EG59G9_Wilson_MSc_dissertation

William J. Wilson 51233726
0
Environmental, safety and business
impact on the Integrity of Subsea
Production Systems in Arctic
environments.
2014
By William J. Wilson
Masters of Science in Subsea Engineering
Student I.D. 51233726
November 2014

i
Abstract
With increased exploration and development of the oil and gas reservoirs it is anticipated
that hazards which arise from extracting hydrocarbons in sub-ice and Arctic environments
will have an inverse impact on the integrity of Subsea Production Systems (SPS). An
environmentally vulnerable ecosystem and reduced integrity of subsea production systems
increases the likelihood and impact of a major oil spill.
To demonstrate that a typical SPS can operate safely and meet a solid financial business
case for field developments in the Arctic region this report proposes a framework to assess
the reliability of a SPS. This report applies the proposed framework to build an accurate
model of a SPS and proves satisfactorily that regular updating of the reliability model can
also update the maintenance strategies of installations as they age. By using this
framework there can be an autonomous and quantifiable methodology for determining the
frequency of testing to ensure that both safety and availability is maintained.
The report also identifies the asset which requires further development to improve safety
for operating in the Arctic is the Surface controlled subsurface safety valve (SCSSV) and
the framework can be used to identify when a hazard is about to occur by using posterior
reliability data updating.

William J Wilson Student I.D. 51233726
ii
Preface
This master’s thesis is based on the principles taught during the MSc Subsea Engineering
programme and it is assumed that the reader has an understanding of the basic concepts of
reliability theory and subsea engineering. It is focused on the reliability of Subsea
Production Systems and the main objective is to develop a framework for the reliability
assessment of Subsea Production Systems in the Arctic environment.
Huge appreciation goes out to the industry supervisors from the DNV GL; Ben Hukins,
Martin Fowlie and others within the DNV GL who all provided their assistance,
enthusiasm and help during this dissertation.
In addition to my new colleagues at DNV GL I would like to extend a warm thank you to
my friends and family, especially Laura, who have endured many lost hours of my
awesome company in order for me to undertake relentless distance learning study. My
friends, my time is now yours… until my PhD.
William Wilson
November 2014

iii
Table of Contents
Abstract ……………………………………………………..………………………………………… i
Preface ……………………………………….…………………………………………....................... ii
Table Of Contents …………………………...…………………………………………....................... iii
List of Tables ………………………………………………………………………………………….. iv
List of Figures ………………………………………………………………………………………… v
List of Abbreviations and Acronyms …………………………………………………………………. vi
List of symbols ……………………………………………………………………………………….. vii
Chapter 1: Introduction ……………………………………………………………….......................2
1.1 Introduction…………………………………………………………………….................. 2
1.2 Background………..……………………..…………………………………….................. 2
1.3 Aims and Objectives …………………………………………………………………….. 4
1.4 Methodology …………………………………………………………............................... 5
Chapter 2: Real World Problem……………………………………………………………………. 7
2.1 Introduction to problem…………….…………………………………………………….. 7
2.2 Stakeholders…………………………………………….………………………………… 9
2.3 Literature review………………………………………………………………………….. 10
2.4 Previous and current research…………………………………………………………….. 12
2.5 Difficulties for Operators…………………………………………………………………..12
2.6 The Arctic Environment…………………………………………………………………... 13
2.7 The North Sea Environment………………………………………………………………. 14
2.8 Problem Assessment……………………………………………………………………… 14
Chapter 3: Frameworks for risk analysis………………………………........................................... 15
3.1 Introduction to Frameworks for risk analysis ………………………………………........ 15
3.2 Proposals for change to existing RAM framework…………….......................................... 17
Chapter 4: Reliability Theory……………………………………………………………………...... 18
4.1 Introduction to Reliability Theory…..………………………………………….................. 18
4.2 Standards and Recommended Practices………………………………………………....... 18
4.3 Reliability and Maintenance data…………………………………………………………. 18
4.4 Objectives of the RAMS analysis………………………………………………………….19
4.5 FTA and RAMS Basics concepts…………………………………………………………. 19
Chapter 5: Proposed Framework…………………………………………………………………… 22
5.1 Proposed framework for RAM analysis…………………………………………………... 22
Chapter 6: Case Study - Production System Components………………………………………… 25
6.1 Introduction to production systems components………………………………………….. 25
6.2 Subsea Surface Isolation Valve (SSIV)…………………………………………………… 26
6.3 Well head and X-Tree…………………………………………………………………….. 26
6.4 Manifold…………………………………………………………………………………... 27
6.5 Flowline…………………………………………………………………………………… 28
6.6 Riser………………………………………………………………………………………. 28
6.7 Surface controlled Subsurface Safety Valve (SCSSV)…………………………………… 29
6.8 Subsea Control system……………………………………………………………………. 30
6.9 Building the SPS…………………………………………………………………………...31
6.10 The Minimum cut sets for the system…………………………………………………….. 32
6.11 The prior data input……………………………………………………………………….. 33
6.12 Maintainability of system components……………………………………………………. 35
6.13 The posterior data updating……………………………………………………………… 38
6.14 Proposed maintenance strategy…………………………………………………………… 41
6.15 Life cycle cost analysis for Subsea Production system…………………………………… 42
Chapter 7 Case study and report findings………………………………………….......................... 44
7.1 General Report findings …..………………………………………….................................44

iv
7.2 Answering the Research Questions………………………………….................................. 44
7.3 Report recommendations…………………………………………………………………. 46
7.5 Future Research Opportunities……………………………………………………………. 47
Chapter 8: References…………………………………………........................................................... 49
Appendices
A SSIV FTA………………………………………………………………………………….. A
B X-tree and well head FTA…………………………………………………………………. B
C Manifold FTA……………………………………………………………………………… C
D Flowline FTA………………………………………………………………………………. D
E Riser FTA………………………………………………………………………………….. E
F Control System FTA……………………………………………………………………….. F
G SCSSV FTA………………………………………………………………………………. G
H Ethical statement for stakeholder engagement…………………………………………….. H
I Microsoft Excel Commands List……………………………………………………………I
J Copy of cover sheet………………………………………………………………………… J
K Quick reference to D-010 for SCSSV testing schedule……………………………………. K
List of Tables
T2.1 CATWOE definition of problem 14
T6.1 Minimum cut sets for each sub-module 32
T6.3 Weighting for delays (Estimated) 36
List of figures
F1.1 Likelihood / Consequence chart for moving towards colder climates 2
F1.2 Macondo oil spill subsea 3
F1.3 Methodology and project timetable 6
F2.1 An illustration subsea Production System sub ice 8
F2.2 Influence diagram for the introduction of a new RP 9
F2.3 Stakeholder relationship 10
F3.1 A comparison of the current Processes in use today 15
F3.2 Integrity Management system life cycle 16
F3.3 Data collection strategy from ISO 14224 17
F4.1 Example of Hydrocarbon leak in a simple pipeline 19
F4.2 Example of series structure Reliability block 19
F4.3 “Bathtub curve” for a typical asset. 20
F4.4 “Bathtub curve” for a typical asset with extended wear-out phase. 21
F4.5 Early intervention based on reliability data Increases Mean time to failure (MTTF) 21
F4.6 Early intervention based on reliability data delays top event, T, occurring for 4 years 22
F5.1 Proposed framework for RAMS analysis adapted from [25][26] 24
F6.1 Area of SPS scope boundary 25
F6.2 Reliability block diagram of the SSIV 26
F6.3 Reliability block diagram of the X-tree and Wellhead (XTX1) 27
F6.4 Reliability block diagram of the Manifold 27
F6.5 Reliability block diagram of the Flowline 28
F6.6 Reliability block diagram of the Riser 29
F6.7 Reliability block diagram of the SCSSV within the X-tree 30
F6.8 Reliability block diagram of the control system 30
F6.9 Reliability block diagram of the SPS 31
F6.10 Bayesian “updating” process ***

v
2
1
21
List of abbreviations and acronyms
API American Petroleum Institute
BN Bayesian Network
BP British Petroleum
CAPEX Capital Expenditure
DNV GL Det Norske Veritas Germanischer Lloyd
FMEA Failure modes and Effects analysis
FMEA Failure Mode and Effect Analysis
FSPOs Floating Production and Storage Offshore
FTA Fault Tree Analysis
IEC International Electrotechnical Commission
IM Integrity Management
ISO International Standards Organisation (also used to identify ISO documents)
MCS Minimum Cut Sets
OPEX Operational Expenditure
OSCR Offshore Safety Case Regulations, 2005
PWV Production Wing Valve
QRA Quantitative Risk Assessment
RAD Reliability Assurance Document
RAMS Reliability, Availability, Maintainability and Safety
RM Reliability and Maintainability
SCE Safety Critical Elements
SCSSV Surface Controlled Sub-surface Valve
SPS Subsea Production system
SSIV Subsea Surface Isolation Valve
SSIC Subsea integrity conference
USGS United States Geological Survey
List of symbols
AND gate, often seen as the series structure:
OR gate, often seen as the series structure:
Element node
Description box
λ Failure rate
ϴ Realisation of the random variable
t time
β Beta Parameter (used in gamma distribution of Bayesian analysis)
α Alpha Parameter (used in gamma distribution of Bayesian analysis)
∧ Random variable
Γ Gamma function
τ Testing interval
𝚯 Random variable used during Bayesian estimation
+
•
TE
XT
TEXT

1
Chapter 1: Introduction
1.1 Introduction
This report will address the particular issue of rising global demand for hydrocarbon
resources that has led to increasing extraction of fossil fuels, which in our lifetime will
become more prevalent. Oil and Gas companies seek to have limitless growth in this small
and limited planet. The rising consumer demand, the advances in technology and the
perception that Arctic sea ice is rapidly disappearing makes the Arctic Circle a now viable
economic possibility for countries such as the United States, Russia, Norway, Denmark,
Canada who may be increasing their interest in the resource rich Arctic Circle [3].
However, there are many organisations; Greenpeace: Save the Arctic, Arctic Circle, Pacific
Environment, Arctic Council, etc who have a very high invested stakes in the protection of
the Arctic Environment. Their priority is to protect the natural beauty and the wildlife
unique to the Circumpolar North by preventing the dangerous extraction of natural
resources. It is unlikely that these organisations will be able to fully prevent the extraction
of fossil fuels in the Arctic and therefore a new approach to dealing with the problem is
needed. It is a fact that poor management of infrastructure and poor governmental policy
leads directly to environmental damage [4] and Russia (a key player in the Arctic fossil
fuel race) alone leaks approximately 1% of their annual production which equates to
approximately 5 million tons of oil being leaked into the environment each year [4].
Before heavy industry gets a strong foothold in the Arctic Circle a solid framework should
be developed which ensures that infrastructures are well designed and properly managed
post commissioning that would appease the environmentalists and return profits to the oil
and gas stakeholders. This zero sum game is a difficult task and one of the biggest
challenges is proving that the reliability and maintenance strategy for Subsea Production
Systems (SPS) is fit for purpose.
This report has been created to establish a framework for oil and gas companies and
governments to use when implementing subsea equipment into the Arctic Circle which
ensures that oil and gas companies have a proactive approach towards social responsibility
and reduce waste in the process. Oil and gas companies who currently operate in the North
Sea might need to adopt a different approach to Arctic subsea field developments in order

