A sequence-based method for dynamic reliability assessment of MPD systems

Zhu, Jingyu; Chen, Guo-Ming; Khan, Faisal; Yang, Ming; Li, Xinhong; Meng, Xiangying; He, Rui

doi:10.1016/j.psep.2020.12.015

Cited by 14 publications

(5 citation statements)

References 40 publications

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“…Data for these parameters can be measured by sensors on the surface and downhole. These datasets are used to build deep-learning models to predict drilling parameters such as total weight and ROP 8 – 10 .…”

Section: Gan and Lstm Fusionmentioning

confidence: 99%

Parameter prediction of coiled tubing drilling based on GAN–LSTM

Zhang

Kai

Chen

et al. 2023

Sci Rep

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With the increasing development of coiled tubing drilling technology, the advantages of coiled tubing drilling technology are becoming more and more obvious. In the operation process of coiled tubing, Due to various different drilling parameters, manufacturing defects, and improper human handling, the coiled tubing can curl up and cause stuck drilling or shortened service life problems. Circulation pressure, wellhead pressure, and total weight have an important influence on the working period of coiled tubing. For production safety, this paper predicts circulation pressure, ROP, wellhead pressure, and finger weight using GAN–LSTM after studying drilling engineering theory and analyzing a large amount of downhole data. Experimental results show that GAN–LSTM can predict the parameters of circulation pressure, wellhead pressure ROP and total weight to a certain extent. After much training, the accuracy is about 90%, which is about 17% higher than that of the GAN and LSTM. It has a certain guiding significance for coiled tubing operation, increasing operational safety and drilling efficiency, thus reducing production costs.

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Section: Gan and Lstm Fusionmentioning

confidence: 99%

Parameter prediction of coiled tubing drilling based on GAN–LSTM

Zhang

Kai

Chen

et al. 2023

Sci Rep

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“…It can be also used to indicate the time duration between a series of successive time points, by which the time‐dependent system dynamics and aging effects on degrading components can be characterized in GO‐FLOW modeling. The GO‐FLOW methodology has been widely applied to the field of nuclear energy, 55 transportation system, 56 offshore oil and gas industry, 26 even logistics network, 57 etc. The wider applications of GO‐FLOW methodology are strongly benefited from the comprehensive representation of all possible system configurations, functions and failures, functioning and failure of physical equipment, physical fluid flow and control information, as well as simplified human‐machine interactions in time‐series sequence within compact phenomenological models.…”

Section: A Success‐oriented Algorithm For Multi‐phase Dynamic Mission...mentioning

confidence: 99%

“…A new GO‐FLOW reliability analysis method handling with time‐dependent and phased‐mission problems is developed by T. Matsuoka in 1988 25 . J. Y. Zhu et al 26 . proposed a GO‐FLOW based sequence analysis method for dynamic reliability assessment of chemical process systems.…”

Section: Introductionmentioning

confidence: 99%

“…A new GO-FLOW reliability analysis method handling with time-dependent and phased-mission problems is developed by T. Matsuoka in 1988. 25 J. Y. Zhu et al 26 proposed a GO-FLOW based sequence analysis method for dynamic reliability assessment of chemical process systems. An integrated safety assessment (ISA) methodology and tool are developed by J. M. Izquierdo et al for automatic generation of dynamic event trees in.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid computing framework for risk‐oriented reliability analysis in dynamic PSA context: A case study

