Risk-based safety analysis of well integrity operations

Abimbola, Majeed; Khan, Faisal; Khakzad, Nima

doi:10.1016/j.ssci.2015.12.009

Cited by 59 publications

(17 citation statements)

References 36 publications

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“…However, for diagnostic analysis, the states of some nodes are instantiated and the updated probabilities of conditionally dependent nodes are calculated. (35)(36)(37)…”

Section: Bayesian Networkmentioning

confidence: 99%

“…It depicts perfect system resilience with negligible hydrocarbon release, achieving a successful performance of the operation. 5 Shuttle tanker (ST) hose connection 1.1E-01 E 6 Hose aging 1.7E-01 E 7 Joints rupture 4.5E-02 E 8 Valves control system 1.0E-03 E 9 Telemetry system 2.4E-02 E 10 Communication 6.2E-03 E 11 Controllable pitch propeller 1.8E-02 E 12 Zone classification in terms of sensitivity 1.5E-01 E 13 Adequate flow control 1.5E-02 E 14 Level monitoring 1.0E-05 E 15 Oil spillage preparedness program 1.0E-01 E 16 Avoid collision 3.1E-03 E 17 Erroneous operations 3.3E-03 E 18 Corrosion management 3.7E-03 E 19 Secure connection 9.9E-02 E 20 Malfunction of alarm system 9.0E-03 E 21 Hydrocarbon release detection system 2.3E-03 E 22 Tension cause by waves height 4.5E-02 E 23 High wind intensity 1.0E-01 E 24 Low visibility 5.5E-04 E 25 Ice management 1.0E-01 E 26 Emergency shutdown system failure 1.3E-04 E 27 Position reference system 2.0E-03 E 28 Dynamic positioning system 5.0E-04 E 29 Avoid risky maneuvering 7.9E-03 E 30 Vessel motion monitoring 1.0E-01 E 31 Distance keeping 3.8E-02 E 32 Avoid drive-off 5.4E-03 E 33 Early detection 7.2E-05 E 34 Available workforce 1.0E-01 E 35 Reactive maintenance 2.3E-03 E 36 Onsite restoration facility 1.7E-01 E 37 Manning competence 2.7E-01 E 38 Lesson learned 1.0E-01 E 39 Toolkit training 1.6E-03 E 40 Good practice guidance 1.0E-03 E 41 Lack of motivation 1.6E-03 E …”

Section: Baseline Scenariomentioning

confidence: 99%

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Resilience Analysis of a Remote Offshore Oil and Gas Facility for a Potential Hydrocarbon Release

et al. 2018

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Resilience is the capability of a system to adjust its functionality during a disturbance or perturbation. The present work attempts to quantify resilience as a function of reliability, vulnerability, and maintainability. The approach assesses proactive and reactive defense mechanisms along with operational factors to respond to unwanted disturbances and perturbation. This article employs a Bayesian network format to build a resilience model. The application of the model is tested on hydrocarbon-release scenarios during an offloading operation in a remote and harsh environment. The model identifies requirements for robust recovery and adaptability during an unplanned scenario related to a hydrocarbon release. This study attempts to relate the resilience capacity of a system to the system's absorptive, adaptive, and restorative capacities. These factors influence predisaster and postdisaster strategies that can be mapped to enhance the resilience of the system.

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“…However, for diagnostic analysis, the states of some nodes are instantiated and the updated probabilities of conditionally dependent nodes are calculated. (35)(36)(37)…”

Section: Bayesian Networkmentioning

confidence: 99%

Section: Baseline Scenariomentioning

confidence: 99%

Resilience Analysis of a Remote Offshore Oil and Gas Facility for a Potential Hydrocarbon Release

et al. 2018

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“…for blowout analysis considering various possible consequences. Recently, BN has been used for blowout analysis for both COBD condition and MPD . Since the focus of this study is on the consequence analysis, readers are referred to the aforementioned resources for further studies.…”

Section: Blowout Risk Analysismentioning

confidence: 99%

Dynamic Blowout Risk Analysis Using Loss Functions

Abimbola

Khan

2017

Risk Analysis

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Most risk analysis approaches are static; failing to capture evolving conditions. Blowout, the most feared accident during a drilling operation, is a complex and dynamic event. The traditional risk analysis methods are useful in the early design stage of drilling operation while falling short during evolving operational decision making. A new dynamic risk analysis approach is presented to capture evolving situations through dynamic probability and consequence models. The dynamic consequence models, the focus of this study, are developed in terms of loss functions. These models are subsequently integrated with the probability to estimate operational risk, providing a real-time risk analysis. The real-time evolving situation is considered dependent on the changing bottom-hole pressure as drilling progresses. The application of the methodology and models are demonstrated with a case study of an offshore drilling operation evolving to a blowout.

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“…This drawback was recently addressed by Kalantarnia et al and Khakzad et al by incorporating Bayesian network. Then the modified BT approach was used for dynamic safety risk analysis of offshore drilling, well integrity operations, and dust explosion scenarios . Numerous researches indicate Bayesian updating is effective in characterizing evolving feature of the failure probability of safety systems within event tree .…”

Section: Introductionmentioning

confidence: 99%

A bow‐tie model for analyzing explosion and fire accidents induced by unloading operation in petrochemical enterprises

Chen

Wang

2018

Process Safety Progress

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Explosion and fire accidents happen frequently in petrochemical enterprises. For improving risk management, Bowtie method is applied to analyze causes, consequences, and control methods of such disasters. Based on fault tree analysis, 42 combination scenarios of primary events leading to explosion and fire accident are achieved. Important order of primary events is determined. Event tree is developed where four consequences, with different occurrence probability and loss degree, are obtained considering of success or failure of emergency evacuation and automatic fire extinguishing system. Structure of Bow-tie model is established where three accident sources, including limit concentration of liquefied petroleum gas, equipment fault or operation error and fire source, are taken into account. After identification of accident causes and consequences, precautionary and loss-reducing measures are proposed. The model was applied to analyze explosion and fire accident occurring in Jinyu group of China, demonstrating poor connection between the pipe and oil tank truck and non-explosion-proof equipment resulted in the accident. The delayed emergency excavation and failure of automatic fire extinguishing system led to fully developed fire and heavy casualties. To reduce such disasters, controlling suggestions in terms of educational training, intelligent monitoring, equipment management, and safety management were provided for Jinyu group.

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Risk-based safety analysis of well integrity operations

Cited by 59 publications

References 36 publications

Resilience Analysis of a Remote Offshore Oil and Gas Facility for a Potential Hydrocarbon Release

Resilience Analysis of a Remote Offshore Oil and Gas Facility for a Potential Hydrocarbon Release

Dynamic Blowout Risk Analysis Using Loss Functions

A bow‐tie model for analyzing explosion and fire accidents induced by unloading operation in petrochemical enterprises

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