A Cloud-Based Computational Framework to Perform Oil-Field Development & Operation Using a Single Digital Twin Platform
- Khairul Chowdhury (IDARE LLC) | Akhteruzzaman Arif (IDARE LLC) | Md Nuruzzaman Nur (IDARE LLC) | Omar Sharif (IDARE LLC)
- Document ID
- Offshore Technology Conference
- Offshore Technology Conference, 4-7 May, Houston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2020. Offshore Technology Conference
- asset integrity, concept development, digital oil field, automated engineering, artificial intelligence
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Building a digital oil-field or digital twin, without involving multiple digital partners, and costly time-consuming digital endeavor is a significant challenge in Oil & Gas industry. In most cases, such systems are capable of data integration and visualization for only a part of an oil-field. Real-time analytics is missing to transform the digitalization into a smart and scalable system. The objective of this paper is to present a digital oil-field creator driven by a computational framework to perform key analytics for oil-field life cycles.
A computational framework, networked with decentralized analytics engines that are equipped with a predictive analytics computation system, and an automated design engineering system has been developed. The predictive analytics computation system performs engineering design using data, pipeline integrity, platform structures, and coupled systems integrity and equipment integrity. The automated design engineering engine performs conceptual and FEED for subsea elements such as pipelines. These computational engines are then integrated into a self-service 3D digital twin creator for the user to operate on the front end and to receive analytical insights.
3D digital twin creator is capable of creating oil-field architecture within a fraction of the time utilizing the traditional model. Additionally, the creator optimizes the subsea architecture based on a selected set of criteria including CAPEX or OPEX costs. The system then performs conceptual or FEED level engineering including finite element analysis (FEA) using automated engineering saving as much as 80% of engineering time. Finally, the digital model is able to conduct asset integrity predictive analytics using machine learning from the monitored data. A Gulf of Mexico (GOM) oil field concept architecture was created by the digital twin creator and the computational framework. The system performed an automated pipeline conceptual optimization, buckling and free span design, and finally conducted riser integrity and equipment integrity. The computational framework is flexible and scalable to incorporate any organization's existing engineering process.
The multidisciplinary complex analytics systems are networked in a way to maximize the integration of multiple oil-field systems from multiple companies and their asset's operational status in a 3D visual without a complex or expensive digital system. This digital system not only integrates multiple phases of the oil-field life cycle but also allows the reuse of all processes across different projects and time.
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Reuters Publication, "Offshore spending surge risks overruns up to $110 billion by 2023: Rystad", https://www.reuters.com/article/us-oil-offshore-costs/offshore-spending-surge-risks-overruns-up-to-110-billion-by-2023-rystad-idUSKCN1TE26H, June 13, 2019
Niclus Newman, Rigzone Article, "Cost Overruns Threaten Offshore Development", https://www.rigzone.com/news/costoverrunsthreatenoffshoredevelopment-01-jul-2019-159178-article/, July 2019
Rui, Z & Peng, Fei & Ling, Kegang & Chang, Hanwen & Chen, Gang & Zhou, Xiyu. (2016). Investigation into the performance of oil and gas projects. Journal of Natural Gas Science and Engineering. 38.10.1016/j.jngse.2016.11.049.
Chowdhury, K., & Lamacchia, D. (2019, November 11). Collaborative Workspace for Employee Engagement Leveraging Social Media Architecture. Society of Petroleum Engineers. 10.2118/197325-MS
Malykhina, Galina. (2018). Digital Twin Technology As A Basis Of The Industry In Future. 416–428. 10.15405/epsbs.2018.12.02.45.
F. Tao, H. Zhang, A. Liu and A. Y. C. Nee, "Digital Twin in Industry: State-of-the-Art," in IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2405–2415, April 2019. 10.1109/TII.2018.2873186