Jiang

et al. 2022

Quality & Reliability Eng

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Nowadays, the emerging digital technologies and digitalization trend in safety‐critical industrial process systems are bringing great opportunities for system performance improvements. However, new big challenges are also encountered in the reliability and safety evaluation of large complex industrial process systems due to its multi‐state, multi‐phase dynamic interactions, resilience on software and inter‐dependencies among digital components. The objectives of this study are i) to present an introductory overview of a hybrid computing framework as a supplementary to conventional fault tree analysis toolkit for risk‐oriented reliability analysis in dynamic probabilistic safety assessment context; ii) to illustrate how to combine the three methods of DDET, Markov/CCMT, and GO‐FLOW for integrated risk solutions by a case study of small‐break LOCA in nuclear power plants. Within the hybrid computing framework, the DDET model is implemented based on graph‐based search and sequence diagram refactoring by linking with Markov/CCMT and GO‐FLOW solver for branch probability estimation. The dynamic event tree model is adopted to represent the accident sequence of small‐break LOCA, where the heading events of system failure of digital RPS and phased‐mission ECCS in realization of safety‐critical functions of reactivity control and emergency core cooling are respectively modeled and analyzed by Markov chain and GO‐FLOW method. The demonstration results show that the failure analysis of complex dynamic process interactions together with time‐dependent mission reliability analysis of safety systems involved in accident prevention and mitigation can be easily implemented with accurate modeling and fast evaluation within the hybrid integration platform. The core algorithms and principles implemented for dynamic risk scenarios development by DDET, modeling, and analysis of dynamic process interactions by Markov/CCMT, time‐dependent and multi‐phase mission reliability analysis by GO‐FLOW as well as their integration to provide comprehensive solutions for the application of dynamic reliability in risk assessment are also discussed as open problems for future research.

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“…The primary advantage of BN is its probability updating and continuous learning ability. In the field of the offshore oil and gas industry, BN is mainly used to evaluate the dynamic risk evolution of major accidents including blowouts and explosions [ 40 , 41 ], as well as the failure probability and reliability of key equipment and systems; examples are blowout preventer [ 42 ], submarine oil and gas pipeline [ 43 , 44 ], drilling riser [ 45 ], crude oil separation system [ 46 ], and managed pressure drilling system [ 47 , 48 ]. However, it is not easy to obtain the prior probability for BN.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Evolution of Major Storm-Disaster-Induced Accidents in the Offshore Oil and Gas Industry

Zhu

Chen

Zhu

et al. 2022

IJERPH

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Storm disasters are the most common cause of accidents in offshore oil and gas industries. To prevent accidents resulting from storms, it is vital to analyze accident propagation and to learn about accident mechanism from previous accidents. In this paper, a novel risk analysis framework is proposed for systematically identifying and analyzing the evolution of accident causes. First, accident causal factors are identified and coded based on grounded theory (GT). Then, decision making trial and evaluation laboratory (DEMATEL) is integrated with interpretative structural modeling (ISM) to establish accident evolution hierarchy. Finally, complex networks (CN) are developed to analyze the evolution process of accidents. Compared to reported works, the contribution is threefold: (1) the demand for expert knowledge and personnel subjective influence are reduced through the data induction of accident cases; (2) the method of establishing influence matrix and interaction matrix is improved according to the accident frequency analysis; (3) a hybrid algorithm that can calculate multiple shortest paths of accident evolution under the same node pair is proposed. This method provides a new idea for step-by-step assessment of the accident evolution process, which weakens the subjectivity of traditional methods and achieves quantitative assessment of the importance of accident evolution nodes. The proposed method is demonstrated and validated by a case study of major offshore oil and gas industry accidents caused by storm disasters. Results show that there are five key nodes and five critical paths in the process of accident evolution. Through targeted prevention and control of these nodes and paths, the average shortest path length of the accident evolution network is increased by 35.19%, and the maximum global efficiency decreases by 20.12%. This indicates that the proposed method has broad applicability and can effectively reduce operational risk, so that it can guide actual offshore oil and gas operations during storm disasters.

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A sequence-based method for dynamic reliability assessment of MPD systems

Cited by 14 publications

References 40 publications

Parameter prediction of coiled tubing drilling based on GAN–LSTM

Parameter prediction of coiled tubing drilling based on GAN–LSTM

A hybrid computing framework for risk‐oriented reliability analysis in dynamic PSA context: A case study

Modeling the Evolution of Major Storm-Disaster-Induced Accidents in the Offshore Oil and Gas Industry

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