2
for them to operate safely and successfully within the Arctic Circle. The consequences of
an oil leak in the Arctic would be much greater than an oil leak in the North Sea due to the
increased distances from mainland bases, the addition of sea ice and harsher weather.
Figure 1.1 shows this relationship between the North Sea and the Arctic consequence and
likelihood estimates, developed from Arctic Resource Management [22], shows that the
risk reduction can be achieved by using a reliability framework as a form of prevention.
The uncertainty with operating in a new environment, such as the Arctic, increases the
likelihood of a major accident occurring. The consequence of an oil leak cannot be
reduced therefore this report will manage this risk by developing an optimised framework
for assessing the safety and reliability of Subsea Production System when operating in
Arctic environments that will reduce the likelihood of a major accident from occurring.
This report will be split into two parts, Part one will: assess the viability for having a new
framework (chapter 2), review the current frameworks for assessing Reliability of SPS
(chapter 4) and format the proposal for a new optimised framework (chapter 5). Part two
of this report will concentrate on carrying out a RAM analysis of a hypothetical SPS using
the new framework and comparing the framework’s effectiveness for operations in colder
climates (chapter 6 and 7).
1.2 Background
Currently around 80% of the World’s energy supply comes from oil and gas [2] and the
demand is still increasing. Even since the early forties demand has been increasing and to
satisfy this growing demand oil companies ventured into the sea to access the rich deposits
below the sea bed and this was first achieved by Kerr-McGee in 1947 when he
North
Sea
Arctic
(future)
Arctic
(now)Likelihood
Consequence
Figure 1.1: Likelihood / Consequence chart for moving towards colder climates [22]
Optimised
framework

3
Figure 1.2: Macondo oil spill subsea [19]
successfully completed an offshore well in Louisiana, in only 4.6m of water. Since then
many offshore facilities have been developed from; Kerr-McGee’s extended pontoons, to
jack up rigs, to conventional fixed platforms, semi-submersibles and most recently
Floating Production, Storage and Offloading facility (FPSOs). These modern facilities use
a Subsea Production Systems (SPS) that allows the well head equipment to be placed on
the sea floor and piping the hydrocarbons up to the platform, using tiebacks. Once on the
sea bed if the equipment fails then any repair or maintenance is a complex and costly task,
especially if that equipment were to fail in a state which results in the loss of life and/or
damage to the environment. An accident that has the potential to cause seriously injury or
death is labelled a “major Accident” in the Offshore Safety Case Regulations (OSCR) [5]
and it is the “duty holders” (Oil and Gas companies) who must adhere to the statutory rule
outlined in the OSCR. One important requirement of the OSCR is regulation 12(1)(c) and
(d) which states that the duty holders must assess all potential major accident hazards,
evaluate the risks and demonstrate that adequate controls are in place to mitigate the risk
[5].
Since the Gulf of Mexico oil spill (Macondo
“major accident” figure 1.2) where British
Petroleum (BP) lost huge public support for
its operations, companies have taken larger
steps to protect themselves from similar
public relation disasters by ensuring that
environmental protection is afforded the
same level of priority as life [6]. Although,
eleven men died in the Macondo disaster,
the greatest impact to global image,
company brand and stakeholder revenue was
a direct result of the sheer scale of environmental damage caused. Thus the game has
surely changed and there is more of a consensus to protect the environment and this makes
good business sense too; cheaper insurance premiums, less waste, less negative press,
stable growth, etc.

4
One of the biggest problems which led to the Macondo disaster was the poor regulation of
the facilities and when compared to the UK offshore industry the regulation in the North
Sea was considered far superior and robust [6]. With the introduction of the UK OSCR the
underwater equipment’s “potential to lead towards a major accident hazard” has been
“evaluated” and the “risks have been reduced” [6]. Production up-time is important to the
operators who wish to generate greater profits but it is also paramount that production
systems function correctly on demand since the failure of a few key elements could
potentially lead to a Major accident hazard. Modelling this is in the form of Reliability,
Availability, Maintenance and Safety (RAMS) analysis for Subsea Production Systems.
The introduction of SPS has moved processing subsea and operators take advantage of the
many benefits that SPS offers thus the first barrier towards preventing oil leaks are now
subsea. Social responsibility is more important now than ever as this industry transfers
from unstable markets into new environments and ensuring that Operators have the correct
data to make informed decisions is the underlying reason for this report.
1.3 Aims and Objectives
1.3.1 The aim of the project is:
“Develop an optimised framework for assessing the safety and reliability of Subsea
Production System when operating in Arctic environments.”
1.3.2 The objectives of the project are:
 Build a reliability model of a typical SPS and assess the critical paths of the
model which would lead to failure, identifying the weak links in the chain.
 Assess the environmental impact Arctic operations will have on SPS and
determine the leading causes of potential risks
 Determine which model of the RAMS is best suited for future use in a
reliability framework when used in new environments.
1.3.3 Research questions to be answered:
 Where can accurate reliability data be sourced and what is the reliability of typical
SPS currently in use in the North Sea?

5
 Will current SPS reliability methodology be suitable for future use?
 What are the options for Reliability studies?
 Who will own such a reliability methodology?
 What are all the risks associated with operating in the Arctic?
 Can operations in the Arctic exist without introducing the risks associated with
using SPS?
1.4 Methodology
The analysis methodology used in this report is firstly to use a quantitative risk assessment
(QRA) by undertaking a Fault Tree Analysis (FTA) to identify and assess the failure
modes that impact both production and safety, with the emphasis on sub units and
individual components that could lead to a direct release of production hydrocarbons or
chemicals into the environment. Following on from the FTA and finding the minimum cut
sets (MCS) a RAMS analysis was carried out to for a typical North Sea SPS. This model
was then used to assess the current SPS reliability towards leaks in the North Sea. Taking
the research data for the Arctic, regulations, and reliability case studies and using systems
understanding to produce a fundamental framework for operating in new harsher
environments.
The project management actions, tasks and timings to undertake the research and
associated reporting activities are recorded in figure 1.3, which can be interpreted as a flow
chart combined with a project gnatt chart running vertically downwards. The time between
the start of the project and the submission date is 30 weeks, with research consuming an
estimated half the available time, modelling and carrying out iterations of modelling was
estimated to take 1/3rd
of the available time and the remaining was to be used to finalise the
reports.

6
Assess real world problem
Week 1-4
Define boundary of
the scope
Week 5-6
Define all possible
system state (QRA)
Week 12-15
Simplify system to
critical path (MCS)
Week 15-16
Collect Arctic
environment,
geophysical and
political data
Week 6-9
Aggregate results
Week 20-22
Produce deliverables:
 RAMS analysis for subsea Production System
 framework for operating in arctic environment subsea
 compare old with new
Week 22-30
Collect reliability data
for systems in the
North Sea (OREDA)
and other sources
Week 6-12
Review rules and
standards and assess
compatibility with
new environment
and political needs
Week 9-13
Model SPS
1
st
iteration Week 18-20
2
nd
Develop framework
1
st
2
nd
Assess Viability of Model
Week 20-22
Time
Finish
Start
Figure 1.3: Methodology and project timetable

7
This method of research was decided upon since the industry currently and actively uses
FTA and RAM analysis and if there was a requirement for them to accept a new
methodology it would be easier for them to accept a methodology which had the
foundations that they are already familiar with. Developing and improving an existing
system that oil and gas operators are already accustomed too would not be rejected as
readily as new concept that is completely different.
In addition, the majority of data was planned to be obtained through secondary research by
willing operators and stakeholders. However, the attempts to gain data through direct
contact with clients were challenging and therefore the study was mostly theoretical. This
meant that the research was more qualitative rather than quantitative since the majority of
reliability data was taken directly from Offshore Reliability Data (OREDA) textbooks.
Inspired by the systems methodology developed in the 1980s by Peter Checkland, Brian
Wilson and Stafford Beer [9] [10] [11] this report will also include some of the tools that
they adopted for project management which are ideal for assessing technical real-world
situations which include many differing perceptions, judgements, and objectives that will
aide in developing the new framework for SPS in new environments and contribute to the
model that compares the differences between the current environment and reliability of
SPS with what would be expected in an Arctic environment.
General assumptions that are made throughout this report are:
1. Only normal operation has been used in this analysis and failure rate data does not
include start-up or shut-down activities.
2. The reliability data used from the Oreda handbook only includes assets used in the
North Sea under typical North sea environmental conditions.

8
Figure 2.1: An illustration subsea Production System sub ice adapted from [33][34]
Chapter 2: Real world problem
2.1 Introduction to problem
There exists a gap in the industry for a recommended practice for determining the
reliability of SPS. Within the current industry individual companies are using internal (in
house) methods to determine their reliability of SPSs and current policy does not govern
how these methods impact on safety and policy. So the methods used between two
different companies for reliability studies vary and their reliability models are not being
used to efficiently demonstrate that minimum safety measures are met. Demonstrating
safety would be vital to operations that contain SPS below sea ice and figure 2.1 shows
how a SPS would look sub ice and highlights the close proximity of hydrocarbon
extraction to the vulnerable Arctic environment.
A recommended practice for the industry could align the different RAM methods and
increase reliability and safety, not just meeting minimum targets. Figure 2.2 shows an
influence diagram which conveys how a good RAMS framework would be beneficial to
the oil and gas industry when operating in the Arctic areas. In addition the local
stakeholders who I had the opportunity to discuss this problem with feared that the current
model is only carried out at the beginning of a project to get executive management buy in
and then once the development is in the operations phase RAM analysis is forgotten about
until the field becomes inefficient and shows repeated failures later in its life.

9
2.2 Stakeholders
The local stakeholders who were approached for analysis were issued with a project code
of ethics (Appendix H) that would ensure their worldview on the matter remained
anonymous and the identities (including organisations) remained confidential. To assess
who all the stakeholders are within the problem and to answer the research question, “Who
will own such a reliability methodology” it was essential to understanding the problem to
see how the stakeholders related to one another, and this can be seen in the relationship
diagram, Figure 2.3. It would not be wise to allow the member states to own such a
methodology since even the sovereign ownership of Arctic areas is still under dispute [16]
Figure 2.2: Influence diagram for the introduction of a new RP
-
-
-
Improving the
Framework
for operating
in the Arctic
Less pressure
from Arctic
organisations
Industry
solutions to
industry
Lower number of
Minority
campaigners who
wish to cause
sabotage and
disruption
Increased
Exploration
of Arctic
Circle
Increasing
Consumer
demand
Reduced
alternative
Energy sources
Introduction of
New Technology
Increasing
Sharehold
Increased
extraction
of fossil
fuels in the
Arctic
Higher
reliability
Greater
uptime
Less
Reduced
likelihood of
major hazard
No negative
press for oil
spill
Increased
Profits
+
+
Less red tape and
enforced
Regulation by
outside industries
More
investment
into
Renewables
Cheaper
Insurance
Less pressure
from Nation
States
Greater
Autonomy for Oil
and Gas
companies
Less wasted
time
Greater
investment
into R&D
Meets
Market
demand

