Accident Precursor Probabilistic Method (APPM) for modeling and assessing risk of offshore drilling blowouts – A theoretical micro-scale application

Pedro Perez, Henry Tan

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper proposes and explains the application of an Accident Precursor Probabilistic Method (APPM) that aims to overcome the usual limitations of existing Quantitative Risk Analyses (QRA), with a focus on offshore drilling blowouts. This limitation is implicit in generic QRAs that do not appropriately reflect the specificities of the rig and its environment, without considering systems arrangements, Risk Influencing Factors (RIF) or current operational conditions.

The proposed method is divided into three pillars: (i) a guideline for modeling the blowout probability considering specific conditions or well, rig, safety barriers and Risk Influencing Factors (RIF) objectives; (ii) a proposed axiom combined with a scoring system to quantify the RIF into the QRA; and (iii) a risk based plan framework, to allow risk update and sequential learning during the operational phase.

The APPM is based on a Bayesian Network (BN) mathematical framework. It allows the pre-defined axiom to be entered into a conditional probability table (CPT). This approach, combined with the assessment of the company’s safety management system, allows the incorporation of RIF into the QRA.

The developed APPM is applied to a theoretical micro-scale calculation. The result demonstrates its suitability for addressing common aspects inherent to the blowout phenomenon, including uncertainty, dependability between variables (common cause factors and redundant failures), and dynamism due to planned or unplanned operational changes in systems, drilling parameters and current conditions of RIF. Limitations of the APPM are also identified, and suggestions are made for future work on this topic.
Original languageEnglish
Pages (from-to)238-254
Number of pages17
JournalSafety Science
Volume105
Early online date28 Feb 2018
DOIs
Publication statusPublished - Jun 2018

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Blowouts
Offshore drilling
Accidents
accident
Safety Management
Uncertainty
Learning
Guidelines
Safety
dynamism
Bayesian networks
Drilling

Cite this

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title = "Accident Precursor Probabilistic Method (APPM) for modeling and assessing risk of offshore drilling blowouts – A theoretical micro-scale application",
abstract = "This paper proposes and explains the application of an Accident Precursor Probabilistic Method (APPM) that aims to overcome the usual limitations of existing Quantitative Risk Analyses (QRA), with a focus on offshore drilling blowouts. This limitation is implicit in generic QRAs that do not appropriately reflect the specificities of the rig and its environment, without considering systems arrangements, Risk Influencing Factors (RIF) or current operational conditions.The proposed method is divided into three pillars: (i) a guideline for modeling the blowout probability considering specific conditions or well, rig, safety barriers and Risk Influencing Factors (RIF) objectives; (ii) a proposed axiom combined with a scoring system to quantify the RIF into the QRA; and (iii) a risk based plan framework, to allow risk update and sequential learning during the operational phase.The APPM is based on a Bayesian Network (BN) mathematical framework. It allows the pre-defined axiom to be entered into a conditional probability table (CPT). This approach, combined with the assessment of the company’s safety management system, allows the incorporation of RIF into the QRA.The developed APPM is applied to a theoretical micro-scale calculation. The result demonstrates its suitability for addressing common aspects inherent to the blowout phenomenon, including uncertainty, dependability between variables (common cause factors and redundant failures), and dynamism due to planned or unplanned operational changes in systems, drilling parameters and current conditions of RIF. Limitations of the APPM are also identified, and suggestions are made for future work on this topic.",
author = "Pedro Perez and Henry Tan",
note = "We appreciate and acknowledge the research's financial support provided by Witt O'Brien's Brazil. Adriano Ranieri and Flavio Andrade for their professional support and reference. Oliver Peters for English proof reading and revision, Rafael Perez for proof reading the article as many times as requested, and for both anonymous peer reviewers for all their valuable comments.",
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N2 - This paper proposes and explains the application of an Accident Precursor Probabilistic Method (APPM) that aims to overcome the usual limitations of existing Quantitative Risk Analyses (QRA), with a focus on offshore drilling blowouts. This limitation is implicit in generic QRAs that do not appropriately reflect the specificities of the rig and its environment, without considering systems arrangements, Risk Influencing Factors (RIF) or current operational conditions.The proposed method is divided into three pillars: (i) a guideline for modeling the blowout probability considering specific conditions or well, rig, safety barriers and Risk Influencing Factors (RIF) objectives; (ii) a proposed axiom combined with a scoring system to quantify the RIF into the QRA; and (iii) a risk based plan framework, to allow risk update and sequential learning during the operational phase.The APPM is based on a Bayesian Network (BN) mathematical framework. It allows the pre-defined axiom to be entered into a conditional probability table (CPT). This approach, combined with the assessment of the company’s safety management system, allows the incorporation of RIF into the QRA.The developed APPM is applied to a theoretical micro-scale calculation. The result demonstrates its suitability for addressing common aspects inherent to the blowout phenomenon, including uncertainty, dependability between variables (common cause factors and redundant failures), and dynamism due to planned or unplanned operational changes in systems, drilling parameters and current conditions of RIF. Limitations of the APPM are also identified, and suggestions are made for future work on this topic.

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