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T HE U S E OF R IS K B A S E D A S S E S S M E N T T E C HN IQU E S T O OP T IMIS E

IN S P E C T ION R E GIME S

G R Bennett, M L Middleton, P Topalis

Det Norske Veritas. Stockport Technical Consultancy, Highbank House. Exchange Street,

Stockport, SK3 OET.

Regulators and insurance companies now recognise the acceptability

of a risk based approach to the optimisation of inspection and

maintenance intervals. Qualitative approaches to Risk Based

Inspection (RBI) have been developed for general use and more

detailed quantitative methods exist for activities with major loss

potential or large preventive expenditure requirem ents. DN V

pioneered the use of RBI in the chemical process industry in 1992 and

has produced a resource document for the API (API 581). DNV

subseque ntly further develo ped Quantified RBI software. A number

of case studies and client projects have been conducted and the RBI

techniques and software are becoming valuable tools for the oil and

chemical industry.

Keyw ords: Risk Based Inspection, Inspection Planning ,

Consequence, Likelihood, Corrosion

WHY CONSIDER RISK IN INSPECTION PLANNING ?

The first duty of business is to survive and the guiding principle of business econom ics is

not the maxim isation of profit, it is the avoidance of loss

(P e te r Dru c k e r )

All industrial organisations use people, equipment and property to add value to a

commodity and thus generate a return on their investments. If the returns are in excess of

the costs, the com pany is in profit, if they are less, a loss occ urs and if the losses co ntin ue

to be sustained, the organisation will ultimately fail.

People and equipment are not infallible. People do make m istakes and equipment does

fail. All mistakes and failures have consequences, some sm all and others large. The

magnitude of those consequences influences the ability of the organisation to achieve a

sustained profit and thus survive . It is therefore good business prac tice to consider the

consequences of failures and how likely they are to occur, i.e. to systematically consider

the risks arising from failures.

All forward thinking organisations practice some form of loss avoidance policy.

Equipment maintenance, inspection and testing, and the training of personnel are all

undertaken to avoid failures and red uce or m itigate their associated losses

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How many organisations however, know in detail how effective their investment in

loss prevention activity is ? Does the extent and frequency of inspection reflect the

mag nitude of the conseq uen ces of an undesired failure ? Is the inspection activity likely to

identify the degradation mechanisms that exist ? Do the planned maintenance routines

actually influence the chance of the equipmen t breaking dow n ? Is mo ney be ing spent on

inspecting and maintaining equipment which, if it fails, has very little effect on the

organisation ?

An organisation's abili ty to consider these questions has traditionally been driven by

legislative rather than business need. For exam ple, inspection of pressure retaining

equipment or structures has been primarily calendar based, driven purely by legislative

requiremen ts. This no longer needs to be the case. The new goal setting legislative

environm ent provides the opportunity to plan loss avoidance activities by linking any

increase in the level of activity, to the reduction in risk ach ieve d by that increased level

of activity.

Both regulators and insurance companies now recognise the acceptabili ty of a risk

based approach to the optimisation of maintenance.

R ISK B A SED I N SPEC TI O N MET H O D S

Risk Based Inspection (RBI) techniques have been developed along two complementary

routes. Qualitative approaches to RBI have been developed for general use and detailed

quantitative methods have been developed and are being refined for activities with major

loss potential or large preventive expend iture requirements. This paper will con centrate

on recent developments in the quantitative assessment approach.

QUALITATIVE RISK BASED INSPECTION

Qualitative RBI is based on answering a series of questions regarding likelihood of failure

and the consequen ces of failure and assigning notional levels ( High / Med ium / Low ) to

the answers to place the item on a risk matrix, see Figure 1.

The closer an item is to the top right corner of the matrix, the more critical the item is,

and the greater the inspection activity warranted. It should be noted how ever, that chan ges

to inspection regimes can only effect the frequency of an event, and not its consequences.

Therefore if an item is in the high risk category primarily due to it 's consequences, then no

amount of additional inspection will improve it , and design changes to the system may be

required instead. Th is statemen t is equa lly true for qua ntitative assessm ent techn iques .

Software packages are now available which allow a qualitative estimate of inspection

frequency to be made. They vary in complexity, and in the degree of "engineering

judgement" to be applied, but they can be used as an effective screening tool in order to

determine w hich equipm ent should be subjected to a more detailed quantified assessment.

An example screen shot from o ne of the DNV software p ackag es is shown in Figure 2.

The software is driven simply by the selection of various options from drop down

menus, which prompt the user to select the most appropriate category for equipment type.

