1CHSME SYMPOSIUM SERIES NO. 144

THE USE OF RISK-BASED ASSESSMENT TECHNIQUES TO OPTIMISE INSPECTION REGIMES

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 requirements. DNV pioneered the use of RBI in the chemical process industry in 1992 and has produced a resource document for the API (API 581). DNV subsequently further developed 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.

Keywords: 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 economics is not the maximisation of profit, it is the avoidance of loss"

(Peter Drucker)

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 company is in profit, if they are less, a loss occurs and if the losses continue to be sustained, the organisation will ultimately fail.

People and equipment are not infallible. People do make mistakes and equipment does fail. All mistakes and failures have consequences, some small 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 practice 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 reduce or mitigate 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 magnitude of the consequences 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 equipment breaking down ? Is money being spent on inspecting and maintaining equipment which, if it fails, has very little effect on the organisation ?

An organisation's ability to consider these questions has traditionally been driven by legislative rather than business need. For example, inspection of pressure retaining equipment or structures has been primarily calendar based, driven purely by legislative requirements. This no longer needs to be the case. The new goal setting legislative environment provides the opportunity to plan loss avoidance activities by linking any increase in the level of activity, to the reduction in risk achieved by that increased level of activity.

Both regulators and insurance companies now recognise the acceptability of a risk based approach to the optimisation of maintenance.

RISK BASED INSPECTION METHODS

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 expenditure requirements. This paper will concentrate 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 consequences of failure and assigning notional levels ( High / Medium / 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 however, that changes 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. This statement is equally true for quantitative assessment techniques.

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 which equipment should be subjected to a more detailed quantified assessment. An example screen shot from one of the DNV software packages 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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SCHEME SYMPOSIUM SERIES NO. \44

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 mechanisms. Based upon these inputs, the software then uses 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 companies:

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 chemical process industry in 1992. In 1993 the API approached DNV with the request to jointly fund a larger development effort, aimed at producing a resource document for how to establish risk based inspection in the petroleum industry. DNV obliged, and in 1994 it produced the Base Resource Document 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 its 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 members. DNV decided to further develop the Quantified Risk Based Inspection Software in 1996. The new software, called ORBIT was initially released in March 1998 and is available to the industry as part of an integrated package of Risk Based Inspection services. ORBIT provides a fast interpolation approach for consequence analysis.

The quantified RBI approach provides a methodology for determining the optimum combination of inspection methods and frequencies. Each available inspection method can be analysed and its relative effectiveness in reducing failure frequency can be estimated. Given this information and the cost of each procedure, an optimisation program can be developed. The key to developing such a procedure is the ability 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.

SYSTEMATIC APPROACH TO REDUCING RISKS

A fully integrated Risk Based Inspection system should contain the steps shown in Figure 3. The system includes inspection activities, inspection data collection and updating, and continuous quality improvement of the system. Risk analysis is "state of knowledge" 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. Although any system, when first established, may lack some needed data, the risk based inspection program can be established based on the available information, using conservative assumptions for unknowns. As knowledge is gained from inspection and testing programs and the database improves, uncertainty in the analysis 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 likelihood) 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 damage rate for the equipment. The calculated damage rate depends upon the item's material of construction, the process fluids it is exposed to, its external environment and the process conditions (pressure, temperature etc.). Details of the current technical modules 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 has an error band associated with the particular technique. These error bands can be established by trials and review of historical surveys. Inspection activity does not change a corrosion rate, it reduces the error band and increases our confidence that we know the actual corrosion rate.

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ICHE.VIE SYMPOSIUM SERIES NO. 144

Statistical methods can be used to evaluate the likelihood that damage severe enough to cause a failure could exist given the amount of appropriate inspection activity that has been performed. As the damage rate is time 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 example of a screen shot of the equipment details from the equipment specification module of the software is shown in Figure 6. A screen shot of the likelihood module is shown in Figure 7.

CONSEQUENCE ANALYSIS

The consequence analysis conducted within the RBI software is based upon look-up tables calculated using the DNV software package PHAST . Consequences 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 sizes, and also determines the release type. After determining whether a release is continuous or instantaneous (as in a vessel rupture), the software calculates the final phase in the environment (liquid or gas) and then determines the toxic or flammable consequences. In evaluating the consequences, the software also allows modeling of account mitigating features such as isolation and shutdown systems.

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

RISK ASSESSMENT

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 where 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 generated to produce a wide range of analyses. These reports include:

• Action damage/mechanism summary reports. • Financial risks. • Inspection planning. • Risk ranking.

