Self-Verification Programme – A Success Story of Major Accident Risk Management via Bowtie Barrier Model

2021 ◽  
Author(s):  
Roman Bulgachev ◽  
Michael Cromarty ◽  
Lee Milburn ◽  
Kevan Davies

Abstract bp's Wells Organization manages its operational risks through what is known as the ‘Three Lines of Defense’ model. This is a three-tiered approach that starts with self-verification as the first line of defense which Wells assets apply to prevent or mitigate operational risks. The second line is conducted by its Safety and Operational Risk function using deep technical expertise. The third line of defense is provided by Group Audit. This paper will discuss the Wells self-verification programme evolution from its first implementation; results, lessons learned, and further steps planned as part of the continuous improvement cycle will be also shared. The company's Wells organization identified nine major accident risks which have the potential to result in significant HSE impacts. Examples include loss of well control, offshore vessel collision and dropped objects. The central Risk team developed bowties for these risks, with prevention barriers on cause legs and mitigation barriers on consequence legs. Detailed risk bowties are fundamental to Wells self-verification, adding technical depth to allow more focused verification to be performed when compared with the original bowties, as verification is now conducted using checklists targeting barriers at their component level – defined as critical tasks and equipment. Barriers are underpinned by barrier enablers – underlying supporting systems and processes such as control of work, safe operating limits, inspection and maintenance and others. Checklists are standardized and are available through a single, global digital application. This permits the verifiers, typically wellsite leaders, to conduct meaningful verification conversations, record the resulting actions, track them to closure within the application and gain a better understanding of any cumulative impacts, ineffective barriers and areas to focus on. Self-verification (SV) results are reviewed at rig, region, Wells and Upstream levels. Rigs and regions analyze barrier effectiveness and gaps and implement corrective actions with contractors at the rig or region level. Global insights are collated monthly and presented centrally to Wells leadership. Common themes and valuable learnings are then addressed at functional level, shared across the organization or escalated by the leadership. The self-verification programme at the barrier component level proved to be an effective risk management tool for the company's Wells organization. It helps to continuously identify risks, address gaps and learn from them. Recorded assessments not only provide the Wells organization with barrier performance data, but also highlight opportunities to improve. Leadership uses the results from barrier verification to gain a holistic view of how major accident risks are managed. Programme evolution has also eliminated duplicate reviews, improved clarity of barrier components, and improved sustainability through applying systematic approach, standardization, digitization and procedural discipline.

2021 ◽  
pp. 1-13
Author(s):  
Roman Bulgachev ◽  
Michael Cromarty ◽  
Lee Milburn ◽  
Kevan Davies

Summary bp’s (“the company’s”) wells organization manages its operational risks through what is known as the “three lines of defense” model. This is a three-tiered approach; the first line of defense is self-verification, which wells assets apply to prevent or mitigate operational risks. The second line of defense is conducted by the safety and operational risk function using deep technical expertise. The third line of defense is provided by group audit. In this paper, we discuss the wells self-verification program evolution from its first implementation and share case studies, results, impact, lessons learned, and further steps planned as part of the continuous improvement cycle. The company’s wells organization identified nine major accident risks that have the potential to result in significant health, safety, and environment (HSE) impacts. Examples include loss of well control (LoWC), offshore vessel collision, and dropped objects. The central risk team developed bowties for these risks, with prevention barriers on cause legs and mitigation barriers on consequence legs. Detailed risk bowties are fundamental to wells self-verification, adding technical depth to allow more focused verification to be performed when compared with the original bowties, because verification is now conducted using checklists targeting barriers at their component level, defined as critical tasks and equipment. Barriers are underpinned by barrier enablers (underlying supporting systems and processes) such as control of work, safe operating limits, inspection and maintenance, etc. Checklists are standardized and are available through a single, global digital application. This permits the verifiers, typically wellsite leaders, to conduct meaningful verification conversations, record the resulting actions, track them to closure within the application, and gain a better understanding of any cumulative impacts, ineffective barriers, and areas to focus on. Self-verification results are reviewed at rig, region, wells, and upstream levels. Rigs and regions analyze barrier effectiveness and gaps and implement corrective actions with contractors at the rig or region level. Global insights are collated monthly and presented centrally to wells leadership. Common themes and valuable learnings are then addressed at the functional level, shared across the organization, or escalated by the leadership. The self-verification program at the barrier component level proved to be an effective risk management tool for the company’s wells organization. It helps to continuously identify risks, address gaps, and learn from them. Recorded assessments not only provide the wells organization with barrier performance data but also highlight opportunities to improve. Leadership uses the results from barrier verification to gain a holistic view of how major accident risks are managed. Program evolution has also eliminated duplicate reviews, improved clarity of barrier components, and improved sustainability through applying a systematic approach, standardization, digitization, and procedural discipline.


