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

2 1

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

+

•

TEXT

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

1st iteration Week 18-20

2nd

iteration Week 20-22

Develop framework 1

st iteration Week 13-16

2nd

iteration Week 20-22

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

B1

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

= 𝐴2𝐴1 + 𝐴2𝐵1 − 𝐴2𝐴1𝐵1

= 𝐴2(𝐴1 + 𝐵1 − 𝐴1𝐵1)

[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: {𝐴1𝐵1}{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

phase Infant

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 5.2 Fault Tree analysis 5.1 Event Tree 5.3

Populate model 8

Collect prior reliability data

6

Fault Mode, Effects and Criticality Analysis 3.1

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 model 9

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) = (𝑆𝑉1𝑆𝑉2𝑆𝑉4𝑆𝑉5 + 𝑆𝑉3 − 𝑆𝑉1𝑆𝑉2𝑆𝑉3𝑆𝑉4𝑆𝑉5)

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) = (𝑋1𝑋2𝑋3𝑋4 + 𝑋5 − 𝑋1𝑋2𝑋3𝑋4𝑋5)(𝑋6𝑋8𝑋9 + 𝑋7𝑋10

− 𝑋6𝑋8𝑋9𝑋7𝑋10)(𝑋11𝑋12𝑋13𝑋14 + 𝑋15 − 𝑋11𝑋12𝑋13𝑋14𝑋15)(𝑋16𝑋17𝑋18

+ 𝑋19𝑋20𝑋21 − 𝑋16𝑋17𝑋18𝑋19𝑋20𝑋21) + 𝑋22 − (𝑋1𝑋2𝑋3𝑋4 + 𝑋5

− 𝑋1𝑋2𝑋3𝑋4𝑋5)(𝑋6𝑋8𝑋9 + 𝑋7𝑋10 − 𝑋6𝑋8𝑋9𝑋7𝑋10)(𝑋11𝑋12𝑋13𝑋14 + 𝑋15

− 𝑋11𝑋12𝑋13𝑋14𝑋15)(𝑋16𝑋17𝑋18 + 𝑋19𝑋20𝑋21 − 𝑋16𝑋17𝑋18𝑋19𝑋20𝑋21)

+ 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

X7 X5

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) = (𝑀1𝑀2𝑀6𝑀7 + 𝑀3𝑀4𝑀5 − 𝑀1𝑀2𝑀6𝑀7𝑀3𝑀4𝑀5)(𝑀10𝑀11𝑀12 + 𝑀8𝑀9 −

𝑀10𝑀11𝑀12𝑀8𝑀9))

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) = (𝐹𝐿2𝐹𝐿3𝐹𝐿5𝐹𝐿6 + 𝐹𝐿7𝐹𝐿1𝐹𝐿4 − 𝐹𝐿1𝐹𝐿2𝐹𝐿3𝐹𝐿4𝐹𝐿5𝐹𝐿6𝐹𝐿7)

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 FL6 FL2

FL1 FL4


29

Figure 6.6: Reliability block diagram of the Riser

R2

R3 R4 R1

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) = (𝑅1𝑅3𝑅4 + 𝑅2𝑅5𝑅6 − 𝑅1𝑅2𝑅3𝑅4𝑅5𝑅6)

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 SC6 SC5 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) = (𝐶𝑆1𝐶𝑆2)

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) = (𝐶𝑆3𝐶𝑆4𝐶𝑆5𝐶𝑆6𝐶𝑆7𝐶𝑆8𝐶𝑆9)

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) = (𝐶𝑆1𝐶𝑆2) + (𝐶𝑆3𝐶𝑆4𝐶𝑆5𝐶𝑆67𝐶𝑆8𝐶𝑆9) − (𝐶𝑆1𝐶𝑆2𝐶𝑆3𝐶𝑆4𝐶𝑆5𝐶𝑆67𝐶𝑆8𝐶𝑆9)

