An integrated framework for maintenance optimisation with petroleum industry

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with petroleum industry.

MOHAMED, Abdelhafiez M.A.

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An Integrated Framework for Maintenance Optimisation

within Petroleum Industry

Abdelhafiez M A Mohamed

A thesis submitted in partial fulfilment of the requirements of

Sheffield Hallam University for the degree of Doctor of

philosophy

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Acknowledgment

During the co u rse of this research , I w as fortunate to receive all possible

support and help from Professor S am eh M S a a d , for which I would like to

take the opportunity to em p h asise my appreciation and gratitude.

I would like to thank all th e participants who provided all th e required d a ta to

com plete this work and th o se who took the time to com plete the

questionnaires.

I am especially grateful to my parents, family and friends for consistently

providing the support and the encouragem ent.

Finally yet importantly, I would like to dedicate this piece of work to th o se

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Nomenclature

AD Asset density in the vicinity o f the event of explosion (

$/m2)

AHP Analytic hierarchy process

AMI Absolutely more important

AR Area under the damage radius (m2)

ARCM Accelerated reliability centred maintenance

A

vd Average demand during lead time (Time)

Av l t Average lead time in days (days)

Awsc Alternative's weight o f sub criteria

Blc Booked labour costs

BP British Petroleum

Bv Financial loss o f the facilities in terms o f explosion or fire ($)

Bpc

Bottleneck penalty cost ($)

C Consequence o f the failure CBM Condition-based maintenance Cl Consistency index

ch

Cost o f delay charges per unit ($) CM Corrective maintenance

Cmc

Corrective maintenance cost

Cm t Time required completing corrective actions(hrs)

Cpd Value of damaged production ($) CR Consistency ratio

Csd Cost o f damaged parts due to failure o f another part ($) Csp Cost of spare parts ($)

Coh.

Cost o f out-house maintenance ($)

C\Vd Cost of cleaning non-hazardous and hazardous materials ($)

r

^wp

Waste disposable cleaning cost ($)

D Demand

d Design age o f part/machine

Dc Damages cost ($)

Dch Delay charges ($)

Dd Daily demand

DEA Domino effect analysis

D Ft Due fine time ($)

DOM Design of maintenance

DPD Drilling and production equipment El Equally important

EOQ Economic order quantity ETA Event Three Analysis EVM Eigenvector method F Failure frequency scales / Frequency factor

FAHP Fuzzy analytic hierarchy process FGI Finished goods inventory

FMECA Failure mode effective and critical analysis FTA' Fault tree analysis

Fm Probability o f failure

G Growth factor

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He Holding cost ($)

HIRA Hazard and identification and ranking analysis

IM Importance factor

IsE Impact on safety and environmental factor

I Lower bound value

Lc Legal fines in case o f environmental damages ($) LOR . Likelihood of risk

Lt Lead time (h)

m Middle bound value MAX Maximum

MC Maintenance cost ($) Mc Maintenance cost factor MCDM Multi criteria decision making

MDT Mean down time between failures MIN Minimum

MM Maintenance management MNV Mean o f the normalised values MTBF Mean time between failure MTBM Mean time between maintenance MTTR Mean time to repair

MRO Maintenance, repairs and operations inventory MSI Maintenance significant items

MUT Mean up time between failures.

N Number o f orders placed NGM Normalised geometric mean OA Operational availability

Oc Order cost

OEM Original equipment manufacturer 0 F Operational flexibility factor 0/ Operational impact factor ORA Optimal risk analysis

P Physical age o f the equipment

PDI Population density in the vicinity o f the event PD1 Number o f people within the ratio o f impacted area

PDF1 Population distribution factor PIRP Point inspection and regular repair

Plc Production losses cost ($)

PM Preventive maintenance

P

r mc Preventive maintenance cost ($)

PRA Probabilistic risk assessment

Prp Probability o f replacement

QFD Quality function deployment QTY Quantity o f the spare parts RA Risk Assessment

RBM Risk based maintenance

RBIM Risk-based inspection and maintenance RCM Reliability centred maintenance

RGBI Reliability growth based inspection RMI Raw material inventory

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RPN Risk priority number

RRCM Reliability and risk Centred maintenance SIL Safety integrity level

SL Service level

Sm h Maintenance personnel hourly rate (h/$)

SMI Strongly more important

Soh Operator’s hourly rate (h/$)

s s

Safety stock

T Expected time between orders TAM Turnaround maintenance TBM Time-Based Maintenance TFN Triangular fuzzy number

tip Time spent by maintenance personnel in carrying out preventive

action (hrs) TM Team Managers

TMC Total maintenance cost

(h/S)

tsu Machine set up time (hrs)

t t c Time spent by the maintenance personnel to repair failure (hrs) t t i Production time loss excluding setup machine time (hrs)

U Upper bound value

UDA Unacceptable damaging level(m2)

UFL Level of an unacceptable financial loss

UFR Unacceptable fatality rate (person)

V Number o f damaged production by barrel VED Vital, Essential and Desirable

VSMI Very strongly more important Weight of performance loss

w b Weight o f financial loss Wc Weight of human health loss WD Weight o f environment loss WIP Work-in-process inventory

Weight o f main criteria

Wsc Weight o f sub criteria

Xi Number o f maintenance personnel

x

2

Number o f operational personnel

z Number o f standard deviations

a Department’s income due to one barrel ($)

a* Standard deviations

a LT Standard deviations for lead time in days

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Research Papers Based on this Research

Saad M Sameh and Mohamed Abdel.(2015). An optimisation model for the scheduling o f preventive maintenance activities and its applications in petroleum industry. Flexible Automation and Intelligent Manufacturing, FAIM2015.

Saad M Sameh, Khan Nufal and Mohamed Abdel.(2015). A fuzzy-AHP multi criteria decision-making model for procurement process. International Journal o f Logistics Systems and Management, Vol. 23, No. 1, 2016.

Saad M Sameh and Mohamed Abdel.(2016). Optimisation o f the maintenance activities and its impact on operation availability within the petroleum industry.

International Journal o f Advanced Manufacturing Technology (IJAMT)(In press).

