Dynamic resilience assessment for process units operating in Arctic environments

Abstract

The Arctic is known for its abundant reserve of natural resources. Last decade has seen some exploration and production activities in this region. The assurance of safe operations in this region is a critical and challenging task because of the harsh environment, the remoteness of operation sites, the limited infrastructure, and resources available in response to emergent situations, the application of costly equipment and facilities, and the sensitive marine environment. For complex process systems operating in a harsh environment, the scope of conventional risk assessment is not enough because of the highly uncertain environment, and its impacts on equipment performance. Risk assessment needs to be extended to include both the pre-failure and the post-failure phases. Additionally, risk assessment approaches under normal operating, and environmental conditions may not be applicable in the Arctic regions with unique and uncertain characteristics of the harsh environment. Therefore, this study aims to develop a quantitative resilience assessment method for process units operating under Arctic extreme conditions. Dynamic Bayesian network (DBN) is applied to model the probabilistic relationships between causes and effects in a dynamic manner. The proposed method is applied to the resilience assessment of a separator (as part of an oil production system). The proposed approach will help reveal the critical operating parameters under extreme conditions for process units. It also helps identify potential design improvement to enhance process safety.

The Resilience of Critical Infrastructure Systems: A Systematic Literature Review

“…Therefore, they should be capable of absorbing various component failures [46]. Absorptive capacity includes a set of proactive actions (e.g., by safer design) that should be considered in the CIS's preparedness phase [136][137][138][139]. Robustness is commonly used to quantify the adsorptive CIS capacity [101].…”

“…Restorative Capacity: This capacity is the degree to which the CIS can effectively restore its damaged performance and is typically affected by available budget and resources (e.g., materials, human, etc.) [17,139,141]. Therefore, this capacity is affected by the CIS's supportability.…”

Self-Assessment in the Electricity Sub-Sector

“…It also helps to identify potential areas where improvements are needed to increase process safety. (Zinetullina et al 2019) The The project should include a description of the synergic effect of the failure of these systems and their impact on impact assessment and the establishment of a dynamic assessment of CI resilience. The practical part of the project was aimed at creating a system for identifying key elements of land transport infrastructure, critical infrastructure in the energy and ICT sectors in the context of their correlation, and in relation to the crisis preparedness of territorial units (Resilience 2015-2019).…”