EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 266 (2021) E3S Web Conf., 266 (2021) 01017 References
Open Access
Issue
E3S Web Conf.
Volume 266, 2021
Topical Issues of Rational Use of Natural Resources 2021
Article Number 01017
Number of page(s) 19
Section New Sustainable Approaches to the Challenges of the Oil and Gas Sector
DOI https://doi.org/10.1051/e3sconf/202126601017
Published online 04 June 2021
  1. P.A. Bern, V.R. Withers, R.J.R. Cairns Wax deposition in crude oil pipelines, European Offshore Petroleum Conference & Exhibition, 206, 571–575 (1980). [Google Scholar]
  2. B. Edmonds, T. Moorwood, R. Szczepanski, X. Zhang Simulating wax deposition in pipelines for Flow Assurance, Energy & Fuels, 22(2), 729–741 (2008). [CrossRef] [Google Scholar]
  3. R.E. Edwards, J. New, L.E. Parker, B. Cui, J. Dong Constructing large scale surrogate models from big data and artificial intelligence, Appl. Energy, 202, 685–699 (2017). [Google Scholar]
  4. E. Ramirez-Jaramillo, C. Lira-Galeana, O. Manero Modeling wax deposition in pipelines, Petroleum Science and Technology, 22(7-8), 821–861 (2004). [Google Scholar]
  5. A. V. Kopteva, V. Y. Koptev Automatic Measuring System for Oil Stream ParaffinDeposits Parameters, IOP Conf. Series: Materials Science and Engineering, 327, 1–7 (2018). [Google Scholar]
  6. Z. Guozhong, L. Gang Study on the wax deposition of waxy crude in pipelines and its application, Journal of Petroleum Science and Engineering, 70, 1–9 (2010). [Google Scholar]
  7. Z. Huang, Application of the fundamentals of heat and mass transfer to the investigation of wax deposition in subsea pipelines. (PhD Dissertation. University of Michigan. 2011) [Google Scholar]
  8. M.E. Mohyaldinn, H. Husin, N. Hasan, M.M.B. Elmubarak, A.M.E. Genefid, M.E.A. Dheeb Challenges during operation and shutdown of waxy crude pipelines, Processing of heavy crude oils-Challenges and opportunities, 1–13 (2019). [Google Scholar]
  9. I.A. Struchkov, M.K. RogachevRisk Challenges of Wax Precipitation in Oil Well, Natural Resources Research, 26(1), 67–73 (2017). [Google Scholar]
  10. A.M. Sousa, H.A. Matos, L. Guerreiro Wax deposition mechanisms and the effect of emulsions and carbon dioxide injection on wax deposition: critical review, Petroleum. 6(3), 215–225 (2020). [Google Scholar]
  11. D.A. Nguyen, H. Scott Fogler, S. Chavadej Fused chemical reactions. 2. Encapsulation: application to remediation of paraffin plugged pipelines, Industrial and Engineering Chemistry Research, 40, 5058–5065 (2001). [Google Scholar]
  12. R.M. Roehner, J.V. Fletcher, F. V. Hanson Comparative compositional study of crude oil solids from the Trans Alaska pipeline system using high-temperature gas chromatography, Energy & Fuels, 16, 211–217 (2002). [Google Scholar]
  13. Z. Jeirani, A. Lashanizadegan, S. Ayatollahi, J. Javanmardi The possibility of wax formation in gas fields: a case study, Journal of Natural Gas Chemistry, 16(3), 293–300 (2007). [Google Scholar]
  14. A.L. Sousa, H.A. Matos, L.P. Guerreiro Preventing and removing wax deposition inside vertical wells: a review, Journal of Petroleum Exploration and Production Technology, ), 2091–2107 (2019). [Google Scholar]
  15. I.A. Struchkov, M.K. Rogachev The challenges of waxy oil production in a Russian oil field and laboratory investigations, Journal of Petroleum Science and Engineering, 163, 91–99 (2018). [Google Scholar]
  16. V.T. Nguyen, M.K. Rogachev, A.N. Aleksandrov A new approach to improving efficiency of gas-lift wells in the conditions of the formation of organic wax deposits in the Dragon field, Journal of Petroleum Exploration and Production Technology, 10, 3663–3672 (2020). [Google Scholar]
  17. R. Venkatesan, The deposition and rheology of organic gels. (PhD Dissertation. University of Michigan. 2004). [Google Scholar]
  18. Z. Huang, M. Senra, R. Kapoor, H. Scott Fogler Wax deposition modeling of oil/water stratified channel flow, AIChE Journal, 57(4), 841–851 (2011). [Google Scholar]
  19. W. Kang, F. Perez de Gracia, R.A. Ratti Oil price shocks, policy uncertainty, and stock returns of oil and gas corporations, Journal of International Money and Finance, 70, 344–359 (2017). [Google Scholar]
  20. Y. Chi, N. Daraboina, C. Sarica Investigation of inhibitors efficacy in wax deposition mitigation using a laboratory scale flow loop, AIChE Journal, 62(11), 4131–4139 (2016). [Google Scholar]
  21. A.D. Handal, Analysis of some wax deposition experiments in a crude oil carrying pipe. (Master Thesis. University of Oslo. 2008). [Google Scholar]
  22. K. Rosvold Wax. Deposition Models. Master Thesis. Department of Petroleum Engineering and Applied Geophysics, (2008). [Google Scholar]
  23. Z. Huang, H.S. Lee, M. Senra, H. Scott Fogler A fundamental model of wax deposition in subsea oil pipelines, AIChE Journal, 57(11), 2955–2964 (2011). [Google Scholar]