Mohamed Abdel and Saad M Sameh (2016) Fuzzy Analytic Hierarchy Process for the Selection o f Maintenance Policies within Petroleum Industry. 31st National Conference on Manufacturing Research(In press)

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Abstract

M aintenance co n cern s abound a s com panies strive to increase production while guaranteeing safety, flow a ss u ra n c e and equipm ent reliability. Therefore, optimisation of th e m aintenance p ro c e ss is essential to increasing th e productivity of the equipm ent a s well a s d ecreasin g the m aintenance expenditure. Thus, this research is aim ed a t proposing an integrated fram ework to optimise the major m aintenance activities a t strategic and operational levels including sp a re p arts control and risk a s s e s s m e n t within th e petroleum industry.

In this research , th e selection of th e m aintenance optimum policy for equipm ent within petroleum industry is dealt with a t strategic level through multi criteria decision making techniques (classic and fuzzy analytic hierarchy p ro c e ss FAHP). At the operational level, a c o st optimisation m athem atical model is proposed to balance the c o sts of failure of a unit during operation ag ain st the co st of preventive m aintenance to e n su re preventive m aintenance activities a re kept a t minimum possible co st without compromising the utilisation of equipm ent's perform ance.

Furthermore, an integrated approach betw een sp a re parts m a n ag em en t and preventive m aintenance activities is developed to create a c o st effective method and ensuring th e availability of parts in the stock while carrying out preventive m aintenance. A risk a s s e s s m e n t model for equipm ent within the petroleum industry is developed to handle th e likelihood of risk and its co n se q u e n ce s. A m athem atical equation is developed to predict the likelihood of risks and identify the optimum inspection interval. In addition, s e t of modified m athem atical equations to evaluate c o n se q u e n c e s of risk and weighing th e severity of risks in specific a re a s is developed.

The findings of this research indicate th at the proposed FAHP will clearly guide th e practitioners in selecting the optimum m aintenance policies a t strategic level by th e consideration of the related criteria and the possible alternatives for petroleum equipm ent. The results from the proposed m athem atical model for scheduling preventive m aintenance activities show ed

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List of Figures

Figure 2-1: AHP for M aintenance in Oil R e fin e ry ... 28

Figure 2-2: Hierarchical Structure of Fuzzy Analytical Hierarchy P r o c e s s ... 33

Figure 2-3: RRCM F ra m e w o rk ...40

Figure 2-4: M aintenance Framework for a Pipeline S y s te m ... 41

Figure 2-5: Simplified Block of Optimal Risk A nalysis... 59

Figure 3-1: S te p s of Selection of the M aintenance P o licies... 69

Figure 3-2: The Hierarchy of th e Proposed M athematical M odel...72

Figure 3-3: A Digram showin th e main connections B etween L evels 76 Figure 3-4: P roposed Integrated M aintenance Fram ew ork...78

Figure 4-1: The Hierarchy Structure for the M aintenance Optimum Policy.. 85

Figure 4-2: Priority P referen ce of Main Criteria Using MNV...94

Figure 4-3: Alternative Priorities with R esp ect to C o s t... 95

Figure 4-4: Priorities of Alternatives Using MNV M ethod... 97

Figure 4-5: Alternative P referen ce With R esp ect to Main Goal (NGM ) 100 Figure 4-6: A hierarchal View of th e Entire S tru ctu re ...101

Figure 4-7: Pairwise Numerical C o m p ariso n s... 102

Figure 4-8: Main Criteria Prioritization and Inconsistency M easu rem en t... 102

Figure 4-9: W eights of Main Criteria, Sub Criteria and A lternatives 103 Figure 4-10: Alternative P reference with R esp e ct to Main Goal Using (EVM) ... 105

Figure 4-11: Different Sensitivity Analysis G ra p h s ... 106

Figure 4-12: Dynamic Sensitivity for the Main C riteria... 106

Figure 4-13: The Intersection betw een T F N s... 107

Figure 4-14: Triangular Fuzzy m e m b e rsh ip ...108

Figure 4-15: Alternative Preference with R esp e ct To Main Goal (T FN ) 117 Figure 4-16: Main Criteria's C om parison Matrix (0 C o n sisten cy )... 120

Figure 4-17: Alternatives Priorities with R esp ect to Main G o a l...121

Figure 5-1: T he Behaviour of (Cmc x FAt) for Minor S e rv ic e ...136

Figure 5-2: T he Behaviour of (Pmc x 1 - FAt) for Minor S e rv ic e ... 137

Figure 5-3: TMC ($/h) for the Minor S e rv ic e ... 138

Figure 5-4: The Behaviour of (Cmc x FAt )for Major S e rv ic e ... 141

Figure 5-5: Behaviour of Pmcfor Major S erv ice ... 141

Figure 5-6: TMC ($/h) for Major S e rv ic e s.. ...143

Figure 5-7: Total C ost Saving ($/year) For Minor S e rv ic e ... 144

Figure 5-8: Total C ost Saving ($/3,5year) Major S e rv ic e ... 145

Figure 5-9: Improved O A for Minor S e rv ic e ...147

Figure 5-10: Improved O A for Major S e rv ic e ... 147

Figure 5-11: PM C ost Higher than CM C ost for Minor S e rv ic e s...149

Figure 6-1: T he Integrated M aintenance and Inventory A p p ro ach ...155

Figure 6-2: Description of M aintenance and Inventory Integrated Approach ...155

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Figure 6-4: P art (3) Inventory Level M ovement under Integrated Approach

... 163

Figure 6-5: M ovement of P art (2) MIN/MAX Policy...169

Figure 6-6: Inventory Level of P art (5) (MIN/MAX Policy) ... 172

Figure 6-7: Inventory Level of P art (5) with SL (90%, 95% and 99.99% )... 174 Figure 6-8: P art (1) M ovement under MAX/MIN Policy...175

Figure 6-9: M ovement of S p a re P art (1) with Different SL for 8 Y e a rs 176

Figure 6-10: The Annual A verage Inventory Level of P art (1 )... 177 Figure 6-11: A verage Inventory C ost of S p are P art under MIN/MAX Policy ... 178 Figure 6-12: P arts C ost for Two Y ears with SL (90%, 95% and 99.99% ).. 180

