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Title: Accident precursor probabilistic method (APPM) for modelling and assessing risk of offshore drilling blowouts
Author: Perez, Pedro Rafael Nonato
ISNI:       0000 0004 7967 6993
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 2019
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Most of the blowout Quantitative Risk Analyses (QRAs) employed in deep water offshore drilling operations are generic as they: (i) fail in addressing the specificities of critical systems related to the well and drilling rig; (ii) do not reflect the specific risk influencing factors (RIF) of the project; and, (iii) are static and not updated when relevant new data becomes available. The generic and static concept of the current practices in that industry relies on the extreme complexity in applying more recent approaches (already in use by the petrochemical sector) in drilling platforms, due to its operational dynamism and associated socio-technical complexity. The main objective of the present Engineering Doctoral thesis is to develop an accident precursor probabilistic method (APPM) for offshore drilling blowouts that overcomes the above-mentioned limitations being, at the same time, feasible to be implement in a real project. The APPM is based on a Bayesian Network (BN) mathematical framework. It decodes a pre-defined axiom into a conditional probability table (CPT) to facilitate the process of tailoring the blowout probability based on objective evidences. This probability update method is integrated to a comprehensive precursor based blowout probability model, composed of the following elements: basic causes, intermediate triggering events, safety barriers systems/ elements and risk influencing factors, including human, organizational and geological and geophysical (G&G). So, the APPM allows the incorporation of RIF into the QRA, as well as performing probability updates when new relevant evidence becomes available. The new evidence are gathered according to a Risk Based Plan (RBP), composed by well integrity governance and operational mechanisms for risk update and planning. The implementation of the method is tested by: (i) a theoretical microscale application; (ii) a real scale theoretical applications; and, (iii) a case study. The method is validated, and its advantages and weaknesses vis-à-vis other methodologies are identified. The evidences are that the APPM is more appropriate for modelling and assessing the risk of blowouts in offshore drilling, given the practical way to address common aspects inherent to drilling operations and the blowout phenomenon, including: socio-technical complexity, uncertainty, interdependency between variables, and dynamism, due to planned or unplanned operational changes.
Supervisor: Tan, Henry ; Ranieri, Adriano Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Offshore gas well drilling ; Oil wells ; Oil spills ; Bayesian statistical decision theory