This paper presents a data-driven approach to predict the pipelines’ corrosion-induced Burst failure. In this approach, different aspects of pit growth progression and spatial distribution of pits are simulated. The proposed approach takes advantage of population characteristics to model these aspects of the degradation paths for each pipe section down to the size of single joints. The insights obtained from simulations are used to project the degradation of each pipe section. Understanding corrosion behavior and field data are used to model the corrosion-related parameters such as corrosion pit dimensions, probability and time of initiation, and location. The failure is modeled using the probabilistic simulation considering degradation rate, interactions among pits, and material properties as stochastic variables. The proposed approach and included models are tested using multiple real-life inline inspection datasets. Validation of predicted properties shows prediction errors ranging from 3%–10% depending on the three remaining strength calculation approaches. This work aimed to serve as an important tool for risk-based maintenance prioritization, inspection interval assessment, and the fitness of service assessment of pipelines.

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The present study aims to investigate the impacts of oil sands development in Canada on the economy, society and the environment as the three pillars of sustainability. Factors such as aquatic ecosystems, land disturbance and reclamation, air quality, public health, safety, aboriginal and local communities, gross domestic product, employment rate and job creation, government revenues and demographic changes have been considered. Based on a review of the available literature, this study shows that the oil sands industry has so far fallen short in keeping a balance among the three pillars of sustainability, with the negative impacts on society (e.g. changing the lifestyle of Aboriginal people) and the environment (e.g. land disturbance) overweighing the relatively positive economic impacts. This, along with the current pace of remedies (e.g. land reclamation), makes it hard to conclude that the oil sands industry is sustainable. @en

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.

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Global concerns around climate change and the volatility of conventional fuel prices have prompted researchers and technologists to make significant efforts to identify and exploit alternative energy sources that are cleaner and more sustainable. Wind energy has seen considerable development among these alternative energy sources, mainly due to its abundance and global availability for extraction and the existing knowledge within the aviation and aerospace fields. Many nations, including European countries, already operate offshore wind farms (OWFs) and are progressively carrying out new projects and expanding on other projects. The Australian offshore environment provides unique opportunities for wind energy extraction, particularly along the southern coast of mainland Australia and the regions around Tasmania, where substantially strong winds blow most of the year. A significant challenge to establishing wind farms is the selection of site locations with optimal outputs. This can become a complex decision-making problem if there are numerous options and no information from previous projects. This paper aims to develop a decision-making framework to select the optimal location for installing OWFs while addressing financial, performance-related, and availability-related objectives. This paper adopts a game-theoretical approach to develop a decision-support tool to account for the interdependencies of influencing factors and possible conflicts amongst the parties. The game model is applied to an OWF development case study in the Bass Strait, known for its dominant and strong winds.

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Failure modelling and reliability assessment of repairable systems has been receiving a great deal of attention due to its pivotal role in risk and safety management of process industries. Meanwhile, the level of uncertainty that comes with characterizing the parameters of reliability models require a sound parameter estimator tool. For the purpose of comparison and cross-verification, this paper aims at identifying the most efficient and minimal variance parameter estimator. Hierarchical Bayesian modelling (HBM) and Maximum Likelihood Estimation (MLE) approaches are applied to investigate the effect of utilizing observed data on inter-arrival failure time modelling. A case study of Natural Gas Regulating and Metering Stations in Italy has been considered to illustrate the application of proposed framework. The results highlight that relaxing the renewal process assumption and taking the time dependency of the observed data into account will result in more precise failure models. The outcomes of this study can help asset managers to find the optimum approach to reliability assessment of repairable systems.

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The significant cost required for implementation of WEC sites and the uncertainty associated with their performance, due to the randomness of the marine environment, can bring critical challenges to the industry. This paper presents a probabilistic methodology for predicting the long-term power generation of WECs. The developed method can be used by the operators and designers to optimize the performance of WECs by improving the design or in selecting optimum site locations. A Markov Chain model is constructed to estimate the stationary distribution of output power based on the results of hydrodynamic analyses on a point absorber WEC. To illustrate the application of the method, the performance of a point absorber is assessed in three locations in the south of Tasmania by considering their actual long-term sea state data. It is observed that location 3 provides the highest potential for energy extraction with a mean value for absorbed power of approximately 0.54MW, while the value for locations 1 and 2 is 0.33MW and 0.43MW respectively. The model estimated that location 3 has the capacity to satisfy industry requirement with probability 0.72, assuming that the production goal is to generate at least 0.5MW power.

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High strength steels such as X80 steels have recently been used more frequently in production of offshore structures. However, they may still be subject to degradation processes such as corrosion considering the conditions in marine environment. Pitting corrosion is a destructive form of corrosion which reduces the material resistance and may result in failure accidents with severe financial, human life and environmental consequences. The process of pitting corrosion is inconsistent and largely stochastic being influenced by a number of parameters with a high level of uncertainty. This makes it very difficult to predict corrosion in terms of its initiation time and spatial behavior. Therefore, it is vital to investigate pitting corrosion phenomena in offshore structures using a probabilistic approach for the assessment of structural reliability and operational safety. In this study, an in-situ experiment has been conducted on X80 steel in an NaCl solution in a laboratory environment to observe the generation and growth of corrosion pits. A probabilistic model based on Hierarchical Bayesian Approach (HBA) is developed for predicting the pitting corrosion growth rate using experimental results. In order to model the process more realistically, the proposed methodology considers the degradation process to be consisting of the time needed for pit initiation and propagation. The results indicate that the proposed methodology is capable of predicting the time required to reach a specific pit size. The methodology developed in this study can be applied to estimate the remaining useful life of subsea structures.