[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

CSX1 XTX16

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

Failu

re p

rob

abili

ty /

%

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; 𝑃𝐹𝐷𝑆𝐶𝑆𝑆𝑉(𝑡) = 38.12% and

𝑀𝑇𝑇𝐹𝑆𝐶𝑆𝑆𝑉 =1

𝜆𝑆𝐶𝑆𝑆𝑉=

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 + 𝐼𝑛𝑆𝐶𝑆𝑆𝑉 + 𝑅𝑢𝑝𝑆𝐶𝑆𝑆𝑉 + 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

𝑎𝐴𝑆𝐶𝑆𝑆𝑉 =𝑅𝑆𝐶𝑆𝑆𝑉

𝑅𝑆𝐶𝑆𝑆𝑉 + 𝑀𝑆𝐶𝑆𝑆𝑉=

2190100.74

2190100.74 + 1457.4= 99.93%

[Eq.6.33]

Where, A is the availability. However, the actual availability is determined by

𝐴𝑜𝑝 = 1 − ((1 −𝑅𝑆𝐶𝑆𝑆𝑉

𝑅𝑆𝐶𝑆𝑆𝑉 + 𝑀𝑆𝐶𝑆𝑆𝑉) + (𝑃 × 𝑆))

[Eq.6.34]

[30]

Where:

A𝑜𝑝 = 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

. of

failu

re o

f n

ew

inst

alle

d

SCSS

V

Chart 6.3: Scheduled SCSSV testing to determine

avaerage number of annual planned shutdowns

Monthly

3 monthly

6 monthly

annual function test


38

So,

𝐴𝑜𝑝 = 1 − (1 − (𝑅𝑆𝐶𝑆𝑆𝑉

𝑅𝑆𝐶𝑆𝑆𝑉 + 𝑀𝑆𝐶𝑆𝑆𝑉) + 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 (𝐴𝑘|𝐵) =𝑅 (𝐴𝑘|𝐵)

𝑅(𝐴1 ∩ 𝐵) + 𝑅(𝐴2 ∩ 𝐵) + 𝑅(𝐴3 ∩ 𝐵) + ⋯ + 𝑅(𝐴𝑛 ∩ 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 = (𝐴𝑘 ∩ 𝐵) = 𝑅(𝐴𝑘)𝑅(𝐵|𝐴𝑘) the Bayes’ theorem

becomes,

𝑅 (𝐴𝑘|𝐵) =𝑅(𝐴𝑘)𝑅(𝐵|𝐴𝑘)

𝑅(𝐴1)𝑅(𝐵|𝐴1) + 𝑅(𝐴2)𝑅(𝐵|𝐴2) + ⋯ + 𝑅(𝐴𝑛)𝑅(𝐵|𝐴𝑛)

[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) =𝛽𝛼

𝛤(𝛼)𝜆𝛼−1𝑒−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= 𝜆2𝑒−𝜆(𝑡1+1) 𝑓𝑜𝑟 𝑡 > 0, 𝜆 > 0

Where,

𝑓𝑇1(𝑡1) = ∫ 𝜆2𝑒−𝜆(𝑡1+1)

∞

0

𝑑𝜆 = 2

(𝑡1 + 1)3 𝑓𝑜𝑟 𝑡 > 0

[Eq.6.39]

[Eq.6.40]

So,

𝑓∧|𝑇1(𝜆 | 𝑡1) =

𝜆2𝑒−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 +

𝐶𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒

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

2𝜆𝜏 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 ∙ 𝐶𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒

2 ∙ 𝜆2 ∙ 𝑓 ∙ 𝐶𝑓𝑎𝑖𝑙𝑢𝑟𝑒

3

= √3 ∙ 1000

2 ∙ 0.456610−62 ∙ 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 𝐶𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 = £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 withPosterior

ISO 10417

No

. of

test

s p

er

mo

nth

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


48

that the system would satisfy the requirements of ISO 13628-1 where there are two barriers

between the reservoir and the environment at all times and that the failure of a single

barrier does not cause the loss of well control. However, it is an interesting observation

that an SCSSV is considered a barrier but is also allowed to have a low leakage rate as

defined in ISO 10417. Knowledge of this must form part of the design phase for an SPS

when an SCSSV is used within an environmentally vulnerable area where the target for

hydrocarbon leakages is zero.