Figure 7-1: Description of Risk Estimation M o d el... 186

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List of Tables

Table 2-1: Com parison betw een MCDM to o ls ... 22

Table 4-1: S cale of Relative Im p o rtan ce... ... 91

Table 4-2: C om parison Matric of Main Criteria... 92

Table 4-3: Random In d ices...93

Table 4-4: T he Normalized Value (MNV)... 93

Table 4-5: W eight of Priorities with R esp e ct to C ost (NMV)...95

Table 4-6: Local, Global Priorities and Alternatives W eight (NMV)...96

Table 4-7: Normalised G eom etric m ean of The Main C riteria... 98

Table 4-8: Local, Global priorities and Alternatives W eight (NGM )...99

Table 4-9: Local, Global priorities and Alternatives W eight (EVM) 104 Table 4-10: Triangular Fuzzy Conversion S c a le ... 109

Table 4-11: Fuzzy C om parisons M atrices for the M aintenance Main Criteria 112 Table 4-12: Criteria, Sub-Criteria and Alternatives W eights (T F N )... 116

Table 4-13: Criteria's W eight and Alternatives for Pairwise Matrix Evaluation 118 Table 4-14: The M aintenance Main Criteria Matrix with 0.0 C onsistency ..1 1 9 Table 4-15: Main Criteria's W eights and Alternatives for Modified Matrix ..1 1 9 Table 5-1: C o sts of S p are P arts for Minor S e rv ic e s ...135

Table 5-2: Preventive M aintenance C o sts for Minor S e rv ic e s ... 135

Table 5-3: CM R elevant C o sts for Minor S e rv ic e ... 136

Table5-4: TMC for Minor Service ($/h)... 138

Table 5-5: S p a re p arts C ost for Major S e rv ic e ... 139

Table 5-6: Preventive M aintenance C o sts for Major S e rv ic e ... 139

Table 5-7: S p a re parts C ost for Major S e rv ic e ... 140

Table 5-8: Preventive M aintenance C o sts for Minor S e rv ic e s ... 140

Table 5-9: TMC for Major Service ($/h)... 142

Table 5-10: TMC ($/h) for Minor Services under (PM=CM C o s ts ) 148 Table 6-1: M aintenance Annual D em and for S p a re P a rts ... 159

Table 6-2: S p a re P arts Minor/Major M aintenance S e rv ic e s ...160

Table 6-3: Calculation of EOQ for Minor/Major S e rv ic e s ... 161

Table 6-4: M ovement of P art (1) C onstant L and D ... 162

Table 6-5: ROP and S S for S p are P arts (1) And (2) With SL 90 % ...165

Table 6-6: ROP and S S for S p are P arts (1) And (2) With SL (95% and 9 9 .9 9 % )...165

Table 6-7: ROP and S S for S p are P arts Used in Major S e rv ic e ... 165

Table 6-8: Number of O rders N and Time betw een O rders T ... 166

Table 6-9: P art (1) M ovement under the Current MAX/MIN P o licy ...167

Table 6-10: M ovement of Part 1 under Different Service Levels... 168

Table 6-11: P art (2) M ovement under the Current MAX/MIN P o licy 169 Table 6-12: P art (2) Inventory M ovement under Different Service Levels.. 170

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Table 6-14: P art (3) M ovement under Different Service L e v e ls... 171

Table 6-15: P art (4) M ovement un d er C urrent MAX/MIN P o licy ... 171

Table 6-16: P art (4) M ovement under Different Service L e v e ls... 172

Table 6-17: P art (5) M ovement under C urrent MAX/MIN P o licy ... 173

Table 6-18: M ovement of P art (5) U nder Different Service Level... 173

Table 6-19: Total P arts C ost for Y ear 1 and 2 under MAX/MIN Policy 178 Table 6-20: Total P arts C ost for Two Y ears with Different S L ... 179

Table 7-1: Assigning Probability... 188

Table 7-2: Perform ance Function... 190

Table 7-3: S p a re P arts A verage MTBF... 195

Table 7-4: LOR and Growth Factor (G) for Mixer 1 0 0 ... 196

Table 7-5: R elated m aintenance C o sts (Mixer 1 0 0 )... 199

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Chapter One

Introduction

This chap ter is divided into six sections. The first section

p re sen ts an introduction to the th esis and th e second

section d e als with th e historical background of the

petroleum industry and m aintenance. The third section

briefly d esc rib e s the problem definition and th e forth

section lists the th e sis's aim s and objectives. T he last

two sectio n s respectively p re sen t the structure of the

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1.1 Background

T he contem porary b u sin ess environm ent h a s raised the strategic im portance

of the m aintenance function within organizations which have significant

investm ent in physical a s s e ts (Tsang 2002). The perform ance and

com petitiveness of manufacturing and production com panies is d e p en d e n t

on the reliability, availability and productivity of their production facilities

(C oetzee, 1997, Madu, 2000, Fleischer et al. 2006, Muchiri e t al 2011).

Therefore, m aintenance function n e e d s to m e et the overall targ et of th e

organization and transform b u sin ess priorities into th e m aintenance priorities.

Within m any large-scale plant-based industries, m aintenance c o sts can

acco un t a s much a s 40% of the operational budget and therefore improving

m aintenance effectiveness is a potential so u rce for making financial savings

(Eti e t al 2006).

The oil and g a s industry is a competitive m arket which requires a high

perform ance in plants th at can be translated into high availability, reliability

and maintainability for equipm ent. Today, the expectations of high reliability

and availability of equipm ent a re so high th at drives th e n ecessity of

optimising m aintenance activity (Calixto 2012).The co st of m aintenance in oil

and g a s production is the third largest c o st within th e production which

n e c e ssita te s th e optimisation of this function (El-Jawhari and Collins 2014).

N evertheless, th e catastrophic c o n se q u e n c e s of any failure within su ch

industry compel the m aintenance to avoid the occurrence of the failure.

T he difficulty to balance betw een both reliability and availability of equipm ent

on one hand and minimising th e c o st of m aintenance on th e other hand while

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et al (2009) indicated th at in so m e petroleum secto rs such a s "petroleum

refineries average downtime can reach 10% o f the production time, and

refineries are often used only at 60% o f their capacity. Thus, important

financial gains and safety improvements can be affected by optimising

maintenance tasks".