10
so the ownership of such a methodology would be Operators who could use the tools to
actively promote the best standards without the difficulties of member state collaboration.
2.3 Literature review
Yong Bai and Qiang Bai’s Subsea Engineering Handbook conveys that a change in
environment causes greater reliability issues [2]. Moving into colder climates will surely
reduce reliability of SPSs and this section will collate a variety of information for assessing
if the current framework for reliability is robust enough for Arctic conditions. As seen
from figure 2.2 companies can benefit from a revised recommended practice towards RAM
analysis. This proposal originated from stakeholder discussions with industry members
who were concerned about the implications of the new EU directive on offshore safety
(2013/30/EU) that will require operators to demonstrate their financial provision to cover
major accidents [15]. In addition the new EU directive is anticipated to additionally
require oil and gas companies to demonstrate the potential cost of a major hazard by
assessing the risks that would contribute to an oil spill. Placing a financial value on the
Figure 2.3: Stakeholder relationship
Oil and Gas Operators
BP
Shell
Nexan
Apache
Chevron, etc
Arctic Wildlife
Polar bears Seals
Birds Fish
Etc.
Regulators
HSE
Manufacturers
Aker Solutions
Oceaneering
Woodgroup Kenny, etc
Rules and Standards
IEC API PFEER
OSCR SCR
DCR PCR
Fishery act
Nation States
Canada
USA
Russia
UK
Norway
Iceland
Greenland
Organisations
Greenpeace, Arctic Council,
Consumers
Oil
Gas
Insurance companies
Share holders

11
cost of a potential clean-up and becoming insured against such incidents would only drive
overheads higher.
The new EU directive on offshore safety also extends to the Arctic as companies who
already operate in the North Sea would be keen to be seen to actively adopt the highest
standards that would be praised by member states of the Arctic council [15]. The financial
cost of a potential clean up in the Arctic is very difficult to determine and there has been no
evidence to suggest a comprehensive study into the financial cost of an oil spill under sea
ice has been conducted but there have been many researches into assessing the possible
risks and recovery problems associated with leaks in the Arctic [17][18]. The best method
to protect the environment is in the prevention of an oil spill and good integrity of subsea
assets will reduce the likelihood of a major accident.
The two main standards and recommend practices that aim to increase the integrity of
subsea assets are currently:
 API 17N
 DNV RP O401
The quantitative reliability information gained from these documents is discussed in more
detail in chapter 4 but the important aspects of both documents, from the objective point of
view for the literature research, was that both considered reliability data collection and
storage as key to good and reasonable “scientific justification for future activities” [12].
Whilst discussing the usage of API 17N with stakeholders it was identified that there is the
common belief that reliability data was used really well for justifying executive buy in for
new projects, however, once into operations the reliability data was either not collected or
implemented well and thus the confidence of reliability data for future operation activities
was reduced.
The literature review also identified that the Risk and Reliability & Maintainability (RM)
[12] needed to have confidence in the reliability data. The direct impact of CAPEX and
OPEX was dependant on the availability of a system and that intervention logistics to
improve reliability needed to be proportional to the risk involved. To increase field value
the investment into greater reliability of hardware needs to be undertaken early and
through the CAPEX. It is known that 60% of subsea wells fail early life of operation [20].

12
Gaining accurate reliability data and implementing the correct reliability strategy would
ensure greater value to any field. There have been many texts on the subject of reliability
theory but one stands out by far and this was Systems Reliability Theory by Marvin
Rausand and Arnljøt Hoyland [13] which was used extensively throughout this report.
This text proved that the field of Reliability and Safety studies goes beyond what is
possible in a single Master’s thesis but did provide advice on the analytical and
quantitative methodology that can be applied to determine the probability of failures, used
in chapter 4 and 6.
2.4 Previous and current research
Previous research in to this particular subject is varied in quality and only a few papers
were found to be of significant interest. The school of engineering, technology and
maritime operations [7] carried out a review of the Monte Carlo method to assess failure
modes and stock control. Whereas Xianwei Hu, et al [8], carried out a risk analysis directly
of a SPS with regards to leakage rates and discovered that fuzzy fault tree methodology
was suitable. Neither report focuses on the framework of for assessing the SPS leakage
rates in new environments where maintenance and reliability data is not readily available
or accurate. An example of the research that is currently being carried out in this field of
study is the Det Norske Veritas Joint industry project (JIP) into the Subsea Integrity
management and this research aims to optimise maintenance and increased confidence in
existing (well-known) subsea equipment, this research has not been considered in this
report since no reports have been produced. Similarly there are multiple initiatives to
increase industry knowledge of subsea integrity and reliability with the Subsea Integrity
Conference (SSIC) 2014 being joined by Shell, Aker Solutions, Oceanering, Statoil, and
many more.
2.5 Difficulties for operators
The research question “What are all the risks associated with operating in the Arctic?”
identified many difficulties that operators will encounter whilst operating in the Arctic and
a summary of the potential problems unique to the SPS are;
 Distance from operating bases
 Weather holds from extreme cold to long lasting heavy storms.
 Intervention access (ice sheets)

13
 It is common for large icebergs to exists for some time in the Arctic Ocean
 Deep water
 Demonstration of adequate oil leak response
 Reduced day light hours
There are many more problems associated with operating in the Arctic that are political in
nature and these have been discounted in this report, it would be expected that any operator
would take reasonable precautions with regards to the territorial mineral rights of Nation
States. In addition, the deep water and potential for drifting ice sheets over particular oil
and gas fields would mean that the most viable solution for many Arctic fields would be
SPS and therefore future operations in the Arctic will include the additional risk of
including SPS. However, it is a common occurrence for icebergs to ground and scour the
seabed in shallower water areas of the Arctic and this would constitute a considerable risk
to Subsea assets in shallow water. Further geophysical data would have to be conducted
for every field to ensure that there is no likelihood of scour occurring near subsea assets. In
addition to the difficulties for the operators the arctic environment is considerable hostile.
2.6 Arctic Environment
The Arctic Circle is a vast 21 million km2
area which stretches from the pole to 66.56ºN
latitude and the U.S. Geological Survey (USGS) estimates that it could contain
“approximately 90 billion barrels of oil, 1,669 trillion feet3
of natural gas and 44 billion
barrels of natural gas liquid” [14]. The Arctic Circle, includes the Brent Sea, the bearing
straight, Norwegian Sea and the Atlantic where we see depths vary from 50m to the
deepest point (Nansen Basin) at 4665 m. With 22% of the worlds undiscovered in the
Arctic [3] and of this 22% approximately 84% oil and gas can be found offshore [14]. The
Arctic Circle seas are severely harsh and the topside temperatures range between -40 ºC
and 20 ºC [21], with wave heights reaching in excess of 12m. Additionally, the amount of
day light in the Arctic cicle decreases to zero for one full month of the year beginning on
the winter solstice and this needs to be considered for operations within this region. When
compared to the North Sea environment the Arctic environment is a risker place to operate
and those who currently operate form the North Sea would have to consider the additional
time it would take for intervention and maintenance, this forms part of the new strategy
formed in chapter 5.

14
2.7 North Sea Environment
In the United Kingdom the oil and gas fields are situated mostly in North Sea shallow
water which is considered by most to be an average of 93m. The North Sea including the
Faroese Shelf has moderately harsh weather with topside water temperatures of between
6ºC to 16ºC and wave heights fluctuating between 1m and 10m [2]. It is assumed that the
data collected through the OREDA handbook is extensively from the North Sea therefore
an uncertainty will arise from the reliability data obtained from this location if it was to be
used for colder climates, even if the data is a mean from a variety of different locations.
2.8 Problem assessment
To ensure there is value in the project the systems tool (CATWOE ) developed from Peter
Checkland [9] was used to determine the root definition of the system and to find the
purpose of the framework that needs to be produced.
Table 2.1: CATWOE definition of problem
Clients Beneficiaries or victims? Oil and Gas Operators
Actors Who are responsible for implementing
this system?
Oil and Gas Operators
Transformation What transformation does this system
bring about?
Working in North sea conditions with current
understanding of RAMS analysis to
embracing new RAMS analysis framework
for operations within the Arctic circle.
Worldview What particular worldview justifies the
existence of this system?
New practices may save time and increase
operational efficiency.
Owner Who has the authority to abolish this
system or change its measures of
performance?
Regulator for Arctic operations
Environmental
constraints
Which external constraints does this
system take as a given?
Dwindling North sea oil and increased
consumer demand
Root definition
If the chosen relevant system is developed to a full root definition it becomes:
“An Arctic Regulator's system to embrace a new recommended practice that
improves subsea production systems capability and safety for Oil and Gas companies, that
will meet increased consumer demand for operations in the Arctic environment.”

15
Chapter 3: Frameworks for risk analysis
3.1 Introduction to framework for risk analysis
Current framework for risk analysis comes from a variety of sources depending on the
level of reliability that is needed from a system life cycle. These can vary from Safety
Integrated Systems (SIS) which comes under the regulation IEC 61508, Safety integrity
levels from IEC 62061 and functional safety regulation IEC 61511 and the differences can
be compared below in figure 3.1.
These flow charts convey similar concepts of the design life cycle for a project however;
they fail to provide an accurate method for carrying out the step-by-step procedure that
incorporates a reliability study from design through to de-commissioning. Only IEC 61508
comes close to providing such a framework, nevertheless, these standards roughly identify
the main stages for reducing risk and these are:
1. Identify the main system functionality
2. Define system boundary
3. Design
4. Install
Figure 3.1: A comparison of the current Processes in use today [23]

16
5. Operate
6. Repair and maintain
7. Decommissioning
IEC 61508 introduces the 16 step life cycle which many people who are familiar with the
integrity management life cycle will be recognise, figure 3.2 shows an integrity life cycle
for a typical project [23], where there are similarities to the IM phases of IEC’s.
Integrity management is important to consider for any project engineer especially if those
systems are safety critical and, it was shown above, the integrity life cycle influences the
individual phase that need to be carried out to ensure that a system meets minimum safety
requirements. The safety critical system in this report is the SPS so the standards above
apply as well as API-RP-17N and DNV-RP-O401. DNV-RP-O401 recommend
identifying sub-elements of a system as safety critical by classifying failure modes through
Failure Mode and Effect Analysis (FMEA) whereas API 17N emphasises the use a
technical risk and reliability effort that would highlight any uncertainty that could impact
on system functionality thus for every project it is a requirement for the project engineer to
produce Reliability Assurance Document (RAD) at the all stages of the design life. API
17N also highlights the importance of accurate data collection and the strategy adopted
from ISO 14224 for data storage and collection is illustrated in figure 3.3, below.
Figure 3.2: Integrity Management system life cycle [23]

17
If reliability data and its analysis it is to be used as supporting evidence for reliability
claims and justification for future activities it needs to form part of the framework. This
would be particularly important for operating in new environments where data is scarce or
non-existent. Even a refined qualification process for new equipment could not provide
the reliability, availability and safety targets set in the design phase if the data is
inaccurate. However, it is still reasonable and probable that reliability analysis could
reduce cost/time/effort and increase safety if applied data is collected and the RAM model
updated at all stages of a project. The literature review identified that the operations phase
is where reliability theory can be the most beneficial for operating in new environments.
3.2 Proposals for change to existing RAM framework
Although the RAM framework constitutes a single element to the bigger integrity
management life cycle it is clear that there is a potential to improve this element to play a
bigger role in determining the safety of an SPS. The Literature review, stakeholder analysis
and topic research identified the following list of recommendations for an improved RAM
framework when assessing the reliability of a SPS when deployed and operated within the
Arctic region:
 Include collection of data and recycle into a RAM analysis at the operations stage
 Use RAM analysis to justify the maintenance strategy instead of spare engineering
capacity driving ad-hoc maintenance tasks.
 Provide RAM analysis as evidence for reducing the likelihood of a major accident.
The data collected can be utilised by Bayesian analysis and API 17N also recommends the
use of Monte Carlo simulations for RAM analysis. The new RAM framework is developed
in Chapter 5.
Design/
Manufacture
RAM
Analysis
Operation and Maintenance
Failure and maintenance
events
Concept
Improvement
Adjustments and
modifications
DATA
Loop
Figure 3.3: Data collection strategy from ISO 14224 [24]