In addition this report has found that current methodologies for determining reliability of

assets suffers from “analysis paralysis” where the operator’s field development evolves

over time with a growing number of assets in the field that all have changing failure rates

and maintenance requirements. Normally for a large complex SPS development a system

team would have to continually revisit the analysis and independently review each

scheduled maintenance programme causing delays to the projects. To address this issue

this report has included, within the framework, the capacity to review and update the

reliability model in real time thus providing operators the capacity to amend the

appropriate maintenance schedules for economy of effort whilst also providing higher

availability and safety. This stochastic application towards maintenance can work to

decrease the likelihood of environmental damage if implemented and supported through

accurate data capture. Finally, this report proposed a new RAM framework for SPS field

developments which should become the key tool within the business plan for operators in

the Arctic Region.


49

Chapter 9: References [1.] OREDA, Offshore Reliability Data 5

th Edition Volume 2 – Subsea Equipment 2009

[2.] Yong Bai and Qiang Bai, Elsevier, Subsea Engineering handbook, 2010.

ISBN 978-1-85617-689-7

[3.] Scott G. Borgerson ,The Coming Arctic Boom: As the Ice Melts, the Region Heats Up,

July/August 2013 Issue of the Foreign Affairs (published by the council of Foreign Relations)

[4.] Nataliya Vasilyeva, AP Business Writer, AP Enterprise: Russia oil spills wreak devastation

December 17, 2011

http://www.boston.com/business/articles/2011/12/17/ap_enterprise_russia_oil_spills_wreak_

devastation/

[5.] The Offshore Installations (Safety Case) Regulations 2005 ISBN 0 11 073610 9

ISBN 0 11 073610 9

[6.] House of Commons: Energy and Climate Change committee, UK Deepwater Drilling

Implication of the Gulf of Mexico Oil Spill (6 January 2011)

[7.] D,McNamara, A Cunningham, Ijenkinson and Jang, School of Engineering, Technology and

maritime Operations, Liverpool John Moores University; A Monte Carlo approach to

maintenance considering failure modes and spare part control

[8.] Xianwei Hu, et al, Offshore Oil and Gas Reserch Center, China University of Petroleum:

Risk analysis of Oil/gas leakage of Subsea Production system based on Fuzzy fault tree.

International Joural of Energy Enginering (IJEE) Volume 2, issue 3, August 2012.

[9.] Peter Checkland, Wiley, Systems Thinking, Systems Practice, (1981)

[10.] Brian Wilson, Systems: Concepts, Methodologies and Applications (1990)

[11.] Stafford Beer, Wiley, Diagnosing the system for Organisations (1985)

[12.] API Recommended Practice 17N: Recommended Practice for Subsea Production System

Reliability and Technical Risk Management, 1st Edition, (2009)

[13.] Marvin Rausand and Arnljot Hoyland, Wiley, Systems Reliability Theory: models, statistical

methods and applications, 2nd

Edition, 2004.

[14.] U.S. Geological Survey (USGS), USGS Fact Sheet 2008-3049 : Circum-Arctic Resource

Appraisal: Estimates of Undiscovered Oil and Gas North of the Arctic Circle (2008)

[15.] Official Journal of the European Union: DIRECTIVE 2013/30/EU OF THE EUROPEAN

PARLIAMENT AND OF THE COUNCIL of 12 June 2013 on safety of offshore oil and gas

operations and amending Directive 2004/35/EC (28.6.2013)

[16.] Brian Van Pay, Office of Ocean and Polar Affairs, U.S. Department of State: National

Maritime Claims in the Arctic (2009)

[17.] David Dickins, Offshore Technology Conference, Behavior of Oil Spills in Ice and

Implications for Arctic Spill Response (2011)

http://www.foreignaffairs.com/author/scott-g-borgerson

http://www.foreignaffairs.com/issues/2013/92/4

http://www.boston.com/business/articles/2011/12/17/ap_enterprise_russia_oil_spills_wreak_devastation/

http://www.boston.com/business/articles/2011/12/17/ap_enterprise_russia_oil_spills_wreak_devastation/


50

[18.] Shannon H. Nudds, et al, International Society of Offshore and Polar Engineers (ISOPE),

