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Technical and Non-technical Challenges of Development of Offshore Petroleum Reservoirs: Characterization and Production

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Abstract

Offshore oil and gas reservoirs comprise a significant portion of the world’s reserve base, and their development is expected to help close a potential gap in the supply of hydrocarbons in the near future. Continuous advances in technology have helped the oil and gas industry to extend the exploration and production activities to deep and ultra-deep waters in harsher environments. Field development in the offshore environment is associated with numerous significant challenges in different phases including exploration, reservoir description and characterization, development planning, drilling, production, improved oil recovery (IOR) and enhanced oil recovery (EOR), transportation, and decommissioning. These challenges are further complicated by economic restrictions, especially in periods when low or unstable oil prices weaken the incentives of investments due to the high risks and uncertainties involved. Environmental concerns including the adverse effects of seismic surveys, drilling activities, discharge of waste material and produced water, and accidental spills and blowouts add another level of complexity to the design and implementation of offshore projects. Safe, reliable, and efficient development of offshore hydrocarbon reservoirs can be achieved through the proper identification of the challenges and use of modern technologies and innovative methods. This work is an effort to provide a comprehensive review of the history, global distribution, production share, classification, characteristics, and characterization methods of offshore reservoirs, and some of the main aspects and challenges of the experimental and modeling works as well as IOR/EOR planning and implementations from the reservoir engineering and production standpoint. These challenges are categorized based on their area of impact, i.e., characterization and recovery (production) phases. Furthermore, economic and environmental challenges are reviewed. After the recent downturn that caused a decline in offshore investments, current investment trends and the forecasts show that the offshore industry is booming again with more focus on cost saving and operational efficiency. The key technologies that can expedite the growth of the offshore petroleum industry have also been discussed.

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References

  • Abedini, A., & Torabi, F. (2013). Implementing artificial neural network for predicting capillary pressure in reservoir rocks. Special Topics & Reviews in Porous Media: An International Journal,4(4), 315–325.

    Google Scholar 

  • Afshar, M., Gholami, A., & Asoodeh, M. (2014). Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling. Korean Journal of Chemical Engineering,31(3), 496–502.

    Google Scholar 

  • Ahmadi, M.-A., Ahmadi, M.-R., Hosseini, S.-M., & Ebadi, M. (2014a). Connectionist model predicts the porosity and permeability of petroleum reservoirs by means of petro-physical logs: Application of artificial intelligence. Journal of Petroleum Science and Engineering,123, 183–200.

    Google Scholar 

  • Ahmadi, M. A., & Ebadi, M. (2014). Evolving smart approach for determination dew point pressure through condensate gas reservoirs. Fuel, 117, 1074–1084.

    Google Scholar 

  • Ahmadi, M. A., & Elsharkawy, A. (2017). Robust correlation to predict dew point pressure of gas condensate reservoirs. Petroleum, 3(3), 340–347.

    Google Scholar 

  • Ahmadi, M. A., & Mahmoudi, B. (2016). Development of robust model to estimate gas–oil interfacial tension using least square support vector machine: Experimental and modeling study. The Journal of Supercritical Fluids,107, 122–128.

    Google Scholar 

  • Ahmadi, M. A., Pournik, M., & Shadizadeh, S. R. (2015a). Toward connectionist model for predicting bubble point pressure of crude oils: Application of artificial intelligence. Petroleum,1(4), 307–317.

    Google Scholar 

  • Ahmadi, M. A., Zahedzadeh, M., Shadizadeh, S. R., & Abbassi, R. (2015b). Connectionist model for predicting minimum gas miscibility pressure: Application to gas injection process. Fuel,148, 202–211.

    Google Scholar 

  • Ahmadi, M.-A., Zendehboudi, S., Dusseault, M. B., & Chatzis, I. (2016). Evolving simple-to-use method to determine water–oil relative permeability in petroleum reservoirs. Petroleum,2(1), 67–78.

    Google Scholar 

  • Ahmadi, M. A., Zendehboudi, S., James, L. A., Elkamel, A., Dusseault, M., Chatzis, I., et al. (2014b). New tools to determine bubble point pressure of crude oils: Experimental and modeling study. Journal of Petroleum Science and Engineering,123, 207–216.

    Google Scholar 

  • Ahmadun, F.-R., Pendashteh, A., Abdullah, L. C., Biak, D. R., Madaeni, S. S., & Abidin, Z. Z. (2009). Review of technologies for oil and gas produced water treatment. Journal of Hazardous Materials,170(2–3), 530–551.

    Google Scholar 

  • Ahmed, T. (2016). Equations of state and PVT analysis: Applications for improved reservoir modeling (2nd ed.). Houston, Amsterdam: Gulf Professional Publishing, Elsevier.

    Google Scholar 

  • Ahmed, T. H. (1988). Comparative study of eight equations of state for predicting hydrocarbon volumetric phase behavior. SPE Reservoir Engineering,3(1), 337–348.

    Google Scholar 

  • Aïfa, T., Baouche, R., & Baddari, K. (2014). Neuro-fuzzy system to predict permeability and porosity from well log data: A case study of Hassi R׳Mel gas field, Algeria. Journal of Petroleum Science and Engineering,123, 217–229.

    Google Scholar 

  • Al-Marhoun, M. A., Ali, S. S., Abdulraheem, A., Nizamuddin, S., & Muhammadain, A. (2014). Prediction of bubble point pressure from composition of black oils using artificial neural network. Petroleum Science and Technology,32(14), 1720–1728.

    Google Scholar 

  • Al-Marhoun, M. A., & Osman, E. A. (2002). Using artificial neural networks to develop new PVT correlations for Saudi Crude Oils. In Abu Dhabi international petroleum exhibition and conference. Abu Dhabi, United Arab Emirates: Society of Petroleum Engineers.

  • Alvarado, V., & Manrique, E. J. (2013). Engineering design challenges and opportunities beyond waterflooding in offshore reservoirs. In Offshore Technology Conference. Houston, Texas, USA: Offshore Technology Conference. https://doi.org/10.4043/24105-ms.

  • Amadei, C. (Ed.) (2005). Petroleum fluid properties. In Encyclopedia of hydrocarbons (Vols. I, Exploration, Production, and Transport). Marchesi Grafiche Editoriali S.p.A. Retrieved February 2017, from http://www.treccani.it/export/sites/default/Portale/sito/altre_aree/Tecnologia_e_Scienze_applicate/enciclopedia/inglese/inglese_vol_1/pag487-508ING3.pdf.

  • Amin, R., & Smith, T. N. (1998). Interfacial tension and spreading coefficient under reservoir conditions. Fluid Phase Equilibria,142(1–2), 231–241.

    Google Scholar 

  • Anifowose, F. A., Labadin, J., & Abdulraheem, A. (2017). Hybrid intelligent systems in petroleum reservoir characterization and modeling: The journey so far and the challenges ahead. Journal of Petroleum Exploration and Production Technology,7(1), 251–263.

    Google Scholar 

  • Asadisaghandi, J., & Tahmasebi, P. (2011). Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields. Journal of Petroleum Science and Engineering,78(2), 464–475.

    Google Scholar 

  • Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In IEEE congress on evolutionary computation. Singapore: IEEE. https://doi.org/10.1109/cec.2007.4425083.

  • Bakhtyar, S., & Gagnon, M. M. (2012). Toxicity assessment of individual ingredients of synthetic-based drilling muds (SBMs). Environmental Monitoring and Assessment,184(9), 5311–5325.

    Google Scholar 

  • Bakke, S., & Øren, P. (1997). 3-D pore-scale modelling of sandstones and flow simulations in the pore networks. SPE Journal,2(2), 136–149.

    Google Scholar 

  • Bakke, T., Klungsøyr, J., & Sanni, S. (2013). Environmental impacts of produced water and drilling waste discharges from the Norwegian offshore petroleum industry. Marine Environmental Research,92, 154–169.

    Google Scholar 

  • Baldwin, J. L., Bateman, R. M., & Wheatley, C. L. (1990). Application of a neural network to the problem of mineral identification from well logs. The Log Analyst,31(5), 279–293.

    Google Scholar 

  • Baldwin, J. L., Otte, D. N., & Whealtley, C. L. (1989). Computer emulation of human mental processes: Application of neural network simulators to problems in well log interpretation. In SPE annual technical conference and exhibition. San Antonio, TX: Society of Petroleum Engineers. https://doi.org/10.2118/19619-ms.

  • Bao, K., Yan, M., Allen, R., Salama, A., Lu, L., Jordan, K. E., et al. (2016). High-performance modeling of carbon dioxide sequestration by coupling reservoir simulation and molecular dynamics. SPE Journal,21(3), 853–863.

    Google Scholar 

  • Batra, R., Miranda, C. R., & Osako, I. (2017). Technology development journey of deepwater technologies portfolio. In Offshore Technology Conference. Houston, TX, USA: Offshore Technology Conference. https://doi.org/10.4043/27915-ms.

  • Bell, J. M., Chin, Y. D., & Hanrahan, S. (2005). State-of-the-art of ultra deepwater production technologies. In Offshore Technology Conference. Houston, TX: Offshore Technology Conference. https://doi.org/10.4043/17615-ms.