18
Chapter 4: Reliability Theory
4.1 Introduction to Reliability Theory
There are many different approaches and methods for predicting reliability of assets and
managing system performance. These range from the Monte Carlo method, Boolean
approximation, Fault Tree analysis (FTA), Bayesian Network (BN), Failure mode and
effects analysis (FMEA). This report will undertake a FTA combined with RAMS for a
typical single manifold SPS with 6 wells. To better understand the methodology and the
principles used in this report this section will describe the basic concepts of FTA and
RAMS and their associated rules and regulations. FTA and RAMS are both techniques
used to predict future performance of a system or component and operators use these tools
to demonstrate that a system can function with an assured level of uptime which will
maximise revenue. This Chapter will identify they key elements of RAMS and identify
possible improvements to incorporate into the new proposed framework.
4.2 Standards and Recommended Practices
There are numerous standards for the reliability theory but for specific applications where
SPS are implemented these are:
 API RP-17N SPS reliability and technical risk management
 IEC 61508
 API 17Q.
There is also the requirement that any data capture system complies with IS0 14224 which
defines the minimum requirements of information to be collected for ensuring that the
quality of RM data is of value to the individuals carrying out RAMS analysis [12].
4.3 Reliability and Maintenance data
Since this is a desk top study all data for this report was collected purely from OREDA:
Offshore Reliability Data 5th
Edition Volume 2 – Subsea Equipment 2009[1]. It is
assumed that all the data obtained is from non-Arctic assets. For the feedback element of
the framework it was assumed that the data returned was the upper failure rate recorded in
the Oreda handbook for those assets that were chosen.

19
4.4 Objectives of the RAMS analysis
The objective of a RAMS analysis is to:
1. Evaluate the availability of a typical Arctic SPS and Mean Time To Failure
(MTTF) over a set period.
2. Highlight the elements of the SPS which contribute the greatest threat to the
environment.
4.5 FTA and RAMS Basics concepts
Fault Tree Analysis (FTA) is a commonly used tool for risk and reliability studies. It links
an undesired critical event in a system, (at the top of the tree), which in this report is the
leak of production hydrocarbons, injection chemicals or control fluids, and the events
which lead to this event. This allows the potential causes of the critical event to be
identified and quantified. A typical fault tree would looked like, figure 4.1, where the top
event is the accident and those elements are contribute to the accident are identified.
Figure 4.1, is adapted from System Reliability Theory [13] to suit this explanation. This
will also be displayed as a reliability block diagram, known also as a series structure;
A1
B1
A2
Figure 4.2 Example of series structure Reliability block
Accident
Threat A1
A
Leak Threat A1
B
1
Barriers against
A1 fail to function
A
Threat A2
Figure 4.1: Example of Hydrocarbon leak in a simple pipeline
+
•

20
So we can see from figure 4.1 and figure 4.2 that an accident will occur if Threat A1
occurs AND Barriers against threat A1 fail or Threat A2 occurs. This can be written as
Q(t) = (𝐴1 ∩ 𝐵1) ∪ 𝐴2
= 𝐴2 𝐴1 + 𝐴2 𝐵1 − 𝐴2 𝐴1 𝐵1
= 𝐴2(𝐴1 + 𝐵1 − 𝐴1 𝐵1)
[Eq4.1]
Where Q(t) is the top event. The fault tree can then be reduced to its minimum cut set. A
minimum cut set (MCS) is defined by Marvin Rausand as “the basic events whose
occurrence (at the same time) ensures that the top event occurs” [13]. The minimum cut
sets for this scenario would be: {𝐴1 𝐵1}{𝐴2}.
The basic assumption of reliability theory is simply that all manmade objects will fail
eventually and it has been known by many industries by observations that the empirical
population failure rates over time, for an asset or system, produces a graph called the
“bathtub curve”, this can be seen in figure 4.3, below. The data collected from field
observations and interventions can used to update the existing reliability model to provide
an accurate determination to the future reliability of an asset or system and more
importantly when. The key phase for such an approach is the operations phase. The
hypothesis is to determine when particular failure event will occur by using the reliability
data and take corrective action to prevent or delay the failure event from occurring.
This curve can be used to represent and overall system of many elements like a SPS. The
objective here is to identify when to decommission an asset at a point when the failure rate
is sufficiently high enough to induce higher OPEX where repair is not viable and
Time
Failure
rate
Constant
failure rate
Wear-out
phaseInfant
mortality
rate
Figure 4.3: “bathtub curve” for a typical asset.
Wear-out
phase

21
decommission is the only option, this would occur somewhere within the Green area on
figure 4.3. However, the improved framework will highlight quickly any small increases
in failure rates and identify the assets which contribute to the highest unreliability. By
taking corrective action early could prolong the field life of an installation and improve
safety. Reducing the rate of change within the wear-out phase would produce a curve
similar to the one represented by the dashed line in figure 4.4, below, creating a longer
field life.
Similarly the same method can be used to determine the probability of failure event, T,
occurring within short time scale, t = 5 years. Thus, operators can take intervention actions
targeting the highest contributors to unreliability to prevent, the top even T occurring. For
example, if the failure rate predicted that there would be a 100% probability of a system
hydrocarbon leak occurring within the next 5 years then the greatest contributor to non-
reliability could be replaced, repaired, or shut down. Figure 4.5 shows how this would
work for the given example by reducing the failure rate.
Time
Failure
rate
Constant
failure rate
Wear-out
phase
Figure 4.4: “bathtub curve” for a typical asset with extended wear-out phase.
Δt
Time
Probability
of failure
Constant
failure rate
Figure 4.5: Early intervention based on reliability data Increases Mean time to failure (MTTF)
Δt
100%
Intervention
reducing the
failure rate

22
By intervention and removal/replacement of the element that contributes to the greatest
failure rate of a system is a logical conclusion if it was practicable to do since Birnbaum’s
measure deduces that the weakest component is also the most likely to cause failure [13].
If the RAM analysis is carried out annually and successful intervention occurs then the
time to failure could be increased drastically. This can be visualised in figure 4.6, below,
where immanent failure occurs four years later.
Using this methodology annually to undertake a 5 year projection could potentially prevent
a major accident hazard from occurring and would be a useful tool to demonstrate that
safety processes are in place to mitigate the possibility of SPS leakage. This report has
applied this method to only the safety critical element: hydrocarbon containment but by
applying the same approach to all safety critical elements then an overall installation‘s
safety can be improved. In addition to early intervention against targeted elements
proposed by here this method will allow management to determine when best to intervene
since the Arctic operations will potentially increase the overall time to repair due to
weather holds, moving ice sheets, day light hours and extreme cold. Thus a dangerous
scenario where multiple system failures are occurring and maintenance teams are unable to
gain access to repair would not exist because there would be limited durations of
overlapping failure and maintenance.
Time, t, years
Probability
of failure
Original
Constant
failure rate
Figure 4.6: Early intervention based on reliability data delays top event, T, occurring for 4 years
Δt
T, 100%
1 2 3 4 5 6
Annual
decrease in
failure rate

23
Chapter 5: Proposed framework
5.1 Proposed Framework for RAM analysis
The hypothesis of this report is to use reliable (real-time) data and feedback this into the
existing reliability model at the operations phase to the evolving failure function rate to
determine when a critical failure will occur and to prevent this from occurring by
maintenance intervention and overhaul, or shutdown based on the results of the Reliability
theory and management decision. This Chapter will build upon the frameworks mentioned
earlier, most notably API 17N and ISO 61508 and propose a new RAM framework
specifically for subsea production systems operating in new environments where reliability
data is not fully comprehensive.
Figure 5.1, below, shows the proposed framework that incorporates the minimum
requirements that would ensure safe operation in new environments. It also includes the
basic procedural outlines for Failure Modes, Effects and Criticality Analysis (FMECA),
Reliability block diagrams (RBD), Fault Tree analysis (FTA) and event trees which are
outlined in API 17N.
The framework also includes prior and posterior data collection into the model that would
be best suited if Bayesian networking and Bayesian learning is used to re-evaluate any
model once built.
The framework would require a management decision to be made between steps 15 and 16
since empirical evidence alone cannot determine what intervention activities can and
should be carried out. It was highlighted during the stakeholder discussion that
management will have a subjective view point when carrying out a detailed reliability
study and these are:
1. Safety and environment
2. Production and availability
The framework outlined in figure 5.1, below, aims to satisfy both the needs for high
availability and higher safety simultaneously.

24
Bayesian
Analysis
Identify
operational
improvements
and carry out
adjustments to
design
10
Define system and
purpose
1
Define scope
boundary
2
Hazard and risk
analysis
3
Define Safety
requirements
4
Create model of
system
5
Reliability block
diagrams
5.2Fault Tree analysis5.1 Event Tree5.3
Populate model8
Collect prior
reliability data
6
Fault Mode, Effects and Criticality Analysis3.1
Construct system block diagram
Identify all potential failure modes and
immediate effects on the system
Agree on Corrective actions and log actions
Identify detection methods and mitigating
options
Assign a severity category and probability for
each failure mode, plot in a matrix
(Risk = PoF x CoF)
Quality check of
data (ISO 12442)
7
Identify the top event
Identify the causes to
ensure top event occurs
Define relationship of
causes, AND/OR gates
Carry out logical
assessment of the tree
Define success of system
(end node)
Divide system into blocks
Construct RBD
Carry out logical and
numerical assessment of
the RBD
Identify the initiating
event for analysis
Identify all possible
outcomes from initiating
event
Evaluate model9
Bayesian Networking
12.1
Software supported
with RAM tool
9.1
Collect posterior
reliability data
from the
operational field
and other sources
available
16
Identify trends in
failure rates
13
Install system into
the field and
operate
11
Quality check of
data (ISO 12442)
17
Re-Evaluate
model (annually)
12
Bayesian Learning
Mitigate, Repair,
intervene or
decommission
15
Annual
Loop
Treat each outcome as a
sub-initiating event
Repeat until the
boundary of scope is
reached
Identify the boundary of
scope
Carry out logical and
numerical assessment of
the Event tree
Identify options to
mitigate and repeat
event tree with
mitigations in place.
Share knowledge
of lessons learnt
with industry
18
Review and update
maintenance strategy
and recommend
improvements to
increase safety
14
Figure 5.1: Proposed framework for RAMS analysis adapted from [25][26]

25
Chapter 6: Case Study – Subsea Production
System
6.1 Introduction to case study and production systems components
This part of the report will review the hypothetical model of the SPS using the proposed
framework. It is important to define the scope boundary and the components which make
up the constituent parts of a Subsea Production System which will aide in determining the
impact on the real world situation. In addition this section will also include the fault tree of
the subsystem for qualitative analysis, considering only the critical elements which would
lead to a release of hydrocarbons or utilities chemicals. These elements include the; well
heads, Christmas trees, manifolds, subsea valves, risers, riser base, flow lines and the
control system, figure 6.1, shows a diagram of the SPS scope boundary. In addition to the
boundary scope the critical event that is being analysed is the leak of production
hydrocarbons or utilities chemicals into the environment. The Microsoft Excel programme,
EG59G9_Wilson_MSc_disseration.xlsx was produced to carry out the FTA analysis and is
submitted as part of the electronic files along with this report at Appendix K and an
additional list of the commands used can be seen in Appendix J.
Manifold Riser
Base
Figure 6.1: Area of SPS scope boundary
Riser
Flowline
Jumper
Control
System
XT
SCSSV
PWV
SSIV