Various books and literature p re sen t different types of m aintenance system

and it h a s b een observed th at som e technical and term inologies such a s

strategy and policy a re used in different context and p laces to m ean the

sa m e thing, so the author feels the need to define them both a s they a re

u sed frequently in this thesis:

Policy: M eans th e type of m aintenance (corrective or preventive).

Strategy: M eans the philosophy to select the policy su ch a s

reliability-centred m aintenance.

1.2

Historical Background o f Petroleum Industry and M aintenance

1.2.1 Petroleum Industry

Oil h a s been u sed for purpose of lighting for th o u san d s of y e ars in a r e a s

w here oil is found in shallow reservoirs. However, it w as not until 1859 th at

"Colonel" Edwin Darke drilled th e first successful oil well, for th e sole p u rp o se

of finding oil (Devoid 2013). T h e se wells w ere shallow wells by m odern

stan d ard s, often le ss than 50 m eters, but could give quite a large production.

Soon oil had replaced m ost other fuels for mobile use. D espite th e early

attem pts a t g a s transportation, it w as not successful until after World W ar 2

with the developm ent of welding techniques.

Today oil and g a s a re produced in different p arts in the world. However,

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environm ental issu e s and restrictive laws a s well a s th e challenges of

su stain ab le energy and their rem arkable challenging prices.

1.2.2 Maintenance

In th e early d ay s of the petroleum industry, m aintenance w as perform ed a s

"n ecessary evil" but over th e time, the role of m aintenance b e co m e s m ore

recognizable. T he need for providing better services with th e petroleum

industry a s any other industries requires the developm ent of m aintenance

program m e. The first recognized policy w as corrective m aintenance or run to

failure policy.

The secon d generation of m aintenance w as the preventive m ain ten an ce in

th e form of the periodic and th e last generation is predictive m ain ten an ce or

condition m aintenance. Different philosophies have been d eveloped to run

th e se policies such a s reliability centred m aintenance (RCM) and risk b a se d

m aintenance, which will be extensively d iscu ssed in the thesis.

1.3

Problem Definition and Research Questions

The petroleum industry is a sensitive industry when it co m es to m ainten an ce

and its recognized role in term s of ad d ed value to the production line. The

reaso n for this sensitivity is the high dem and of controlling th e high reliability

and availability of equipm ent and the catastrophic c o n se q u e n c e s of failures

in such industry.

The general conception of the function of m aintenance is to prevent the

failure of occurrence, which is correct to so m e extent. However, to clearly

identify the role of m aintenance, considerations to the re a so n s of failure,

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operato r and a s a s e q u e n c e of imperfect m aintenance planning should be

analysed. Therefore, the role of m aintenance is to create a program m e that

utilizes the equipm ent productivity, to minimise th e interruption to the

production line and within the least spending.

In th e petroleum industry, m aintenance plays a significant role to bring th e

a s s e ts reliability and availability into a desirable predefined level while

decreasin g the e x p en se s. In order to explicitly understand th e problem th e

author sum m arizes it in th e following questions:

1. W hat a re th e major activities that should be included within the

integrated m aintenance fram ework?

2. W hat is the m ost appropriate m aintenance philosophy th at can

com prehensively lead to the selection of th e m ost appropriate

m aintenance policy?

3. W hat is the m ost suitable m ethodology to recognize th e optimum

m aintenance intervals?

4. As the sp are parts have a big portion of the m ain ten an ce's cost, w hat

is the m ost appropriate approach to optimise th e sp a re p arts

inventory used for m aintenance?

5. Finally, risk a s s e s s m e n t is o n e of the major activities with the

petroleum industry and, therefore, how can it be improved to reflect

the likelihood and c o n se q u e n ce s of the failures?

Solving th e se problem s d em an d s a successful cooperation betw een

m aintenance departm ent and other involved dep artm en ts such a s inventory

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generic integrated framework is proposed and the next section d isc u sse s the

main aim and objectives of building the framework.

1.4 Aims and Objectives

1.4.1 Aim

The principle aim of this research is to develop an integrated fram ew ork that

optim ises major m aintenance activities a t strategic level, operational level,

inventory control and risk a s s e s s m e n t within the petroleum co m p an ies in

order to maximize availability, reliability, and safety of a s s e ts while

minimizing th e co st of m aintenance.

1.4.2 Objectives

In order to achieve the aim stated above, the following objectives will be

undertaken to optimise th e m ajor m aintenance activities within petroleum

industry:-D evelopm ent of a strategic m aintenance policy for petroleum

industry which covers the whole a re a of the field by using classic

and fuzzy analytical hierarchy p ro c e ss to identify criteria, s u b

criteria and alternatives that can lead to the optimisation of

m aintenance policy's selection.

D evelopm ent o f a m athem atical model for the operational level to

identify the optimum interval at which th e preventive m ainten an ce

activities should be carried out (schedule of preventive

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D evelopm ent of an integrated approach betw een m aintenance and

inventory m an ag em en t to control th e availability and the level of

sp a re parts required at the time of m aintenance activities.

D evelopm ent of a risk a s s e s s m e n t model th at guides the

m aintenance on a ss e ss in g th e risk for the petroleum equipm ent.

1.5 Structure of the Thesis

This section outlines the structure of the thesis including a sum m ary of each

chapter. It provides guidance to th e read er to understand th e direction of th e

study and know the se q u e n c e and placem ent of various concepts.

1.5.1

Chapter One: Introduction

The author provides th e re a d er with an introduction to the re search focus, the

aim s and objectives and th e context of th e study. In addition, the ch ap ter

provides the re a d er with the problem definition and th e research qu estion s

that interested the author to carry out this research .

1.5.2 Chapter Two: Literature Review

C hapter two com prehensively covers related literature review regarding

m aintenance generally and within the petroleum industry. It includes the

various strateg ies for th e selection of the m aintenance policies and different

m ethods for the scheduling of preventive m aintenance activities. M oreover,

th e ch ap ter provides literature review related to th e inventory m a n ag em en t

and existing fram ew orks for controlling the policy of sp a re parts a s well a s

insights to m odels and fram ew orks for controlling m aintenance within th e

petroleum industry. The a s s e s s m e n t of risk is covered including th e

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industry. Finally, th e conclusion is drawn and the research g a p s are

e m p hasised .

