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Benefits of Risk Based Inspectionto the Oil and Gas Industry

Paper presented at the RBI Conference,Presented in Perth, Australia 27-28 April 2000

Jens P. Tronskar Lynne C. KaleyDet Norske Veritas Det Norske Veritas,Singapore Houston, Texas USA

ABSTRACTRISK BASED INSPECTION (RBI) is a methodology which prioritisesinspection activities on the basis of risk. RBI uses the results ofquantified risk and production availability analyses to assess thepotential failure consequences. It combines material technology withload and resistance models to determine the probability of failure. Aquantified ranking of process equipment, piping and transmissionpipelines in terms of personnel and environmental risk, loss ofproduction and damage cost, focuses the inspection towards high riskcomponents and potential/active failure damage mechanisms for anoptimal utilisation of inspection resources on key assets. Case studiesfor downstream and upstream applications are discussed.

1 INTRODUCTION

The oil and gas industry, which operates offshore installations and topside processingfacilities, pipelines, petrochemical and petroleum refining plants, handles a wide range offlammable and toxic materials which are potentially hazardous. These industries have agood safety record compared to the industry as a whole [1]; however, occaisonal incidentsoccur emphasizing the inherent hazards involved in the business. As a result, locallegislation has been imposed in various parts of the world to define requirements formanagement of these plants and facilities that require operators to demonstrate thatpotential hazards have been identified and plans developed for maintaining safe facilitiesthrough periodic inspection and mitigation activities. In addition, increasing businesspressures require efficient use of company resources while production schedules requirehigh operating equipment efficiency.

Risk Based Inspection (RBI) methodology is a systematic approach which prioritisesinspection activities on the basis of risk. The fundamentals of Risk Based Inspection applyuniversally to all types of oil and gas processing and petrochemical processes, eitheronshore or offshore installations, as well as to other industries. There are, however, somedifferences in the application of these basic fundamentals depending on the specifics ofthe process and the type of installation.

Leaks of flammable/hazardous material in a plant originate from a variety of causes, andmaterial related damage is only one of these causes. Important causes for leaks can beoperator errors (simply opening live process equipment), incorrect operation, improper jobtraining, lack of procedures for maintenance, bad weather conditions, etc. Inspectioncannot prevent leaks associated with these causes; what is required in such cases is

Benefits of Risk Based Inspection for the DET NORSKE VERITASOffshore and Onshore Oil & Gas Industry

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management actions of different kind. Loss Control Management (LCM), inspectionmanagement and RBI can be seen as a part of this effort and all are part of RiskManagement.

Inspection management is an ongoing activity within a plant and a number of resourcesand different disciplines are involved. Figure 1 illustrates the different steps withininspection management. RBI is a tool for identifying active/potential damagemechanisms, for risk ranking and finally for inspection planning and risk reduction, i.e.inspection and monitoring for the high risk items and corrective actions for the lower riskitems.

2 ACTIVITIES

A typical Risk Based Inspection (RBI) programme contains the following activities:

• Screen operating units within a process system or plant; identify high risk equipmentor piping items

• Calculate the probability of failure taking into account ongoing degradation• Calculate consequence of failure; effect of release of process fluid, business loss due

to failure, repair costs.• Calculate the risk value associated with the operation of each equipment item as the

product of the probability of failure and the consequence of failure• Prioritise the equipment based on calculated risk• Design risk reduction programmes to lower total risk; by inspection, monitoring,

repair, mitigation and replacement/re-design• Calculate Risk Reduction and Cost Optimization of activities for budget allocation of

resources and planning• Systematic management of operating risk to avoid unexpected equipment failures and

production losses over timeThe approach and methodology demonstrated here was developed by the API Committeeon Refinery Equipment and is outlined in the API Base Resource Document on RiskBased Inspection (API Publication 581). The methodology was developed as a part of aJoint Industry sponsored project between 23 major oil and gas industry companies andDNV.

3 METHODOLOGY

Analysing all equipment items in a plant can be a time-consuming effort; thus methodsshould be used to minimise the work and to focus on the high risk items. A qualitativeplant system risk screening is an efficient tool for RBI analysis. The systems screeningapproach allows documented ‘screening out’ of low risk systems with little or no safety orproduction impact that are candidates for a corrective maintenance program. The systems‘screened in’ are analysed quantitatively. In this way, data analysis time is focused on theequipment with the most impact on risk and production.