  • Berg, S., Ott, H., Klapp, S. A., Schwing, A., Neiteler, R. B., Leu, L., et al. (2013). Real-time 3D imaging of Haines jumps in porous media flow. Proceedings of National Academy of Sciences of the United States of America,110(10), 3755–3759.

    Google Scholar 

  • Beucher, H., & Renard, D. (2005). Reservoir charcterization. Retrieved December 2016, from http://cg.ensmp.fr/bibliotheque/public/BEUCHER_Rapport_00318.pdf.

  • Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford: Clarendon Press.

    Google Scholar 

  • Blunt, M. J., Bijeljic, B., Dong, H., Gharbi, O., Iglauer, S., Mostaghimi, P., et al. (2013). Pore-scale imaging and modelling. Advances in Water Resources,51, 197–216.

    Google Scholar 

  • Blunt, M. J., Jackson, M. D., Piri, M., & Valvatne, P. H. (2002). Detailed physics, predictive capabilities and macroscopic consequences for pore-network models of multiphase flow. Advances in Water Resources,25(8–12), 1069–1089.

    Google Scholar 

  • Bomba, J., Chin, D., Kak, A., & Meng, W. (2018). Flow assurance engineering in deepwater offshore—Past, present, and future. In Offshore Technology Conference. Houston, TX, USA: Offshore Technology Conference. https://doi.org/10.4043/28704-ms.

  • Bondor, P. L., Hite, J. R., & Avasthi, S. M. (2005). Planning EOR projects in offshore oil fields. In SPE Latin American and Caribbean Petroleum Engineering Conference. Rio de Janeiro, Brazil: Society of Petroleum Engineers.

  • Boukadi, F., Al-Alawi, S., Al-Bemani, A., & Al-Qassabi, S. (1999). Establishing PVT correlations for Omani oils. Petroleum Science and Technology,17(5–6), 637–662.

    Google Scholar 

  • Breuer, E., Stevenson, A., Howe, J., Carrol, J. L., & Shimmield, G. B. (2004). Drill cutting accumulations in the Northern and Central North Sea: A review of environmental interactions and chemical fate. Marine Pollution Bulletin,48(1–2), 12–25.

    Google Scholar 

  • Camero, C. B. (2000). Drilling fluids design and field procedures to meet the ultra deepwater drilling challenge. In Nigerian annual international conference and exhibition. Abuja, Nigeria: Society of Petroleum Engineers. https://doi.org/10.2118/66061-ms.

  • Campos, M. D., Akkutlu, I. Y., & Sigal, R. F. (2009). A molecular dynamics study on natural gas solubility enhancement in water confined to small pores. In SPE annual technical conference and exhibition. New Orleans, LA: Society of Petroleum Engineers. https://doi.org/10.2118/124491-ms.

  • Carpenter, C. (2015). Development of a stranded tight gas field in the North Sea with hydraulic fracturing. Journal of Petroleum Technology,67(04), 102–105.

    Google Scholar 

  • Chakrabarti, S. (2005). Handbook of offshore engineering. Amsterdam: Elsevier.

    Google Scholar 

  • Chamkalani, A., Mae’soumi, A., & Sameni, A. (2013a). An intelligent approach for optimal prediction of gas deviation factor using particle swarm optimization and genetic algorithm. Journal of Natural Gas Science and Engineering,14, 132–143.

    Google Scholar 

  • Chamkalani, A., Zendehboudi, S., Bahadori, A., Kharrat, R., Chamkalani, R., James, L., et al. (2014). Integration of LSSVM technique with PSO to determine asphaltene deposition. Journal of Petroleum Science and Engineering,124, 243–253.

    Google Scholar 

  • Chamkalani, A., Zendehboudi, S., Chamkalani, R., Lohi, A., Elkamel, A., & Chatzis, I. (2013b). Utilization of support vector machine to calculate gas compressibility factor. Fluid Phase Equilibria,358, 189–202.

    Google Scholar 

  • Chandrasekar, S., Sorial, G. A., & Weaver, J. W. (2006). Dispersant effectiveness on oil spills—Impact of salinity. ICES Journal of Marine Science,63(8), 1418–1430.

    Google Scholar 

  • Chanton, J., Zhao, T., Rosenheim, B. E., Joye, S., Bosman, S., Brunner, C., et al. (2015). Using natural abundance radiocarbon to trace the flux of petrocarbon to the seafloor following the deepwater horizon oil spill. Environmental Science and Technology,49(2), 847–854.

    Google Scholar 

  • Cheng, R., Jin, Y., Olhofer, M., & Sendhoff, B. (2016). A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation,20(5), 773–791.

    Google Scholar 

  • Cho, Y., Ahmed, A., Islam, A., & Kim, S. (2015). Developments in FT-ICR MS instrumentation, ionization techniques, and data interpretation methods for petroleomics. Mass Spectrometry Reviews,34(2), 248–263.

    Google Scholar 

  • Clark, M. R., Althaus, F., Schlacher, T. A., Williams, A., Bowden, D. A., & Rowden, A. A. (2016). The impacts of deep-sea fisheries on benthic communities: A review. ICES Journal of Marine Science,73(suppl_1), i51–i69.

    Google Scholar 

  • Clarkston, B. J., Dhuldhoya, N. P., Mileo, M. A., & Moncrief, J. R. (2001). Gulf of Mexico ultra-deepwater development study. In Offshore Technology Conference. Houston, TX: Offshore Technology Conference. https://doi.org/10.4043/12964-ms.

  • Claverie, M., Hansen, S. M., Daungkaew, S., Prickett, Z., Akinsanmi, O. B., & Pillai, P. S. (2007). Applications of NMR logs and borehole images to the evaluation of laminated deepwater reservoirs. In Asia Pacific oil and gas conference and exhibition. Jakarta, Indonesia: Society of Petroleum Engineers. https://doi.org/10.2118/110223-ms.

  • Clayton, F. M., & Gordon, N. C. (1990). The Leman F and G development: Obtaining commercial production rates from a tight gas reservoir. In European petroleum conference. The Hague, The Netherlands: Society of Petroleum Engineers. https://doi.org/10.2118/20993-ms.

  • Cordes, E. E., Jones, D. O., Schlacher, T. A., Amon, D. J., Bernardino, A. F., Brooke, S., et al. (2016). Environmental impacts of the deep-water oil and gas industry: A review to guide management strategies. Frontiers in Environmental Science,4, 58.

    Google Scholar 

  • Creek, J. L., Subramanian, S., & Estanga, D. A. (2011). New method for managing hydrates in deepwater tiebacks. In Offshore Technology Conference. Houston, TX, USA: Offshore Technology Conference. https://doi.org/10.4043/22017-ms.

  • Curtis, M. E., Sondergeld, C. H., Ambrose, R. J., & Rai, C. S. (2012). Microstructural investigation of gas shales in two and three dimensions using nanometer-scale resolution imaging. AAPG Bulletin. https://doi.org/10.1306/08151110188.

    Article  Google Scholar 

  • Danesh, A. (2003). PVT and phase behaviour of petroleum reservoir fluids (3rd ed.). Amsterdam: Elsevier.

    Google Scholar 

  • Danesh, A., Xu, D.-H., & Todd, A. C. (1991). Comparative study of cubic equations of state for predicting phase behaviour and volumetric properties of injection gas-reservoir oil systems. Fluid Phase Equilibria,63(3), 259–278.

    Google Scholar 

  • Dekker, M., McEvilly, M., Beattie, M., Laws, B., Yanosek, K., Goodwin, D., et al. (2016). Energizing worldwide oil and gas deepwater developments. In Offshore Technology Conference. Houston, TX, USA: Offshore Technology Conference. https://doi.org/10.4043/27317-ms.

  • DeLeo, D. M., Ruiz-Ramos, D. V., Baums, I. B., & Cordes, E. E. (2016). Response of deep-water corals to oil and chemical dispersant exposure. Deep-Sea Research Part II: Topical Studies in Oceanography,129, 137–147.

    Google Scholar 

  • Demianov, A., Dinariev, O., & Evseev, N. (2010). Density functional modelling in multiphase compositional hydrodynamics. The Canadian Journal of Chemical Engineering,89(2), 206–226.

    Google Scholar 

  • Dhanapal, K., Devegowda, D., Zhang, Y., Contreras-Nino, A. C., Civan, F., & Sigal, R. (2014). Phase behavior and storage in organic shale nanopores: Modeling of multicomponent hydrocarbons in connected pore systems and implications for fluids-in-place estimates in shale oil and gas reservoirs. In SPE Unconventional Resources Conference. The Woodlands, TX, USA: Society of Petroleum Engineers. https://doi.org/10.2118/169008-ms.

  • Didar, B. R., & Akkutlu, I. Y. (2013). Pore-size dependence of fluid phase behavior and properties in organic-rich shale reservoirs. In SPE International Symposium on Oilfield Chemistry. The Woodlands, TX, USA: Society of Petroleum Engineers. https://doi.org/10.2118/164099-ms.

  • Dixit, A. B. (1999). Pore-scale modeling of wettability effects and their influence on oil recovery. SPE Reservoir Evaluation & Engineering,2(1), 25–36.