1.5.3 Chapter Three: Research Methodologies and the Proposed

Integrated Framework

T he third ch ap ter d em o n strates concisely each m ethodology u sed to

optim ise a certain activity within th e proposed integrated m aintenance

framework. As th e major m aintenance activities a re targeted to be optimised,

e ach activity’s m ethod will be outlined and later in corresponding chapters,

th e se m ethods will be extensively illustrated. The proposed integrated

m aintenance framework is then p resen ted with an outline of th e relevant

term inologies fhat a re u sed such a s, optimisation and the required data.

1.5.4 Chapter Four: Maintenance Strategic Level

In this chapter, the problem of the selection of the m ost appropriate

m aintenance policy is presen ted . The proposed model for the selection of the

m aintenance policy within the petroleum industry is defined and th e

application of different classic analytic hierarchy "Pairwise Matrix Evaluation"

m ethods is dem onstrated. Fuzzy analytic hierarchy p ro c e ss is applied and

com pared a t the end of the ch ap ter to the results calculated from classic AHP

including sensitivity analysis.

1.5.5 Chapter Five: Maintenance Operational Level

C hapter five d em o n strates th e proposed m athem atical model for the

optimisation of preventive m aintenance scheduling intervals. T he

m athem atical model is explained and th e developed eq u atio n s a re

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a re dem onstrated and a t th e end of th e chapter, the author draw s the

conclusion of th e applied m ethod.

1.5.6 Chapter Six: An Integrated Approach between Preventive

Maintenance and Spare Part Control

C hapter six illustrates th e integrated approach betw een m aintenance

intervals and sp a re parts control. It d isc u sse s the relationship betw een the

m aintenance intervals and the m ovem ent of sp are parts. A c a s e study is

p resen ted to show the im pact of the integrated approach on the inventory

level with different assu m p tio n s of lead-time.

1.5.7 Chapter Seven: Risk Assessment Model for Petroleum Equipment

C hapter sev en p resen ts the proposed risk a s s e s s m e n t model for equipm ent

within the petroleum industry. A developm ent of risk equation is p re sen te d to

a s s e s s the likelihood of th e risk b e sid e s qualitative risk a s s e s s m e n t. The

c o n se q u e n c e s of the risk are also evaluated a s a loss on four main a re a s

including system perform ance loss, financial loss, hum an health lo ss and

environm ent loss.

1.5.8 Chapter Eight: Conclusions, Contribution to knowledge and Future

Work

C hapter eighth sum m arizes the conclusion of th e th esis and the outline of th e

proposed m ethodologies and contribution to knowledge within th e th e sis a re

d iscu ssed . The recom m endations for future work that a re related and

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1.6 Conclusion

In this C hapter, an introduction to the petroleum industry and m ainten an ce's

im portance w ere provided. The historical developm ent of the petroleum

industry and m aintenance philosophy w ere d iscu ssed to understand the

nature of such competitive industry. T he definition of the problem, th e aim s

and objectives w ere d iscu ssed to highlight the main targ et of the th esis. The

structure of the th esis in term s of ch ap ters w as outlined to simplify it for the

read ers. The next ch ap ter will deliver literature review and latest

developm ents in m aintenance and th e related topics a sso c iate d with the

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Chapter Two

Literature Review

This ch ap ter provides a com prehensive background to

th e m aintenance within the petroleum industry. The

definition of m aintenance, its policies and its role within

th e petroleum industry a re extensively dem onstrated.

T he literature review also covers topics that a re related to

th e selection of m aintenance policy, determ ining the

optimum m aintenance interval, sp are p arts control and

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2.1 Introduction

C ontem porary b u sin ess environm ent h a s raised th e strategic im portance of

th e m aintenance function in organizations that have significant investm ent in

physical a s s e ts . The high co st proportion of m aintenance in operation c o sts

within m anufacturing and production industries h a s drawn attention to the

im portance of planning and controlling of m aintenance actions to minimize

th e operational costs.

The aim of this ch ap ter is to p resen t a com prehensive and yet concise

literature review of the major a re a s that related to m aintenance in general

and within the petroleum secto r in particular. The notion for this ch ap ter is to

provide the re a d ers of this th esis with a background of the relevant topics to

provide a better understanding of the contribution su g g ested by the author.

The chapter starts with a background of m aintenance m an ag em en t within the

oil and g a s industry, including the history of m aintenance, th e current existing

strateg ies to sele c t m ain ten an ce's policy and the m ethods u sed to sele c t the

time of conducting m aintenance overhauls w here the majority of the

m aintenance actions a re carried out. This ch ap ter ex ten d s to explain

inventory m an ag em en t and relevant topics such a s inventory control,

econom ic reorder point and safety stocks. A nother a sp e c t covered within th e

literature review is risk a s s e s s m e n t within the petroleum industry and

different m ethodologies u sed to estim ate the likelihood and the

c o n se q u e n c e s of the risks.

2.2 Definition o f Maintenance

T he term m aintenance can be defined a s all actions appropriate for retaining

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2002). M arquez (2007) defined m aintenance m an ag em en t (MM) a s all

activities of the m an ag em en t that determ ine m aintenance objective or

priorities assig n ed and accep ted by the m an ag em ent and m aintenance

team s, "strategies" defined a s the m an ag em en t m ethodology to achieve

m aintenance objectives, and responsibilities and th e im plem entations m e an s

such a s m aintenance planning and m aintenance control. A debim pe et al

(2015) extended on the definition of m aintenance and its actions that

includes th e repair of broken equipm ent, the preservation of equipm ent

conditions and the prevention of their failure. This ultimately re d u c es

production lo sse s and downtime and also red u ces environm ental and

asso ciated safety h azard s. Due to the increasing p re ssu re of high

competition and stringent environmental and safety regulations, m aintenance

h as shifted from being perceived a s a “n e ce ssa ry evil” to being recognized a s

an effective tool for increased profitability. M aintenance h a s b ecom e an

integrated part of the production p ro c e ss rather than a supporting or

peripheral activity. Developing effective and optimum m aintenance strateg ies

and m odels h a s thus becom e a subject of research in both acad em ic and

industry a re a s .