2021 ◽  
Vol 14 (3) ◽  
pp. 139
Author(s):  
José Ruiz-Canela López

Operational risk is defined as the potential losses resulting from events caused by inadequate or failed processes, people, equipment, and systems or from external events. One of the most important challenges for the management of the company is to improve its results through its operational risk identification and evaluation. Most of Enterprise Risk Management (ERM) scholarship has roots in the finance/risk management and insurance (RMI) discipline, mainly in the banking sector. This study proposes an innovative operational risk assessment methodology (OpRAM), to evaluate operational risks focused on telecommunications companies (TELCOs), on the basis of an operational risk self-assessment (OpRSA) process and method. The OpRSA process evaluates operational risks through a quantitative analysis of estimates which inputs are the economic impact and the probability of occurrence of events. The OpRSA method is the “engine” for calculating the economic risk impact, applying actuarial techniques, which allow estimation of unexpected losses and expected losses distributions in a TELCO. The results of the analyzed business unit in the field work were compared with standardized ratings (acceptable, manageable, critical, or catastrophic), and contrasted against the company’s managers, proving that the OpRSA framework is a reliable and useful management tool for the business, and leading to more research in other sectors where operational risk management is key for the company success.


Author(s):  
Sarah Maslen

Since the 1990s there has been an increasing interest in knowledge, knowledge management, and the knowledge economy due to recognition of its economic value. Processes of globalization and developments in information and communications technologies have triggered transformations in the ways in which knowledge is shared, produced, and used to the extent that the 21st century was forecasted to be the knowledge century. Organizational learning has also been accepted as critical for organizational performance. A key question that has emerged is how knowledge can be “captured” by organizations. This focus on knowledge and learning demands an engagement with what knowledge means, where it comes from, and how it is affected by and used in different contexts. An inclusive definition is to say that knowledge is acquired theoretical, practical, embodied, and intuitive understandings of a situation. Knowledge is also located socially, geographically, organizationally, and it is specialized; so it is important to examine knowledge in less abstract terms. The specific case engaged with in this article is knowledge in hazardous industry and its role in industrial disaster prevention. In hazardous industries such as oil and gas production, learning and expertise are identified as critical ingredients for disaster prevention. Conversely, a lack of expertise or failure to learn has been implicated in disaster causation. The knowledge needs for major accident risk management are unique. Trial-and-error learning is dangerously inefficient because disasters must be prevented before they occur. The temporal, geographical, and social scale of decisions in complex sociotechnical systems means that this cannot only be a question of an individual’s expertise, but major accident risk management requires that knowledge is shared across a much larger group of people. Put another way, in this context knowledge needs to be collective. Incident reporting systems are a common solution, and organizations and industries as a whole put substantial effort into gathering information about past small failures and their causes in an attempt to learn how to prevent more serious events. However, these systems often fall short of their stated goals. This is because knowledge is not collective by virtue of being collected and stored. Rather, collective knowing is done in the context of social groups and it relies on processes of sensemaking.


2007 ◽  
Vol 15 (2) ◽  
pp. 223-233 ◽  
Author(s):  
J. Engels ◽  
D. Dixon-Hardy ◽  
C. McDonald ◽  
K. Kreft-Burman

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