    Google Scholar 

  • Dumont, H., Garcia, G., Mullins, O. C., Nighswander, J., Mishra, V. K., El-Khoury, J., et al. (2017). asphaltene onset pressure uncertainty and other asphaltene issues in field development planning. In Offshore Technology Conference. TX, USA: Offshore Technology Conference.

  • Dutta, S., & Gupta, J. (2010). PVT correlations for Indian crude using artificial neural networks. Journal of Petroleum Science and Engineering,72(1–2), 93–109.

    Google Scholar 

  • Eckle, P., Burgherr, P., & Michaux, E. (2012). Risk of large oil spills: A statistical analysis in the aftermath of deepwater horizon. Environmental Science and Technology,46(23), 13002–13008.

    Google Scholar 

  • Edge, K. J., Johnston, E. L., Simpson, S. L., Kutti, T., & Bannister, R. J. (2016). Sub-lethal effects of water-based drilling muds on the deep-water sponge Geodia barretti. Environmental Pollution,212, 525–534.

    Google Scholar 

  • EIA. (2016a). Offshore oil production in deepwater and ultra-deepwater is increasing. (U. E. Administration, Producer) Retrieved May 2018, from https://www.eia.gov/, https://www.eia.gov/todayinenergy/detail.php?id=28552.

  • EIA. (2016b). Offshore production nearly 30% of global crude oil output in 2015. (U. E. Administration, Producer) Retrieved February 2018, from https://www.eia.gov/todayinenergy/detail.php?id=28492.

  • El-Sebakhy, E. A. (2009). Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme. Journal of Petroleum Science and Engineering,64(1–4), 25–34.

    Google Scholar 

  • El-Sebakhy, E. A., Sheltami, T., Al-Bokhitan, S. Y., Shaaban, Y., Raharja, P. D., & Khaeruzzaman, Y. (2007). Support vector machines framework for predicting the PVT properties of crude oil systems. In Middle east oil and gas show and conference. Manama, Bahrain: Society of Petroleum Engineers. https://doi.org/10.2118/105698-ms.

  • Elsharkawy, A. M. (1998). Modeling the properties of crude oil and gas systems using RBF network. Perth, Australia: Society of Petroleum Engineers. https://doi.org/10.2118/49961-MS.

    Book  Google Scholar 

  • Elsharkwy, A. M., & Gharbi, R. B. (2001). Comparing classical and neural regression techniques in modeling crude oil viscosity. Advances in Engineering Software,32(3), 215–224.

    Google Scholar 

  • Epstein, N., Bak, R. P., & Rinkevich, B. (2000). Toxicity of third generation dispersants and dispersed egyptian crude oil on red sea coral larvae. Marine Pollution Bulletin,40(6), 497–503.

    Google Scholar 

  • Fadjarijanto, A., Rachmadi, A., Setiawan, A. S., Praptono, A., Suriyo, K., Simatupang, M. H., et al. (2018). Three petrophysics techniques applied for the thin lamination reservoir: The impact towards significant reserve addition. In Offshore Technology Conference Asia. Kuala Lumpur, Malaysia: Offshore Technology Conference Asia. https://doi.org/10.4043/28412-ms.

  • Fang, H., & Duan, M. (2014). Offshore operation facilities: Equipment and procedures (1st ed.). Houston: Gulf Professional Publishing.

    Google Scholar 

  • Farasat, A., Shokrollahi, A., Arabloo, M., Gharagheizi, F., & Mohammadi, A. H. (2013). Toward an intelligent approach for determination of saturation pressure of crude oil. Fuel Processing Technology,115, 201–214.

    Google Scholar 

  • Ferreira, C. (2001). Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems,13(2), 87–129.

    Google Scholar 

  • Fisher, C. R., Demopoulos, A. W., Cordes, E. E., Baums, I. B., White, H. K., & Bourque, J. R. (2014). Coral communities as indicators of ecosystem-level impacts of the deepwater horizon spill. BioScience,64(9), 796–807.

    Google Scholar 

  • Folger, M., Alkatiri, F., Nguyen, T. A., Daungkaew, S., Khunaworawet, T., Duangprasert, T., et al. (2018). Building systematic approach for upscaling flow potential in South East Asia deepwater. In Offshore Technology Conference Asia. Kuala Lumpur, Malaysia: Offshore Technology Conference.

  • Gaafar, G. R., Tewari, R. D., & Zain, Z. M. (2015). Overview of advancement in core analysis and its importance in reservoir characterisation for maximising recovery. In SPE Asia Pacific enhanced oil recovery conference. Kuala Lumpur, Malaysia: Society of Petroleum Engineers. https://doi.org/10.2118/174583-ms.

  • Gagnon, M. M., & Bakhtyar, S. (2013). Induction of fish biomarkers by synthetic-based drilling muds. PLoS ONE,8(7), e69489.

    Google Scholar 

  • Galliero, G., & Montel, F. (2009). Understanding compositional grading in petroleum reservoirs thanks to molecular simulations. In EUROPEC/EAGE conference and exhibition. Amsterdam, The Netherlands: Society of Petroleum Engineers. https://doi.org/10.2118/121902-ms.

  • Gharbi, R. B., & Elsharkawy, A. M. (1997). Universal neural network based model for estimating the PVT properties of crude oil systems. In SPE Asia Pacific oil and gas conference and exhibition. Kuala Lumpur, Malaysia: Society of Petroleum Engineers. https://doi.org/10.2118/38099-ms.

  • Gharbi, R. B., & Elsharkawy, A. M. (1999). Neural network model for estimating the PVT properties of middle east crude oils. SPE Reservoir Evaluation & Engineering,2(3), 255–265.

    Google Scholar 

  • Gholami, R., Moradzadeh, A., Maleki, S., Amiri, S., & Hanachi, J. (2014). Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs. Journal of Petroleum Science and Engineering,122, 643–656.

    Google Scholar 

  • Ghoodjani, E., Kharrat, R., Vossoughi, M., & Bolouri, S. H. (2012). A review on thermal enhanced heavy oil recovery from fractured carbonate reservoirs. In SPE heavy oil conference Canada. Calgary, AB, Canada: Society of Petroleum Engineers. https://doi.org/10.2118/150147-ms.

  • Goda, H. M., El-M Shokir, E. M., Fattah, K. A., & Sayyouh, M. H. (2003). Prediction of the PVT data using neural network computing theory. In Nigeria annual international conference and exhibition. Abuja, Nigeria: Society of Petroleum Engineers. https://doi.org/10.2118/85650-ms.

  • Goodbody-Gringley, G., Wetzel, D. L., Gillon, D., Pulster, E., Miller, A., & Ritchie, K. B. (2013). Toxicity of deepwater horizon source oil and the chemical dispersant, Corexit® 9500, to coral larvae. PLoS ONE,8(1), e45574.

    Google Scholar 

  • Goodyear, S. G., Koster, M., Marriott, K., Paterson, A., Sipkema, A., & Young, I. (2011). Moving CO2 EOR offshore. Society of Petroleum Engineers. In Offshore Europe. Aberdeen, UK: Society of Petroleum Engineers. https://doi.org/10.2118/146280-ms.

  • Gordon, J., Gillespie, D., Potter, J., Frantzis, A., Simmonds, M. P., Swift, R., et al. (2003). A review of the effects of seismic surveys on marine mammals. Marine Technology Society Journal,37(4), 16–34.

    Google Scholar 

  • Gouth, F., Collell, J., Galliero, G., & Wang, J. (2013). Molecular simulation to determine key shale gas parameters, and their use in a commercial simulator for production forecasting. In EAGE annual conference & exhibition incorporating SPE Europec. London, UK: Society of Petroleum Engineers. https://doi.org/10.2118/164790-ms.

  • Grachev, N. E. (2012). Digital core analysis—The future of petrophysics. In SPE Russian oil and gas exploration and production technical conference and exhibition. Moscow, Russia: Society of Petroleum Engineers. https://doi.org/10.2118/160499-ms.

  • Gray, J. S., Clarke, K. R., Warwick, R. M., & Hobbs, G. (1990). Detection of initial effects of pollution on marine benthos: An example from the Ekofisk and Eldfisk oilfields, North Sea. Marine Ecology Progress Series,66, 285–299.

    Google Scholar 

  • Griebel, M., Zumbusch, G., & Knapek, S. (2007). From the Schrödinger equation to molecular dynamics. In T. J. Barth, M. Griebel, D. E. Keyes, R. M. Nieminen, D. Roose, & T. Schlick (Eds.), Numerical simulation in molecular dynamics. Texts in computational science and engineering (Vol. 5). Berlin: Springer. https://doi.org/10.1007/978-3-540-68095-6_2.

    Chapter  Google Scholar 

  • Guédon, G. R., Hyman, J. D., Inzoli, F., Riva, M., & Guadagnini, A. (2017). Influence of capillary end effects on steady-state relative permeability estimates from direct pore-scale simulations. Physics of Fluids,29(12), 123104.

