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This paper assesses what is needed to sustain the integrity of assets in Trinidad and Tobago's Energy Sector by identifying gaps in knowledge. The next step in continuing research outside the scope of this paper will be to conceptualise and propose a framework for an Asset Integrity Management System (AIMS). The approach used was to compare global and local AIMS to establish best practices and investigate the critical success factors required for a sustainable AIMS implementation. Next an analysis was done of global governmental regulations shaped by major accidents, and local safety regulations and the gap between AIMS research and practice identified. Finally, the findings of the National Facility Integrity Audit conducted in 2016 were explored. This research was limited to organisations in the local energy sector of Trinidad and Tobago. A large gap in knowledge was identified both in AIMS and associated governmental regulation. A robust AIMS will provide assurance to organisations that major accidents which are potentially business eliminating can be averted. It is also of paramount importance to the energy sector whose contribution to GDP was 42% in 2014, making that sector the major revenue provider to the government. The focus is how to communicate a standard and consistent message of a robust AIMS, and how to ensure that it is integrated into the existing business management system. Future research can focus on testing the proposed framework developed from the gaps identified with a larger sample of energy sector companies and use the findings to develop a national framework for AIMS for the energy sectors. [ABSTRACT FROM AUTHOR]

Highlights: [•] Likelihood of failure of corroded pipes is calculated using inspection data. [•] Inspection data simultaneously suffer from variability, uncertainty and imprecision. [•] Two hybrid probabilistic–possibilistic models for handling imperfect data are presented. [•] Strengths and weaknesses of different approaches are discussed. [Copyright &y& Elsevier]

Purpose – The purpose of this paper is to identify and discuss maintenance challenges and maintenance practices for subsea petroleum production systems. Design/methodology/approach – Maintenance challenges, current practices and factors that influence the maintenance and support practices were identified by a literature review and by using a case study conducted in the Norwegian oil and gas industry. The case study was based on semi-structured face-to-face interviews with a number of experts working in the subsea systems' design, installation and support services in the Norwegian oil and gas industry. Findings – The paper identifies and discusses subsea petroleum production system failures, maintenance, inspection, modification and support practices. Findings from literature are validated, and new challenges are identified and discussed. Research limitations/implications – The research is based on a case study in the Norwegian petroleum industry, but may be applicable in other countries as well. The subsea production systems are critical production systems, and failures may result in long downtime and costly maintenance, inspection and support services. Hence, inspection, maintenance and modification intervention support services requires careful project planning, implementation and execution, taking into account all influencing factors. Originality/value – The identified challenges can be used by decision makers in offshore maintenance projects. [ABSTRACT FROM AUTHOR]

Abstract: In an efficient and effective pipe integrity management programme, maintenance engineers often use the risk-based inspection (RBI) and maintenance strategy. Unfortunately, the calculation of risk is a daunting task because in order to calculate the risk of failure, a maintenance engineer needs to predict the rate of growth of a defect, the effect of the defect on the integrity of the structure and the consequence of failure. Unfortunately precise calculation for either of these parts is quite difficult. Fuzzy logic is a mathematical tool suitable for handling imprecise information in the real world. The benefit of this approach lies in its ability to include personal experiences along with acceptable deterministic models in the calculation. The structure of the model also allows easy calibration of the model to suit a particular plant condition. This approach can thus help to reduce the dependence upon the precise data, allow modelling even when a phenomenon is incompletely understood, and reduce the difficulties arising due to the complex computation required by more traditional methods. This paper presents a proposed methodology, based on fuzzy logic framework, for the establishment of an RBI programme for pipes. The paper also presents in detail a section of the methodology that can be used for calculating the estimated rate of CO2 corrosion in carbon steel pipes. In this technique the plant operating parameters (temperature, gas and liquid flow rates, total pressure, CO2 partial pressure and pH) are taken as fuzzy variables and used to calculate the Predicted Rate of Corrosion. The inspected rate of corrosion and the efficiency of inspection are also considered as fuzzy variables and are used to calculate Trust in Inspection Results and Trust in Predicted Results. By combining all the modules an estimated rate of corrosion is calculated. This estimated rate of corrosion can then be used for developing the risk-based inspection programme. [Copyright &y& Elsevier]

Abstract: The number of spare parts required for an item can be effectively estimated based on its reliability performance. The reliability characteristics of an item are influenced by different factors such as the operational environment, maintenance policy, operator skill, etc. However, in the majority of reliability based spare part provision studies, the effect of these influence factors has not been considered, and the only variable of interest is the operating time. The aim of this paper is to demonstrate the application of the available reliability models with covariates in the field of spare part predictions by means of a case study. [Copyright &y& Elsevier]

