An integrated methodology for dynamic risk evaluation of deepwater blowouts
TL;DR: This study presents an integrated methodology that considers the interaction among the drilling risk factors and assesses the blowout risk throughout the deepwater drilling operation's lifecycle and demonstrates the methodology's effectiveness in assessing and evaluatingblowout risk during the drilling operation lifecycle.
Abstract: Offshore drilling operations face technological and operational challenges combined with harsh environmental conditions. The well blowouts are the most feared offshore process operational accident. Many methodologies have been proposed to assess the blowout risk. Most of these studies consider the risk factors' independence and focus on a specific drilling life cycle stage. This study presents an integrated methodology that considers the interaction among the drilling risk factors and assesses the blowout risk throughout the deepwater drilling operation's lifecycle. This integrated methodology is developed based on the index-based risk evaluation system, which comprises hazard identification, interaction analysis, indices weights, and risk evaluation. The Decision Making Trial and Evaluation Laboratory method is used to identify and assess risk factors' interaction. The uncertainty associated with the data is addressed using the Z-numbers method. The risk indices are dynamic to capture the hazards during the lifecycle of the drilling operation. The application of the methodology is tested on a deepwater drilling operation. The application demonstrates the methodology's effectiveness in assessing and evaluating blowout risk during the drilling operation lifecycle.
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TL;DR: Wang et al. as mentioned in this paper proposed a data-driven Bayesian Network (BN) model integrating physical information for risk analysis, which combined prior knowledge with structure learning and parameter learning to obtain a BN model.
Abstract: The coupling of multiple factors stemming from propagation effects and interdependency relationships among risks is prone to generate major accidents. It is of necessity to develop a feasible model with limited cases, which can generate reliable causal relationship evolution. To prioritize risk-influencing factors (RIFs) and investigate their relationships, we proposed a data-driven Bayesian Network (BN) model integrating physical information for risk analysis. Based on collected data, we combined prior knowledge with structure learning and parameter learning to obtain a BN model. In structure learning, we compared three structure learning algorithms including Bayesian search (BS), greedy thick thinning (GTT), and PC algorithm to obtain a robust directed acyclic graph (DAG). In parameter learning, we selected the expectation maximization (EM) algorithm to quantify the dependence and determine the probability distribution of node variables. This study provides a method to capture crucial factors and their interdependent relationships. To illustrate the applicability of the model, we developed a data-driven BN by taking the blowout accident as the case study. Eventually, we introduced vulnerability and resilience metrics for prioritizing risks through network propagation to conduct emergency plans and mitigation strategies .
12 citations
TL;DR: In this article , the authors present a dynamic quantitative risk assessment method for drilling well control by integrating multi types of risk factors, such as human errors, equipment failure, and internal mechanisms.
Abstract: Drilling well control is complex and dynamic with high uncertainty because of the complicated geological conditions and operational environments. Until now, quantitative risk assessment on drilling well control is not integrating human errors, equipment failure, and internal mechanisms together. To address the research gap, this paper presents a novel dynamic quantitative risk assessment method for drilling well control by integrating multi types of risk factors. The entire dynamic Bayesian network is composed of models of human factors, mechanical factors, and environmental factors. The presented method is demonstrated by a case of well section. The dynamic characteristics of the overall risk and individual risks caused by different types of factors are obtained. The influences of human, mechanical and environmental factors on drilling well control risk are researched. It shows that environmental factors have the highest influence on the whole drilling well control risk in Breccia formation. The parameters uncertainty analysis on collapse pressure and fracture pressure of three different rock formations are carried out. It is found out that in-situ stresses have the highest influence on the collapse and fracture pressure.
5 citations
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01 Jan 1985
TL;DR: Analytic Hierarchy Process (AHP) as mentioned in this paper is a systematic procedure for representing the elements of any problem hierarchically, which organizes the basic rationality by breaking down a problem into its smaller constituent parts and then guides decision makers through a series of pairwise comparison judgments to express the relative strength or intensity of impact of the elements in the hierarchy.
Abstract: This chapter provides an overview of Analytic Hierarchy Process (AHP), which is a systematic procedure for representing the elements of any problem hierarchically. It organizes the basic rationality by breaking down a problem into its smaller constituent parts and then guides decision makers through a series of pair-wise comparison judgments to express the relative strength or intensity of impact of the elements in the hierarchy. These judgments are then translated to numbers. The AHP includes procedures and principles used to synthesize the many judgments to derive priorities among criteria and subsequently for alternative solutions. It is useful to note that the numbers thus obtained are ratio scale estimates and correspond to so-called hard numbers. Problem solving is a process of setting priorities in steps. One step decides on the most important elements of a problem, another on how best to repair, replace, test, and evaluate the elements, and another on how to implement the solution and measure performance.
16,547 citations
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TL;DR: The concept of a Z-number has a potential for many applications, especially in the realms of economics, decision analysis, risk assessment, prediction, anticipation and rule-based characterization of imprecise functions and relations.
865 citations
TL;DR: This paper introduces the application of probability adapting in dynamic safety analysis rather than probability updating, and illustrates how Bayesian network (BN) helps to overcome limitations in BT.
440 citations
TL;DR: In this paper, the authors reviewed past progress in the development of methods and models for process safety and risk management and highlighted the present research trends; also it outlines the opinions of the authors regarding the future research direction in the field.
361 citations
TL;DR: The integration of different types of analyses and methods of system modeling is put forward for capturing the inherent structural and dynamic complexities of critical infrastructures and eventually evaluating their vulnerability and risk characteristics, so that decisions on protections and resilience actions can be taken with the required confidence.
293 citations