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2021 Vol.58, Issue 2 Preview Page

Technical Report

April 2021. pp. 150-160
Bhowmik, S., 2019. Digital twin of subsea pipelines: Conceptual design integrating IoT, Machine learning and data analysis. Offshore Technology Conference, Houston, USA. 10.4043/29455-MS
Bishop, C.M., 2006. Pattern recognition and machine learning, New York: Springer, USA, 738p.
Cornor's Blog, 2021.01.26,
Fletcher, L, Katkovnik, V, Steffens, F., and Engelbrecht, A., 1998. Optimizing the number of hidden nodes of a feedforward artificial neural network. Proc. of IEEE International Joint Conference on Neural Networks, IEEE, Anchorage, AK, USA, p.1608-1612.
Grange, E.L., 2018. A road-map for adopting a digital lifecycle approach to offshore oil and gas production. Offshore Technology Conference, Houston, USA. 10.4043/28669-MS
Grieves, M.W., 2019. Virtually intelligent product systems: Digital and physical twins. Complex Systems Engineering: Theory and Practice, AIAA, Virginia, p.175-200. 10.2514/5.9781624105654.0175.0200
Jang, S., Lee, T., Shin, C., Yoon, H., and Kang, W., 2019. The development of virtual reality platform for off-shore oil & gas field. Proc. of the 2nd conf. on gas engineering, KIGAS, Jeju, p.61.
Ki, S., Seo, J., Kwon, O., and Jang, I., 2019. Prediction of missing tubing head pressure using recurrent neural network. Journal of The Korean Society of Mineral and Energy Resources Engineers, 56(5), p.416-426. 10.32390/ksmer.2019.56.5.416
Lee, K., Lim, J., Yoon, D., and Jung, H., 2019. Prediction of shale-gas production at Duvernay formation using deep- learning algorithm. SPE Journal, 24(6), p.2423-2437, SPE- 195698-PA. 10.2118/195698-PA
Lee, S., 2019. Applications and prospects of digital twin technology in mineral and energy resource engineering. Journal of The Korean Society of Mineral and Energy Resources Engineers, 56(5), p.427-434. 10.32390/ksmer.2019.56.5.427
Matt, Z., 2018. Finding meaning, Application for the much discussed digital twin. J Pet Technol, 70(6), p.26-32. 10.2118/0618-0026-JPT
Lin, S.Y., Chiang, C.C., Hung, Z.S., and Zou, Y.H, 2017. A Dynamic Data-Driven Fine-Tuning Approatch for Stacked Auto-Encoder Neural Network. In 2017 IEEE 14t h International Coference on e-Business Engineering (IECBE), p.226-231. 10.1109/ICEBE.2017.43
Okhuijsen, B. and Wade, K., 2019. Real-time production optimization - Applying a digital twin model to optimize the entire upstream value chain. International Petroleum Exhibition & Conference, Society of Petroleum Engineers, Abu Dhabi, UAE, SPE-197693-MS. 10.2118/197693-MS
Renzi, D., Maniar, D., McNeill, S., and Del Vecchio, C., 2017. Developing a digital twin for floating production systems integrity management. Offshore Technology Conference, Houston, Texas, USA, May p.1-4. 10.4043/28012-MS
Raschka, S., Julian, D., and Hearty J., 2017. Python: deeper insights into machine learning, Packt Publishing Ltd, UK, p.916.
Solaris AI Lab., 2021.01.26.,
Tofte, B.L., Vennemann, O., Mitchell, F., Millington, N., and McGuire, L., 2019. How digital technology and standardization can improve offshore operation. Offshore Technology Conference, Houston, USA. 10.4043/29225-MS
Tygesen, U.T., Jepsen, M.S., Vestermark, J., Dollerup, N., and Pedersen, A., 2018, The true digital twin concept for fatigue re-assessment of marine structure. Proc. of Int. Conf. on Ocean, Offshore and Arctic Engineering, Madrid, Spain, June p.17-22. 10.1115/OMAE2018-77915
Woodman, M.R., Rodriguez, J., Wade, K.C., and Samsatli, N.J., 2017. New integrated technology for full production and facilities modelling and optimization. Proc. of production enhancement and cost optimization, Kuala Lumpur, Malaysia, November p.7-8. 10.2118/189263-MS
  • Publisher :The Korean Society of Mineral and Energy Resources Engineers
  • Publisher(Ko) :한국자원공학회
  • Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
  • Journal Title(Ko) :한국자원공학회지
  • Volume : 58
  • No :2
  • Pages :150-160
  • Received Date :2020. 07. 24
  • Revised Date :2021. 01. 29
  • Accepted Date : 2021. 04. 27