26
Figure 6.2: Reliability block diagram of the SSIV
SV1 SV2 SV4 SV5
SV3
The building of the SPS model is a theoretical application using the basics of Reliability
and probability theory, as described above. The model is built using the concept of
modular decomposition of the system which is simply building the subsystems into their
series structures then building the whole system with simplified models. This is already
common practice within the energy industry.
6.2 Subsea Surface Isolation Valve (SSIV)
The SSIV is the last barrier between the flowline and the topside riser; it is of paramount
importance to ensure that this valve is serviceable and will operate on demand. It was the
failure of this component to close which caused the Gulf of Mexico oil spill. The FTA and
reliability data for the SCSSV can be found at Appendix A and the system structure
function of SCSSV is:
∅ 𝑆𝑉(𝑡) = 𝑆𝑉𝑋1 ∩ 𝑆𝑉𝑋3
∅ 𝑆𝑉(𝑡) = (((𝑆𝑉2 ∪ 𝑆𝑉3 ∩ 𝑆𝑉5 ∩ 𝑆𝑉6) ∩ (𝑆𝑉1 ∩ 𝑆𝑉4)
∅ 𝑆𝑉(𝑡) = (𝑆𝑉1 𝑆𝑉2 𝑆𝑉4 𝑆𝑉5 + 𝑆𝑉3 − 𝑆𝑉1 𝑆𝑉2 𝑆𝑉3 𝑆𝑉4 𝑆𝑉5)
Where, ∅ 𝑆𝑉(𝑡) is the reliability of the SSIV module (which are the leakages SVX2 and
control barrier SVX1). This can be seen diagrammatically from the reliability block
diagram figure 6.2.
[Eq.6.1]
[Eq.6.2]
[Eq.6.3]
6.3 Well head and X-Tree
The well head is the element which provides the interface between the subsea entrance
point to the hydrocarbon reservoir, known as the well bore, and the production equipment.
As part of the SPS it will be installed on the seabed and link directly to the manifold, via a
jumper spool (a short pipeline which is fabricated to fit exactly between the manifold and
the wellhead. Attached to the well head is the subsea Christmas tree. The subsea tree
(XT) contains the valves, interfaces and piping that controls the hydrocarbons flowing
from the reservoir. The FTA analysis for the well head and Christmas tree can be seen in

27
Figure 6.4: Reliability block diagram of the Manifold
M1 M2 M6 M7
M3 M4 M5
M10 M11 M12
M8 M9
Appendix B and the series structure built from the FTA can be seen in figure 6.3. The
corresponding reliability system structure is:
∅ 𝑋(𝑡) = (𝑋𝑋1 ∪ 𝑋𝑋2 ∪ 𝑋𝑋3 ∪ 𝑋𝑋4) ∩ 𝑋22
∅ 𝑋(𝑡) = ((𝑋1 ∪ 𝑋2 ∪ 𝑋3 ∪ 𝑋4) ∩ 𝑋5) ∪ ((𝑋6 ∪ 𝑋8 ∪ 𝑋9) ∩ (𝑋7 ∪ 𝑋10))
∪ ((𝑋11 ∪ 𝑋12 ∪ 𝑋13 ∪ 𝑋14) ∩ 𝑋15) ∪ ((𝑋16 ∪ 𝑋17 ∪ 𝑋18)
∩ (𝑋19 ∪ 𝑋20 ∪ 𝑋21)) ∩ 𝑋22
∅ 𝑋(𝑡) = (𝑋1 𝑋2 𝑋3 𝑋4 + 𝑋5 − 𝑋1 𝑋2 𝑋3 𝑋4 𝑋5)(𝑋6 𝑋8 𝑋9 + 𝑋7 𝑋10
− 𝑋6 𝑋8 𝑋9 𝑋7 𝑋10)(𝑋11 𝑋12 𝑋13 𝑋14 + 𝑋15 − 𝑋11 𝑋12 𝑋13 𝑋14 𝑋15)(𝑋16 𝑋17 𝑋18
+ 𝑋19 𝑋20 𝑋21 − 𝑋16 𝑋17 𝑋18 𝑋19 𝑋20 𝑋21) + 𝑋22 − (𝑋1 𝑋2 𝑋3 𝑋4 + 𝑋5
− 𝑋1 𝑋2 𝑋3 𝑋4 𝑋5)(𝑋6 𝑋8 𝑋9 + 𝑋7 𝑋10 − 𝑋6 𝑋8 𝑋9 𝑋7 𝑋10)(𝑋11 𝑋12 𝑋13 𝑋14 + 𝑋15
− 𝑋11 𝑋12 𝑋13 𝑋14 𝑋15)(𝑋16 𝑋17 𝑋18 + 𝑋19 𝑋20 𝑋21 − 𝑋16 𝑋17 𝑋18 𝑋19 𝑋20 𝑋21)
+ 𝑋22)𝑋22
Where, ∅ 𝑋(𝑡) is the overall reliability of the X-tree and Wellhead sub unit. This can
also be derived from the X-tree and Wellhead reliability block diagram, figure 6.3,
below.
[Eq.6.4]
[Eq.6.5]
[Eq.6.6]
6.4 Manifold
The manifold, often referred to as a PLEM (pipeline end manifold), is installed on the
seabed and is designed to optimise the flow assurance of a subsea system by tying multiple
wells together via jumpers. The manifold mixes the hydrocarbon mixture from the wells
then monitors and controls the downstream flow. The fault tree analysis for the manifold
can be found at Appendix C and the resulting reliability block diagram is below in figure
6.4.
Figure 6.3: Reliability block diagram of the X-tree and Wellhead (XTX1)
X1 X2 X3
X7X5
X4 X6 X8
X10
X9
X11 X12 X13
X15
X14 X16 X17 X18
X19 X20 X21

28
From the Reliability block diagram of the Manifold, figure 6.4 we can see that the corresponding
series structure equation would be,
∅ 𝑀(𝑡) = 𝑀𝑋1 ∪ 𝑀𝑋2
∅ 𝑀(𝑡) = ((𝑀1 ∪ 𝑀2 ∪ 𝑀6 ∪ 𝑀7) ∩ (𝑀3 ∪ 𝑀4 ∪ 𝑀5)) ∪ ((𝑀10 ∪ 𝑀11 ∪ 𝑀12) ∩ (𝑀8
∪ 𝑀9))
∅ 𝑀(𝑡) = (𝑀1 𝑀2 𝑀6 𝑀7 + 𝑀3 𝑀4 𝑀5 − 𝑀1 𝑀2 𝑀6 𝑀7 𝑀3 𝑀4 𝑀5)(𝑀10 𝑀11 𝑀12 + 𝑀8 𝑀9 −
𝑀10 𝑀11 𝑀12 𝑀8 𝑀9))
Where, ∅ 𝑀(𝑡) is the overall reliability of the manifold sub unit.
[Eq.6.7]
[Eq.6.8]
[Eq.6.9]
6.4 Flowline
The flowline is the main transport link for hydrocarbons between the SPS and the
installation. Flowlines can be fabricated in a multitude of ways to protect itself from the
environment with pipe-in-pipe systems that increase on the floor stability, thermal
properties and resistance to Upheaval buckling. The fault tree analysis for the flowline can
be found at Appendix D and the reliability block diagram for a flowline can be seen in
figure 6.5, below.
From the Reliability block diagram for the flowline the corresponding series structure
would be,
∅ 𝐹𝐿(𝑡) = 𝐹𝐿𝑋1 ∩ 𝐹𝐿𝑋2
∅ 𝐹𝐿(𝑡) = (𝐹𝐿2 ∪ 𝐹𝐿3 ∪ 𝐹𝐿5 ∪ 𝐹𝐿6) ∩ (𝐹𝐿7 ∪ 𝐹𝐿1 ∪ 𝐹𝐿4)
∅ 𝐹𝐿(𝑡) = (𝐹𝐿2 𝐹𝐿3 𝐹𝐿5 𝐹𝐿6 + 𝐹𝐿7 𝐹𝐿1 𝐹𝐿4 − 𝐹𝐿1 𝐹𝐿2 𝐹𝐿3 𝐹𝐿4 𝐹𝐿5 𝐹𝐿6 𝐹𝐿7)
Where, ∅ 𝑆𝑉(𝑡) is the reliability of the flowline module.
[Eq.6.10]
[Eq.6.11]
[Eq.6.12]
6.6 Riser
The riser is the pressure containing portion of a flowline that connects the subsea
production system or flowline to the topside facility. The Nominal Bore of a riser can vary
between 3” and 16” and the length is defined by water depth, riser configuration, topside
facility type (FPSO, semi-sub, etc), and geographical location. The geographical location is
Figure 6.5: Reliability block diagram of the Flowline
FL7
FL3 FL5 FL6FL2
FL1 FL4

29
Figure 6.6: Reliability block diagram of the Riser
R2
R3 R4R1
R5 R6
important to consider for the type of riser configuration to use since there are many factors
that affect a riser such as:
 Subsea currents
 likelihood of marine growth
 storm frequency
 maximum storm amplitude (increases vessel offset and thus riser dynamic
properties)
It is anticipated that any offshore installation operating in the Arctic Circle would be a
floating installation and therefore the riser would more likely be a flexible instead of
ridged. The data used for the riser configuration in this report is a floating installation riser
to reflect what would actually be deployed into the Arctic areas. The fault tree analysis for
the riser can be found at Appendix E and the reliability block diagram for a riser can be
seen below in figure 6.6
From the Reliability block diagram for the Riser the corresponding series structure would
be,
∅ 𝑅(𝑡) = (𝑅𝑋1 ∩ 𝑅𝑋2)
∅ 𝑅(𝑡) = ((𝑅1 ∪ 𝑅3 ∪ 𝑅4) ∩ (𝑅2 ∪ 𝑅5 ∪ 𝑅6))
∅ 𝑅(𝑡) = (𝑅1 𝑅3 𝑅4 + 𝑅2 𝑅5 𝑅6 − 𝑅1 𝑅2 𝑅3 𝑅4 𝑅5 𝑅6)
Where, ∅ 𝑅(𝑡) is the reliability of the Riser module.
[Eq.6.13]
[Eq.6.14]
[Eq.6.15]
6.7 Surface-Controlled Subsurface safety Valve (SCSSV)
The SCSSV is the last barrier between the reservoir and the wellhead; it is of paramount
importance to ensure that this valve is serviceable and will operate on demand. The FTA
and reliability data for the SCSSV can be found at Appendix G and the series structure
function of SCSSV is:

