Implementation of Virtual Reality Model for Offshore Gas Field Platform and Evaluation of Gas Hydrate Formation for Subsea Production Pipeline using AI

Sangyup Jang; Byoungjun Kim; Heesung Yoon; Youngjin Seo; Huoryun Lee; Joonwhoan Lee; Taeyeob Lee

doi:10.32390/ksmer.2021.58.2.150

All Issue

2021 Vol.58, Issue 2 Preview Page Next Page

Technical Report

Implementation of Virtual Reality Model for Offshore Gas Field Platform and Evaluation of Gas Hydrate Formation for Subsea Production Pipeline using AI 해상플랫폼 가상현실모델 구현 및 인공지능을 활용한 해저배관 하이드레이트형성 평가

30 April 2021. pp. 150-160

PDF XML

Abstract

The subsea gas field environment involves considerable physical and economic difficulties in the offshore environment, including drilling and production processes, design modification, maintenance, facility monitoring, etc. Therefore, operating companies manage these facilities using various techniques to prevent safety and reliability problems in offshore platforms. Although major exploration and production companies operate their own operating facility integrated management systems, plans have been recently announced to use digital twin and artificial intelligence (AI) technology to secure the reliability of offshore structures. However, virtual models are still used for general safety education and disaster prevention training. This research introduces methods and processes for implementing offshore platforms as reliable virtual models and introducing concepts and functions for intelligent operating solutions by incorporating data communication and AI technologies.

Keywords

Digital twin

Offshore platform

Artificial intelligence (AI)

Gas hydrate

Subsea pipeline

해양 유·가스전은 해양환경에 의한 시추 및 생산의 어려움, 설계변경, 유지보수 및 생산설비의 모니터링 등으로 상당한 물리적, 경제적인 어려움이 수반된다. 특히 해양 유·가스전 생산시설을 운영하는 운영사에서는 장시간 생산설비 사용으로 인한 건전성 저하, 해양환경 노출로 인한 강구조물의 부식 등으로 인해 해양생산설비의 안전성과 신뢰성 문제를 해결하기 위해 여러 가지 기법을 활용하여 관리해오고 있다. 최근 들어 디지털트윈(digital twin), 인공지능(artificial intelligence, AI)기술을 도입하려는 움직임이 있으나 아직까지 교육이나 재난방지의 목적으로 한정되어 사용된다. 본 연구에서는 현재 운영 중인 해상플랜트구조물을 대상으로 3차원 스캐너(scanner)를 이용하여 가상모델을 구현한 내용을 소개하고자 하며, 가상모델을 기반으로 데이터 통신기술과 가스하이드레이트 형성예측과 관련한 인공지능기술을 접목함으로써 해상플랜트의 스마트 운영솔루션 개발에 대한 내용을 소개하고자 한다.

키워드

디지털트윈

해상플랫폼

인공지능

가스하이드레이트

해저파이프라인

References

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, www.wjddyd66.github.io/tensorflow/Tensorflow-AutoEncoder/.

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., www.solarisailab.com/archives/1182.

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

Information

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
DOI :https://doi.org/10.32390/ksmer.2021.58.2.150

Journal of the Korean Society of Mineral and Energy Resources Engineers ISSN:2288-0291(Print) 2288-2790(Online) 한국자원공학회지

All Issue