Technical health, remaining useful life and life extension of subsea equipment
Abstract
This PhD thesis proposes concepts, tools and processes for analyses of technical health
and remaining useful life (RUL) for decision-making regarding life extension of subsea
equipment. The thesis is the outcome of the Integrated Operations (IO) programme,
hosted at the Norwegian University of Science and Technology, Trondheim. The
mission of the programme is to develop new methods and tools, which can be
embedded in improved work processes in the oil companies and enhanced products
and services from the suppliers. Program 3, subsection 3.1, of the IO, has the mandate
to create a generic framework or a toolbox for remaining useful life prediction.
This work clarifies the meaning of the useful life and the RUL of equipment. It
then discusses the terms useful life and the life termination criteria. It explains the
weaknesses of using the mean remaining life and the ‘technical health index’ for
determination of the RUL. It defines the concept of technical health, presents the
factors that influence the technical health and shows the relation between technical
health and RUL.
This work provides a process to determine a technical condition vector by
bringing in the idea of multi-parameter monitoring. It presents technical health as a
process of assessment of the elements of the technical condition vector, resulting in the
formulation of a technical health picture.
The work clarifies the terms tacit knowledge and explicit knowledge in the
context of technical health. It explains that the technical health is a tacit knowledge and
that all the factors influencing RUL are in tacit form. The knowledge needs to be
converted into explicit form. The work explains the meaning of community of practice
(CoP), and how this can be used to achieve the knowledge conversion.
The work presents various failure modes and mechanisms that are commonly
observed in the subsea industry and recommends suitable modelling approaches based
on several references from top-side (oil and gas), nuclear and avionics industries. It
evaluates the suitability of Bayesian Belief Network (BBN) as a modelling tool for
technical health assessment in the subsea oil and gas industry.
The work explains how to utilize expert judgment, knowledge conversion and
knowledge sharing for technical health assessment and for model selection. A new
approach, combining elements from storytelling and root cause analysis, to address
knowledge conversion and knowledge sharing is presented. A flexible RUL prediction
process is suggested that is adaptable to both objective (classical, frequentist) and subjective (Bayesian) interpretations of probability depending on the availability of
data and suggestions from experts.
Condition based maintenance (CBM) for RUL prediction is not yet a practice
in the subsea oil and gas industry because the real time data acquisition process is not
established. The PhD thesis also describes the work done by various standardization
groups to ensure interoperability among sensors and interfaces i.e., making them smart.
In particular, it informs on how these divergent standards complement each other and
how they can work together to get real time operational data.
The main contributions of the thesis are: (1) Clarification of the terms
technical condition and technical health, (2) Explanation of the relationship between
technical condition, technical health, RUL and decision-making related life extension,
and (3) Presentation of a step-wise process for determination of technical health and
RUL that can be used in the subsea oil and gas industry.