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Research Papers: Offshore Technology

Dynamic Bayesian Network-Based Risk Assessment for Arctic Offshore Drilling Waste Handling Practices

[+] Author and Article Information
Yonas Zewdu Ayele

Department of Engineering and Safety,
UiT The Arctic University of Norway,
Tromsø 9037, Norway
e-mail: yonas.z.ayele@uit.no

Javad Barabady

Department of Engineering and Safety,
UiT The Arctic University of Norway,
Tromsø 9037, Norway
e-mail: javad.barabady@uit.no

Enrique Lopez Droguett

Department of Mechanical Engineering,
University of Chile,
Santiago 8370448, Chile
e-mail: elopezdroguett@ing.uchile.cl

1Corresponding author.

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received November 7, 2015; final manuscript received May 18, 2016; published online June 17, 2016. Assoc. Editor: David R. Fuhrman.

J. Offshore Mech. Arct. Eng 138(5), 051302 (Jun 17, 2016) (12 pages) Paper No: OMAE-15-1116; doi: 10.1115/1.4033713 History: Received November 07, 2015; Revised May 18, 2016

The increased complexity of Arctic offshore drilling waste handling facilities, coupled with stringent regulatory requirements such as zero “hazardous” discharge, calls for rigorous risk management practices. To assess and quantify risks from offshore drilling waste handling practices, a number of methods and models are developed. Most of the conventional risk assessment approaches are, however, broad, holistic, practical guides or roadmaps developed for off-the-shelf systems, for non-Arctic offshore operations. To avoid the inadequacies of traditional risk assessment approaches and to manage the major risk elements connected with the handling of drilling waste, this paper proposes a risk assessment methodology for Arctic offshore drilling waste handling practices based on the dynamic Bayesian network (DBN). The proposed risk methodology combines prior operating environment information with actual observed data from weather forecasting to predict the future potential hazards and/or risks. The methodology continuously updates the potential risks based on the current risk influencing factors (RIF) such as snowstorms, and atmospheric and sea spray icing information. The application of the proposed methodology is demonstrated by a drilling waste handling scenario case study for an oil field development project in the Barents Sea, Norway. The case study results show that the risk of undesirable events in the Arctic is 4.2 times more likely to be high (unacceptable) environmental risk than the risk of events in the North Sea. Further, the Arctic environment has the potential to cause high rates of waste handling system failure; these are between 50 and 85%, depending on the type of system and operating season.

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Figures

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Fig. 1

Schematic flowchart showing separation of drill cuttings from drilling fluids and options for cuttings disposal. Modified from Bernier et al. [42].

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Fig. 2

Qualitative part of the proposed DBN-based risk assessment methodology

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Fig. 3

Quantitative part of the proposed DBN-based risk assessment methodology

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Fig. 4

Johan Castberg oil field: (a) Johan Castberg oil field [43] and (b) North Sea – reference area, Barents Sea – target area

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Fig. 6

Solids-control system installed in the rig. Adapted from Bernier et al. [42]. (Reprinted with permission from the International Association of Oil & Gas Producers.)

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Fig. 7

The original static BN fragment

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Fig. 8

The extension of the static BN into two time slices of DBNs. Z denotes a set of any initiating (trigger) events, which are the risk-influencing factors, and C denotes the control measure, which can be a winterization measure—enclosure of the solids-control systems. W denotes the main risk event—a system or component failure, which shows that the cause of the trigger Z produces the effect W. E denotes the consequence of the system failure, which is an environmental risk (marine pollution). M denotes the mitigating event that prevents any cause E, such as rapid emergency response that avoids or reduces the consequence event.

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Fig. 9

Probability transition diagram for four-state Markov chain of the snow

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Fig. 10

Estimated posterior environmental risks for the month of March, for both regions: Arctic and North Sea (see online for color version)

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Fig. 11

Post shale shaker reliability versus operating time. The degraded and fully operating states are expressed as posterior reliability and the failed state of the shale shaker is expressed as posterior unreliability (1 − R(t)).

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Fig. 12

Environmental risk (ER) (%) versus operating time (days): (a) % higher ER versus operating time (days), (b) % medium ER versus operating time (days) (c) % lower ER versus operating time (days)

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