2.3 Maintenance Policies

T rem endous ch an g e have occurred in engineering since the industrial

revolution, but p erh ap s the m ost dram atic c h an g e s have occurred in th e last

sixty y ears (Parida and Kumar 2006). As a result of th e se c h a n g e s a s well a s

th e growth of the complexity of a s s e ts , m aintenance m an ag em en t h a s

developed to maintain the plant's m ore complicated m achinery with re sp e c t

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Important reaso n of the developm ent of m aintenance is the dem and on

productivity, availability, quality, safety and environm ent (Arunraj and Maiti

2007). M aintenance can be categorised into different classifications

according to its actions. M aintenance can mainly b e classified into two types:

Corrective M aintenance (CM) and Preventive M aintenance (PM)

(W aeyenbergh and Pintelon 2004; Li e t al., 2006). Planned m aintenance

could b e tim e-based or condition-based (Duffuaa e t al., 1998). Each plant's

a s s e ts or equipm ent can be asso ciated with o n e or m ore m aintenance policy

throughout its lifecycle (H ossam et al 2003).

The m an ag em en t system of Point Inspection and R egular R epair (PIRR) is

regarded a s the p resen t core of m aintenance and m an ag em en t for o n shore

and offshore oil field equipm ent, which mainly ad o p t corrective m aintenance

(CM), Tim e-Based M aintenance (TBM) and Condition-Based M aintenance

(CBM) (P errons e t al 2013 and D oostparast e t al 2014).

2.3.1 Corrective Maintenance (CM)

In the c a s e of CM policy, an item is allowed to fail before m aintenance is

im plem ented. CM is still being used until today d u e to th e fact th at CM can be

useful and add value to the plant un d er certain criteria such a s th e criticality

of the m achine and th e effect of th e failure (W aeyenbergh and Pintelon,

2002). B alasah eb and Milind (2012) m entioned th at CM is th e first and the

basic policy that ap p eared in th e industry. CM is also referred to a s, failure

b a se d m aintenance, breakdown m aintenance or run to failure strategy.

2.3.2 Preventive Maintenance (PM)

PM refers to th e action of m aintenance im plem ented to prevent the

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predict the w ear and te a r or life of equipm ent by using different a p p ro a c h e s

to prevent failure from taking a place. In general, the am ount of equipm ent

failure can be reduced if the preventive m aintenance strateg ies a re correctly

selected (B alasaheb and Milind 2012). Most com m on form s of PM a re time-

b a se d m aintenance (TBM) and condition-based m aintenance (CBM).

2.3.2.1 Time-Based Maintenance (TBM)

In this policy, m aintenance is scheduled in a d v an ce to prevent failure. It

fo c u ses on preventing failures through replacing com ponents a t particular

time. It a s s u m e s that th e m achine com ponent’s life is predictable, and

m aintenance is b a se d on hours of run or calen d ar time elap sed . This is

suitable for rep eatab le degradation m odes, w ear p ro c e ss for exam ple. In this

policy replacem ent or repair is carried out a t a fixed time after th e installation

of a facility, which is generally independent of its condition. T he time period

u sed to construct a m aintenance schedule can be either calen d ar tim e or

com ponent running time (Ahmad e t al 2011).

2.3.2.2 Condition Based Maintenance (CBM)

M aintenance decision is m ad e depending on m easu red data. Vibration

monitoring, lubrication analysis, therm ography, visual inspection and

ultrasonic testing a re commonly u sed ap p ro a ch e s to collect d ata (Mobley

2002). B ased on th e d ata analysis, w henever th e monitoring level value

e x c e e d s the normal the com ponent is either repaired or replaced. T he u s e of

CBM m ay lead to considerable reductions in production cost, capital

investm ent and increm ents in the quality rate, profits, and m arket sh are.

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and accuracy of the condition-based m aintenance strategy (Alnajjar and

Alsyouf 2003).

2.4 The Role of Maintenance

"The primary objective of planned m aintenance is the minimization of total

c o st of inspection and repair, and equipm ent downtime (m easured in lost

production capacity or reduced product quality" (Mann e t al 1995).

Luxhoj e t al (1997); Pradhan and Bhol (2006) stated that the im portance of

m aintenance to industry can be m easu red by:

• Accounting of the total m aintenance cost.

• P e rcen tag e of m aintenance co st to total production cost or capital co st

in a ss e ts .

• Total num ber of personnel working with m aintenance or p ercen tag e of

m aintenance personnel to total num ber of production personnel.

• Possible c o n se q u e n ce s for lack of m aintenance: financial,

environmental, hum an, equipm ent d am age.

Bevilacqua and Bragliab (2000) described th e role of m aintenance in so m e

industries and how it is th e second highest or even the highest elem en t of

operating costs. As a result, in only 30 y e ars it h a s moved from alm ost

;

now here to th e top of co st control priority.

According to Utne et al (2012), scheduled and unplanned shutdow ns such a s

repairs, overhauls, replacem ents and require p arts to shutdow n a re the m ajor

contributor for the e x p e n s e s to oil drilling platform s and processing plants.

Apart from th e se long period m aintenance activities, sh o rter types of planned

and unplanned shutdow ns a re also c a u s e for production loss and rev en u e

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surplus or insufficient m aintenance, exorbitant m aintenance c o sts and

increasing failure frequency, which have c au se d a g reat influence to

production safety and econom ic co st in th e oil and g a s exploitation p ro cess.

2.5 M aintenance Policy Selection Problem

Effective m aintenance m an ag em en t involves a multidisciplinary ap p ro ach

w here m aintenance is viewed strategically from the overall b u sin ess

perspective to translate th o se priorities into m aintenance task s. Estimating

th e b est s e t of m aintenance policies for different failure m o d es is a hard and

com plex task. This selection requires th e knowledge of various factors such

a s safety a sp e c ts, environmental problem s, c o sts and budget constraints,

m anpow er utilization, m ean time betw een failure (MTBF) and m ean time to

repair (MTTR) for each piece of equipm ent (Bertolini and Bevilacqua 2006).

