Production Performance Analysis : Reliability, Maintainability and Operational Conditions

Abstract

With the increasing demand for energy over recent decades, the Arctic region has become an interesting area for future exploration and development for the oil and gas industry. The Arctic region is known to have a harsh climate and a sensitive environment in a remote location. The severe and complex operational conditions in the Arctic can significantly affect the lifetime of a system, the repair processes and the support activities. Hence, it is important to consider the effect of the operational conditions on the performance of the production facility/systems/equipment and machines, and the related reliability and maintainability characteristics. The aim of this thesis is to study, analyze and suggest a methodology for production performance analysis considering operational conditions. Furthermore, the study focuses on developing and modifying the available statistical approach for prediction of maintainability performance and spare part provision considering the effect of time-dependent and time-independent covariates (influence factors). In this research study, firstly a brief survey of technological and operational challenges in the Arctic region from a maintainability and reliability performance point of view is presented. Then, available statistical approaches for reliability and maintainability performance analysis considering the effect of covariates are reviewed. Thereafter, a methodology is developed and proposed for production performance analysis considering time-dependent and time-independent covariates. The methodology is based on the concept of the proportional hazard model (PHM) and the proportional repair model (PRM), as well as their extensions. A case study from the mining industry is presented to demonstrate how the proposed methodology can be applied. In the second part of this research study, the application of the extension of PHM is developed and discussed in order to predict the maintainability performance considering time-dependent covariates. Furthermore, the existing methods for calculating the number of spare parts on the basis of the reliability characteristics, without the consideration of time-dependent iv ABBAS BARABADI covariates, is modified and improved to enhance their application in the presence of time-dependent covariates. The applications of these methods are demonstrated and discussed using a case study. The result of the study shows that the operational conditions may have a significant effect on the reliability and maintainability performance of a component. This also consequently affects the number of the required spare parts for a given operational condition. The result also shows that considering time-dependent covariates as time-independent covariates may lead to wrong results in the prediction of reliability and maintainability performance as well as the required spare parts. Therefore, before any analysis, the timedependency of covariates must be checked. Thereafter, based on the result of the analysis, the appropriate statistical approach must be selected.