Simulating Oil Spill Evolution in Water and Sea Ice in the Beaufort Sea (2013)

ISBN 978-1-880653-99–9

[19.] {PICTURE} Laurel Brubaker Calkins, Margaret Cronin Fisk and Jef Feeley, Bloomberg; BP

Seeks $3.4 Billion Reduction in Fines Related to Macondo Disaster

[20.] David Saul BP Advisor, BP’s Subsea Reliability Strategy10 years on: Subsea System

Reliability

MCE Deepwater Development (April 2014)

[21.] Janine L. Murray, Arctic Monitoring and Assessment Programme (AMAP) Assessment

Report: Physical/Geographical Characteristics of the Arctic (2009)

[22.] A joint report from Det Norske Veritas and Fridtjof Nansen Institute (DNV and FNI): Arctic

Resource Development; Risks and Responsible Management (2012)

[23.] Det Norske Veritas, Recommended Practice F116 (DNV-RP-F116): Integrity Management

Of Submarine Pipeline Systems (2009)

[24.] ISO 14224: Petroleum, petrochemical and natural gas industries

Collection and exchange of reliability and maintenance data for equipment (2006)

[25.] American Petroleum Institute (API) Recommended Practice 17N, Recommended Practice for

subsea Production Systems Reliability and Technical Risk Management (2009)

[26.] Norsk Elektroteknisk Komite & International Standards Organisation, (NEC ISO) 61508 – 1:

Functional safety of electrical/electronic/programmable electronic safety-related systems Part

1: General requirements (2010)

[27.] {PICTURE} Arctic Monitoring and Assessment Programme (AMAP) Assessment Report:

Arctic Pollution issues (2007)

[28.] George E. King, Reliability of Downhole Equipment: A One Day Course on Understanding

Well Equipment and Workover Failures and Improving Well Production Reliability. (2010)

[29.] International Association of Oil & gas Producers, Risk Assessment Data Directory: Report

No. 434 – A1 (2010) www.ogp.org.uk

[30.] Remi Eriksen, Det Norske Veritas, Essential Ram Theories: Guideline RAM4 (1999)

[31.] NORSOK Standard; D-010: Well integrity in drilling and well operations (2013)

[32.] Remi Eriksen, Det Norske Veritas, Reliability Centred Maintenance Analysis: Guideline

RAM13 (1999)

[33.] {PICTURE} Greenpeace: "ICEBERG" Print Ad by Grey, Istanbul

http://www.coloribus.com/adsarchive/prints/greenpeace-iceberg-15241905/

[34.] {PICTURE} Aker solutions, Ingenuity subsea

http://www.akersolutions.com/en/Global-menu/Products-and-Services/Subsea-technologies-

and-services/Subsea-production-systems-and-technologies/

http://www.ogp.org.uk/

http://www.coloribus.com/adsarchive/prints/greenpeace-iceberg-15241905/

http://www.akersolutions.com/en/Global-menu/Products-and-Services/Subsea-technologies-and-services/Subsea-production-systems-and-technologies/

http://www.akersolutions.com/en/Global-menu/Products-and-Services/Subsea-technologies-and-services/Subsea-production-systems-and-technologies/


A

Leakage through the SSIV(SVX2)

Figure A.1: SSIV Fault

SV3

Leakage and loss of barrier (SSIV)

+

Failure of SSIV barriers (EPU) (SVX1)

•

SV1 SV2 SV4 SV5

Figure A2: Reliability block diagram of the SSIV

SV1 SV2 SV4 SV5

SV3

Appendix A: SSIV FTA

Table A1: SSIV Reliability Data

Title Description Sub unit

assembly

No.

of

units

No. of

failure

Mean

Failure

rate

(106hrs)

Upper

Failure

rate

(106hrs)

SD MTTR

(hrs)

Calendar

time

(106hrs)

OREDA

Pg No.