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location, fluid type and inventory, business interruption consequence, and specific details

relating to the material of construction of the equipment, and its susceptibility to various

failure m ech anis ms . Based up on these inpu ts, the software then use s a set of pre-defined

rule sets to calculate a risk ranking and a corresponding recommendation for the next

inspection interval.

THE QUANTIFIED RISK BASED INSPECTION METHOD

The Quantified Risk Based Inspection philosophy and method has been endorsed and is

being promoted by the American Petroleum Institute Committee on Refining Equipment.

The group is comprised of representatives from the following com panie s:

Amoco

Aramco

Arco

Ashland

BP

Chevron

Citgo

Conoco

Dow

DNO Heather

DSM

Exxon

Fina

Koch

Marathon

Mobil

Pennzoil

Petro Canada

Phillips

Shell

Sun

Texaco

Unocal

DNV pioneered the use of RBI in the chem ical process industry in 1 992. In 1993 the

API approach ed D NV with the reque st to join tly fund a larger dev elop me nt effort, aimed

at producing a resource document for how to establish risk based inspection in the

petroleum industry. DN V obliged, and in 1994 it produced the Base Resource Docum ent

on Risk-Based Inspection. This document is now being promoted by API as a standard

referred to as API 581, it will be followed shortly by API 580 which will become an API

Recommended Practice.

In 1995, the API commissioned DNV to develop software for i ts members to allow

them to automate some of the processes required by API 581 . This software based system

has now been used successfully on a number of studies conducted both by DNV, and the

sponsor group mem bers. DNV d ecided to further develop the Quantified R isk Based

Inspection Software in 1996. The new software, called OR BIT w as initially released in

March 1998 and is available to the industry as part of an integrated package of Risk Based

Inspection services. ORB IT provides a fast interpolation approac h for consequ ence

analysis.

The quantified RBI approach provides a methodology for determining the optimum

combination of inspection method s and frequencies. Each available inspection method

can be analysed and its relative effectiveness in reducing failure frequency can be

estimated . Give n this information and the cost of each proce dur e, an optimisation

program can be developed. The key to developing such a procedure is the abili ty to

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quantify the risk associated with each item of equipment and then to determine the most

appropriate inspection techniques for that piece of equipment.

SY STEMA TI C A PPR O A C H TO R ED U C I N G R I SK S

A fully integrated Risk Based Inspection system should contain the steps shown in Figure

3. The system includ es inspe ction activities, inspection data collection and updatin g, and

continuous quality improv emen t of the system. Risk analysis is "state of kn ow ledg e"

specific and, since processes and systems are changing with time, any risk study can only

reflect the situation at the time the data were collected . Althou gh any syste m, whe n first

established, may lack some needed data, the risk based inspection program can be

established based on the available information, using conservative assumptions for

unkno wns. A s know ledge is gained from inspection and testing prog ram s and the

database imp rove s, unce rtainty in the analys is will be reduced. This results in reduced

uncertainty in the calculated risks.

The combination of elements required as inputs to a quantitative RBI analysis are

shown in Figure 4.

The two major elements of a quantitative RBI analysis, as with any risk based study are

an assessment of the probability (or l ikelihood) of an event occurring, and its

consequences should it occur.

LIKELIHOOD ANALYSIS

When considering the likelihood of a failure occurring, the RBI process utilises a series of

technical modules, in order to establish a dam age rate for the equipment. Th e calculated

dam age rate depends upon the item's material of construction, the process fluids it is

exposed to, i ts external environment and the process conditions (pressure, temperature

etc.).

Details of the current technical mod ules are shown in Figure 5.

It is also necessary to consider the current inspection regime, and to identify when the

equipment was last inspected, how it was inspected, and what the results of those

inspections were.

For example, if an equipment is subject to damage by corrosion or erosion, and if no

inspections have been performed, then the likelihood of failure may be high. If however,

many inspections of sufficient quality have been performed ( and the equipment still meets

its design intent ) then the likelihood of failure will be quite low, even if there has been

significant corrosion, as the rate of corrosion will be well understood.

Inspection activity is not however guaranteed to provide precise details of actual

corrosion rates, each inspection h as an error band associated with the particular technique.

These error bands can be established by trials and review of historical surve ys. Inspection

activity does not change a corrosion rate, it reduces the error band and increases our

confidence that we k now th e actual corrosion rate.

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Statistical m ethods can be used to evaluate the likelihood that damage severe enoug h to

cause a failure could exist given the am oun t of app ropria te inspection activity that has

been performed. As the damage rate is t ime based, future inspection techniques and

intervals can be planned based upon the amount of damage expected to be seen at some

point in the future. A proper balance must be established between advancing damage and

increased knowledge of the amount of damage, to ensure safe and economic operation.