    Google Scholar 

  • Ha, G. T., Tran, N. D., Vu, H. H., Takagi, S., Mitsuishi, H., Hatakeyama, A., et al. (2012). Design & implementation of CO2 huff-n-puff operation in a vietnam offshore field. In Abu Dhabi international petroleum conference and exhibition. Abu Dhabi, UAE: Society of Petroleum Engineers. https://doi.org/10.2118/161835-ms.

  • Håheim, S., Dutsch, D., Pires, L., Baima, J., Galgoul, D., & Caldas, L. (2016). Subsea sampling performed in the deepwater BC-10 field offshore Brazil. In Offshore Technology Conference. Houston, TX, USA: Offshore Technology Conference. https://doi.org/10.4043/27162-ms.

  • Haidar, S., Rylance, M., Tybero, G., & Van-Batenburg, J. D. (1996). Novel fracture technology proves marginal viking prospect economic, Part II: Well clean-up, flowback and testing. In SPE annual technical conference and exhibition. Denver, CO: Society of Petroleum Engineers. https://doi.org/10.2118/36473-ms.

  • Hall-Spencer, J., Allain, V., & Fosså, J. H. (2002). Trawling damage to Northeast Atlantic ancient coral reefs. Proceedings Biological Sciences,269(1490), 507–511.

    Google Scholar 

  • Han, C., Delshad, M., Sepehrnoori, K., & Pope, G. A. (2007). A fully implicit, parallel, compositional chemical flooding simulator. SPE Journal,12(3), 322–338.

    Google Scholar 

  • Han, M., Xiang, W., Zhang, J., Jiang, W., & Sun, F. (2006). Application of EOR technology by means of polymer flooding in bohai oilfields. In International oil & gas conference and exhibition in China. Beijing, China: Society of Petroleum Engineers. https://doi.org/10.2118/104432-ms.

  • Hawkins, A. D., Pembroke, A. E., & Popper, A. N. (2015). Information gaps in understanding the effects of noise on fishes and invertebrates. Reviews in Fish Biology and Fisheries,25(1), 39–64.

    Google Scholar 

  • Haykin, S. S. (1999). Neural networks: A comprehensive foundation (2nd ed.). Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Hazlett, R. (1995). Simulation of capillary-dominated displacements in microtomographic images of reservoir rocks. Transport in Porous Media,20(1), 21–35.

    Google Scholar 

  • Helmy, T., Fatai, A., & Faisal, K. (2010). Hybrid computational models for the characterization of oil and gas reservoirs. Expert Systems with Applications,37(7), 5353–5363.

    Google Scholar 

  • Herdes, C., Totton, T. S., & Müller, E. A. (2015). Coarse grained force field for the molecular simulation of natural gases and condensates. Fluid Phase Equilibria,406, 91–100.

    Google Scholar 

  • Honarpour, M. M., Djabbarah, N. F., & Sampath, K. (2005). Whole core analysis—Experience and challenges. SPE Reservoir Evaluation & Engineering,8(6), 460–469.

    Google Scholar 

  • Honarpour, M. M., Nagarajan, N. R., & Sampath, K. (2006). Rock/fluid characterization and their integration—Implications on reservoir management. Journal of Petroleum Technology,58(9), 120–130.

    Google Scholar 

  • Huang, D. D., & Honarpour, M. M. (1998). Capillary end effects in coreflood calculations. Journal of Petroleum Science and Engineering,19(1–2), 103–117.

    Google Scholar 

  • Huang, Y., Gedeon, T. D., & Wong, P. M. (2001). An integrated neural-fuzzy-genetic-algorithm using hyper-surface membership functions to predict permeability in petroleum reservoirs. Engineering Applications of Artificial Intelligence,14(1), 15–21.

    Google Scholar 

  • Huang, Y., Wong, P. M., & Gedeon, T. D. (1996). An improved fuzzy neural network for permeability estimation from wireline logs in a petroleum reservoir. In 1996 IEEE TENCON. Digital signal processing applications (Vol. 2, pp. 912–917). Perth, WA. https://doi.org/10.1109/tencon.1996.608469.

  • Huang, Z., Shimeld, J., Williamson, M., & Katsube, J. (1996b). Permeability prediction with artificial neural network modeling in the Venture gas field, offshore eastern Canada. Geophysics,61(2), 422–436.

    Google Scholar 

  • Hutahaean, J., Demyanov, V., & Christie, M. (2016). Many-objective optimization algorithm applied to history matching. In IEEE symposium series on computational intelligence (SSCI). Athens, Greece: IEEE. https://doi.org/10.1109/ssci.2016.7850215.

  • Hutahaean, J., Demyanov, V., & Christie, M. (2019). Reservoir development optimization under uncertainty for infill well placement in brownfield redevelopment. Journal of Petroleum Science and Engineering,175, 444–464.

    Google Scholar 

  • Hutahaean, J., Demyanov, V., & Christie, M. A. (2017). On optimal selection of objective grouping for multiobjective history matching. SPE Journal,22(4), 1296–1312.

    Google Scholar 

  • Hutahaean, J. J., Demyanov, V., Arnold, D., & Vazquez, O. (2014). Optimization of well placement to minimize the risk of scale deposition in field development. In Abu Dhabi international petroleum exhibition and conference. Abu Dhabi, UAE: Society of Petroleum Engineers. https://doi.org/10.2118/171733-ms.

  • Hutahaean, J. J., Demyanov, V., & Christie, M. A. (2015). Impact of model parameterisation and objective choices on assisted history matching and reservoir forecasting. In SPE/IATMI Asia Pacific oil & gas conference and exhibition. Nusa Dua, Bali, Indonesia: Society of Petroleum Engineers. https://doi.org/10.2118/176389-ms.

  • Idowu, N., & Blunt, M. (2010). Pore-scale modelling of rate effects in waterflooding. Transport in Porous Media,83(1), 151–169.

    Google Scholar 

  • Iglauer, S., Favretto, S., Spinelli, G., Schena, G., & Blunt, M. J. (2010). X-ray tomography measurements of power-law cluster size distributions for the nonwetting phase in sandstones. Physical Review E. https://doi.org/10.1103/physreve.82.056315.

    Article  Google Scholar 

  • Iglauer, S., Wang, S., & Rasouli, V. (2011). X-ray micro-tomography measurements of fractured tight sandstone. In SPE Asia Pacific oil and gas conference and exhibition. Jakarta, Indonesia: Society of Petroleum Engineers. https://doi.org/10.2118/145960-ms.

  • Ippoliti, E. (2011). What is molecular dynamics? German Research School for Simulation Sciences. Retrieved April 06, 2017, from http://www.grs-sim.de/, http://www.grs-sim.de/cms/upload/Carloni/Tutorials/SiScLab/Preparatory_material.pdf.

  • Irani, R., & Nasimi, R. (2011). Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir. Expert Systems with Applications,38(8), 9862–9866.

    Google Scholar 

  • Israelachvili, J. N. (2011). Intermolecular and surface forces (3rd ed.). Cambridge: Academic Press.

    Google Scholar 

  • Ivanova, M. (2017). Top 10 offshore producing countries in 2017. (Offshore) Retrieved July 23, 2018, from https://www.offshore-mag.com/articles/print/volume-77/issue-1/departments/data/top-10-offshore-producing-countries-in-2017.html.

  • Jayasekera, A. J., & Goodyear, S. G. (2002). Improved hydrocarbon recovery in the United Kingdom Continental shelf: Past, present and future. In SPE/DOE improved oil recovery symposium. Tulsa, OK: Society of Petroleum Engineers. https://doi.org/10.2118/75171-ms.

  • Jernelöv, A., & Lindén, O. (1981). Ixtoc I: A case study of the world’s largest oil spill. Ambio,10(6), 299–306.

    Google Scholar 

  • Jones, D. O., Hudson, I. R., & Bett, B. J. (2006). Effects of physical disturbance on the cold-water megafaunal communities of the Faroe–Shetland Channel. Marine Ecology Progress Series,319, 43–54.

    Google Scholar 

  • Joye, S. B., Bracco, A., Ozgokmen, T. M., Chanton, J. P., Grosell, M., MacDonald, I. R., et al. (2016). The Gulf of Mexico ecosystem, six years after the Macondo oil well blowout. Deep-Sea Research Part II: Topical Studies in Oceanography,129, 4–19.

    Google Scholar 

  • Kang, P.-S., Lim, J.-S., & Huh, C. (2016). Screening criteria and considerations of offshore enhanced oil recovery. Energies. https://doi.org/10.3390/en9010044.

    Article  Google Scholar 

  • Kang, X., Zhang, J., Sun, F., Zhang, F., Feng, G., Yang, J., et al. (2011). A review of polymer EOR on offshore heavy oil field in Bohai Bay, China. In SPE enhanced oil recovery conference. Kuala Lumpur, Malaysia: Society of Petroleum Engineers. https://doi.org/10.2118/144932-ms.

  • Karimnezhad, M., Heidarian, M., Kamari, M., & Jalalifar, H. (2014). A new empirical correlation for estimating bubble point oil formation volume factor. Journal of Natural Gas Science and Engineering,18, 329–335.

