Stochastic modelling for the analysis of blowout risk in exploration drilling

Andersen, Lasse Berg

doi:10.1016/s0951-8320(97)00067-7

Cited by 17 publications

(3 citation statements)

References 3 publications

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“…On the use of FT, Bercha was among the pioneering authors who modeled the occurrence of blowout during overbalanced drilling for both offshore and artificial island locations in the Beaufort arctic region. Berg Andersen analyzed a kick scenario while having the drill bit at the bottom‐hole, from which low equivalent circulating density (ECD) and loss of mud to external environment and formation were identified as the probable causative elements. In using FT, Torstad developed a relational hierarchy of events for QRA of well control in COBD.…”

Section: Blowout Risk Analysismentioning

confidence: 99%

“…Blowout software such as BlowFlow has also been developed for a risk‐based evaluation of a blowout scenario in determining blowout flow rates, volumes, and durations. Probabilistic models for analyzing blowouts using the conventional overbalanced drilling (COBD) technique and the constant bottom‐hole pressure (CBHP) technique of managed pressure drilling (MPD) have been developed in recent years. Apparently, there exists minimal work on the consequence analysis of a blowout.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Blowout Risk Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Blowout Risk Analysis Using Loss Functions

Abimbola

Khan

2017

Risk Analysis

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“…Other studies reported probabilities for a blowout accident in the order of 10 -4 (blowouts/drilled well) for both natural gas and oil exploration (e.g. OGP, 2010;Andersen, 1998).…”

Section: Blowoutsmentioning

confidence: 99%

Energy from the earth - Deep geothermal as a resource for the future?

Hirschberg¹,

Wiemer²,

Burgherr³

2014

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Geothermal energy offers the prospect of supplying base-load power in a decentralized fashion, while remaining heat can be used in district heating networks or industrial processes. No electric power is currently generated from deep geothermal energy sources within Switzerland that is available to end consumers -despite the large resource potential. Résumé LV Options technologiques fondamentalesLes ressources conventionnelles disponibles sous forme de chaleur souterraine (autrement dit hydrothermales) reposent sur l'interaction de trois facteurs :The project benefited from the support of the Swiss Federal Office of Energy (SFOE). Finally, the study would not have been possible without huge in-kind contributions by its authors and their home organizations.

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Methodology for Quantified Risk Assessment

Vinnem

2013

Springer Series in Reliability Engineering

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Stochastic modelling for the analysis of blowout risk in exploration drilling

Cited by 17 publications

References 3 publications

Dynamic Blowout Risk Analysis Using Loss Functions

Dynamic Blowout Risk Analysis Using Loss Functions

Energy from the earth - Deep geothermal as a resource for the future?

Methodology for Quantified Risk Assessment

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