Purpose – The purpose of this paper is to identify and discuss various factors that will influence the process of installing and maintaining subsea equipment in the oil and gas (O&G) industry. Design/methodology/approach – Influencing factors and their attributes are identified using a case study on subsea installations conducted in the Norwegian O&G industry. A number of experts were interviewed. A survey was conducted to rank the importance of the influence factors. Findings – The paper identifies, analyzes and validates the factors and attributes that may impact the installation and maintenance strategy of subsea components. The factors are ranked according to importance and as practiced in the industry. Research limitations/implications – The research is based on a case study in the Norwegian O&G industry, but the approach and results could be adapted to other industries as well. Originality/value – The identified factors can be used by decision makers in the development of offshore projects. [ABSTRACT FROM AUTHOR]

Abstract: Throughput capacity of a production facility plays an important role in supporting managers and engineers in the decision making processes related to the design and optimization of a production plant. System throughput capacity is affected in complex ways by the reliability, maintainability, and capacity of its components. These in turn are considerably affected by operational environment such as ambient temperature, icing, dust, wind, etc. Therefore, in order to have an effective throughput capacity analysis (TCA), all influence factors (covariates) on these terms need to be identified; furthermore, their effect must be modeled and quantified by an appropriate statistical approach. The aim of this paper is to develop a methodology for throughput capacity analysis considering environment condition. A simple case study is used to demonstrate how the methodology can be applied in a real case. [Copyright &y& Elsevier]

Purpose – The purpose of this paper is to discuss operation and maintenance challenges under Arctic conditions and to propose a methodology to assess systems' reliability, maintainability and maintenance costs under the influence of the Arctic operational environment. Design/methodology/approach – A model is suggested for quantifying maintenance costs while taking into account uncertainty due to lack of appropriate data and operational experience using the proportional hazard model and proportional repair model as well as Monte Carlo simulation. Findings – The results show that the operating environment has a considerable influence on the number of failures, the maintenance and repair times and consequently on maintenance cost. Forecasting the maintenance costs based on technical characteristics (e.g. reliability and maintainability) and considering the operational environment, as well as including uncertainty analysis using Monte Carlo simulation, provide more trustworthy information in the decision-making process. Practical implications – There are few data and little experience available regarding the operation of offshore oil and gas production systems in the Arctic region. Using the available data collected from similar systems, but in a different operational environment, may result in uncertain or incorrect analysis results. Hence, the method that is used for maintenance cost analysis must be able to quantify the effect of the operating environment on the system reliability and maintainability as well as to quantify the uncertainty. Originality/value – The paper presents a statistical approach that will be useful in predicting maintenance cost considering the lack of appropriate reliability data from equipment operated in Arctic conditions. The approach presented is valuable for the industrial practitioners in the Arctic region, and may also be adapted to other areas where there is lack of data and operational experience. [ABSTRACT FROM AUTHOR]

Abstract: Traditional parametric methods for assessing maintainability most often only consider time to repair (TTR) as a single explanatory variable. However, to predict availability more precisely for high availability systems, a better model is needed to quantify the effect of operational environment on maintainability. The proportional repair model (PRM), which is developed based on proportional hazard model (PHM), may be used to analyze maintainability in the present of covariates. In the PRM, the effect of covariates is considered to be time independent. However this assumption may not be valid for some situations. The aim of this paper is to develop the Cox regression model and its extension in the presence of time-dependent covariates for determining maintainability. A simple case study is used to demonstrate how the model can be applied in a real case. [Copyright &y& Elsevier]

Abstract: As the oil and gas (O&G) industry is increasing the focus on petroleum exploration and development in the Arctic region, it is becoming increasingly important to design exploration and production facilities to suit the local operating conditions. The cold and harsh climate, the long distance from customer and suppliers’ markets, and the sensitive environment may have considerable influence on the choice of design solutions and production performance characteristics such as throughput capacity, reliability, availability, maintainability, and supportability (RAMS) as well as operational and maintenance activities. Due to this, data and information collected for similar systems used in a normal climate may not be suitable. Hence, it is important to study and develop methods for prediction of the production performance characteristics during the design and operation phases. The aim of this paper is to present an approach for prediction of the production performance for oil and gas production facilities considering influencing factors in Arctic conditions. The proportional repair model (PRM) is developed in order to predict repair rate in Arctic conditions. The model is based on the proportional hazard model (PHM). A simple case study is used to demonstrate how the proposed approach can be applied. [Copyright &y& Elsevier]