30
Figure 6.7: Reliability block diagram of the SCSSV within the X-tree
XTX1
SCV1
∅ 𝑆𝐶𝑉(𝑡) = (𝑋𝑇𝑋1 ∪ 𝑆𝐶𝑉1)
∅ 𝑆𝐶𝑉(𝑡) = (𝑋𝑇𝑋1 + 𝑆𝐶𝑉1 − 𝑋𝑇𝑋1 ∙ 𝑆𝐶𝑉1)
Where, ∅ 𝑅(𝑡) is the reliability of the SCSSV within the scope of each X-tree
since the SSIV is unique to the wells. The Reliability block diagram for a riser
can be seen below in figure 6.7
[Eq.6.16]
[Eq.6.17]
6.8 Subsea Control System
The control system of the SPS is the primary system that provides both data acquisition
and control for operators. There are a few types of control system which would be suitable
for use in the Arctic but it is expected that most will be multiplexed electro-hydraulic
systems that offers reduced costs and greater efficiency for multiple wells to be operated
from the same control umbilical. The control system could lead to a potential hydrocarbon
leak if it fails to operate correctly on demand. The loss of a safety barrier could occur due
to the complete loss of utilities or electrical control where the system would not be able to
respond to an incident sufficiently. This failure of leakage control would lead to a major
accident hazard, thus containment control would be lost. The control system FTA has two
top events: failure of the electrical control and failure of hydraulic control. The FTA for
the control system can be found in Appendix F and the reliability block diagram for the
control system can be seen below in figure 6.8.
The corresponding series structure for both the hydraulic and electric controls are,
SC1 SC2
SC3 SC4 SC6SC5 SC7 SC8 SC9
Figure 6.8: Reliability block diagram of the control system
CSX2
CSX1

31
∅ 𝐶𝑆𝑋1(𝑡) = (𝐶𝑆𝑋1)
∅ 𝐶𝑆𝑋1(𝑡) = (𝐶𝑆1 ∪ 𝐶𝑆2)
∅ 𝐶𝑆𝑋1(𝑡) = (𝐶𝑆1 𝐶𝑆2)
Where, ∅ 𝐶𝑆𝑋1(𝑡) is the reliability of the control system electrical node, and,
[Eq.6.18]
[Eq.6.19]
[Eq.6.20]
∅ 𝐶𝑆𝑋2(𝑡) = (𝐶𝑆𝑋2)
∅ 𝐶𝑆𝑋2(𝑡) = (𝐶𝑆3 ∪ 𝐶𝑆4 ∪ 𝐶𝑆5 ∪ 𝐶𝑆6 ∪ 𝐶𝑆7 ∪ 𝐶𝑆8 ∪ 𝐶𝑆9)
∅ 𝐶𝑆𝑋2(𝑡) = (𝐶𝑆3 𝐶𝑆4 𝐶𝑆5 𝐶𝑆6 𝐶𝑆7 𝐶𝑆8 𝐶𝑆9)
Where, ∅ 𝐶𝑆𝑋2(𝑡) is the reliability of the control system hydraulic node.
Thus the series structure for the control system is:
∅ 𝐶𝑆𝑋1(𝑡) = (𝐶𝑆𝑋1 ∩ 𝐶𝑆𝑋2)
∅ 𝐶𝑆𝑋1(𝑡) = (𝐶𝑆1 𝐶𝑆2) + (𝐶𝑆3 𝐶𝑆4 𝐶𝑆5 𝐶𝑆67𝐶𝑆8 𝐶𝑆9) − (𝐶𝑆1 𝐶𝑆2 𝐶𝑆3 𝐶𝑆4 𝐶𝑆5 𝐶𝑆67𝐶𝑆8 𝐶𝑆9)
[Eq.6.21]
[Eq.6.23]
[Eq.6.24]
[Eq.6.25]
[Eq.6.26]
6.9 Building the SPS
The entire system can be represented as a reliability block diagram, figure 6.9, below. It is
anticipated that there would be 6 wells, therefore there would be six SCSSVs and six
Christmas trees. The overall system reliability block diagram would look like figure 6.9
and it can be seen from figure 6.9 that the SCSSV is the final barrier but failure of the
entire Control System could contribute to the loss of control of final barrier (SCSSV) thus
potentially increasing the likelihood of a major accident hazard. The model reflects the
only the reliability of the system and not how the system works and this is not a functional
bock diagram.
Figure 6.9 shows clearly the two paths and these are broken down into the
leakage/defects/ruptures and the failure of leakage controls. Thus the series structure for
the system becomes
Figure 6.9: Reliability block diagram of the SPS
FL
SV
R
M
CSX2
CSX1XTX16
SCV16

32
∅ 𝑆𝑃𝑆(𝑡) = ((𝑀 ∪ 𝐹𝐿) ∪ (𝑅 ∩ 𝑆𝑉)) ∩ ((XTX1 ∩ SCV1)6
) ∪ (𝐶𝑆𝑋1 ∩ 𝐶𝑆𝑋1))
∅ 𝑆𝑃𝑆(𝑡) = ((𝑀 ∙ 𝐹𝐿) ∙ (𝑅 + 𝑆𝑉 − 𝑅 ∙ 𝑆𝑉)) + (((XTX1 + SCV1 − XTX1 ∙ SCV1)6
)
∙ (𝐶𝑆𝑋1 + 𝐶𝑆𝑋1 − 𝐶𝑆𝑋1 ∙ 𝐶𝑆𝑋2)) − (((𝑀 ∙ 𝐹𝐿) ∙ (𝑅 + 𝑆𝑉 − 𝑅 ∙ 𝑆𝑉))
∙ ((XTX1 + SCV1 − XTX1 ∙ SCV1)6
)
∙ (𝐶𝑆𝑋1 + 𝐶𝑆𝑋1 − 𝐶𝑆𝑋1 ∙ 𝐶𝑆𝑋2))
Where, ∅ 𝑆𝑃𝑆(𝑡) is the reliability of the SPS with leakage and barriers control.
[Eq.6.27]
[Eq.6.28]
6.10 The Minimum cut sets for the system
Minimum cut sets define the minimum number of assets that, if they failed, will cause the
entire system to stop functioning properly and, similarly, in this case study the minimum
cut set defines the minimum number of assets that, if they failed, will lead to a major oil
spill. The minimum cut sets for this case study can are identified by sub-module only in
table 6.1, below.
Table 6.1: Minimum cut sets for each sub-module.
SSIV {𝑆𝑉1, 𝑆𝑉3} {𝑆𝑉2, 𝑆𝑉3} {𝑆𝑉4, 𝑆𝑉3} {𝑆𝑉5, 𝑆𝑉3}
Wellhead and X-tree
{𝑋𝑇1, 𝑆𝑉5}{𝑋𝑇2, 𝑆𝑉5}{𝑋𝑇3, 𝑆𝑉5}{𝑋𝑇4, 𝑆𝑉5}
{𝑋𝑇6, 𝑆𝑉7}{𝑋𝑇6, 𝑆𝑉10}
{𝑋𝑇8, 𝑆𝑉7}{𝑋𝑇8, 𝑆𝑉10}
{𝑋𝑇9, 𝑆𝑉7}{𝑋𝑇9, 𝑆𝑉10}
{𝑋𝑇11, 𝑆𝑉15}{𝑋𝑇12, 𝑆𝑉15}{𝑋𝑇13, 𝑆𝑉15}{𝑋𝑇14, 𝑆𝑉15}
{𝑋𝑇16, 𝑆𝑉19}{𝑋𝑇16, 𝑆𝑉20}{𝑋𝑇16, 𝑆𝑉21}
{𝑋𝑇17, 𝑆𝑉19}{𝑋𝑇17, 𝑆𝑉20}{𝑋𝑇17, 𝑆𝑉21}
{𝑋𝑇18, 𝑆𝑉19}{𝑋𝑇18, 𝑆𝑉20}{𝑋𝑇18, 𝑆𝑉21}
Maniford
{𝑀1, 𝑀3}{𝑀1, 𝑀4}{𝑀1, 𝑀5}
{𝑀2, 𝑀3}{𝑀2, 𝑀3}{𝑀2, 𝑀3}
{𝑀6, 𝑀3}{𝑀6, 𝑀3}{𝑀6, 𝑀3}
{𝑀7, 𝑀3}{𝑀7, 𝑀3}{𝑀7, 𝑀3}
{𝑀10, 𝑀8}{𝑀10, 𝑀9}
{𝑀11, 𝑀8}{𝑀11, 𝑀9}
{𝑀12, 𝑀8}{𝑀12, 𝑀9}
Flowline
{𝐹𝐿2, 𝐹𝐿7}{𝐹𝐿2, 𝐹𝐿1}{𝐹𝐿2, 𝐹𝐿4}
Riser
{𝑅1, 𝑅2}{𝑅1, 𝑅5}{𝑅1, 𝑅6}
{𝑅3, 𝑅2}{𝑅3, 𝑅5}{𝑅3, 𝑅6}
{𝑅4, 𝑅2}{𝑅4, 𝑅5}{𝑅4, 𝑅6}
Control system {𝐶𝑆1, 𝐶𝑆3}{𝐶𝑆1, 𝐶𝑆4}{𝐶𝑆1, 𝐶𝑆5}{𝐶𝑆1, 𝐶𝑆6}{𝐶𝑆1, 𝐶𝑆7}{𝐶𝑆1, 𝐶𝑆8}{𝐶𝑆1, 𝐶𝑆9}
{𝐶𝑆2, 𝐶𝑆3}{𝐶𝑆2, 𝐶𝑆4}{𝐶𝑆2, 𝐶𝑆5}{𝐶𝑆2, 𝐶𝑆6}{𝐶𝑆2, 𝐶𝑆7}{𝐶𝑆2, 𝐶𝑆8}{𝐶𝑆2, 𝐶𝑆9}
SCSSV {𝑆𝐶𝑆𝑆𝑉1}

33
6.11 The prior data input
The prior data obtained from the Oreda handbook for all the specific nodes and elements
are populated in the appendix tables; A1, B1, C1, D1, E1 and F1. To calculate the overall
reliability the following equation is utilised,
𝑅(𝑡) = ∏ 𝑒−𝜆∙𝑡
𝑛
𝑖−1
[Eq.6.29]
where R(t) is the reliability and can be considered as the Probability of success thus the
probability of failure becomes
𝐹(𝑡) = 1 − ∏ 𝑒−𝜆∙𝑡
𝑛
𝑖=1
[Eq.6.30]
where F(t) is the failure probability of the control system against time. In this case study,
with the given prior data, analysis has shown that the failure probability for each element
of the system will be as seen in chart 6.1, below.
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Failureprobability/%
Time (t) / years
Chart 6.1: Failure Probability of the sub systems
SSIV
XT
Manifold
Flowline
Riser
Control system total
SCSSV mean

34
If the design life was for 20 years then an assessment can be made to discover the main
contributor to unreliability, Chart 6.2, below.
The proposed framework step 13 requires the reliability of the system to be assessed and
any trends identified. In this case study for the initial 20 year design life it was identified
that the greatest unreliability was due to the SCSSV. The SCSSV had the highest
probability of failure; 𝑃𝐹𝐷 𝑆𝐶𝑆𝑆𝑉(𝑡) = 38.12% and
𝑀𝑇𝑇𝐹𝑆𝐶𝑆𝑆𝑉 =
1
𝜆 𝑆𝐶𝑆𝑆𝑉
=
1
0.4566 × 106
= 2190100.74𝐻𝑟𝑠
[Eq.6.31]
This appears very unusual considering this item is an integral safety element and although
the model was re-analysed with multiple iterations the failure of the SCSSV was always
prominent. This was identified as being due to the data source from old and aging assets
and that the data was referenced against failure rate instead of probability of failure on
demand (PFD). Additionally, the model included six wet wells thus the reliability of the
SCSSV was amplified to the power of six. From the data source for the SCSSV [29] it was
also clear that wet wells had a much higher failure rate of SCSSVs than dry wells and this
would be consistent with the harsher environments in which the wet SCSSVs would be
expected to work.
Chart 6.2: Contributor to unreliability over 20
years
SSIV
XT
Manifold
Flowline
Riser
Control system total
SCSSV