    Google Scholar 

  • Karimpouli, S., Fathianpour, N., & Roohi, J. (2012). A new approach to improve neural networks’ algorithm in permeability prediction of petroleum reservoirs using supervised committee machine neural network (SCMNN). Journal of Petroleum Science and Engineering,73(3–4), 227–232.

    Google Scholar 

  • Kaydani, H., Mohebbi, A., & Eftekhari, M. (2014). Permeability estimation in heterogeneous oil reservoirs by multi-gene genetic programming algorithm. Journal of Petroleum Science and Engineering,123, 201–206.

    Google Scholar 

  • Kazemi, K., Moradi, S., & Asoodeh, M. (2013). A neural network based model for prediction of saturation pressure from molecular components of crude oil. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects,35(11), 1039–1045.

    Google Scholar 

  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95—International conference on neural networks (Vol. 4, pp. 1942–1948). Perth, Australia: IEEE. https://doi.org/10.1109/icnn.1995.488968.

  • Khoukhi, A. (2012). Hybrid soft computing systems for reservoir PVT properties prediction. Computers & Geosciences,44, 109–119.

    Google Scholar 

  • Kim, E., Cho, E., Moon, S., Park, J.-I., & Kim, S. (2016). Characterization of petroleum heavy oil fractions prepared by preparatory liquid chromatography with thin-layer chromatography, high-resolution mass spectrometry, and gas chromatography with an atomic emission detector. Energy & Fuels,30(4), 2932–2940.

    Google Scholar 

  • Knackstedt, M. A., Pinczewski, W. V., Fogden, A., & Senden, T. (2011). Improved characterization of EOR processes in 3D. Characterizing mineralogy, wettability and residual fluid phases at the pore scale. In SPE enhanced oil recovery conference. Kuala Lumpur, Malaysia: Society of Petroleum Engineers. https://doi.org/10.2118/145093-ms.

  • Kokal, S., & AL-Kaabi, A. (2010). Enhanced oil recovery: Challenges & opportunities. In Technology and innovations (pp. 64–69). Retrieved September 11, 2018, from https://pdfs.semanticscholar.org/d36c/ffd9c2b47509bf1d736544b241adb1126266.pdf?_ga=2.68940437.52770906.1569109883-433272377.1569109883.

  • Koperna, G. J. (2011). Linking CO2-EOR and CO2 storage in the offshore Gulf of Mexico. In Offshore Technology Conference. Houston, TX, USA: Offshore Technology Conference. https://doi.org/10.4043/21986-ms.

  • Kuila, U., Prasad, M., Derkowski, A., & McCarty, D. K. (2012). Compositional controls on mudrock pore-size distribution: An example from Niobrara Formation. In SPE annual technical conference and exhibition. San Antonio, TX, USA: Society of Petroleum Engineers. https://doi.org/10.2118/160141-ms.

  • Kumar, M., Middleton, J. P., Sheppard, A., Senden, T., & Knackstedt, M. A. (2009). Quantifying trapped residual oil in reservoir core material at the pore scale: Exploring the role of displacement rate, saturation history and wettability. In International petroleum technology conference. Doha, Qatar: International Petroleum Technology Conference. https://doi.org/10.2523/iptc-14001-ms.

  • Langford, M. E., Holland, B., Green, C. A., Bocaneala, B., & Norris, M. R. (2013). Offshore horizontal well fracturing: Operational optimisation in the Southern North Sea. In SPE Offshore Europe oil and gas conference and exhibition. Aberdeen, UK: Society of Petroleum Engineers. https://doi.org/10.2118/166550-ms.

  • Latham, A. J. (2002). Commercial realities in deep and ultra deepwater. In 17th world petroleum congress. Rio de Janeiro, Brazil: World Petroleum Congress.

  • Lavenson, D. M., Kelkar, A. V., Joshi, N., & Portman, L. (2017). Kinetics of fluid phase behavior changes: A critical uncertainty for offshore field development. In Offshore Technology Conference. Houston, TX, USA: Offshore Technology Conference. https://doi.org/10.4043/27604-ms.

  • Law, R. (1978). Determination of petroleum hydrocarbons in water, fish and sediments following the Ekofisk blow-out. Marine Pollution Bulletin,9(12), 321–324.

    Google Scholar 

  • Lawrence, J. J., Chorneyko, D. M., Smith, C. K., & Nagarajan, N. (2008). Representative reservoir fluid sampling: Challenges, issues, and solutions. In International petroleum technology conference. International Petroleum Technology Conference. https://doi.org/10.2523/iptc-12401-ms.

  • Lebrato, M., & Jones, D. O. (2009). Mass deposition event of Pyrosoma atlanticum carcasses off Ivory Coast (West Africa). Limnology and Oceanography. https://doi.org/10.4319/lo.2009.54.4.1197.

    Article  Google Scholar 

  • Lemmens, H., Butcher, A., Richards, D., Laughrey, C., & Dixon, M. L. (2011). Imaging techniques for 2D and 3D characterization of unconventional reservoirs core and cuttings samples-and how to integrate them. In North American unconventional gas conference and exhibition. The Woodlands, TX, USA: Society of Petroleum Engineers. https://doi.org/10.2118/144308-ms.

  • Lim, J.-S. (2005). Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea. Journal of Petroleum Science and Engineering,49(3–4), 182–192.

    Google Scholar 

  • Liu, F.-J., Fan, M., Wei, X.-Y., & Zong, Z.-M. (2016). Application of mass spectrometry in the characterization of chemicals in coal-derived liquids. Mass Spectrometry Reviews,36(04), 543–579.

    Google Scholar 

  • Loucks, R. G., Reed, R. M., Ruppel, S. C., & Hammes, U. (2012). Spectrum of pore types and networks in mudrocks and a descriptive classification for matrix-related mudrock pores. AAPG Bulletin,96(6), 1071–1098.

    Google Scholar 

  • Makimura, D., Kunieda, M., Liang, Y., Matsuoka, T., Takahashi, S., & Okabe, H. (2013). Application of molecular simulations to CO2-enhanced oil recovery: Phase equilibria and interfacial phenomena. SPE Journal,18(2), 319–330.

    Google Scholar 

  • Malallah, A., Gharbi, R., & Algharaib, M. (2006). Accurate estimation of the world crude oil PVT properties using graphical alternating conditional expectation. Energy & Fuels,20(2), 688–698.

    Google Scholar 

  • Malki, H. A., Baldwin, J. L., & Kwari, M. A. (1996). Estimating permeability by use of neural networks in thinly bedded shaly gas sands. SPE Computer Applications,8(2), 58–62.

    Google Scholar 

  • Martinez, V., & Ascencio, F. (2018). A new practical water injection system in offshore fields. In Offshore Technology Conference. Houston, TX, USA: Offshore Technology Conference. https://doi.org/10.4043/28741-ms.

  • Martins, J. P., Leung, K. H., Jackson, M. R., Stewart, D. R., & Carr, A. H. (1992). Tip screenout fracturing applied to the ravenspurn south gas field development. SPE Production Engineering,07(03), 252–258.

    Google Scholar 

  • Martinsen, A., & Weiss, J. (2018). 100 Offshore projects to be sanctioned in 2018. (Rystad Energy) Retrieved June 7, 2018, from https://www.rystadenergy.com/newsevents/news/press-releases/100-offshore-projects/.

  • McDougall, S. R., & Sorbie, K. S. (1995). The impact of wettability on waterflooding: Pore-scale simulation. SPE Reservoir Engineering,10(3), 208–213.

    Google Scholar 

  • McPhee, C. (2015). Special core analysis: Challenges, pitfalls and solutions. In SPE London Evening Dinner meetings. London, UK: Society of Petroleum Engineers.

  • Meller, J. (2001). Molecular dynamics. Encyclopedia of Life Sciences. https://doi.org/10.1038/npg.els.0003048.

    Article  Google Scholar 

  • Milliken, K. L., Rudnicki, M., Awwiller, D. N., & Zhang, T. (2013). Organic matter–hosted pore system, Marcellus Formation (Devonian), Pennsylvania. AAPG Bulletin,97(2), 177–200.

    Google Scholar 

  • Mishra, V. K., Zuo, J. Y., Dumont, H., & Mullins, O. C. (2012). Permeable tar mat formation described within context of novel asphaltene science. In SPE Kuwait international petroleum conference and exhibition. Kuwait City, Kuwait: Society of Petroleum Engineers. https://doi.org/10.2118/163292-ms.

  • Mohaghegh, S., Arefi, R., Ameri, S., & Hefner, M. H. (1994). A methodological approach for reservoir heterogeneity characterization using artificial neural networks. I SPE annual technical conference and exhibition. New Orleans, LA: Society of Petroleum Engineers. https://doi.org/10.2118/28394-ms.

  • Mohaghegh, S., Arefi, R., Ameri, S., & Rose, D. (1995). Design and development of an artificial neural network for estimation of formation permeability. SPE Computer Applications,7(6), 151–154.

    Google Scholar 

  • Montagna, P. A., Baguley, J. G., Cooksey, C., Hartwell, I., Hyde, L. J., Hyland, J. L., et al. (2013). Deep-sea benthic footprint of the deepwater horizon blowout. PLoS ONE,8(8), e70540.