SV1 Fail to function on

demand (HPU) HPU 15 2 3.77 11.36 3.85 3 0.3652 77

SV2 Internal utility leakage

(HPU) HPU 15 2 3.77 11.36 3.85 3 0.3652 77

SV3 External process

leakage (HPU) HPU 15 1 1.86 8.13 3.17 3 0.3652 77

SV4 Internal utility leakage

(SCM) SCM 13 1 1.47 4.61 1.64 24.0 0.3652 78

SV5 Fail to function on demand Solenoid

control valve (SCM)

SCM 148 1 0.13 0.39 0.14 24.0 0.3652 78


B

Leakage from choke module

+

leakage through the X-mas tree and well head (XTX1)

X1

Failure of leakage control in choke

module

•

X2

X3

X4

X5

Figure B1: Fault Tree for X-tree and well head (including choke and flowbase)

Leakage form choke module

Leakage from Flowbase

+

X6

Failure of leakage control in Flowbase

•

X8 X9 X10 X7

Leakage form Flowbase

•

Leakage from X-Tree

+

X16

Failure of leakage control in X-Tree

•

X17 X18 X20 X19

Leakage form X-Tree

•

Leakage from Wellhead

+

Failure of leakage control in Wellhead

•

X12 X13 X14

X15

Leakage form Wellhead

X11

X21

•

Leakage from Christmas tree and Wellhead

+

X22

Appendix B: X-tree and well head FTA

Table B1: X-tree and well head Reliability Data

Title Fault Description Sub unit

assembly

No.

of

units

No. of

failure

Mean

Failure

rate

(106hrs)

Upper

Failure

rate

(106hrs)

SD MTTR

(hrs)

Calenda

r time

(106hrs)

ORED

A Pg

No.

X1 External process leakage Choke module 34 1 1.06 4.53 1.75 72.0 9.1129 138

X2 Chemical Injection connector

Failure Choke module 6 0 7.20 27.66 10.19 - 9.1129 138

X3 Connector external process

leakage Choke module 95 1 0.38 1.66 0.64 72.0 9.1129 138

X4 Piping failure (rupture) Choke module 1 0 43.22 165.98 61.13 - 9.1129 138

X5 Valve fail to function Choke module 32 1 1.13 4.95 1.93 1.27 9.1129 138

X6 External utility Leakage Flowbase 105 2 0.62 2.78 1.10 12 9.1129 138

X7 Loss of barrier Flowbase 105 1 0.24 1.16 0.48 - 9.1129 138

X8 Frame External utility Leakage Flowbase 109 1 0.25 1.37 0.70 12 9.1129 138

X9 Valve External utility Leakage Flowbase 172 1 0.26 1.40 0.64 - 9.1129 138

X10 Valve fail to close on demand Flowbase 172 1 0.14 0.43 0.16 - 9.1129 138

X11 External process leakage Wellhead 261 3 0.26 1.20 0.49 288 9.1129 138

X12 Annulus failure - External

process leakage Wellhead 413 2 0.11 0.34 0.12 - 9.1129 138

X13 Casing failure - External

process leakage Wellhead 444 1 0.05 0.17 0.16 288 9.1129 139

X14 Conductor failure - External

process leakage Wellhead 256 1 0.10 0.42 0.16 - 9.1129 139

X15 Well head housing Wellhead 247 - 0.06 0.23 0.08 - 9.1129 139

X16 External process leakage X-Tree 270 1 0.10 0.42 0.16 12 9.1129 139

X17 Internal process Leakage X-Tree 270 7 0.58 2.34 0.88 28.3 9.1129 139

X18 Internal utility leakage X-Tree 270 1 0.16 0.85 0.55 - 9.1129 139

X19 Loss of barrier (PWV) X-Tree 270 3 0.3 1.68 0.85 136.7 9.1129 139

X20 Plugged blocked X-Tree 270 2 0.23 1.30 0.67 4 9.1129 139

X21 Structural deficiency X-Tree 270 3 0.28 1.33 0.54 16.7 9.1129 139 X22 Production wing valve Failure X-tree Isolation 2267 26 0.34 0.83 0.25 78.9 9.1129 140


C

Leakage of manifold

+

Major Leak through the Manifold

•

Figure C1: Fault Tree for the manifold

Valve, process isolation

+

Control and barrier failure

•

M12

M11

M10 M8

Valve Leakage

•

M9

Leakage of manifold

•

M2

M1

M7

M6

M5

M4

M3

Control Barrier failure

•

Figure C2: Reliability block diagram of the Manifold

M1 M2 M6 M7

M3 M4 M5

M10 M11 M12

M8 M9

Appendix C: Manifold FTA

Table C1: Manifold Reliability Data

Title Fault Description Sub unit assembly

No.

of

units

No. of

failure

Mean

Failure

rate

(106hrs

)

Upper

Failure

rate

(106hrs)

SD MTTR

(hrs)

Calenda

r time

(106hrs)

ORED

A Pg

No.

