- Adegbite J.O., Al-Shalabi E.W., Ghosh B. (2017) Modeling the effect of engineered water injection on oil recovery from carbonate cores, in: SPE International Conference on Oilfield Chemistry, Society of Petroleum Engineers, pp. 1–33. https://doi.org/10.2118/184505-MS. [Google Scholar]
- Adegbite J.O., Al-Shalabi E.W. (2020) Optimization of engineered water injection performance in heterogeneous carbonates: a numerical study on a sector model, J. Petrol. Explor. Prod. Technol. 10, 3803–3826. https://doi.org/10.1007/s13202-020-00912-6. [Google Scholar]
- Bernard G.G. (1967) Effect of floodwater salinity on recovery of oil from cores containing clays, in: Society of Petroleum Engineers – SPE California Regional Meeting, CRM. https://doi.org/10.2523/1725-ms. [Google Scholar]
- Bidhendi M.M., Garcia-Olvera G., Morin B., Oakey J.S., Alvarado V. (2018) Interfacial viscoelasticity of crude oil/brine: An alternative enhanced-oil-recovery mechanism in smart waterflooding, SPE J. 23, 03, 0803–0818. https://doi.org/10.2118/169127-pa. [Google Scholar]
- Bourbiaux B. (2020) Low salinity effects on oil recovery performance: underlying physical mechanisms and practical assessment, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 75, 37. https://doi.org/10.2516/ogst/2020030. [Google Scholar]
- Breitenbach E.A. (1991) Reservoir simulation: State of the art, J. Petrol. Technol. 43, 09, 1033–1036. [Google Scholar]
- Brooks R.H., Corey A.T. (1964) Hydraulic properties of porous media, Hydrology Papers, no. 3, Colorado State University. [Google Scholar]
- Burden F., Winkler D. (2008) Bayesian regularization of neural networks, Methods Mol. Biol. 458, 25–44. [PubMed] [Google Scholar]
- Correia M., Hohendorff J., Gaspar A.T.F.S., Schiozer D. (2015) UNISIM-II-D: Benchmark case proposal based on a carbonate reservoir, in: SPE Latin American and Caribbean Petroleum Engineering Conference Held in Quito, Ecuador 1, 18–20. https://doi.org/10.2118/177140-ms. [Google Scholar]
- Dake L.P. (2015) Fundamentals of reservoir engineering, Elsevier, Netherlands, pp. 1–498. https://doi.org/10.1016/B978-0-08-098206-9.00004-X. [Google Scholar]
- Dang C., Nghiem L., Nguyen N., Chen Z., Nguyen Q. (2015) Modeling and optimization of low salinity waterflood, in: SPE Reservoir Simulation Symposium, Houston, Texas, USA, pp. 1–10. https://doi.org/10.2118/173194-ms. [Google Scholar]
- Dang C., Nghiem L., Nguyen N., Chen Z., Nguyen Q. (2016) Mechanistic modeling of low salinity water flooding, J. Petrol. Sci. Eng. 146, 191–209. https://doi.org/10.1016/j.petrol.2016.04.024. [Google Scholar]
- Dang C.T.Q., Nghiem L.X., Chen Z.J., Nguyen Q.P. (2013) Modeling low salinity waterflooding: Ion exchange, geochemistry and wettability alteration, in: SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA, pp. 1–22. https://doi.org/10.2118/166447-ms. [Google Scholar]
- Fabbri C., de-Loubens R., Skauge A., Hamon G., Bourgeois M. (2020) Effect of initial water flooding on the performance of polymer flooding for heavy oil production, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 75, 19. https://doi.org/10.2516/ogst/2020008. [CrossRef] [Google Scholar]
- Fathi S.J., Austad T., Strand S. (2011) Water-based enhanced oil recovery (EOR) by “smart water”: Optimal ionic composition for EOR in carbonates, Energy Fuels 25, 11, 5173–5179. https://doi.org/10.1021/ef201019k. [Google Scholar]