35
This high failure rate represents two possible consequences depending on the subjective
view of analyst and these are:
1. Reduced safety
2. Reduced availability and uptime
The framework outlined in Chapter 5 aimed to satisfy both viewpoint s and this is achieved
by applying a quantitative approach to maintenance management. Since the SCSSV is
safety critical then a failure of this element would cause the system to be shut down until
the element was repaired. Shutting out the well that contains the failed SCSSV would
ultimately reduce production revenue and can be considered as important as safety so the
SCSSV was used for further analysis and examples in this case study.
6.12 Maintainability of system components
For each subsystem identified within the scope boundary accurate maintenance data is
required to make a quantified assessment of the cost of maintenance and reliability.
The maintainability of a system component can be broken down into constituent parts and
for Arctic operations where delays are anticipated they should form part of the assessment.
Figure 6.11 shows an expansion of the maintainability for an asset in the Arctic region.
Where, Tran.= Transport to site, F= Fabrication and Procurement, I=Installation,
Func=Functional Testing, D=Delays and Rup= Ramp up
State
Operating
Failed
Time
MTTF
Maintainability
Figure 6.10: illustration of basic availability

36
The total maintainability, M, of the system, especially for Arctic operations would require
an awareness of the delays that could be exhibited via severe winter storms, Sea ice at the
site or poor ambient light conditions that hinder human intervention at the site, etc.
Quantifying these delays can be achieved by weighting each and using this weighted delay
for every component under assessment, a break down for common Arctic delays can be
seen in table 6.3 with estimated delays times that can be used to provide a total weighted
delay.
Table 6.3: Weighting for delays (Estimated)
Type of delay
Probability
(1)
Effect (time/ hrs)
(2)
Weighting (D/hrs)
(1) X (2)
Sea ice at site location 0.5 504 (21 days) 252
Severe Storm 0.5 336 (14 days) 168
Harsh Storm 0.6 168 (7 days) 100.8
light Storm 0.7 48 (2 days) 33.6
Vendor delay 0.3 120 (5 days) 36
Installation issues 0.5 120 (5 days) 60
Total Weighted Delay (D/hrs) 650.4
The case study has identified the SCSSV as the greatest contributor to unreliability and
further assessment will be applied to determine if the framework is suitable for
maintenance, safety and availability. Assuming ship availability with 31 days (F=744hrs)
and transit time for the vessel of 14 days (Trans=504hrs). The installation time, testing and
State
Operating
Failed
Time
Total Maintainability
Figure 6.11: Expanding the known Maintainability for Arctic region
MTBF
F
Trans.
In
Func.
D
D
Rup.

37
ramp up for a typical SCSSV can take between 3-6 days. Therefore the total installation
time would take approximately 144hrs with,
𝑀𝑇𝑅 = 𝑀 = 𝐹𝑆𝐶𝑆𝑆𝑉 + 𝑇𝑟𝑎𝑛𝑠𝑆𝐶𝑆𝑆𝑉 + 𝐼𝑛 𝑆𝐶𝑆𝑆𝑉 + 𝑅𝑢𝑝 𝑆𝐶𝑆𝑆𝑉 + 𝐹𝑢𝑛𝑐. 𝑆𝐶𝑆𝑆𝑉+ 𝐷
= 744 + 504 + 144 + 650.4 = 1457.4 ℎ𝑟𝑠
[Eq.6.32]
Where, MTR is the Mean time to Repair in hours and M is the Maintainability, these terms
are interchangeable. The availability for the SCSSV is defined as
𝑎𝐴 𝑆𝐶𝑆𝑆𝑉 =
𝑅 𝑆𝐶𝑆𝑆𝑉
𝑅 𝑆𝐶𝑆𝑆𝑉 + 𝑀𝑆𝐶𝑆𝑆𝑉
=
2190100.74
2190100.74 + 1457.4
= 99.93%
[Eq.6.33]
Where, A is the availability. However, the actual availability is determined by
𝐴 𝑜𝑝 = 1 − ((1 −
) + (𝑃 × 𝑆))
[Eq.6.34]
[30]
Where:
A 𝑜𝑝 = Operational Availability, P = number of planned shutdowns per year, S =
Mean time for planned shutdowns per year
From NORSOK standard D-010 [31] table 8: Downhole safety valve monitoring (this can
be seen in appendix I) the functionality of the SCSSV is tested in accordance with ISO
10417 and this stipulates that a new SCSSV should be tested monthly for three months and
then once every three months for three tests and then once every six months. Each test will
last approximately 2 hours (time for function test and leak check). However, if failures are
discovered during these tests, the testing schedule repeats itself and could potentially last a
few years that will require manual management. Chart 6.3 conveys the possible testing
frequencies depending on serviceability of the SCSSV providing a quantified average
number of planned shutdowns per year and calculated to be approximately 8 times.
3
5
9
6
9
9
27
15
1
6
12
6
2
2
4
3
0 12 24 36 48 60
no failure
2 failures
3 failures
Average
Time/ months
No.offailureofnewinstalled
SCSSV
Chart 6.3: Scheduled SCSSV testing to determine
avaerage number of annual planned shutdowns
Monthly
3 monthly
6 monthly
annual function test

38
So,
𝐴 𝑜𝑝 = 1 − (1 − (
) + 𝑃 × 𝑆) = 1 − (1 − 0.9993) + (
8
8760
× 2))
= 99.74%
[Eq.6.35]
[30]
The operational availability includes both corrective and scheduled maintenance as well as
the associated time lost for operating in the Arctic region mentioned in table 6.3.
The above description was for scheduled maintenance that is mandated through ISO 10417
and API-RP-14P. However, the new framework implies that the Operator should be able to
determine a new quantitative maintenance strategy that would determine the frequency of
testing based on analysis for each system element. Therefore, if the reliability data is
updated regularly then a suitable maintenance strategy can be applied at any stage of the
installation life cycle. The new quantitative maintenance, paragraph 6.11, can be applied to
any sub-unit and for this to be achieved an appropriate method for updating posterior data
is to be adopted.
6.13 The posterior data updating
Using just the prior data allows an initial assessment of the reliability at the design phase
but does not consider the addition of posterior data once the system is within the operations
phase. Ultimately it is very difficult assure that the installed assets in the Arctic will have a
failure rate predicted using the prior data since it is only empirical and the future reliability
is affected by a multitude of factors. One of these factors could be the introduction of new
technology to overcome the low temperatures anticipated or better data about equipment as
manufacturers and Operators collate higher quality information on assets. Posterior data
could either increase or decrease our assessment of unreliability and for effective integrity
maintenance scheduling it is advantageous to increase accuracy by incorporating posterior
data. To use posterior data successfully Bayesian analysis provides a reasonable
methodology for such problems. Bayesian theory was briefly introduced within the Safety
and Reliability module of MSc Subsea Engineering course and this method allows an
updating process that forms step 12 of the proposed framework, this can be seen
diagrammatically in figure 6.10, below.

39
The Prior data however, has been collated from the OREDA handbook and thus is a non-
informative Prior distribution giving the constant failure rate, λ is used in the model.
However, as the proposed framework outlines in step 16, updated information on the
reliability of components will be shared between asset owners and manufacturers thus the
posterior data for individual components becomes readily available and accurate if the
quality satisfies the requirements of ISO 12442. Using this new data can highlight assets
that have a changing probability of failure on demand (PFD) and this could influence the
maintenance strategy for particular safety critical elements. In Bayes’ theorem the prior
belief can be updated by
𝑅 (𝐴 𝑘|𝐵) =
𝑅 (𝐴 𝑘|𝐵)
𝑅(𝐴1 ∩ 𝐵) + 𝑅(𝐴2 ∩ 𝐵) + 𝑅(𝐴3 ∩ 𝐵) + ⋯ + 𝑅(𝐴 𝑛 ∩ 𝐵)
[Eq.6.35]
[13]
Where, R is the probability of success (reliability), A1, A2, … , An are a mutually exclusive
event that make up the posterior sample and B is an event from the prior data, such that
R(B)>0. Additionally, when 𝑅 = (𝐴 𝑘 ∩ 𝐵) = 𝑅(𝐴 𝑘)𝑅(𝐵|𝐴 𝑘) the Bayes’ theorem
becomes,
𝑅 (𝐴 𝑘|𝐵) =
𝑅(𝐴 𝑘)𝑅(𝐵|𝐴 𝑘)
𝑅(𝐴1)𝑅(𝐵|𝐴1) + 𝑅(𝐴2)𝑅(𝐵|𝐴2) + ⋯ + 𝑅(𝐴 𝑛)𝑅(𝐵|𝐴 𝑛)
[Eq.6.36]
[13]
In terms of Bayesian updating and using those functions mentioned in figure 6.10 bayes’
theorem from equation 6.36 becomes,
𝑓𝑋|𝛩(𝜃|𝑥) =
𝑓𝑋|𝛩(𝜃|𝑥) ∙ 𝑓𝛩(𝜃)
𝑓𝑋(𝑥)
[Eq.6.37]
Figure 6.10: Bayesian “updating” process. [13]
Model for observed data
Density: 𝑓𝑋|𝛩(𝜃|𝑥) 𝑋 = 𝑥
Observed Data
&
Posterior information about θ
Posterior density: 𝑓𝑋|𝛩(𝜃|𝑥)
Prior information about θ
Prior density: 𝑓𝛩(𝜃)

40
Where, 𝑓𝑋|𝛩(𝜃|𝑥)is the posterior density and X= x is the new observed failure data for a
system component. The probability distribution function provided in the OREDA
handbook for all the prior data is assumed to be gamma distributed and the prior density is
therefore,
𝑓𝑇|∧(𝑡|𝜆) =
𝛽 𝛼
𝛤(𝛼)
𝜆 𝛼−1
𝑒−𝛽𝜆𝑡
𝑓𝑜𝑟 𝑡 > 0, 𝜆 > 0
[Eq.6.38]
When, ∧ is a random variable contributing to the failure rate. Assuming that the basic
gamma distribution has the parameters α1 = 2 and β1=1. Thus combining the prior density
with updated data (equation 6.38 with equation 6.37, respectively) we now have
𝑓𝑇1,∧(𝜆, 𝑡1) =
𝜆𝑒−𝜆𝑡
∙ 𝜆𝑒−𝜆
𝜆𝑒−𝜆𝑡
= 𝜆2
𝑒−𝜆(𝑡1+1)
𝑓𝑜𝑟 𝑡 > 0, 𝜆 > 0
Where,
𝑓𝑇1
(𝑡1) = ∫ 𝜆2
𝑒−𝜆(𝑡1+1)
∞
0
𝑑𝜆 =
2
(𝑡1 + 1)3
𝑓𝑜𝑟 𝑡 > 0
[Eq.6.39]
[Eq.6.40]
So,
𝑓∧|𝑇1
(𝜆 | 𝑡1) =
𝜆2
𝑒−𝜆(𝑡1+1)
∙ (𝑡1 + 1)3
2
[Eq.6.41]
This will provide an updated failure distribution for one new failure occurring at T1 but
this can be repeated for increasing failures T2, T3, …., Tn (posterior data) by updating the
Gamma distribution parameters. As a summary the Alpha and Gamma distribution changes
by
𝛼1 = 2 𝑎𝑛𝑑 𝛽1 = 1 PRIOR data
𝛼2 = 𝛼1 + 1 𝑎𝑛𝑑 𝛽2 = 𝛽1 + 𝑡1
𝛼3 = 𝛼1 + 1 + 1 𝑎𝑛𝑑 𝛽3 = 𝛽1 + (𝑡1 + 𝑡2)
[Eq.6.42]
[Eq.6.43]
[Eq.6.44]
Where, increasing new time to failure data influences the prior belief of the system. The
case study identified that the SCSSV had a failure rate of 0.4556 per million hours. As an
example of posterior updating it was assumed that the upper failure rate provided by the
data gathering for SCSSV would be used as the posterior failure rate in this case study and