M1 External process leakage Manifold module 110 4 1.33 7.30 3.57 70.3 3.5069 103 M2 Internal process leakage Manifold module 110 1 0.30 1.57 1.25 35.0 3.5069 103 M3 Plugged/choked Manifold module 110 5 2.67 14.87 7.72 15 3.5069 103 M4 External utility leakage Manifold module 110 4 1.10 3.21 1.06 37.3 3.5069 103 M5 Internal utility leakage Manifold module 110 5 1.84 9.33 4.02 10.4 3.5069 103 M6

Coupling connector

external process leakage

Chemical Injection

coupling 689 2 0.09 0.41 0.16 24.0 3.5069 103

M7 Piping external process

leakage Manifold 261 1 0.23 1.32 0.70 168.0 3.5069 103

M8 external process leakage Valve, choke 3 1 9.72 30.51 10.90 240.0 3.5069 103 M9 external process leakage

Valve, process

isolation 1111 29 0.22 1.24 0.65 15.2 3.5069 103

M10 external utility leakage Valve, process

isolation 1111 5 0.11 0.28 0.09 30.0 3.5069 103

M11 Fail to close on demand Valve, process

isolation 1111 13 0.26 1.35 0.59 18.9 3.5069 103

M12 Leakage in closed position Valve, process

isolation 1111 6 0.17 0.64 0.24 26.2 3.5069 103


D

+

Major Leak through pipeline

Figure D1: Fault tree of the flowline

FL7

FL1

FL4

Blockage

•

External Process leakage

FL3

FL5

FL6

•

FL2

Pipe barrier

•

Figure D2: Reliability block diagram of the Flowline

FL7

FL3 FL5 FL6 FL2

FL1 FL4

Appendix D: Flowline FTA

Table D1: Flowline Reliability Data


No.

of

units

No. of

failure

Mean

Failure

rate

(106hrs

)

Upper

Failure

rate

(106hrs)

SD MTTR

(hrs)

Calenda

r time

(106hrs)

ORED

A Pg

No.

FL1 Plugged/blocked pipe 310 2 0.23 1.23 0.57 51 11.6842 95

FL2 external process leakage pipe 310 2 0.15 0.88 0.48 2 11.6842 95

FL3 internal process leakage pipe 310 1 0.11 0.61 0.36 5 11.6842 95

FL4 structural deficiency pipe 310 1 0.07 0.39 0.21 6 11.6842 95

FL5 external process leakage connector 506 1 0.1 0.55 0.29 5 11.6842 95

FL6 external process leakage flexible spool 137 2 0.29 1.08 0.4 2 11.6842 95

FL7 Valve fail to open pipeline isolation 23 2 2.41 5.79 1.74 5 11.6842 95


E

Riser leakage

•

Figure E1: Fault Tree of Riser

R5 R2 R5

R3 R1

Riser barrier failure

•

+

Leak through Pipe

R6

Figure E2: Reliability block diagram of the Riser

R2

R3 R4 R1

R5 R6

Appendix E: Riser FTA

Table E1: Riser Reliability Data


No.

of

units

No. of

failure

Mean

Failure

rate

(106hrs)

Upper

Failure

rate

(106hrs)

SD MTTR

(hrs)

Calenda

r time

(106hrs)

OREDA

Pg No.

