization for subsurface flow model calibration, Water Resour. Res., 47, W06517, 2011. DOI: 10.1029/2010WR009982.

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