41
by using the Bayesian updating method Operators can update their belief about the random
variable, Δ as new reliability data is obtained and this will assist in demonstrating that they
have a proactive approach to assessing the safety and reliability of a SPS. Regularly
updating the reliability model is an ideal opportunity to assess the maintenance strategies
of installations as they age and by using this model there can be an autonomous
quantifiable methodology for determining the frequency of testing to ensure that both
safety and availably is maintained whilst also ensuring that there is an economy of effort.
6.14 Proposed maintenance strategy
The framework proposes that there should be a reliability cantered maintenance strategy
and as the posterior data evolves the prediction of reliability in the system model then
planned maintenance is affected. It was observed from with ISO 10417 that the average
testing for a single SCSSV is 8 times a year. However, a quantitative approach to
maintenance strategy can be determined by
𝑡𝑜𝑡𝑎𝑙 𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑐𝑜𝑠𝑡 =
1
2
∙ 𝜆 ∙ 𝜏 ∙ 𝑓 ∙ 𝐶𝑓𝑎𝑖𝑙𝑢𝑟𝑒 +
𝐶 𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒
𝜏
[Eq.6.38]
[32]
Where,
τ : the test interval
f: the frequency of demand
Cfailure: the cost of shut-down failure and
Cmaint: cost per maintenance
The economic and optimal test interval can then calculated but in this case study the
designed SPS has six SCSSV and
1
2
𝜆𝜏 becomes
(𝜆𝜏)2
3
when there is redundancy in the
system and letting the total expected cost equal to zero will represent optimal testing
frequency as
𝜏 = √
3 ∙ 𝐶 𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒
2 ∙ 𝜆2 ∙ 𝑓 ∙ 𝐶𝑓𝑎𝑖𝑙𝑢𝑟𝑒
3
= √
3 ∙ 1000
2 ∙ 0.456610−6
2
∙ 1 ∙ 10000000
3
= 896.065 ℎ𝑟𝑠
[Eq.6.39]
[32]
Where, 𝜏 is in hours and using the reliability failure rate data for the SCSSV with the
assumption that, 𝑓 = 1since it would only be used for a single emergency shutdown,
𝐶𝑓𝑎𝑖𝑙𝑢𝑟𝑒 = £10𝑚 (potential for hydrocarbon leak, production loss and intervention of
SCSSV) and 𝐶 𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 = £1000. Here 896.065 hrs would equate to 1.22 tests per

42
month (2 rounded up). This demonstrates that the current testing strategy under ISO 10417
is not sufficient for this type of SCSSV with the prior failure rate of 0.4556 per million
hours. Additionally, computing the same testing frequency for the new posterior failure
rate (1.2257x10-6
hrs) gives a monthly failure test frequency of 3 per month and comparing
with ISO 10417 is illustrated in Chart 6.4, below, conveys that ISO 10417 would not be
sufficient for this asset.
6.15 Life cycle cost analysis for Subsea Production system
Having determined the maintenance strategy for each critical element within the SPS an
analysis can be carried out for the whole system to determine the overall cost of the
maintenance strategies on OPEX; manning costs, repair costs, materials, support
equipment, logistics, etc. The Operators can then allocate maintenance effort as a result of
the full SPS analysis to ensure overall installation is financially viable and this case study
shows that a quantified approach to SPS asset integrity management can be carried out for
and SPS whilst considering the impact of a new environment.
0
0.5
1
1.5
2
2.5
3
3.5
Prior Updated with
Posterior
ISO 10417
No.oftestspermonth
Chart 6.4: Comparision of mainteance
strategies
permonth (actual value)
per month (rounded up)

43
Chapter 7: Report findings
7.1 General Report Findings
Although, the case study model of a typical SPS includes; 6 identical wellheads, 1
manifold, single flowline and supporting topside units: HPU, EPU, MCS, this model
provides the basis for a larger study of an entire SPS. It was demonstrated that RAMS
analysis can be used as a tool to identify when a hazard is about to occur by using posterior
reliability data and when to intervene by quantitative maintenance strategy updating.
Operating in the Arctic will have the most profound effect on operations and maintenance
times (MTTR), especially since adequate delay data is unknown. This unknown delay data
decreases the confidence for quantified assessment for repair time and increases the
likelihood that a second component failure could occur before the original component is
repaired. Multiple failures in the system would increase the likelihood of a major accident
hazard.
7.2 Answering the research questions
Where can accurate reliability data be sourced and what is the reliability of typical SPS
currently in use in the North Sea?
As this report proved accurate reliability data is hard to obtain for specific components, the
OREDA handbook is useful for prior assessment but detailed vendor research and ongoing
data collection through-out the life of assets was paramount to confident predictions of
reliability. Reliability and data collection was discussed in detail throughout this report and
it was identified during the literature review that a growing data collection will lead to
lower confidence but greater accuracy that needs to be considered into any reliability
model. Thus a larger sample of assets with failure rate data will have a larger standard
deviation.

44
What are the options for Reliability studies?
A comprehensive study for Reliability model for a project can be carried out by using the
proposed framework in this repot to complete a full life cycle, including life extension, of
an installation. Reliability studies can be in the form of Monte Carlo simulations, Bayesian
Analysis, FMECA, FTA and RAMS. Chapter 4 contains greater information on reliability
methodology.
Will current SPS reliability methodology be suitable for future use?
The current Reliability methodology would benefit from the updating that should consider
the bigger picture for operational adjustments to maintenance strategy which the proposed
model, identified in Chapter 5, does.
Who will own such a reliability methodology?
Whilst carrying out the stakeholder analysis in chapter two it was evident that the
sovereign ownership of the Arctic areas are still debated even today [16] but with every
member state acknowledging that exploration and extraction of the Arctic fossil fuels will
occur in the near future it is in everyone’s best interest to pursue the highest standards
towards environmental and safety practice. This report has proposed a feasible and viable
framework for the Operators to adopt ensuring that future Arctic operations are safer. It
was clear that the owner of such a methodology would be the Operators who would be best
placed to implement it into their projects and to take a pro-active approach to mitigating
the risks of operating the Arctic.
What are all the risks associated with operating in the Arctic?
Oil and gas is an integral part of politics, technology and society. These aspects have not
been considered in detail within the scope of the report but it does open the possibility of
future research for companies to gain the bigger picture of operating in the Arctic region.
The risks identified within this report were: the extreme cold, the weather holds, and
reduced reliability knowledge of SPS components. The consequence of these risks was
identified as a major oil spill under sea ice and the loss of up-time due to unavailability due

45
to a poor maintenance strategy and low reliability of equipment. A good example of the
complications that would exist if there was an oil spill below Arctic sea ice is illustrated in
figure 8.1, where the complexity of a clean-up would be huge and so the best method to
protect the environment is through prevention. The financial impact of recovery post an
oil spill under these conditions would be substantially greater than Macondo but further
research into this topic would be required for an accurate quantitate assessment.
Can operations in the Arctic exist without introducing the risks associated with using
SPS?
It is likely that any operation in the Arctic will require the use of SPS since the likelihood
of sea ice and severe weather on the surface will make operations difficult. The most
likely solution identified in this report will be to use a SPS with a long export flowline that
can then be connected to a riser and FPS which is geographically located in an area where
there is a lower likelihood sea ice. This was in agreement with discussion held with
industry supervisors who concur that this is a feasible and naturally logical route which
operators will most likely use.
Figure 8.1: Consequence of oil spill under sea ice [27]

46
7.3 Report Recommendations
 DNV-RP-O401 should emphasise the potential impact of ice and low temperatures
on maintenance of field equipment.
 Due to the anticipated delay in response time and recovery early leak detection
technology should be incorporated in highly vulnerable environmental zones.
 The proposed model, identified in this report, for RAM analysis of a new SPS field
development should be incorporated into API-17N.
 Just as ISO 13628-6 states that demonstration of reliability targets should form part
of equipment acceptance criteria then demonstration of system reliability
competence and targets should form part of Arctic operating procedures and
performance standards.
 Certification for every sub-unit in the SPS and not just for those within the 500m
radius of a platform would ensure robust quality control in the production phase
that will improve lifelong reliability of assets.
 Regularly updating the reliability data and model to assess the maintenance
strategies should be mandatory for operating in the Arctic region.
7.4 Future Research opportunities
From this report it was identified that additional research is needed to determine the impact
that a hydrocarbon leak below a sheet of ice from a SPS and the financial cost to the
operator of a large recovery in Arctic environment. This would provide a more holistic
view of the commitment that Operators would have to take on as part of any subsea
development. In addition, a detailed investigation into the collection and distribution of
data between those who operate a particular asset in the Arctic would be required for there
to be any confidence and accuracy of any reliability studies carried out in the future.
Another suitable research programme that would be highly beneficial would be a
comprehensive RAM analysis of a SPS using a reliability tool like MAROS or RAM
Commander utilising the framework outlined in this report to allow engineering
management to make informed decisions about operating and schedule maintenance
planning in the Arctic.

47
7.5 Final Conclusion.
From the problem analysis this report found that an operator may have one of two different
ways of evaluating failures and their consequences when carrying out a detailed reliability
study and these are:
1. Safety and environment
2. Production and availability
Regardless of the subjective view of the operator this model provides the ideal solution to
determining the reliability of the system that harmonises both safety and production targets
by reducing; unplanned downtime, increasing longevity of a field, reducing likelihood of
major accidents and proactively updating maintenance strategies for economy of effort
through the entire life cycle of a project. Some key points were highlighted during the
report and these were:
 The Oil and Gas operators will utilise Subsea Production Systems to extract
hydrocarbons from Arctic reservoirs due to the cost savings and other benefits that
SPS offers over conventional wells.
 Maintenance and intervention times will vary considerably with the new
environmental conditions and this should be assessed as part of the safety strategy
and Delay and repair data for this should be collated and distributed between
Operators.
 The maintenance data within the OREDA handbook is regarded with very low
confidence by industry stakeholders and greater effort should be made to record
the Mean Time to Repair data and the breakdown for delays, especially for new
environments.
 Social responsibility should be the driving force for operators to carry out RAMS
analysis of SPS in new environments and this RAM analysis should form part of
the demonstration that a system is safe to use.
The reliability case study use within this report was based upon first principles and with
Excel to demonstrate that the scientific approach to reliability modelling for the purpose of
demonstrating safety and economy was valid. However, the complexity and size of a large
development like a SPS would require a RAM software tool such as MAROS or RAM
commander. It was identified that the accuracy of the RAM analyses is only as good as the
model that represents the actual system, this case study does that effectively by ensuring

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