R1 External Process leakage Riser 66 1 0.46 2.31 0.5 168 1.9824 120

R2 Structural deficiency Riser 66 3 1.29 5.78 2.29 53 1.9824 120

R3 Internal Utility Leakage Riser 66 1 0.95 5.25 2.59 20 1.9824 120

R4 External Process leakage pipe 65 2 1.2 5.39 2.13 94 1.9824 120

R5 Structural deficiency pipe 65 3 1.34 6.74 2.89 53 1.9824 120

R6 Riser base Structural

deficiency Riser base 12 1 0.21 7.26 2.65 5.1 5.5556 117


F

Figure F1: Fault Tree of control system

+

Loss of control Barrier

Loss of control (MCS) CSX1

CS1

CS2

Loss of electrical control

• •

CS3 CS4 CS5 CS6 CS7 CS8 CS9

Loss of Hydraulic control

Leakage of control utilities CSX2

Appendix F: Control System FTA

Table F1: Control system Reliability Data

Title Fault Description Sub unit assembly No. of

units

No. of

failure

Mean

Failure

rate

(106hrs)

Upper

Failure

rate

(106hrs)

SD MTTR

(hrs)

Calendar

time

(106hrs)

OREDA

Pg No.

CS1 Fail to function on

demand MCS 2 1 13.84 40.45

13.4

1 2 0.5781 83

CS2 Control /signal failure MCS 2 1 13.84 40.45 13.4

1 0.5 0.5781 83

CS3 External Untility leakage Umbilical Hydraulic

and chemical line 272 1 0.09 0.31 0.11 4 0.5781 84

CS4 Internal utility leakage Umbilical Hydraulic

and chemical line 272 1 0.09 0.31 0.11 6 0.5781 84

CS5 External utility Leakage SCM 82 2 0.79 2.43 0.85 16.5 0.5781 85

CS6 Internal utility leakage SCM 82 1 0.62 2.68 1.04 6 0.5781 85

CS7 External utility Leakage: SDM: Chemical

injection coupling 524 1 0.05 0.19 0.07 4 0.5781 86

CS8 External utility Leakage: SDM: Hydraulic

coupling 1202 1 0.02 0.08 0.03 1 0.5781 86

CS9 External utility Leakage:

SDM:

Hyydraulic/chemical

jumper

285 12 1.02 2.07 0.56 29 0.5781 86


G

Failure of SCSSV

Figure G1: SCSSV Fault

SCV1

Leakage and loss of barrier primary

(SCSSV)

+

Leakage through the X-mas tree and well head (XTX1)

•

See Figure B1

Figure G2: Reliability block diagram of the SCSSV within the X-tree

XTX1

SCV1

Collection of this data was obtained from an external source was limited but reliability data

could be found at reference [28]. However, the data from reference [28] was given as the

failure rate per year for the North Sea, the values included in table G1 was converted to

Failure rate per 106 hours by 0.004/8760 = 0.4566x10

-6. Additionally, the upper failure rate

was based the highest recorded failure rate of a wet tree, which belonged to company A

[28] and this was presented as 0.011/8760 = 1.2557 x10-6

.

Appendix G: SCSSV FTA

Table G1: SSIV Reliability Data

Title Description Sub unit

assembly

No. of

units

No. of

failure

Mean

Failure

rate

(106hrs)

Upper

Failure

rate

(106hrs)

SD MTTR

(hrs)

Calendar

time

(106hrs)

OREDA

Pg No.

SCV1 Mechanical failure

of SCSSV Valve n/k n/k

0.4566 [28]

1.2557 [28]

n/k n/k n/k n/a

XTX1

Leakage through the X-mas tree and

well head (XTX1)

X-tree Appendix

B

Appendix

B

Appendix

B Appendix

B Appendix

B

Appendix

B Appendix B Appendix B


H

Statement of ethics and principles in researching the Reliability of an Subsea

Production System

1. The views and opinions of the engineering, business management, Arctic organisation

and independent bodies are of equal value and all viewpoints will be recognised.

2. The researcher will respect the experiences and views that all the stakeholders provide

and the systems practitioner will respect the differences between them for example,

ethnicity and age.

3. The methods used for understanding the complexity of this engineering problem will be

chosen which will include everyone and their opinions. The systems practitioner my

request feedback about the stakeholders feelings towards being heard, because this

might assist the practitioner’s further learning and development.

4. All information will be regarded as confidential. It will not be used so that people can

be identified.