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Produced water is a waste of significant concern due to its high volume being produced every day and complex chemical composition. In order to meet environmental regulations and standards, different techniques can be used to treat produced water. This paper first summarizes produced water composition, its related environmental impact, regulations, and standards, as well as a possible combination of different treatment techniques. This paper aims to develop a generic framework for a risk-based approach to produced water management. The proposed methodology considers the integration of environmental, technical, and economic risks in the decision-making process for produced water management. Environmental risk assessment is conducted by DREAM, Failure Mode and Effects Analysis is used to estimate technical risk, and cost-benefit analysis is performed to calculate economic risk. To integrate all the risk values, acceptable risk levels are set and compared to the calculated risk values. Experts assign weighting factors by using pair-wise comparison. The sum of the multiplied weighting factors to the ratio of calculated-acceptable risk values gives the final integrated risk. This framework can help to examine and select the most suitable treatment or reuse technique or identify potential areas for improvement in a specific site. The estimated risk can be used to justify the selection process. A case study on the produced water treatment in Thunder Horse Oil Field is presented to demonstrate the application of the proposed framework.

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Wave energy converters (WEC) are reaching a pinnacle in their prototype phase. World leaders in the energy sector are looking for renewable energy sources to replace the expiry of fossil-fuel energy capacity. For WECs to become a viable solution to the fossil-fuel challenges, there is a need to have a deeper understanding of the associated costs and the operational impacts of this technology. This study investigates the relationship between these two characteristics and finds an improved implementation strategy by developing a dynamic risk-based methodology. The methodology developed from this study will aid WEC technology to move towards a commercialised state by implementing an array or farm of WEC devices. Bayesian network (BN) is adopted to analyse the probability of a collision accident within the farm as well as the likelihood of meeting the desired level of power production. The BN is later extended to an influence diagram (ID) for selecting the optimum configuration of the WEC farm. The ID assists in decision-making based on the investigated probabilities, required capital investment, and economic impact of the accident scenario. To demonstrate the application of the developed method, a case study is adopted including three decision alternatives, each representing a farm with different layouts of point absorber WECs. The performance of the facility is assessed under real-life offshore environmental conditions. The developed methodology assists in finding the WEC layout which minimises the economic risk of an array implementation and also increases the reliability of these structures.@en

Degradation of subsea pipelines in the presence of corrosive agents and cyclic loads may lead to the failure of these structures. In order to improve their reliability, the deterioration process through pitting and corrosion-fatigue phenomena should be considered simultaneously for prognosis. This process starts with pitting nucleation, transits to fatigue damage and leads to fracture and is influenced by many factors such as material and process conditions, each incorporating a high level of uncertainty. This study proposes a novel probabilistic methodology for integrated modelling of pitting and corrosion-fatigue degradation processes of subsea pipelines. The entire process is modelled using a Dynamic Bayesian Network (DBN) methodology, representing its temporal nature and varying growth rates. The model also takes into account the factors influencing each stage of the process. To demonstrate its application, the methodology is applied to estimate the remaining useful life of high strength steel pipelines. This information along with Bayesian updating based on monitoring results can be adopted for the development of effective maintenance strategies.

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This paper focuses on the safety analysis of a nonlinear hydro-generating unit (HGU) running under different loads. For this purpose, a dynamic balance experiment implemented on an existing hydropower station in China is considered, to qualitatively investigate the stability of the system and to obtain the necessary indices for safety assessment. The experimental data are collected from four on-load units operating at different working heads including 431 m, 434 m, 437 m, and 440 m. A quantitative analysis on the safety performance of the four units was carried out by employing an integration of entropy weights method with grey correlation analysis. This assisted in obtaining the safety degree of each unit, providing the risk prompt to the operation of nonlinear hydro-generating units. The results confirm that unit 4 has the highest level of safety while unit 3 operates with the lowest safety condition. This provides the optimal operational schedule of HGUs to cope with the fluctuations of electricity demand in the studied station. The proposed methodology in this paper is not only applicable to the HGUs in the studied station but could also be adopted to assess the safety degree of any hydropower facility.

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A methodology for risk analysis applicable to shipping in arctic waters is introduced. This methodology uses the Bowtie relationship to represent an accident causes and consequences. It is further used to quantify the probability of a ship accident and also the related accident consequences during navigation in arctic waters. Detailed fault trees for three possible ship accident scenarios in arctic transits are developed and represented as bowties. Factors related to cold and harsh conditions and their effects on grounding, foundering, and collision are considered as part of this study. To illustrate the application of the methodology, it is applied to a case of an oil-tanker navigating on the Northern Sea Route (NSR). The methodology is implemented in a Markov Chain Monte Carlo framework to assess the uncertainties arisen from historical data and expert judgments involved in the risk analysis.@en

Using the emissions produced during the entire life-cycle of a fuel or a product, Life-cycle assessment (LCA) is an effective technique widely used to estimate environmental impacts. However, most of the conventional LCA methods consider the impacts of voluntary releases such as discharged toxic substances and overlook involuntary risks such as risk of accidents associated with exploration, production, storage, process and transportation. Involuntary risk of hazardous materials such as fuels could be quite significant and if ignored may result in inaccurate LCA. The present study aims to develop a methodology for accident risk-based life cycle assessment (ARBLCA) of fossil fuels by considering both the voluntary and involuntary risks. The application of the developed methodology is demonstrated for liquefied natural gas (LNG) and heavy fuel oil (HFO) as fuels of a hypothetical power plant. Adopting a Bayesian network approach, the comparative analysis of the fuels helps an analyst not only overcome data uncertainty but also to identify holistically greener and safer fuel options.@en