    Google Scholar 

  • Moore, S. E., Reeves, R. R., Southall, B. L., Ragen, T. J., Suydam, R. S., & Clark, C. W. (2012). A new framework for assessing the effects of anthropogenic sound on marine mammals in a rapidly changing arctic. BioScience,62(3), 289–295.

    Google Scholar 

  • Moreno, J. E., Gurpinar, O., Liu, Y., & Gossuin, J. (2018). Effective use of laboratory measurements on Eor planning. In Offshore Technology Conference. Houston, TX, USA: Offshore Technology Conference. https://doi.org/10.4043/29057-ms.

  • Muehlenbachs, L., Cohen, M. A., & Gerarden, T. (2013). The impact of water depth on safety and environmental performance in offshore oil and gas production. Energy Policy,55, 699–705.

    Google Scholar 

  • Mullins, O. C., Primio, R. D., Zuo, J. Y., Uchytil, S., Mishra, V. K., Dumont, H., et al. (2016). Reservoir fluid geodynamics; The link between petroleum systems and production concerns relating to fluids and tar distributions in Re. In SPE annual technical conference and exhibition. Dubai, UAE: Society of Petroleum Engineers. https://doi.org/10.2118/181535-ms.

  • Myers, J. K., & Pace, C. N. (1996). Hydrogen bonding stabilizes globular proteins. Biophysical Journal,71(4), 2033–2039.

    Google Scholar 

  • Nadeev, A., Mikhailov, D., Chuvilin, E., Koroteev, D., & Shako, V. (2013). Visualization of clay and frozen substances inside porous rocks using X-ray micro-computed tomography. Microscopy and Analysis,27(2), S9–S11.

    Google Scholar 

  • Nagarajan, N. R., Honarpour, M. M., & Sampath, K. (2007). Reservoir-fluid sampling and characterization—Key to efficient reservoir management. Journal of Petroleum Technology. https://doi.org/10.2118/103501-jpt.

    Article  Google Scholar 

  • Naseryan-Moghadam, J., Salahshoor, K., & Kharrat, R. (2011). Introducing a new method for predicting PVT properties of Iranian crude oils by applying artificial neural networks. Petroleum Science and Technology,29(10), 1066–1079.

    Google Scholar 

  • Neff, J. (2002). Bioaccumulation in marine organisms: Effect of contaminants from oil well produced water. Amsterdam: Elsevier.

    Google Scholar 

  • Nguyen, V. H., Sheppard, A. P., Knackstedt, M. A., & Pinczewski, W. V. (2006). The effect of displacement rate on imbibition relative permeability and residual saturation. Journal of Petroleum Science and Engineering,52(1–4), 54–70.

    Google Scholar 

  • Nieukirk, S. L., Mellinger, D. K., Moore, S. E., Klinck, K., Dziak, R. P., & Goslin, J. (2012). Sounds from airguns and fin whales recorded in the mid-Atlantic Ocean, 1999–2009. The Journal of the Acoustical Society of America,131(2), 1102–1112.

    Google Scholar 

  • Olatunji, S. O., Selamat, A., & Abdulraheem, A. (2012). A hybrid model through the fusion of type-2 fuzzy logic systems and extreme learning machines for modelling permeability prediction. Information Fusion,16, 29–45.

    Google Scholar 

  • Olsen, D. (2011). CO2 EOR production properties of chalk. In SPE EUROPEC/EAGE annual conference and exhibition. Vienna, Austria: Society of Petroleum Engineers. https://doi.org/10.2118/142993-ms.

  • Osman, E. A., Abdel-Wahhab, O. A., & Al-Marhoun, M. A. (2001). Prediction of oil PVT properties using neural networks. In SPE middle east oil show. Manama, Bahrain: Society of Petroleum Engineers. https://doi.org/10.2118/68233-ms.

  • Patzek, T. W. (2001). Verification of a complete pore network simulator of drainage and imbibition. SPE Journal,6(2), 144–156.

    Google Scholar 

  • Pedersen, K. S., Christensen, P. L., & Azeem Shaikh, J. (2015). Phase behavior of petroleum reservoir fluids (2nd ed.). Boca Raton/Abingdon: CRC Press/Taylor & Francis Group.

    Google Scholar 

  • Pettingill, H. S., & Weimer, P. (2002). Worlwide deepwater exploration and production: Past, present, and future. The Leading Edge,21(4), 371–376.

    Google Scholar 

  • Pinto, D. (2007). Retrieved August 17, 2018, from Wikipedia-Public Domain, https://en.wikipedia.org/wiki/File:Worlds5.png.

  • Piri, M., & Blunt, M. J. (2005). Three-dimensional mixed-wet random pore-scale network modeling of two-and three-phase flow in porous media. I. Model description. Physical Review E,71(2), 026301.

    Google Scholar 

  • Pivel, M. A., Freitas, C. M., & Comba, J. L. (2009). Modeling the discharge of cuttings and drilling fluids in a deep-water environment. Deep Sea Research Part II: Topical Studies in Oceanography,56(1–2), 12–21.

    Google Scholar 

  • Pizarro, J. O., & Branco, C. C. (2012a). Challenges in implementing an EOR project in the pre-salt province in deep offshore Brasil. In SPE EOR conference at oil and gas West Asia. Muscat, Oman: Society of Petroleum Engineers. https://doi.org/10.2118/155665-ms.

  • Pizarro, J. O., & Branco, C. C. (2012b). Planning and implementing an EOR projects for the pre-salt Lula. World Oil, 233(8). https://www.worldoil.com/magazine/2012/august-2012/supplement/planning-and-implementing-an-eor-project-for-the-pre-salt-lula-field.

  • Rafiee-Taghanaki, S., Arabloo, M., Chamkalani, A., Amani, M., Zargari, M. H., & Adelzadeh, M. R. (2013). Implementation of SVM framework to estimate PVT properties of reservoir oil. Fluid Phase Equilibria,346, 25–32.

    Google Scholar 

  • Rahman, A. S., & Fuad, M. I. (2018). Improved reservoir characterisation in Malay Basin through seismic stratigraphy and rock physics modelling. In Offshore Technology Conference. Kuala Lumpur, Malaysia: Offshore Technology Conference. https://doi.org/10.4043/28404-ms.

  • Ravi, A., & Reiersen, L. M. (2018). Offshore costs continue to fall. (Rystad Energy) Retrieved June 11, 2018, from https://www.rystadenergy.com/newsevents/news/press-releases/offshore-review-subsea-market/.

  • Rocha, L. A., Junqueira, P., & Roque, J. L. (2003). Overcoming deep and ultra deepwater drilling challenges. In Offshore Technology Conference. Houston, TX: Offshore Technology Conference. https://doi.org/10.4043/15233-ms.

  • Rodgers, R. P., Schaub, T. M., & Marshall, A. G. (2005). Petroleomics: MS returns to its roots. Analytical Chemistry,77(1), 20A–27A.

    Google Scholar 

  • Rosa, M. B., & Branco, C. C. (2012). Optimizing CO2 injection in an offshore field considering operational constraints. In SPE Latin America and Caribbean petroleum engineering conference. Mexico City, Mexico: Society of Petroleum Engineers. https://doi.org/10.2118/153049-ms.

  • Rylance, M., Haidar, S., Sykes, G., & Pyecroft, J. (1996). Novel fracture technology proves marginal viking prospect economic, Part I: Implementation of fracture treatments. In SPE annual technical conference and exhibition. Denver, CO: Society of Petroleum Engineers. https://doi.org/10.2118/36472-ms.

  • Saadatfar, M., Francois, N., Arad, A., Madadi, M., Cruikshank, R., Alizadeh, M., et al. (2012). 3D mapping of deformation in an unconsolidated sand: A micro mechanical study. SEG Technical Program Expanded Abstracts,2012, 1–6.

    Google Scholar 

  • Saadatfara, M., Shepparda, A. P., Sendena, T. J., & Kabla, A. J. (2012). Mapping forces in a 3D elastic assembly of. Journal of the Mechanics and Physics of Solids,60(1), 55–66.

    Google Scholar 

  • Saemi, M., Ahmadi, M., & Yazdian-Varjani, A. (2007). Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. Journal of Petroleum Science and Engineering,59(1–2), 97–105.

    Google Scholar 

  • Sah, P., Gurdial, G., Pedersen, K., Izwan Deraman, H., & Ramli, F. (2012). Equation-of-state modeling for reservoir-fluid samples contaminated by oil-based drilling mud using contaminated-fluid pressure/volume/temperature data. SPE Reservoir Evaluation & Engineering,15(2), 139–149.

    Google Scholar 

  • Saint-Marcoux, J. F. (2014). Recent trends and future of ultra deepwater oil fields. In The 24th international Ocean and polar engineering conference. Busan, Korea: International Society of Offshore and Polar Engineers.

  • Salim, M., Harley, J., & Faizal, Z. (2012). Process design aspects for taking CO2 enhanced oil recovery offshore. In SPE EOR conference at oil and gas West Asia. Muscat, Oman: Society of Petroleum Engineers. https://doi.org/10.2118/155527-ms.