- Fjelde I., Asen S.M., Omekeh A.V. (2012) Low salinity water flooding experiments and interpretation by simulations, in: SPE Improve Oil Recovery Symposium, Tulsa, Oklahoma, USA, pp. 1–22. https://doi.org/10.2118/154142-ms. [Google Scholar]
- Ghahramani Z. (2004) Unsupervised Learning, in: Advanced Lectures on Machine Learning: ML Summer Schools 2003, Springer, Berlin Heidelberg, pp. 72–112. https://doi.org/10.1007/978-3-540-28650-9_5. [Google Scholar]
- Ghosh B., Sun L., Osisanya S. (2016) Smart-water EOR made smarter a laboratory development, in: International Petroleum Technology Conference, Bangkok, Thailand, pp. 1–13. https://doi.org/10.2523/18988-ms. [Google Scholar]
- Hayashi S.H.D. (2006) Value of flexibility and information in field development by modules, Master Thesis, Faculty of Mechanical Engineering, State University of Campinas, 138 p. (in Portuguese). [Google Scholar]
- Hirasaki G., Zhang D.L., Rice U. (2004) Surface chemistry of oil recovery from fractured, oil-wet, carbonate formations, in: International Symposium on Oilfield Chemistry, Houston, Texas, USA, pp. 151–163. https://doi.org/10.2118/88365-pa [Google Scholar]
- Jerauld G.R., Lin C.Y., Webb K.J., Seccombe J.C. (2006) Modeling low-salinity waterflooding, in: SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, pp. 1–13. https://doi.org/10.1002/app.30886. [Google Scholar]
- Kayri M. (2016) Predictive abilities of Bayesian regularization and levenberg-marquardt algorithms in artificial neural networks: A comparative empirical study on social data, Math. Comput. Appl. 21, 2, 1–11. https://doi.org/10.3390/mca21020020. [Google Scholar]
- Lie K.A. (2012) An Introduction to Reservoir Simulation Using MATLAB/GNU Octave, Vol. 21, Cambridge University Press, pp. 1–659. https://doi.org/10.1016/j.solener.2019.02.027. [Google Scholar]
- Mohaghegh S. (2000) Virtual-intelligence applications in Petroleum Engineering: Part 1 – Artificial Neural Networks, J. Petrol. Technol. 52, 09, 64–73, 64–71. [Google Scholar]
- Morrow N.R., Tang G.Q., Valat M., Xie X. (1998) Prospects of improved oil recovery related to wettability and brine composition, J. Petrol. Sci. Eng. 20, 3–4, 267–276. https://doi.org/10.1016/S0920-4105(98)00030-8. [Google Scholar]
- Mustafiz S., Islam M.R. (2008) State-of-the-art petroleum reservoir simulation, Petrol. Sci. Technol. 26, 10–11, 1303–1329. https://doi.org/10.1080/10916460701834036. [Google Scholar]
- Rajasekaran S., Pai G.A.V. (2017) Neural networks, fuzzy systems, and evolutionary algorithms: Synthesis and applications, PHI Learning Pvt. Ltd, New Delhi, India, pp. 1–442. [Google Scholar]
- Reginato L.F., Carneiro C.C., Gioria R.S., Sampaio M.A. (2019) Prediction of wettability alteration using the artificial neural networks in the salinity control of water injection in carbonate reservoirs, Offshore Technology Conference Brazil, Rio de Janeiro, Brazil, pp. 1–17. https://doi.org/10.4043/29916-ms. [Google Scholar]
- Saikia B.D., Mahadevan J., Rao D.N. (2018) Exploring mechanisms for wettability alteration in low-salinity waterfloods in carbonate rocks, J. Petrol. Sci. Eng. 164, 595–602. https://doi.org/10.1016/j.petrol.2017.12.056. [Google Scholar]
- Sampaio M.A., Barreto C.E.A.G., Schiozer D.J. (2015) Assisted optimization method for comparison between conventional and intelligent producers considering uncertainties. J. Petrol. Sci. Eng. 133, 268–279. https://doi.org/10.1016/j.petrol.2015.06.023. [Google Scholar]