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3.1 Qualitative AnalysisThe screening process is done in close co-operation with the operating company andshould involve experienced personnel from the plant (inspection, operation and materials)in addition to personnel with RBI assessment experience. This may be organised instructured working sessions. Typically these analyses can be completed in a week or lesstime.

The Probability and Consequence Categories are evaluated separately and subsequentlycombined in a risk matrix to produce a risk ranking for the unit. The ProbabilityCategory is assigned by evaluating and combining the primary factors affecting theprobability of failure (small, large or catastrophic failure) listed below:1. Number of equipment items2. In-service damage mechanism potential3. Appropriateness of inspection methods used (inspection effectiveness)4. Current equipment condition, inspection history and findings5. Process and Operating conditions6. Equipment design basis

The damage Consequence Category is determined from a combination of six elements orsub-factors that determine the fire and / or explosion hazards:1. Ignition probability2. Quantity that can be released3. Ability to flash to a vapour4. Possibility of escalation from minor to serious conditions, location of equipment5. Engineered safeguards in place6. Degree of exposure to damage7. Damage effect on production loss, redundancy and repair.

The results are combined and plotted on a simplified risk matrix shown in Figure 2 tolocate areas of potential concern and determine process unit areas in need of the mostinspection attention or other measures of risk reduction. This approach shows at a glancethe number of equipment items that are high or low risk, as well as whether the risk isdominated by probability of failure (a good candidate for inspection) or consequence offailure (a good candidate for mitigation), or both. Likewise components with low risk areidentified and assigned a corrective maintenance strategy.

3.2 Quantitative AnalysisThe quantitative RBI approach begins with the extraction of process, equipment and otherinformation from the plant management database. Then each equipment item within thearea of interest is evaluated with regard to probability of failure and consequence offailure. The consequence and probability for each scenario are combined to obtain therisk. Based on the risk prioritisation the inspection efforts are focused, aiming atopportunities to reduce overall risk and cost. Similar plant expertise is involved as for thequalitative analysis, most importantly in identification of damage mechanisms andsusceptibilites, as well as process stream identification for consequence modelingconsiderations.


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3.2.1 Probability of Failure AnalysisThe probability of failure analysis uses experienced personnel to identify and evaluatepossible material degradation in all covered equipment. This is accomplished bysystematically identifying the environment/material interaction, actual design data,operational history, experience with this or similar services, and other aspects drivingdamage rates. The first step in the probability determination is to use the aboveinformation to identify the potential damage mechanisms and damage rates, determine thecurrent condition, and evaluate damage tolerance, failure modes and remaining life, ifapplicable, for each piece of equipment. Damage Modules are used to evaluate thenecessary conditions for failure and calculation of the probability of failure. The actualmethodology is used in the software in order to simplify and reduce the time required inthe anlysis effort. New mechanisms are being added periodically, as required.

The failure mechanisms currently covered by the methodology are:• External Corrosion including atmospheric corrosion and cracking and corrosion or

cracking under insulation (CUI) or lagging• General and localised corrosion in various environments (streams containing water,

sea water, CO2, Hydrochloric, Sulphuric, and Hydrofluoric acids, etc.)• Stress corrosion cracking in various environments (Caustic, Amine, Chlorides, H2S,

etc.)• High temperature phenomena (oxidation, hydrogen attack, thermal fatigue, creep, etc.)• Brittle Fracture and other Embrittlement mechanisms• Fatigue caused by vibration, flow effects (slugging/choking), and ship movements

(FPSO’s)

The calculation method for probability of failure associated with the failure mechanisms isbased on Structural Reliability Analysis (SRA) [3], where the stochastic uncertainty in thebasic variables, in particular the uncertainty in the determination of the damage rate, aretaken into account. An important feature of this theory is its ability to include both theprior damage estimates and the outcome from the inspections in the derivation of theupdated posterior probability of failure (Bayes’ Theorem). For inspection updating, theadditional information gained from the inspection is utilised in the determination of thefuture behaviour of the equipment.

The level of information gained from an inspection depends heavily on the quality andextent (coverage) of the inspection performed. The inspection quality is modelled eitherby discrete probabilities for the inspection effectiveness (i.e. the probability that theinspection method will detect the ongoing damage) or by Probability of Detection (POD)curves [4] for the inspection method applied. The latter defines the probability ofdetecting existing degradation as a function of the characteristic dimension of thedegradation.