An exam ple of a screen shot of the equipmen t details from the equipmen t specification

mo dule of the software is shown in Figu re 6. A screen shot of the likelihood m odu le is

shown in Figure 7.

C O N SEQ U EN C E A N A LY SI S

The consequ ence analysis conducted w ithin the RBI software is based upon look-up tables

calculated using the DNV software package

PHAST .

Conseq uences are calculated in

terms of the area of equipment damage, and the areas within which personnel will be

adversely affected by flames, explosions, or the toxic effects of the product concerned.

Using the input data on process pressure and temperature, material properties, and

inventories, the system determines the release rate for a range of representative hole siz es,

and also determines the release type. After d etermining w hether a release is continu ous or

instantaneous (as in a vessel rupture), the software calculates the final phase in the

environm ent (liquid or gas) and then determines the toxic or flammable co nsequ ences. In

evaluating the consequences, the software also allows modeling of account mitigating

features such as isolation and shu tdown systems.

An example of the Consequence data module can be seen in Figure 8.

R I SK A SSESSMEN T

Having evaluated both the likelihood of an event, and its consequences, the system then

combines this data to produce the overall risk evaluation for each piece of equipment.

This allows the assessment of the overall risk levels of the plant, and the identification of

whe re the high risk items on the plant are. so that inspection effort can be focused initially

on the high risk items. Various reports can be automatically g enerated to produ ce a w ide

range of analyses. These reports include:

Action damag e/mechan ism summary reports.

Financ ial risks.

Inspection planning.

Risk rank ing.

Graphs can also be produced for specific i tems of equipment showing the optimised

number of inspections (cost of risk per number of inspections versus years of inspection)

and the percentage of equipment versus the percentage of risk. An exam ple plot is

illustrated in Figure 10. The curve demonstrates the general principle that 80-90% of the

risk is contributed by only 10-20% of a plants fixed equipment.

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PLA N T EX PER I EN C E

It should be clear from the forgoing that regardless of whether a qualitative or quantitative

approach is followed, i t cannot be implemented without the active involvement of

personnel familiar with the plant and its operation. Corrosion engineering experience is

required to determine the damage mechanisms possible. Inspection management personnel

are required to extract the knowledge of past inspection, and operations personnel are

required to assist with establishing the safety and prod uction im plication s of failures. It

must be a team effort, in order to be effective.

CASE STUDIES

DNV has now completed a number of botii pilot and full scale studies, in order to validate

the data in the model, and to evaluate the usage of the technique. Results demo nstrate that

the RBI techniques and software are becoming valuable tools for the oil and chemical

industry.

On one site, an RBI analysis of nearly 2,000 piping sections in an ethylene plant

show ed that less than 10% fell into the high risk category. Failure of those items

cons tituted a busines s interruption and asset dam age risk of SI 1.5 million per year. Th e

application of improved inspection techniques reduced this risk to $4.1 million per year, a

saving of S7.4 million per year. The re was of course a cost associated with this risk

reduction, and the improved inspection technology utlised was estimated to cost $250,000

per year. In this case the benefits clearly outw eigh ed the cos ts. On the other hand, a

review of the bottom 10% of risk items showed that the application of the same inspection

techniques could still result in a risk reduction from $12,000 per year, to $4,300 per year a

saving of $7,700 per year. Ho wev er the costs of the improved inspection of those items

would still cost $250,000, and this was clearly not cost effective.

On another site. 10 vessels were removed from the annual inspection plan, at an annual

cost saving of $25,000 per vessel, and some pipework materials were upgraded at a 20%

increased cost of materials, but avoiding a possible $3 million loss in bu siness

interruption.

S U M M A R Y

The RBI methodology and its supporting computer program has already gone a long way

toward an integrated risk management program.

The technology is sti l l being developed by DNV, and the author wishes to take this

opportunity to thank the RBI sponsor group for their advice and support during the

develo pm ent phase. The RBI philosophy and database has drawn upon the experience of

both DNV and the project sponsors in order to evaluate failure mechanisms, corrosion

rates,

consequences etc.

The author would also like to acknowledge the contribution of his colleagues Mark

Middleton (DNV Stockport), Panos Topalis (DNV Software Products), Angus Lyon

(DNV Aberdeen) and Gert Koppen (DNV Rotterdam) to the production of this paper.

