Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

Link to publishers version: 10.1016/j.ress.2016.03.004We consider the assessment of the availability of oil and gas processing facilities operating under Arctic conditions. The novelty of the work lies in modelling the time-dependent effects of environmental conditions on the components failure and repair rates. This is done by introducing weather-dependent multiplicative factors, which can be estimated by expert judgements given the scarce data available from Arctic offshore operations. System availability is assessed considering the equivalent age of the components to account for the impacts of harsh operating conditions on component life history and maintenance duration. The application of the model by direct Monte Carlo simulation is illustrated on an oil processing train operating in Arctic offshore. A scheduled preventive maintenance task is considered to cope with the potential reductions in system availability under harsh operating condition

Examining the Drivers of C-130J Maintenance Requirements through Multiple Regression Analysis

As a result of increasing system complexity and cost, new aircraft acquisition, upgrade and repair timelines continue to lengthen. As a result, aircraft are kept in service longer than originally intended. Therefore, age-related wear continues to play a large part in determining mission-capable status, and therefore, aircraft availability (AA) rates. Combined with decreasing fleet sizes and manpower resource pools, each aircraft declared not mission capable (NMC) exerts an out-sized influence upon fleet AA rates. This research used multiple regression analysis to identify and quantify the effects of age, Major Command (MAJCOM) and operating location ambient weather on unscheduled not mission capable time. The research found that age and ambient weather have a small but statistically significant effect upon unscheduled not mission capable time, while MAJCOM does not appear to have a statistically significant effect. The research serves as a foundational study to identify and propose new and more in-depth research into the root causes of the identified effects

Simulation of Meteorological and Oceanographic Parameters: An Application in Spray Icing Prediction

Sea spray icing is considered as a major environmental challenge in the Arctic Ocean, which poses a critical risk not only to the vessels and industrial operations but also to human safety. Although some studies have been carried out to estimate spray icing rate (e.g., RIGICE04 and ICEMOD models), such models suffer from some unrealistic modeling assumptions and limited verification. Moreover, limited researches have been conducted on the prediction of icing rates in the long-term, as well as climatological information on spray icing for long-term risk-based decisions in the Arctic offshore industrial applications. In this study, simulation of meteorological conditions to improve prediction of sea spray icing for offshore industrial applications in the Arctic region is purposed. The applications of Bayesian inference as well as Monte Carlo methods comprised of Sequential Importance Sampling (SIS) and Markov Chain Monte Carlo (MCMC) in the prediction of meteorological and oceanographic parameters to improve the estimation of sea spray icing in the Arctic region is purposed. Reanalysis data from NOrwegian ReAnalysis 10km (NORA10) during 33 years are applied to evaluate the performance of the models. Consequently, using the 32-year data, the parameters are predicted and compared for the last one-year on a daily basis. The predicted parameters are considered as input for the newly introduced icing model namely Marine-Icing Model for the Norwegian COast Guard (MINCOG) and the results are evaluated and discussed. Apart from the prediction of sea spray icing, the applied prediction and simulation techniques can play useful roles in industrial application, especially, when new data and information are collected using which the meteorological and atmospheric conditions are predicted for future junctures. This provides the decision-maker with valuable information for planning offshore activities in the future (e.g., offshore fleet optimization). Accordingly, sea voyages with relatively lower risks can be selected based on the predicted parameters and icing rates

Reliability analysis and optimisation of subsea compression system facing operational covariate stresses

This paper proposes an enhanced Weibull-Corrosion Covariate model for reliability assessment of a system facing operational stresses. The newly developed model is applied to a Subsea Gas Compression System planned for offshore West Africa to predict its reliability index. System technical failure was modelled by developing a Weibull failure model incorporating a physically tested corrosion profile as stress in order to quantify the survival rate of the system under additional operational covariates including marine pH, temperature and pressure. Using Reliability Block Diagrams and enhanced Fusell-Vesely formulations, the whole system was systematically decomposed to sub-systems to analyse the criticality of each component and optimise them. Human reliability was addressed using an enhanced barrier weighting method. A rapid degradation curve is obtained on a subsea system relative to the base case subjected to a time-dependent corrosion stress factor. It reveals that subsea system components failed faster than their Mean time to failure specifications from Offshore Reliability Database as a result of cumulative marine stresses exertion. The case study demonstrated that the reliability of a subsea system can be systematically optimised by modelling the system under higher technical and organisational stresses, prioritising the critical sub-systems and making befitting provisions for redundancy and tolerances