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3.2.2 Consequence AnalysisFor a process plant, the most important consequence elements are the personnel safety andthe financial loss due to shut-down or deferred production. The focus on safety is obviousas the process unit may contain large volumes of flammable and/or toxic component. Thesafety aspects can be addressed using established risk analysis methods (QRA-Quantitative Risk Analysis). This type of analysis is often carried out as a part of thedesign process, and the analysis should ideally be updated on a regular basis duringoperation to include effects of changes in the operation and modification to the facility.The consequence parts of the QRA analysis can be transferred to RBI analysis in the formof PLL-values (Potential Loss of Life) and asset damage depending on the size of leak andextent of the simulated accident scenario.

The QRA methodology facilitates modelling of leakage, dispersion, ignition, fire andexplosion, as well as escalation of the initial event to other process segments or foroffshore platforms, to the load bearing structure. QRA analyses are comprehensive andcan be utilised in the RBI analysis with considerable savings in the overall analysis effort.

In the absence of such analysis, simplified consequence assessment can be performed. TheRBI methodology is based on an event tree approach, similar to that applied for standardQRA’s, with pre-simulated effect-scenarios. The five main consequence categories are:(see Figure 2)

• Flammable events (fire/explosion)• Toxic Releases• Environmental Risks (cost of environmental clean-up)• Business Interruption (lost/deferred production)• Asset repair after failureCost data related to lost production and asset repair used should be based on actual plantdata; otherwise default data from industry experience can be used.

3.2.3 Risk RankingAs we have already stated, risk is a function of probability and consequence of failure.Experience has shown that the use of a risk matrix is an effective tool for communicatingrisk qualitatively. Both consequence and probability of failure are categorised in 5 groupsgiving a total of 25 risk combinations. Additionally, a matrix plot as shown in Figure 3can be used by setting iso-risk lines to identify levels of action required. Each of thesemethods of communication allows comparison of equipment to prioritise the effortrequired.

Risk can be communicated in terms of personnel safety, economic loss or a combinationof both personnel and economic impact.

The overall safety requirement(s) can then be converted to an acceptance line in the safetyrisk matrix and used for inspection/maintenance planning. An example is shown inFigure 4. For components above the acceptance line, actions should be taken to reducethe risk. For components below and for all components with only financial risk, a costoptimisation scheme should be considered.


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4 RISK BASED INSPECTION PLANNING

From the risk based inspection point of view, inspection planning is a matter of gainingenough knowledge about the equipment through inspections to accurately predict the rateof degradation and thereby determine when equipment should be re-inspected, be repairedor be replaced. A unique feature of RBI is the ability to determine the level of inspectionand effectiveness appropriate to provide the knowledge required to achieve or maintainthe acceptable risk level. The inspection approach is flexible enough to allow multipleinspections with less coverage (less effective) or a single inspection with extensivecoverage as well as intrusive methods requiring internal access and surface preparation ornon-intrusive techniques allowing effective in-service inspections.

4.1 Risk Reduction through InspectionThe effectiveness of the inspection methods to detect the damage mechanisms is evaluatedand characterised based on five inspection effectiveness categories: Highly effective,Usually effective, Fairly effective, Poorly effective and Ineffective. Assignment ofcategories is based on professional judgement and expert opinion. One of the mostimportant criteria is the capability of the inspection methods to detect the characteristics ofthe relevant damage mechanisms. The damage mechanisms generally considered are listedin Section 3.2.1.

4.2 Inspection Planning TechniquesIdeally, cost optimisation should be performed for every item of equipment evaluated inthe study. The availability of software to automate and facilitate this step is an importantcost effective tool. Several simplified techniques for inspection planning may be appliedbut the following are the most commonly used:• Risk Matrix method• Remaining Life method• Limit on Probability of Failure

The simplest method is the Risk Matrix method. For this method the inspection intervalis directly dependent on the risk of each equipment item and the location of the equipmentitem in the risk plot; requiring high-risk items to be given short intervals and low-riskitems longer intervals. This method is simple to apply and is commonly used by theindustry. The disadvantages with this method are that the damage rate and lifetime are notaccounted for explicitly and the effect of past inspections sometimes is not considered in aconsistent manner.