Oil and g a s com panies a s any other com panies a re in favour of optimisation

of m aintenance, to provide the highest availability of equipm ent with

minimization of the running m aintenance co st with resp ect to environm ent

and safety (Azadeh e t al 2009).

M aintenance m an ag em en t p ro cess can b e divided into two main levels:

strategic level "including th e definition of the policy to be im plem ented" and

operational level "considers the im plem entations of the strategy" (M arquez

2007). In addition to that M arquez (2007) stated that strategic level is

normally neglected by th e m aintenance departm ent which lead s to the

probability of selecting inappropriate m aintenance policy for a m achine. Hong

e t al (2012) acknow ledged th at th e wrong selection of the optimal

m aintenance policy lead s to the in crease in failure rate and also affects the

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Different a p p ro a ch e s and philosophies have been im plem ented to m anag e

m aintenance on the strategic levels within th e oil and g a s co m p an ies such a s

Reliability centred m aintenance (RCM) and risk b a se d m aintenance (RBM).

Each approach h a s its u n iq u en ess and priorities to deal with m anaging

m aintenance. In the following section, explanations of the m ost applied

a p p ro a ch e s to m an ag e m aintenance on the strategic level a re described.

2.5.1 Reliability-Centered maintenance (RCM)

Reliability-centred m aintenance (RCM) w as first developed within the aircraft

industry and later ad ap ted by several oth er industries and military b ran ch es

(R ausand 1998). Reliability-centred m aintenance (RCM) is an engineering

framework that e n ab le s th e definition of a com plete m aintenance regim e and

a s the nam e indicates, reliability is the main point (Selvik and Aven

2011).

RCM is a w ell-established analysis method for preventive m aintenance

planning. As its nam e indicates, reliability is the main point of reference for

th e planning of m aintenance. However, c o n se q u e n c e s of failures a re also

a s s e s s e d . RCM is o ne of preventive m aintenance strateg ies to incorporate

new understanding to the w ays equipm ent fail (D eshpande an d M odak

2002).

The m ethodology h a s been available in the industry for over

30

y ears, and

h a s proved to offer an efficient strategy for preventive m ain ten an ce

optimisation . RCM is a technique initially developed by th e airline industry

that fo c u ses on prevention of failures w hose c o n se q u e n c e s a re likely to be

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2.5.1.1

Reliability Centered Maintenance Phases

The RCM analysis p ro c e ss fo c u se s on the functions of plant and equipm ent,

the c o n se q u e n c e s of failure and m e a su re s to prevent or cope with functional

failure. Moubray (1997) identified th e ste p s to implement RCM including

determ ining key functions and perform ance stan d ard s, determining possible

function failures, determining likely failure m o d es and their effects, selecting

feasible and effective m aintenance strategies, scheduling and implementing

selected strategies, and optimising tactics and program s. Selvik and Aven

(2011) sum m arised the p h a se s of RCM methodology by th e following th ree

p h a se s.

• Identification of M aintenance Significant Items (MSI).

• A ssignm ent of suitable PM ta sk s for the MSI.

• Implementation and update of th e PM tasks.

Moubray (2000) su g g ested th at d u e to the time consum ing nature of the

classical failure m ode effective and critical analysis (FMECA), in m any p laces

only critical equipm ent a re analysed by RCM method. R ausand (2008) stated

that RCM h a s significant start-up c o sts asso ciated with staff training and

equipm ent n eed s, a s well a s sav in gs potential is not readily s e e n by

m anag em en t which can be a huge d isad v a n ta g e of using RCM. Selvik and

Aven (2011) stated that one of th e shortcom ings of RCM is traced to the

limited a s s e s s m e n ts of risk and uncertainties. Jag ath y and D eepak (2013)

su g g ested that refineries and p ro cess plants find it difficult to ad o p t this

standardized m ethodology of RCM mainly d u e to the complexity and the

large am ount of analysis th at n e e d s to be done, resulting in a long draw n out

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2.5.2 Risk Based Maintenance (RBM)

K rishnasam y et al (2005) defined RBM a s th e selection of m aintenance

policy depending on the a s s e s s m e n t of the risk. Dey (2001) developed a risk

b a se d model that red u ces the am ount of time sp en t on inspection and to

reduce the co st of maintaining petroleum pipelines. He u sed Analytic

Hierarchy P ro ce ss (AHP) to identify th e factors th at influence failure on

specific seg m e n ts and analyzed their effects by determining probability of risk

factors and the severity of failure is determ ined through co n se q u e n ce

analysis. Accordingly he su g g este d th e inspection time and the m aintenance

type for the pipeline.

Tixier e t al (2002) listed and identified different risk analysis m ethodologies,

and categorized them from diverse referen ces into deterministic, probabilistic,

and combination of deterministic and probabilistic ap p ro ach es. The main aim

of this m ethodology is to reduce th e overall risk th at m ay result a s the

co n se q u e n ce of unexpected failures of operating facilities (Khan and

H addara, 2004). They divided RBM m ethodology into four m odules:

identification of the scope, risk a ss e ss m e n t, risk evaluation, and m aintenance

planning. Dey et al (2004) u sed risk-based m aintenance model to sele c t

specific inspection and m aintenance m ethod for specific section in line with

its probability and severity of failure for oil and g a s pipeline in the Gulf of

Thailand. Their work cam e to g e n erate guidelines for planning th e pipeline

m aintenance program (Arunraj and Maiti, 2007). The inspection and

m aintenance activities which are planned using RBM a re prioritized on the

b asis of quantified risk c au se d du e to failure of the com ponents, so th at th e

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maintained usually with g reater frequency and thoroughness and a re

m aintained in a g reater m anner, to achieve tolerable risk criteria. They

classified th ree categ o ries of the method (deterministic, probabilistic and

determ inistic and probabilistic) into qualitative, quantitative and sem i-

quantitative and identified the risk-based m aintenance m ethodology to

consist of six m odules which are - hazard analysis, likelihood a s s e ss m e n t,

c o n se q u e n ce a ss e ss m e n t, risk estim ation, risk acc e p tan c e and m aintenance

planning.