5. Nobody will be involved in the project without their direct consent. This means that

everyone knows that:

a) they can choose not to assist in the project

b) they can retract their opinions or withdraw from the project at any time

c) What they must do if the take part

d) What will happen to the information they present during the project.

6. All information will be shared with a third party: The University of Aberdeen and its

associated tutors.

7. Everyone involved in the project will have a de-brief and they can see the results and

outcomes which their information went towards and they will be invited to provide

further comments, if they wish.

Figure H1: Statement of ethics [Adapted from William J. Wilson’s B.Eng Dissertation]

Appendix H: Ethical statement for

stakeholder engagement


I

Appendix I: Quick reference to D-010 for

SCSSV testing schedule


J

Appendix J: Microsoft Excel Commands List

Table J1: Microsoft Excel Commands List

Parameter Excel command

Reliability per

component

[Eq.6.29]

Where X=EXP((-$EX (λX)) *1*10^-6)*((AA$3(year)*(24*365)))

SSIV Reliability

[Eq.6.30]

=(F4(SV1)*F5(SV2)*F7(SV4)*F8(SV5)+F6(SV3)-

(F4(SV1)*F5(SV2)*F6(SV3)*F7(SV4)*F8(SV5)

XT and wellhead

Reliability

[Eq.6.6]

=((G4*G5*G6*G7)+G8-(G4*G5*G6*G7*G8))*((G9*G11*G12)+(G10*G13)-

(G9*G10*G11*G12*G13))*((G14*G15*G16*G17)+G18-

(G14*G15*G16*G17*G18))*((G19*G20*G21)+(G22*G23*G24)-

(G19*G20*G21*G22*G23*G24))+G26(PWV)- ((G4*G5*G6*G7)+G8-

(G4*G5*G6*G7*G8))*((G9*G11*G12)+(G10*G13)-

(G9*G10*G11*G12*G13))*((G14*G15*G16*G17)+G18-

(G14*G15*G16*G17*G18))*((G19*G20*G21)+(G22*G23*G24)-

(G19*G20*G21*G22*G23*G24))*G26(PWV)

Manifold

Reliability

[Eq.6.9]

=((G12*G11)+(G14*G15*G13)-

(G11*G12*G13*G14*G15))*((G4*G5*G9*G10)+(G6*G7*G8)-

(G4*G5*G6*G7*G8*G9*G10))

Flowline

Reliability

[Eq.6.12] =((G5*G6*G8*G9)+(G10*G4*G7)-(G4*G5*G6*G7*G8*G9*G10))

Riser Reliability

[Eq.6.15] =(G4*G6*G7)+(G5*G8*G9)-(G4*G5*G6*G7*G8*G9)

Subsea control

system

Reliability

[Eq.6.26]

=G13+G15-(G4*G5*G6*G7*G8*G9*G10*G11*G12)

Where, G13 is the electrical control system and G15 is the hydraulic control system

SCSSV

reliability

[Eq.6.29] =EXP(((-$F4)*1*10^-6)*((G$3*(24*365))))

XT and Wellhead

including SCSSV

[Eq.6.17]

=(J7(XT and wellhead Reliability)+J14(SCSSV reliability)-(J7(XT and wellhead

Reliability)*J14(SCSSV reliability))^6

Riser including

SSIV =J10(Riser Reliability)+J6(SSIV Reliability)-(J6(SSIV Reliability)*J10(Riser Reliability))

Overall

Production

System

Reliability

[Eq.6.28]

=(J8(Manifold Reliability)*J9(Flowline Reliability)*J16(Riser including

SSIV))+(J15(XT and Wellhead including SCSSV)*J13(Subsea control system

Reliability))-(J8(Manifold Reliability)*J9(Flowline Reliability)*J13(Subsea control

system Reliability)*J15(XT and Wellhead including SCSSV)*J16(Riser including SSIV))

Failure

probability of

each element

[Eq.6.30]

=1- Overall Production System Reliability


K

Appendix K: Microsoft Excel and Report CD

CD

Disk contains:

1. EG59G9_Wilson_MSc_dissertation.docx

2. EG59G9_Wilson_MSc_dissertation.xlsx

eg59g9_wilson_msc_dissertation

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