The Lifetime method is a method where the inspection intervals are set as a function ofthe estimated lifetime of the component. This function will be related to the componentrisk and the predictability of the damage rate. The predictability is related to theuncertainty in any damage model applied and/or the effectiveness of the inspectionsperformed of the component. This model is calibrated using more complex calculationalgorithms where POD-information is included.

Limit on Probability of Failure is a method similar to the methods applied for structuralmechanics analysis of structures and pipelines. This model estimates the failureprobabilities for individual equipment and updates these estimates based on the outcomefrom the inspections carried out. The inspection intervals are defined from restrictions on


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the allowable failure probability for the equipment. The limiting allowable failureprobability triggering an inspection may be set from consideration of the failureconsequences, or by means of any company standards. A cost optimised procedure fordefining the limiting failure probability may be applied for critical components.

4.3 Risk Based Inspection OptimisationThe cost optimal level of inspection may be determined based upon cost-benefit analysesidentifying which equipment item should get thorough inspection and which should getlittle or none. If the analysis reveals that high levels of inspection activity are required forsome equipment, often the inspections can be “paid” for by the savings generated byreduced inspection activity on low risk equipment.

Inspection optimisation involves focusing the inspection efforts in order to reduce risk offailure and save cost. An essential part of the inspection optimisation is to establish themost cost-effective approach satisfying the failure acceptance or acceptable probability offailure criterion. The key to inspection optimisation is to use the method of inspectionupdating described in Section 4.1.

Inspection plans are most often developed by defining the target risk value for inspectioncosts, repair costs and expected risk costs. Based on the defined target value, theinspection interval for a specific inspection quality and coverage is determined after theinspection updating analysis in the Limit on Probability of Failure approach described inSection 4.2. Occasionally a more complex determination is used and includes the netpresent value sum of all the cost terms added to the project over the remaining service life,accounting for maintenance costs, inspection costs, expected repair costs and expectedrisk costs.It is possible to meet defined risk targets by changing the inspection methods andintervals. It is often necessary to inspect more frequently or use different methods toachieve the desired risk targets in some cases while other equipment requres less frequentinspection but require extensive internal examination. In all cases however, the ultimategoal is to minimise the total costs without compromising safety or production capabilities.

5 SUMMARY OF METHODOLOGY

At the highest level, Risk Based Inspection gives management the tools needed to makecost/benefit decisions regarding inspection and related activities. The key to turning RiskBased Inspection into Risk Based Management is via economic evaluation modules thatevaluate relevant costs such as equipment repair and replacement, business interruption,and cost of injuries to personnel. The quantitative approach for Risk Based Prioritisationdescribed in this paper can be illustrated schematically as shown in Figure 5.

One essential feature of the approach is that it includes the option for Fitness-For-Serviceassessment should the inspection findings not be acceptable based on code requirements.The Fitness-For-Service (FFS) option is included to assess the criticality of inspectionfindings, employing less conservative and more detailed assessment procedures than thegenerally conservative acceptance criteria of the design, inspection, repair, maintenanceand alteration codes. FFS methodology is also applied to estimate remaining lives ofequipment items less conservatively than that based on the methods specified in the codes.


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Application of FFS contributes to further cost savings as expensive repair can be avoidedor the useful life of the plant can be extended whilst maintaining risk at an acceptablelevel.

6 CASE STUDIES

6.1 CASE STUDY 1The RBI study was performed on a Coker Unit to determine which equipment wouldrequire inspection over the next four years based on an established risk criteria. The studywas performed in advance of a scheduled fall shutdown to evaluate the equipment needsfor inspection to safely operate for the next four year run and evaluate the impact of thatinspection plan on cost and risk. The equipment covered in the study is illustrated inTable 1.

The objectives of the study were to create flexible, credible inspection plan guidelines to:• Reduce plant operating risk• Optimize inspection/maintenance dollars per activity.• Demonstrate cost benefit of RBI to the plant management.• Create useful equipment inspection plans.

6.1.1 ResultsThe current risk of the unit was compared to the risk after the targetted four year run.Risk in 2002 is shown in Figure 6 assuming no inspection was performed and comparedto the unit risk in 2002, assuming the recommended inspection plan is implemented. Riskis shown in terms of Financial Risk. Financial Risk includes the likelihood of failure,consequence of failure, and production loss.