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Figure 1 Exam ple Risk Ma trix

Increasing

Likelihood 3

2

1

; MecJiium@l ' m

w s . w

: : -

. .V... ,

. ; . .

i

. - - . V .

Mediun

1 2

3 4 5

Increasing Consequence

Figure Example of Qualitative RBI Software

R~~

z

qipmenH den#co(Jon

3

C iM e* tao af

C a ra a ue n C s t h e o r y f c ? ;R f c M & f e l M dH i g h R i t k

-Irw&oCtcn RocomrandabcfT:

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Fi gur e3 Risk Based Inspec t ion Pr og ram for In-Service Eq uip me nt

-

PLANT DATABASE

RISK BASED PRIORITISATION

INSPECTION PLANNING '

r

INSPECTION RESU LTS

FITNESS FOR SERVICE

I

- . ' a

a i - .

-.

INSPECTION UPDATING

. - . . , ' . . .

3

SYSTEM AUDIT

Fi gur e4 Overview of Risk Based Inspect ion

125.vsd

; . - . . * .

:

. . . . . - . . .

PROCESS SAFETY

MANAGEMENT

IMPROVEMENTS

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Figure 5 RBI Technical Modules

Thinning

Modu le

Corrosion

U nder

Insulla l ion

Techn ica l M odu les

Set -up

Stress

Corros ion

Cracking

High

Temperature

Hydrogen

Attack

Fatigue

Brittle

Fracture

No

Me ch a n isms

App ly

HCI

HT Sulf idat ion

HT H.S/H.

H j S O ,

HF

Sour Water

Amine

HT Oxidat ion

C U I

Caustic H

Amine

Carbonate

Sulphide

HIC/SOHIC - H

3

S

HSC/HF

HIC/SOHIC - HF

Fatigue

Brittle

Fracture

Figure 6 General Equipment Module

' ; Fquipm ent Deta i l D ata

Consequence

e n e r a l

Likelihood

Results

- Tempetatuie and Pressute -

Op tempetature: J293

Op pressuie:

102000

Design temperatuie: |263

Design pressure:

Miri.

|263

|99000

Max.

|323

|103000

K

Pa

T empetatuie and Pressure Check j

-Add i t iona l Data

Material ot ICarbon S teel

Construction;

PJID-

ZJ

100-1000 Re v

1

Equipment Dimensions-

Wall Thickness: [5

Tank Sha pe: |Verti

Internal Length : ("

Internal Diameter lg.0

Internal Height |3.rj

D a t e ;

]Current Service Starting Data

11987-01 04 1

; Insulated fr-es) I~ PWHT [Vast [""

;

Exterior Coating fTest 17 Notmalized

[Yes):

T j

: Vessel Lining

(Yes):

l~" Impact Test (Yes ): F \

3

Help-1

Notes

5 1 1

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Fi gur e7 Like l ihood Analys i s Module

8

E q u i p m e n t D e t a i l D a t a

eneia l J L ik e l ih ood Consequence

- D a m a g e - T y p e s

Jhinnkig

SCC

I - Failure Freauency -

U iiltfe Fracture

r

No M echanisms App(y

3

r

Small

Medium

Large

Rupture

Leak Frequency

(peiyeaf|

4.00E-05

1.00E-Q4

100E-05

200E-O5

Cfosa

Help

Fi gur e8 Consequence Analys i s Module

I t q u r p m e n t D e t a i l D a t a

Senetal ] Lfcelihood Consequence]_ Results

Chemical Name : |KER0SENE

Look up Table Name: |Keiosene

Inibal Fluid State: jLiquid Vesse l Type : [Pressurized Liquid

Chemical.

Vessel TypeandInitial Fluid Sta te

Method: |

Lookup Melhod 3

Inventory -

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Figure 9 Results Module

Eq u ip me n t De ta i l Da ta

:

U k d h o o d R esu K s

Ukelhood Categoiy

Consequence Resul t s

Area Based Results

3.81E-03 Events/yeaf

i C o n s eq u en ce

F a c t a

3 6 8 . 0 0 1 s q .

Categoiy ;

2 2 4 1

j Equipment Damage:

FataU y:

ToacAfea:

1.13E*Q2

| s q m

|

3.68E-02

I sqm

[

O.O0E+OQ

| sq m" j

2241

Inspection Plann ing Calculations

Inspection Planning J

.

CJoae Help Notes

F ig u re

10

Risk Results

Percentage of Equipmentvs.Percentage o f Risk

i

I

s,

100

o~

80

70

m

50

4 0

30

20

10

0

i j j

P e r c e n t a g e o f E q u i p m e n t

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