  • Sambo, C. H., Hermana, M., Babasari, A., Janjuhah, H. T., & Ghosh, D. P. (2018). Application of artificial intelligence methods for predicting water saturation from new seismic attributes. In Offshore Technology Conference Asia. Kuala Lumpur, Malaysia: Offshore Technology Conference. https://doi.org/10.4043/28221-ms.

  • Sanchez Bujanos, J. L., Astudillo Abundes, A. V., Rodriguez-De La Garza, F., Morales, J., & Rodriguez Torres, A. (2005). Nitrogen injection in the cantarell complex: Results after four years of operation. In SPE Latin American and Caribbean petroleum engineering conference. Rio de Janeiro, Brazil: Society of Petroleum Engineers. https://doi.org/10.2118/97385-ms.

  • Sandøy, E. V. (2018). Offshore oil and gas investments expected to grow starting in 2019. (PennWell Corporation) Retrieved June 18, 2018, from Offshore Magazine: https://www.offshore-mag.com/articles/print/volume-78/issue-1/market-outlook/offshore-oil-and-gas-investments-expected-to-grow-starting-in-2019.html.

  • Satik, C., Robertson, C., Kalpakci, B., & Gupta, D. (2004). a study of heavy oil solution gas drive for Hamaca field: Depletion studies and interpretations. In SPE international thermal operations and heavy oil symposium and western regional meeting. Bakersfield, CA: Society of Petroleum Engineers. https://doi.org/10.2118/86967-ms.

  • Schlacher, T. A., Baco, A. R., Rowden, A. A., O’Hara, T. D., Clark, M. R., Kelley, C., et al. (2013). Seamount benthos in a cobalt-rich crust region of the central Pacific: Conservation challenges for future seabed mining. Diversity and Distributions,20(5), 491–502.

    Google Scholar 

  • Schrama, E., Naughton-Rumbo, R., van der Bas, F., Norris, M. R., & Shaoul, J. R. (2011). First true tight gas (< 0.1 mD) horizontal multiple fracture well in the North Sea. In SPE European formation damage conference. Noordwijk, The Netherlands: Society of Petroleum Engineers. https://doi.org/10.2118/143166-ms.

  • Schweitzer, P. H., & Szebehely, V. G. (2004). Gas evolution in liquids and cavitation. Journal of Applied Physics,21(12), 1218–1224.

    Google Scholar 

  • Seifi, M., & Abedi, J. (2012). An efficient and robust saturation pressure calculation algorithm for petroleum reservoir fluids using a neural network. Petroleum Science and Technology,30(22), 2329–2340.

    Google Scholar 

  • Seyyedattar, M., Zendehboudi, S., & Butt, S. (2019). Molecular dynamics simulations in reservoir analysis of offshore petroleum reserves: A systematic review of theory and applications. Earth-Science Reviews,192, 194–213.

    Google Scholar 

  • Shafer, J. (2007). Coring and core analysis: Challenges of offshore ultra deep water reservoirs. In International symposium of the society of core analysts, Calgary, Canada.

  • Shafer, J. (2013). Recent advances in core analysis. Petrophysics,54(6), 554–579.

    Google Scholar 

  • Shaoul, J., Park, J., Bakhtiyarov, A., Fekkai, S., Jeffs, P., & Mandiwall, D. (2013). Clipper south field: Fracturing operations and production matching in a low permeability, sandstone gas reservoir in the North Sea. In EAGE annual conference & exhibition incorporating SPE Europec. London, UK: Society of Petroleum Engineers. https://doi.org/10.2118/164826-ms.

  • Silcox, W. H., Bodine, J. A., Burns, G. E., Reeds, C. B., Wilson, D. L., & Sauve, E. R. (1987). Offshore operations (1987 PEH Chapter 18). Society of Petroleum Engineers.

  • Silin, D. B., & Kneafsey, T. J. (2011). Gas shale: From nanometer-scale observations to well modeling. In Canadian unconventional resources conference. Calgary, AB, Canada: Society of Petroleum Engineers. https://doi.org/10.2118/149489-ms.

  • Skopec, R. A. (1992). Recent advances in rock characterization. The Log Analyst,33(3), 270–285.

    Google Scholar 

  • Sloan, D., Koh, C., Sum, A. K., Ballard, A. L., Creek, J., Eaton, M., et al. (2011). Natural gas hydrates in flow assurance. Houston: Gulf Professional Publishing. https://doi.org/10.1016/C2009-0-62311-4.

    Book  Google Scholar 

  • Sondergeld, C. H., Newsham, K. E., Comisky, J. T., Rice, M. C., & Rai, C. S. (2010). Petrophysical considerations in evaluating and producing shale gas resources. In SPE unconventional gas conference. Pittsburgh, PA, USA: Society of Petroleum Engineers. https://doi.org/10.2118/131768-ms.

  • Southall, B. L., Bowles, A. E., Ellison, W. T., Finneran, J. J., Gentry, R. L., Jr., & Greene, C. R. (2008). Marine mammal noise-exposure criteria: Initial scientific recommendations. Bioacoustics,17(1–3), 273–275.

    Google Scholar 

  • Spikes, K. T. (2017). Lithology and fluid facies identification from post-stack seismic inversion. In Offshore Technology Conference. Houston, TX, USA: Offshore Technology Conference. https://doi.org/10.4043/27685-ms.

  • Springer. (2006). Chapter 17: Molecular dynamics. In H. Czichos, T. Saito, & L. Smith (Eds.), Springer handbook of materials measurement methods (pp. 915–952). Berlin: Springer.

    Google Scholar 

  • Sun, S., Hu, C., & Tunnell, J. W., Jr. (2015). Surface oil footprint and trajectory of the Ixtoc-I oil spill determined from Landsat/MSS and CZCS observations. Marine Pollution Bulletin,101(2), 632–641. https://doi.org/10.1016/j.marpolbul.2015.10.036.

    Article  Google Scholar 

  • Sun, Y., & Zhong, L. (2011). Enhance offshore heavy oil recovery by cyclic steam-gas-chemical co-stimulation. In SPE heavy oil conference and exhibition. Kuwait City, Kuwait: Society of Petroleum Engineers. https://doi.org/10.2118/149831-ms.

  • Sun, Y., Zhong, L., Hou, J., Sun, Y., Liu, H., Li, Y., Zhao, L. (2013). Case study: Thermal enhance bohai offshore heavy oil recovery by co-stimulation of steam and gases. In SPE heavy oil conference-Canada. Calgary, AB, Canada: Society of Petroleum Engineers. https://doi.org/10.2118/165410-ms.

  • Suykens, J., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters,9(3), 293–300.

    Google Scholar 

  • Suykens, J. A., Lukas, L., & Vandewalle, J. (2000). Sparse approximation using least squares support vector machines. In 2000 IEEE international symposium on circuits and systems. Emerging technologies for the 21st century. Proceedings (IEEE Cat No.00CH36353). Geneva, Switzerland: IEEE. https://doi.org/10.1109/iscas.2000.856439.

  • Suykens, J. A., VanGestel, T., DeBrabanter, J., DeMoor, B., & Vandewalle, J. (2002). Least squares support vector machines (1st ed.). New Jersey: World Scientific Publishing Co., Pte. Ltd.

    Google Scholar 

  • Sweatman, R. E., Crookshank, S., & Edman, S. (2011). Outlook and technologies for offshore CO2 EOR/CCS projects. In Offshore Technology Conference. Houston, TX, USA: Offshore Technology Conference. https://doi.org/10.4043/21984-ms.

  • Szemat Vielma, W. E., Misenta, M., Ponticiello, I., & Secco, G. (2010). Downhole electrical heating system: feasibility of heavy oil implementation in offshore congo. In Abu Dhabi international petroleum exhibition and conference. Abu Dhabi, UAE: Society of Petroleum Engineers. https://doi.org/10.2118/136857-ms.

  • Tahmasebi, P., & Hezarkhani, A. (2012). A fast and independent architecture of artificial neural network for permeability prediction. Journal of Petroleum Science and Engineering,86–87, 118–126.

    Google Scholar 

  • Tarbuck, E. J., Lutgens, F. K., & Tasa, D. G. (2008). Earth: An introduction to physical geology (9th ed.). Upper Saddle River: Pearson Prentice Hall.

    Google Scholar 

  • Thomas, B. G., Iliyas, A., Johansen, T. E., Hawboldt, K., & Khan, F. (2010). Towards sustainable and environmentally friendly enhanced oil recovery in offshore Newfoundland, Canada. In Offshore Technology Conference. Houston, TX, USA: Offshore Technology Conference. https://doi.org/10.4043/20385-ms.

  • Tippee, B. (2012). Research seeks new chemical methods to improve CO2 floods. (PennWell Corporation) Retrieved June 28, 2018, from Oil & Gas Journal: https://www.ogj.com/articles/print/vol-110/issue-4/general-interest/special-report-eor-heavy-oil-survey/research-seeks-new-chemical.html.

  • Torabi, F., Akhlaghinia, M., & Chan, C. W. (2015). Investigation of three-phase relative permeabilities for heavy oil systems using simulated annealing technique; effect of oil viscosity. Journal of Porous Media,18(2), 99–111.