The reliability of the unit was calculated in terms of mean-time-between-failure (MTBF)and was improved 16-fold. These risk reductions and improved reliability were achievedby redirecting the inspection dollars to the high risk equipment. As a result, 43 pieces ofequipment were removed from the work list and 13 were added. The overall maintenanceand inspection budget savings was $225,000 showing a project Cost Benefit of $175,000,as shown in Table 2.

The thirteen equipment items added to the work list represented unacceptable risk due toactive damage mechanisms not previously identified. The equipment was analyzed usingthe identified H2S levels, presence of water, pH and fabrication practices. Inspectionrevealed one drum with significant wet H2S damage that had gone undetected.6.2 Case Study 2In general, refineries have increased the corrosivity of crude slates in recent years. In thecontinuing drive to improve profitability, one refinery requested an evaluation to measurethe impact of increasing sulfur and naphthenic acid crude content on the equipment lifeand inspection requirements and costs of a sweet crude facility.

The refinery typically runs a 0.1% sulfur crude with naphthenic acid of 0.15 TAN and hasexperienced few corrosion related problems in the last 30 years of operation. A study wasinitiated to evaluate the impact of increasing crude sulfur and naphthenic acidcomposition. The objectives of this case study were to:


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1. Identify equipment that would experience significantly increased corrosion as a resultof the crude slate changes;

2. Quantify the impact of the test crude slates on unit risk compared to the currentoperation;

3. Quantify the potential inspection and maintenance cost increases when compared tothe current inspection program; and

4. Provide guidelines and recommendations to manage equipment risk in the future.

6.2.1 ResultsCurrent risk of the crude unit was evaluated to provide the baseline equipment condition.A 5 X 5-risk matrix of the equipment showing current risk is shown in Figure 7. Threedifferent crude corrosivities were modeled and risk evaluated for a ten year plan period todetermine the impact of the feed changes on risk and maintenance and inspection costs.The risk category distribution for equipment in the study by crude content is shown inTable 3.The impact of processing different corrosivity crude through the crude unit was analyzedon the basis of associated financial risk for each feed. As shown in Figure 8, the financialrisk of processing 0.5% sulfur and 0.28 TAN would be 19% higher than that forprocessing a 0.1% sulfur crude feed for the same 10 year operating period. At the end ofthe 10 year plan period, the financial risk of processing 0.8% sulfur and 0.67 TAN crudewould be 77% higher than the financial risk of processing a 0.1% sulfur crude.

If risk is held constant, the cost of inspection can be determined for each case. The cost ofinspection between 0.1% and 0.5% crudes did not change. However, the difference incost of inspection for the 0.8% sulfur crude modeled was significant. The model indicatedthat for the ten year plan period, the cost of inspection for processing the 0.8% sulfur, 0.26TAN feed would increase 63% over the 0.1% and 0.5% crude, as shown in Figure 9.

Processing the 0.8% sulfur, 0.67 TAN feed significantly increases the inspection costs forthe plan period. If an increase in inspection is not considered, the total unit risk andfinancial risk exposure of the unit will increase by over 50%. Investigating the resultsshows that the 50% items affected by sulfidation would have a higher failure frequency asa result of the increased sulfur content. This increase in the number of failures increasesthe Financial Risk Exposure of the unit. If the higher sulfur crude were to be processed asa permanent operational change, replacement of the higher likelihood items would requirea material upgrade rather than increased inspection.

6.3 Case Study 3RBI analyses were performed on the topsides on the North Sea offshore platforms. Theplatforms produce a total of 14.6 bill scm gas/year and 6 mill tonnes of NGL/year.

The scope of work and purpose of the RBI analyses conducted included:

• Performing risk ranking of all topside piping at the field comprising 8294 pipe tags• Using existing QRA analysis (done by DNV previously using the OHRAT software)• Using existing RAM results• Estimating probability of failure per tag (due to internal and external damage)• Estimating risk per tag• Indicating most likely types of damage


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6.3.1 Most of the piping was fabricated in CRA (corrosion resistant alloys 6Mo and duplex).

The results of the analyses of all the 8294 pipe tags on the Sleipner A platform have beensummarised in the diagram shown in Figure10. It is seen that only a small percentage(0.1%) of the equipment items fall in the category VH (very high risk). Further, onlyrelatively few items fall under the categories high (H) and medium (M) risk, i.e., 0.7% and1.9% respectively.