2.5.3 Multi Criteria Decision Making (MCDM)

Multi Criteria Decision Making (MCDM) refers to finding of th e b e st solution

or decision to a problem from all of th e feasible alternatives in the p re se n c e

of multiple, usually conflicting, decision criteria. Priority-based, outranking,

d istan ce-b ased and mixed m ethods could b e considered a s the primary

c la s s e s of the current m ethods. T he MCDM techniques generally allow to

structure the problem clearly and system atically. With this characteristic,

decision m akers have th e possibility to easily exam ine and scale the problem

in acco rd an ce with their requirem ents (l§iklar and Buyukozkan

2007).

Cinelli

e t al

(2014)

stated that in their com parison study betw een MCDM tools "the

review has shown that there is not a clear agreement among different

authors concerning som e comparison criteria". Therefore, th e selection of a

certain MCDM tools to fit the problem under investigation is an essen tial step

to e n su re th e utility of each tool. V elasquez and H ester

(2013)

and Cinelli e t

al

(2014)

presen ted an analytical com parison study of multi-criteria decision

making m ethods including the stren g th s and w e ak n e ss of each m ethod a s

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Table 2-1: Comparison between MCDM Tools

Method Strengths Weakness

Analytic Hierarch Process (AHP)

• Availability of software with good graphical capabilities • Clear hierarchy structure. • Use of qualitative

• Possibility of trades of between criteria

• Rank reversal can occur

•The use of rigid scale might not reflect uncertainty

Analytic Network Analysis ANP

•Applicable in the case of strong horizontal

interrelationship between sub-criteria

• It requires a large amount of questionnaire to fill in. • It might be too complicated Multi Attribute

Utility Theory (MAUT)

•Availability of software •Takes uncertainty into

account

• Can incorporate preferences.

• Limited graphical capabilities • Needs a lot of input

• Preferences need to be precise

• Not flexible in terms of trade offs between criteria

• Possibility of re-evaluating results if new information becomes available is limited Case-Based

Reasoning (CBR)

• Not data intensive

• Requires little maintenance • Can improve over time • Can adapt to changes in

environment

• Sensitive to inconsistent data • Requires many cases

Goal

Programming (GP)

• Capable of handling large- scale problems

• Can produce infinite alternatives

• It’s ability to weight coefficients

•Typically needs to be used in combination with other MCDM methods to weight coefficients

Elimination and Choice Expressing Reality

(ELECTRE)

•Takes uncertainty and vagueness into account

• Its process and outcome can be difficult to explain in layman’s terms

• Outranking causes the strengths and

weaknesses of the alternatives to not be directly identified Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE)

• Easy to use; • Does not require

assumption that criteria are proportionate

• Does not provide a clear method by which to assign weights

Technique for Order

Preferences by Similarity to Ideal Solutions (TOPSIS)

• Has a simple process • Easy to use and program • The number of steps

remains the same

regardless of the number of attributes

• Its use of Euclidean Distance does not consider the correlation of attributes • Difficult to weight and

[image:42.614.58.557.45.743.2]

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2.5.4 Analytic Hierarchy Process (AHP)

The analytic hierarchy p ro c e ss (AHP) is a structured technique for organizing

and analysing complex decisions, b a se d on m athem atics and psychology. It

w as developed by T hom as L. S aaty in th e 1970s and h a s been extensively

studied and refined since then (Wind and S aaty 1980). S aaty (1982)

discu ssed the application of AHP and m entioned that the selection of

m aintenance policy for a pipeline to be a team effort which require a group of

people to m ake the decision.

S aaty (1990) stated th at t AHP not only helps th e analysts to arrive a t the

b e st decision, but also provides a clear rationale for the choices m ad e a s well

a s en ab le s th e analyst to m easu re consistency. (AHP) (Torfi et al 2010).

Decision-making b eco m es m ore sophisticated since the selection m ethods

have different level of p referen ces in m entioned criteria (Shaverdi and Barzin

2012).

Bevilacqua and Braglia (2000) m entioned th e main s te p s of Analytic

hierarchy p ro c e ss AHP th at S aaty had identified:

2.5.4.1 Step 1: Model the Hierarchy

Structure:-This is do n e by including the main goal a t the top, followed by th e main

criteria, sub criteria and alternatives. U nderstanding th e nature of the industry

is important to identify th e elem en ts of each level.

2.5.4.2 Step 2: Establish Priorities among the Elements of the Hierarchy

This is perform ed by making a series of judgm ents b a se d on pairwise

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determ ine the im portance of alternatives' utilities in the context of specific

criteria.

2.5.4.3 Step 3: Check the Consistency of the Matrix

The AHP en ab le s th e an alysts to evaluate ju d g em en ts with th e consistency

ratio (CR). The ju dg em en ts can be considered accep tab le if consistency ratio

is less than 0.1 (“10%”), and if it is m ore than 10% then th e m atrices which

have the expert's judgm ents a re not accep tab le and should b e carried out

again. S aaty (1977) h a s proposed a consistency index (Cl), which is related

to the eigenvalue m ethod (Ishizaka and Labib 2009).

2.5.4.4 Step 4: Pairwise Matrix Evaluation

O nce a judgem ent matrix h a s been developed, a priority vector to w eight th e

elem en ts of the matrix is calculated. Several m ethods for deriving local

priorities ("the local w eights of criteria and the local sco res of alternatives")

from judgm ent m atrices have been developed. W ang e t al (2007) listed so m e

of the pairwise matrix evaluation method:

Mean of the normalised values (MNV).

The norm alised geom etric m ean (NGM).

The Eigenvector Method (EVM).

2.5.4.4.1 Mean of the Normalised Values (MNV)

This method is considered to be the oldest m ethod (Ishizaka and Lusti 2006).

T he m ethod consists of th ree main s te p s a s followed:

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All the elem ents of the column j of the matrix a re sum m ed up a s shown in

equation (2-1) (Ishizaka and Labib 2009). In this c ase , the com parison of the

alternatives i and j is given by Pi/Pj

W

here:-i and j a re any alternatives of th e matrix.

pi is the priority of the alternative i.

2. Normalization of the column j

In this step, the normalized value is calculated by dividing