    Google Scholar 

  • Total. (2018a). Deep offshore: Technical responses to new challenges. Retrieved August 10, 2018, from https://www.ep.total.com/en/areas/deep-offshore/deep-offshore-technical-responses-new-challenges.

  • Total. (2018b). Flow assurance: A sensitive issue for any oil development project. Retrieved August 23, 2018, from https://www.ep.total.com/en/areas/deep-offshore/flow-assurance-sensitive-issue-any-oil-development-project.

  • Tyagi, A. K., Bastia, R., & Das, M. (2008). Identification and evaluation of the thin bedded reservoir potential in the east coast deep water Basins of India. In 7th international conference and exposition on petroleum. Hyderabad, India: 7th International Conference and Exposition on Petroleum.

  • Ungerer, P., Lachet, V., & Tavitian, B. (2006). Applications of molecular simulation in oil and gas production and processing. Oil & Gas Science and Technology-Revue de l’IFP,63(3), 387–403.

    Google Scholar 

  • Ungerer, P., Nieto-Draghi, C., Rousseau, B., Ahunbay, G., & Lachet, V. (2007). Molecular simulation of the thermophysical properties of fluids: From understanding toward quantitative predictions. Journal of Molecular Liquids,134(1–3), 71–89.

    Google Scholar 

  • Valdez, R., Jimenez, J., Adamson, G. R., Agarwal, B., & Yeap, Y. C. (2011). Gas EOR methodology and integrated basin forecast of offshore Sarawak fields, Baram delta operations, Malaysia. In SPE enhanced oil recovery conference. Kuala Lumpur, Malaysia: Society of Petroleum Engineers. https://doi.org/10.2118/144662-ms.

  • Vallejo, V. G., Olivares, A., Crespo Hdez, P., Roman, E. R., Maia, C. R., & Guajardo, M. (2011). Case history: Lessons learned from retrieval of coiled tubing stuck by massive hydrate plug when well testing in an ultra deep water gas well in Mexico. In SPE/IADC drilling conference and exhibition. Amsterdam, The Netherlands: Society of Petroleum Engineers. https://doi.org/10.2118/140228-ms.

  • Van der Zwaag, C. H., Stallmach, F., Basan, P. B., Hanssen, J. E., Soergaard, E., & Toennessen, R. (1997). New methodology to investigate formation damage using non-destructive analytical tools. In SPE European formation damage conference. The Hague, The Netherlands: Society of Petroleum Engineers. https://doi.org/10.2118/38161-ms.

  • Vapnik, V. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks,10(5), 988–999. https://doi.org/10.1109/72.788640.

    Article  Google Scholar 

  • Vapnik, V., Golowich, S. E., & Smola, A. J. (1997). Support vector method for function approximation, regression estimation and signal processing. In Neural information processing systems, 1996.

  • Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.

    Google Scholar 

  • Varotsis, N., Gaganis, V., Nighswander, J., & Guieze, P. (1999). A novel non-iterative method for the prediction of the PVT behavior of reservoir fluids. In SPE annual technical conference and exhibition. Houston, TX: Society of Petroleum Engineers. https://doi.org/10.2118/56745-ms.

  • Varotsis, N., & Guieze, P. (1996). Reservoir fluid characterisation using gas chromatography–mass spectrometry. Journal of Petroleum Science and Engineering,15(1), 81–89.

    Google Scholar 

  • Vinegar, H. J., & Wellington, S. L. (1987). Tomographic imaging of three-phase flow experiments. Review of Scientific Instruments,58(1), 96–107.

    Google Scholar 

  • Walls, J. D., & Sinclair, S. W. (2011). Eagle ford shale reservoir properties from digital rock physics. First Break,29(6), 97–101.

    Google Scholar 

  • Wang, Z.-Y., Sun, B.-J., Cheng, H.-Q., & Gao, Y.-H. (2008). Prediction of gas hydrate formation region in the wellbore of deepwater drilling. Petroleum Exploration and Development,35(6), 731–735.

    Google Scholar 

  • Wang, Z.-Y., Sun, B.-J., Wang, X.-R., & Zhang, Z.-N. (2014). Prediction of natural gas hydrate formation region in wellbore during deep-water gas well testing. Journal of Hydrodynamics, Series B,26(4), 568–576.

    Google Scholar 

  • Watling, L. (2014). Trawling exerts big impacts on small beasts. Proceedings of the National Academy of Sciences of the United States of America,111(24), 8704–8705.

    Google Scholar 

  • Welch, W. R., & Piri, M. (2015). Molecular dynamics simulations of retrograde condensation in nanoporous shale. In Unconventional resources technology conference. San Antonio, TX, USA: Unconventional Resources Technology Conference. https://doi.org/10.15530/urtec-2015-2172738.

  • Wiener, J. M., Rogers, J. A., & Moll, R. E. (1991). Predicting carbonate permeabilities from wireline logs using a back-propagation neural network. In 1991 SEG annual meeting. Houston, TX: Society of Exploration Geophysicists.

  • Wikimedia Commons-Public Domain. (2007). Retrieved August 17, 2018, from https://commons.wikimedia.org/wiki/File:Formation_of_passive_margins.png.

  • Wong, P. M., Jian, F. X., & Taggart, I. J. (1995a). A critical comparison of neural networks and discriminant analysis in lithofacies, porosity and permeability predictions. Journal of Petroleum Geology,18(2), 191–206.

    Google Scholar 

  • Wong, P. M., Taggart, I. J., & Gedeon, T. D. (1995b). Use of neural network methods to predict porosity and permeability of a petroleum reservoir. AI Applications,9(2), 27–38.

    Google Scholar 

  • Wu, J., He, J., Torsater, O., & Zhang, Z. (2012). Effect of nanoparticles on oil–water flow in a confined nanochannel: A molecular dynamics study. In SPE international oilfield nanotechnology conference and exhibition. Noordwijk, The Netherlands: Society of Petroleum Engineers. https://doi.org/10.2118/156995-ms.

  • Wu, R., & Rosenegger, L. (1999). Comparison of PVT properties from equation of state analysis and PVT correlations for reservoir studies. In Annual technical meeting. Calgary, AB: Petroleum Society of Canada. https://doi.org/10.2118/99-38.

  • Xavier-de-Souza, S., Suykens, J. A., Vandewalle, J., & Bolle, D. (2010). Coupled simulated annealing. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics),40(2), 320–335.

    Google Scholar 

  • Yang, S., Lian, L., Yang, Y., Ma, D., Tang, J., & Su, X. (2016). Molecular dynamics simulation of miscible process in CO2 and crude oil system. In Abu Dhabi international petroleum exhibition & conference. Abu Dhabi, UAE: Society of Petroleum Engineers. https://doi.org/10.2118/182907-ms.

  • Yonebayashi, H., Miyagawa, Y., Ikarashi, M., Watanabe, T., Maeda, H., & Yazawa, N. (2016). How many back up prepared for asphaltene onset pressure measurement using costly collected single phase bottomhole fluid samples? In SPE annual technical conference and exhibition. Dubai, UAE: Society of Petroleum Engineers. https://doi.org/10.2118/181278-ms.

  • Youssef, S., Bauer, D., Bekri, S., Rosenberg, E., & Vizika-kavvadias, O. (2010). 3D in-situ fluid distribution imaging at the pore scale as a new tool for multiphase flow studies. In SPE annual technical conference and exhibition, 19–22 September. Florence, Italy: Society of Petroleum Engineers. https://doi.org/10.2118/135194-ms.

  • Zendehboudi, S., Shafiei, A., Bahadori, A., James, L. A., Elkamel, A., & Lohi, A. (2014). Asphaltene precipitation and deposition in oil reservoirs—Technical aspects, experimental and hybrid neural network predictive tools. Chemical Engineering Research and Design,92(5), 857–875.

    Google Scholar 

  • Zhao, Y., Wang, Z., Zhang, J., Pan, S., Yu, J., & Sun, B. (2017). Flow assurance during deepwater gas well testing: Addressing hydrate associated problems at reduced cost. In Offshore Technology Conference. Houston, TX, USA: Offshore Technology Conference. https://doi.org/10.4043/27671-ms.

  • Zirczy, H., Hwang, Y. K., & Gelinsky, S. (2018). Quantitative seismic reservoir modeling—Optimizing field development value through early and continuous data integration. In Offshore Technology Conference Asia. Kuala Lumpur, Malaysia: Offshore Technology Conference. https://doi.org/10.4043/28251-ms.

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Acknowledgments

The authors would like to acknowledge the financial support of Memorial University, Natural Sciences and Engineering Research Council of Canada (NSERC), InnovateNL (formerly RDC), and Equinor (formerly Statoil) Canada.

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  1. Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL, Canada

    Masoud Seyyedattar, Sohrab Zendehboudi & Stephen Butt

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  1. Masoud Seyyedattar
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Seyyedattar, M., Zendehboudi, S. & Butt, S. Technical and Non-technical Challenges of Development of Offshore Petroleum Reservoirs: Characterization and Production. Nat Resour Res 29, 2147–2189 (2020). https://doi.org/10.1007/s11053-019-09549-7

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