- Seethepalli A., Adibhatla B., Mohanty K.K. (2004) Physicochemical interactions during surfactant flooding of fractured carbonate reservoirs, SPE J. 9, 04, 411–418. https://doi.org/10.2118/89423-pa. [Google Scholar]
- Shobha G., Rangaswamy S. (2018) Machine Learning, in: Handbook of Statistics, 1st ed., Vol. 38, Elsevier B.V., pp. 1–32. https://doi.org/10.1016/bs.host.2018.07.004. [Google Scholar]
- Strand S., Høgnesen E.J., Austad T. (2006) Wettability alteration of carbonates - Effects of potential determining ions (Ca2+ and SO42−) and temperature, Colloids Surf. A Physicochem. Eng. Aspects 275, 1–3, 1–10. https://doi.org/10.1016/j.colsurfa.2005.10.061. [CrossRef] [Google Scholar]
- Strik D.P.B.T.B., Domnanovich A.M., Zani L., Braun R., Holubar P. (2005) Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB Neural Network Toolbox, Environ. Model. Softw. 20, 6, 803–810. https://doi.org/10.1016/j.envsoft.2004.09.006. [Google Scholar]
- Talabis M.R.M., McPherson R., Miyamoto I., Martin J.L., Kaye D. (2015) Chapter 1 - Analytics defined, in: Information Security Analytics, Syngress, Boston, USA, pp. 1–12. [Google Scholar]
- Webb K.J., Black C.J.J., Al-Ajeel H., Members S. (2004) Low salinity oil recovery-log-inject-log, in: Fourteenth Symposium on Improved Oil Recovery, pp. 17–21. https://doi.org/10.2118/89379-MS. [Google Scholar]
- Xiao R., Gupta R., Glotzbach R.C., Sinha S., Teletzke G.F. (2018) Evaluation of low-salinity waterflooding in Middle East carbonate reservoirs using a novel, field-representative coreflood method, J. Petrol. Sci. Eng. 163, 683–690. https://doi.org/10.1016/j.petrol.2017.10.070. [Google Scholar]
- Yousef A.A., Al-Saleh S., Al-Jawfi M.S. (2011) Smart waterflooding for carbonate reservoirs: salinity and role of ions, in: SPE Middle East Oil and Gas Show and Conference Held in Manama, Bahrain, September, pp. 1–11. https://doi.org/10.2118/141082-ms. [Google Scholar]
- Yousef A.A., Al-Saleh S., Al-Kaabi A., Al-Jawfi M., Aramco S. (2010) Laboratory investigation of novel oil recovery method for carbonate reservoirs, Canadian Unconventional Resources & International Petroleum Conference, Calgary, Alberta, Canada, pp. 1–35. https://doi.org/10.2118/137634-ms. [Google Scholar]
- Zaheri S.H., Khalili H., Sharifi M. (2020) Experimental investigation of water composition and salinity effect on the oil recovery in carbonate reservoirs, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 75, 21. https://doi.org/10.2516/ogst/2020010. [CrossRef] [Google Scholar]
- Zhang P., Tweheyo M.T., Austad T. (2007) Wettability alteration and improved oil recovery by spontaneous imbibition of seawater into chalk: Impact of the potential determining ions Ca2+, Mg2+, and SO42−, Colloids Surf. A Physicochem. Eng. Aspects 301, 199–208. https://doi.org/10.1016/j.colsurfa.2006.12.058. [Google Scholar]
- Zhang T., Li Y., Li C., Sun S. (2020) Effect of salinity on oil production: review on low salinity waterflooding mechanisms and exploratory study on pipeline scaling, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 75, 50. https://doi.org/10.2516/ogst/2020045. [Google Scholar]
Open Access
Issue |
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 76, 2021
|
|
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Article Number | 13 | |
Number of page(s) | 14 | |
DOI | https://doi.org/10.2516/ogst/2020094 | |
Published online | 03 February 2021 |
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