Graphs can also be produced for specific items 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 example 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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EGHEME SYMPC5TUM 3 O. 144

PLANT EXPERIENCE

It should be clear from the forgoing that regardless of whether a qualitative or quantitative approach is followed, it 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 production implications 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 demonstrate 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 showed that less than 10% fell into the high risk category. Failure of those items constituted a business interruption and asset damage risk of SI 1.5 million per year. The application of improved inspection techniques reduced this risk to $4.1 million per year, a saving of S7.4 million per year. There 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 outweighed the costs. 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. However 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 business interruption.

SUMMARY

The RBI methodology and its supporting computer program has already gone a long way toward an integrated risk management program.

The technology is still 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 development 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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LCHEME SYMPOSIUM SERIFS N(

Figure 1 Example Risk Matrix

Increasing Likelihood 3

2

1

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ws&.w

•

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• • . ; . . i . - - ' . V .

Mediun

1 2 3 4 5 Increasing Consequence

Figure 2 Example of Qualitative RBI Software

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"3 C»iM<wtnc«- - . - . . . . . . . .

13 w5 [±] 000 psg (I), :

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ICHEME SYMPOSIUM SERIES NO. 1^

Figure 3 Risk Based Inspection Program for In-Service Equipment

-

PLANT DATABASE

RISK BASED PRIORITISATION

INSPECTION PLANNING '

^^r INSPECTION RESULTS

FITNESS FOR SERVICE I — - — . ' a a—i—•-. -.

INSPECTION UPDATING

! — . - . . , ' . . . 3

SYSTEM AUDIT

Figure 4 Overview of Risk Based Inspection

- -125.vsd

; . - . . * . : . . . . . - • . . .

PROCESS SAFETY MANAGEMENT

IMPROVEMENTS

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SCHEME SYMPOSIUM SCRIES NO. 144

Figure 5 RBI Technical Modules

Thinning Module

Corrosion Under

Insullalion

Technical Modules Set-up

Stress Corrosion Cracking

High Temperature

Hydrogen Attack

Fatigue Brittle Fracture

No Mechanisms

Apply

HCI

HT Sulfidation

HT H.S/H.

HjSO,

HF

Sour Water

Amine

HT Oxidation

CUI Caustic • H

Amine

Carbonate

Sulphide

HIC/SOHIC - H3S HSC/HF

HIC/SOHIC - HF

Fatigue •Brittle Fracture

Figure 6 General Equipment Module

'; Fquipment Detail Data

Consequence general 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

-Additional Data !

Material ot I Carbon Steel Construction;

PJID-

ZJ 100-1000 Rev 1

• Equipment Dimensions-

Wall Thickness: [5

Tank Shape: |Verti

Internal Length: ("

Internal Diameter lg.0

Internal Height |3.rj

j - Date—— ;

] 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

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1CHEME SYMPOSIUM S5H;E5 NO N

Figure 7 Likelihood Analysis Module

"8 Equipment Detai l Data

£eneial J Likelihood Consequence

-Damage-Types—

Jhinnkig

SCC

—I - Failure Freauency-

Uiiltfe Fracture

r

No Mechanisms App(y 3r

Small

Medium

Large

Rupture

Leak Frequency (peiyeaf|

4.00E-05

1.00E-Q4

100E-05

200E-O5

Cfosa Help

Figure 8 Consequence Analysis Module

I tqurpment Detai l Data

Senetal ] Lfcelihood C o n s e q u e n c e ] _ Results

Chemical Name: |KER0SENE

Lookup Table Name: |Keiosene

Inibal Fluid State: jLiquid Vessel Type: [Pressurized Liquid

Chemical. Vessel Type and Initial Fluid State Method: | Lookup Melhod 3

Inventory '• ••—-—

<•" Item Inventory: J6000

r - ,:, - -.-, I

Elevation: h 000

k 9 I Dike Area:

Inventory Group: f"

Calculate 3

Surface Type: [ 5 ^ ^

Consequence Calculation

EJ Scutate

Areas button is only available loi selection immediately loUbwing calculation.

Qose Help Notes

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Figure 9 Results Module

! Equipment Detail Data

:

Ukdhood ResuKs

Ukelhood Categoiy

Consequence Results

Area Based Results

3.81E-03 Events/yeaf i Consequence

Facta 368.001 sq .

Categoiy ;

2241 j Equipment Damage:

FataUy:

ToacAfea:

1.13E*Q2 | s q m

| 3.68E-02 I sqm

[ O.O0E+OQ | sqm" j

2241

Inspection Planning Calculations

Inspection Planning J

. CJoae Help Notes

Figure 10 Risk Results

Percentage of Equipment vs. Percentage of Risk

i 1 I s, %

100 o~ 80 70

m 50 40 30 20 10 0

i j j

Percentage of Equipment

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