  24. Z. Huang, S. Zheng, H. Scott Fogler, Wax deposition. Experimental characterizations, theoretical modeling, and field practices. (CRC Press. 2015). [Google Scholar]
  25. M.A. Theyab, S.Y. Yahya Introduction to wax deposition, International Journal of Petrochemistry and Research, 2(1), 126–131 (2018). [Google Scholar]
  26. R. Li, Q. Huang, D. Zhang, X. Zhu, J. Shan, J. Wang An aging theory-based mathematic model for estimating the wax content of wax deposits using the Fick’s second law, AIChE Journal, E16892, 1–13 (2019). [Google Scholar]
  27. D. Dall’Acqua, M. Benucci, F. Corvaro, M. Leporini, R. Cocci Grifoni, A. Del Monaco, A. Di Lullo, C. Passucci, B. Marchetti Experimental results of pipeline dewatering through surfactant injection, Journal of Petroleum Science and Engineering,159, 542–552 (2017) [Google Scholar]
  28. T.S. Golczynski, E.C. Kempton Understanding wax problems leads to deepwater flow assurance solutions, World Oil, 227, 7–10 (2006). [Google Scholar]
  29. B. Wei Recent advances on mitigating wax problem using polymeric wax crystal modifier, Journal of Petroleum Exploration and Production Technology, 5(4), 391–401 (2014). [Google Scholar]
  30. J.P. Vitkovsky, P.J. Lee, M.L. Stephens, M.F. Lambert, A.R. Simpson, J.A. Liggett Leak and blockage detection in pipelines via an impulse response method. Pumps, Electromechanical Devices and Systems Applied to Urban Water Management, 1, 423430 (2003) [Google Scholar]
  31. V.Y. Koptev, A. V. Kopteva Improving paraffin deposits detection methodology for better ecological safety during hydrocarbon transportation, International Journal of Applied Engineering Research, 12(5), 618–621 (2017). [Google Scholar]
  32. C. F. Colebrook Turbulent flow in pipes, with particular reference to the transition region between the smooth and rough pipe laws, Journal of the ICE, 11(4), 133–156 (1939). [Google Scholar]
  33. T. Ribeiro, C. Rigueiro, L. Borges, A. Sousa A comprehensive method for fatigue life evaluation and extension in the context of predictive maintenance for fixed ocean structures, Applied Ocean Research, 95, 1–15 (2020). [Google Scholar]
  34. M.S. Forsthoffer, Forsthoffer’s more best practices for rotating equipment. Chapter 11: Predictive and Preventive Maintenance. (Butterworth-Heinemann. 2017). [Google Scholar]
  35. G. Carvajal, M. Maucec, S. Cullick, (1st. ed.) 2018 Intelligent digital oil and gas fields: concepts, collaboration, and right-time decisions. (Gulf Professional Publishing. 2018). [Google Scholar]
  36. O. Bello, C. Teodoriu, T. Yaqoob, J. Oppelt, J. Holzmann, A. Obiwanne Application of artificial intelligence techniques in drilling system design and operations: a state of the art review and future research pathways, SPE J. Pet. Technol, SPE-184320-MS, 1–22 (2016). [Google Scholar]
  37. C.E. Bravo, L. Saputelli, F. Rivas, A.G. Perez, M. Nickolaou, G. Zangl, N. De Guzman, S.D. Mohaghegh, G. Nunez State of the Art of Artificial Intelligence and Predictive Analytics in the E & P Industry: A Technology Survey, SPE J. Pet. Technol, 19(4), 547–563 (2014). [Google Scholar]
  38. G. Litjens, C.I. Sanchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, P. Bult, B. Van Ginneken, J. Van Der LaakDeep learning as a tool for increased accuracy and efficiency of histopathological diagnosis, Sci. Rep, 6(26286), 1–11 (2016). [Google Scholar]
  39. M. Abambres, K. Rajana, K.D. Tsavdaridis, T.P. Ribeiro Neural network-based formula for the buckling load prediction of I-section cellular steel beams, Computers, 8(2), 1–16 (2018). [Google Scholar]
  40. G.A. Susto, A. Schirru, S. Pampuri, S. Mcloone, A. Beghi Machine Learning for predictive maintenance: A multiple classifier approach, IEEE Transactions on Industrial Informatics, 11(3), 812–820 (2015). [Google Scholar]
  41. M. Nadakatti, A. Ramachandra, A.N.S. Kumar, Artificial intelligence-based condition monitoring for plant maintenancefissembly Automation, 28(2), 143–150 (2008). [Google Scholar]
  42. A. Mirani, R. Samuel Mitigating vibration induced drill string failures using data analytics: workflow and case study,IADC/SPE Drilling Conference and Exhibition;, SPE-178849-MS, 1–13. (2016) [Google Scholar]
  43. A.S. Popa, C.G. Popa, M. Malamma, J. Hicks Case-Based reasoning approach for well failure diagnostics and planning, SPE Western Regional and Pacific Section AAPG Joint Meeting. SPE, 114229, 1–7 (2008). [Google Scholar]
  44. S. Montani, L.C. Jain. Successful case-based reasoning applications-2. Studies in computational intelligence, Springer-Verlag (2010). [Google Scholar]
  45. A. Singh, H. S. Lee, P. Singh, C. Sarica: Flow Assurance: Validation of wax deposition models using field data from a subsea pipeline, Offshore Technology Conference, OTC-21641-MS, (2011). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.