System resilience denotes the capacity to uphold desired system performance in the face of disruptions. Evaluating the resilience of a process system necessitates a thorough consideration of the intricate interplay between its components and the pivotal role of process parameters in reflecting the repercussions of disruptions on the system. This paper introduces an integrated methodology that takes into account component interactions and leverages process data for the resilience assessment of a process system. The proposed methodology comprises four key components: system structure analysis, disruption impacts analysis, process simulation, and resilience assessment. Firstly, the system structure is meticulously scrutinized using a P-graph model. This analysis encompasses the assessment of the significance and interplay of components, as well as the evaluation of how component failures affect the system's overall processes. Secondly, a Markov model is devised to examine the state transition process of components and quantifies the maintenance time needed for failed components. Subsequently, a simulation model is formulated to acquire real-time process parameters in the presence of disruptive events. Finally, the system's performance response function (PRF) is derived from the normalization of these process parameters. Building upon this foundation, a resilience assessment is conducted with a focus on the PRF. To illustrate the effectiveness of this methodology, an LNG terminal system is employed as an exemplar.

Managed Pressure Drilling (MPD) system is widely used in the deepwater drilling operation. Reliability assessment plays a critical role in the MPD system in the management of drilling operation risk and the prevention of blowouts. However, the reliability assessment of the MPD system is challenged due to its sequential operations and multiple processes. Consequently, the present work proposes a sequence-based dynamic reliability assessment method, which focuses on the dynamic modeling of sequential operations for the MPD system by integrating GO-FLOW and dynamic Bayesian Network (DBN). GO-FLOW models are firstly used to define the time interaction between multiple phases for complex systems. A sequence-based mapping method is also proposed for the DBN to construct the reliability model of the MPD system throughout the entire drilling cycle. In the end, the case study analyzed by the proposed framework indicates that the reliability of the MPD system decreases with increasing drilling depth, and the reliability of “tripping in” is highest among four different phases, while the “drilling process” is the lowest. The method provides an important technique that can be implemented with online condition monitoring tools to assess and monitor the reliability of the MPD operation in real-time.

The emergent hazards of chemical process systems cannot be wholly identified and are highly uncertain due to the complicated technical-human-organizational interactions. Under uncertain and unpredictable circumstances, resilience becomes an essential property of a chemical process system that helps it better adapt to disruptions and restore from surprising damages. The resilience assessment needs to be enhanced to identify the accident's root causes on the level of technical-human-organizational interactions, and development of the specific resilience attributes to withstand or recover from the disruptions. The outcomes of resilience assessment are valuable to identify potential design or operational improvements to ensure complex process system functionality and safety. The current study integrates the Functional Resonance Analysis Method and dynamic Bayesian Network for quantitative resilience assessment. The method is demonstrated through a two-phase separator of an acid gas sweetening unit. Aspen Hysys simulator is applied to estimate the failure probabilities needed in the resilience assessment model. The study provides a useful tool for rigorous quantitative resilience analysis of complex process systems on the level of technical-human-organizational interactions.

Urban gas pipelines usually have high structural vulnerability due to long service time. The locations across urban areas with high population density make the gas pipelines easily exposed to external activities. Recently, urban pipelines may also have been the target of terrorist attacks. Nevertheless, the intentional damage, i.e. terrorist attack, was seldom considered in previous risk analysis of urban gas pipelines. This work presents a dynamic risk analysis of external activities to urban gas pipelines, which integrates unintentional and intentional damage to pipelines in a unified framework. A Bayesian network mapping from the Bow-tie model is used to represent the evolution process of pipeline accidents initiating from intentional and unintentional hazards. The probabilities of basic events and safety barriers are estimated by adopting the Fuzzy set theory and hierarchical Bayesian analysis (HBA). The developed model enables assessment of the dynamic probabilities of consequences and identifies the most credible contributing factors to the risk, given observed evidence. It also captures both data and model uncertainties. Eventually, an industrial case is presented to illustrate the applicability and effectiveness of the developed methodology. It is observed that the proposed methodology helps to more accurately conduct risk assessment and management of urban natural gas pipelines.

Subsea gas release is an industrial hazard that can impose fire hazards on offshore facilities near the gas surfacing area. However, risk assessment of the fire caused by subsea gas release is challenged due to inadequate recognition of the knowledge of subsea gas release mechanism and resulting hazards. At present, minimal researches involving risk assessment of offshore fire resulting from a subsea gas release were reported, and this paper is an extension of the previous works on subsea gas behavior. This paper focuses on modeling fire risk on offshore facilities due to subsea gas release. A numerical simulation is carried out using the Computational Fluid Dynamic technique of Fire Dynamics Simulator (FDS) to analyze fire propagation characteristics and assess the impact of fire on personnel and assets. A probit model is adopted to calculate the probabilities of injury or death caused by fire hazards. This study also investigates the effect of wind speed, gas release rate and the distance between gas pool and platform on fire impacts and casualty probabilities. The present study can support safety measure design to mitigate or avoid the impacts of offshore fire events from subsea gas release.

University chemical laboratory is a high-risk place for teaching and scientific research due to the presence of various physical and chemical hazards. In recent years, university chemical laboratory accidents occur frequently. This urges the need to enhance university chemical lab safety. A semi-quantitative methodology comprising Matter-Element Extension Theory (MEET) implemented with Combination Ordered Weighted Averaging (C-OWA) operator is proposed to assess the risk of a university chemical laboratory. First, an index-based risk assessment system of university chemical laboratory is built by identifying various risk factors from a system perspective. Then, C-OWA operator is used to calculate the weight of assessment indices, whereas MEET is employed to determine the correlation degree of assessment indices. Finally, the comprehensive risk of university chemical laboratories is assessed, and some safety measures are proposed to reduce the risk of university chemical laboratories. The applicability of the proposed methodology is tested using a practical case. It is observed that the methodology can be a useful tool for risk assessment and management of university chemical laboratories.

Ultra-deep oil and gas wells have become a new development trend in onshore oil and gas exploitation. However, Ultra-deep oil and gas wellbore casing is with high failure risk due to the harsh environment. It is essential to evaluate the reliability of wellbore casing. This paper assesses the operational reliability of wellbore casing using data statistics and numerical simulation. Firstly, the theoretical model for reliability analysis of wellbore casing is established, and the variables in the model are determined, including rock mechanics, cement ring, and casing string strength factors. Subsequently, considering the random distribution of model variables, many statistics and analyses are performed to determine the distribution parameters of the model variables. Eventually, Monte Carlo based numerical simulations are carried out to obtain the residual strength distribution and the reliability of wellbore casing. The production casing in the ultra-deep well with a depth of 6.5 km in China as an industrial case is used to illustrate the present study. It is observed that this study can be useful to guide a more accurate assessment of the reliability of ultra-deep wellbore casing.

Ensuring the operational safety of subsea pipelines is a growing challenge. It requires monitoring of failure causation factors and detailed safety analysis. This paper proposes an integrated framework for comprehensive safety analysis of subsea pipelines. This framework is developed based on an index-based risk evaluation system, which incorporates interdependency analysis of failure causal factors, hazard coupling analysis and risk grade evaluation in an integrated manner. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is utilized to analyze the interdependencies among risk factors. The fuzzy reasoning algorithm is employed to calculate the risk grades of these factors in the evaluation system. This framework has also considered the effect of hazard coupling (combination of hazards) on subsea pipeline failure. The framework is tested on a case study. The case study demonstrates the practicality and usability of the proposed framework. This framework will serve an important tool for swiftly assess and manage the risk of subsea pipeline failures.