Resilience, Reliability, and Recoverability (3Rs)

Recent natural and human-made disasters, mortgage derivatives crises, and the need for stable systems in different areas have renewed interest in the concept of resilience, especially as it relates to complex industrial systems with mechanical failures. This concept in the engineering systems (infrastructure) domain could be interpreted as the probability that system conditions exceed an irrevocable tipping point. But the probability in this subject covers the different areas that different approaches and indicators can evaluate. In this context, reliability engineering is used the reliability (uptime) and recoverability (downtime) indicators (or performance indicators) as the most useful probabilistic tools for performance measurement. Therefore, our research penalty area is the resilience concept in combination with reliability and recoverability. It must be said that the resilience evaluators must be considering a diversity of knowledge sources. In this thesis, the literature review points to several important implications for understanding and applying resilience in the engineering area and The Arctic condition. Indeed, we try to understand the application and interaction of different performance-based resilience concepts. In this way, a collection of the most popular performance-based resilience analysis methods with an engineering perspective is added as a state-of-the-art review. The performance indicators studies reveal that operational conditions significantly affect the components, industry activities, and infrastructures performance in various ways. These influential factors (or heterogeneity) can broadly be studied into two groups: observable and unobservable risk factors in probability analysis of system performance. The covariate-based models (regression), such as proportional hazard models (PHM), and their extent are the most popular methods for quantifying observable and unobservable risk factors. The report is organized as follows: After a brief introduction of resilience, chapters 2,3 priorly provide a comprehensive statistical overview of the reliability and recoverability domain research by using large scientific databases such as Scopus and Web of Science. As the first subsection, a detailed review of publications in the reliability and recoverability assessment of the engineering systems in recent years (since 2015) is provided. The second subsection of these chapters focuses on research done in the Arctic region. The last subsection presents covariate-based reliability and recoverability models. Finally, in chapter 4, the first part presents the concept and definitions of resilience. The literature reviews four main perspectives: resilience in engineering systems, resilience in the Arctic area, the integration of “Resilience, Reliability, and Recoverability (3Rs)”, and performance-based resilience models

An integrated model for asset reliability, risk and production efficiency management in subsea oil and gas operations

PhD ThesisThe global demand for energy has been predicted to rise by 56% between 2010 and 2040 due to industrialization and population growth. This continuous rise in energy demand has consequently prompted oil and gas firms to shift activities from onshore oil fields to tougher terrains such as shallow, deep, ultra-deep and arctic fields. Operations in these domains often require deployment of unconventional subsea assets and technology. Subsea assets when installed offshore are super-bombarded by marine elements and human factors which increase the risk of failure. Whilst many risk standards, asset integrity and reliability analysis models have been suggested by many previous researchers, there is a gap on the capability of predictive reliability models to simultaneously address the impact of corrosion inducing elements such as temperature, pressure, pH corrosion on material wear-out and failure. There is also a gap in the methodology for evaluation of capital expenditure, human factor risk elements and use of historical data to evaluate risk. This thesis aims to contribute original knowledge to help improve production assurance by developing an integrated model which addresses pump-pipe capital expenditure, asset risk and reliability in subsea systems. The key contributions of this research is the development of a practical model which links four sub-models on reliability analysis, asset capital cost, event risk severity analysis and subsea risk management implementation. Firstly, an accelerated reliability analysis model was developed by incorporating a corrosion covariate stress on Weibull model of OREDA data. This was applied on a subsea compression system to predict failure times. A second methodology was developed by enhancing Hubbert oil production forecast model, and using nodal analysis for asset capital cost analysis of a pump-pipe system and optimal selection of best option based on physical parameters such as pipeline diameter, power needs, pressure drop and velocity of fluid. Thirdly, a risk evaluation method based on the mathematical determinant of historical event magnitude, frequency and influencing factors was developed for estimating the severity of risk in a system. Finally, a survey is conducted on subsea engineers and the results along with the previous models were developed into an integrated assurance model for ensuring asset reliability and risk management in subsea operations. A guide is provided for subsea asset management with due consideration to both technical and operational perspectives. The operational requirements of a subsea system can be measured, analysed and improved using the mix of mathematical, computational, stochastic and logical frameworks recommended in this work