An important observation is that 57.5% of the piping items fall under the low (L) riskcategory and 39.8% under the very low (VL) risk category.

The risk matrix containing the number of piping items in each risk category from theSleipner A topside analysis is shown in Table 4.

Based on the results of the analyses, Statoil concluded that they could reduce the currentinspection activities by 25%. Based on their positive experience with RBI, Statoil decidedto implement the methodology for inspection planning of all their offshore installationswithin three years and onshore plants (refineries) within five years [6].

6.4 Case Study 4An RBI analyses was performed on a number of pipelines in the North Sea. A typicalproject involved analysis of a 13 km long, 12” seamless API 5L Grade X52 offshoresubsea pipeline. The multiphase pipeline, carrying produced gas, oil and water, had beeninstalled at 70 meters water depth in 1990, with a specified design life of 15 years.

The pipeline was commissioned in September 1990 and the first intelligent pig inspection(British Gas Pig) was performed in March 1993. Extensive internal corrosion wasobserved during this inspection, and a corrosion inhibitor program was initiatedimmediately. The pipeline was re-inspected in 1994 and 1996 with significantly improvedsizing accuracy of the inspection tool. Based on the outcome of the last two inspectionsthere were indications that corrosion was still taking place, in spite of the ongoinginhibitor program.

Inspection plan recommendations were made for the pipeline depending on the detectionand sizing accuracy of the intelligent pigs being applied. DNV conducted this study basedon an assessment of the present condition of the pipeline and the predicted futurecorrosion rate, defined from the previously conducted pigging inspections in ‘93, ‘94 and‘96.

6.4.1 ResultsThe relevant failure modes considered were leakage due to through-thickness pittingcorrosion and burst of the pipeline associated with grooving corrosion. The “limit onprobability of failure” approach for determining the inspection intervals was applied. Asburst of the pipeline typically will result in a more severe failure scenario than leakage,two different limiting annual failure probabilities triggering a new inspection wereconsidered for these failure scenarios; for leakage 10-3 and for burst 10-4.

In Figure 11 the predicted future annual failure probabilities for leakage and burst failurecaused by internal corrosion are shown, where the uncertainties associated with the abilityof the pigging inspections to accurately assess the condition of the pipeline are accountedfor. It is observed that a new pigging inspection of the pipeline should be carried out


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within three years of last inspection (1999). Given that this inspection does not reveal anyunexpected increase in the level of degradation, no further inspections of the pipelinewould be required over the remaining service life, as shown by the updated annual failureprobabilities in Figure 11.

It was found that the pipeline, in spite of the significant level of corrosion initiallyobserved, might be operated safely throughout its intended design life with a defined riskcontrolled inspection plan. Another more expensive alternative to the use of RBI planningwould for this case be to introduce a periodic inspection/monitoring program of thepipeline with possible early abandonment.

7 CONCLUSIONS

• Risk Based Inspection gives management the tools needed to make cost/benefitdecisions regarding inspection and related maintenance activities; hence RBI is a subsetof Risk Based Management, using risk as the criterion for action or inaction for anyactivity affecting safety or reliability of equipment.

• Risk is a good criterion for prioritising inspection efforts because :− Highest priority items are easily identified as the highest risk items− Risk can be measured as economic loss (or gain from reduction of risk)− Activities can be justified on a cost/benefit basis

• DNV’s experience shows that application of RBI methodology can lead to substantialcost savings for the oil and gas industry in terms of reduced time required for turn-around inspection, deferral of inspection and by specification of optimised on-lineinspection to replace intrusive inspection.

• However, the cost optimisation should not only be focused on reduction in the directinspection costs but on an overall risk cost reduction. This may in some cases lead tohigher inspection costs.


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8 REFERENCES

[1] Robin Pitblado and Robin Turney: “Risk Assessment in The Process Industry”.Institution of Chemical Engineers, Rugby Warwickshire, UK 1996

[2] API Committee on Refinery Equipment: “Base Resource Document on Risk basedInspection”. Prepared by DNV Industry Inc. Houston Texas January 1998.

[3] Madsen, H.O., Lind, N.C. and Krenk, S., “Methods of Structural Safety”,Prentice-Hall, Engelwood Cliffs, N.J. 1986

[4] Førli, Olav: “Development and Optimization of NDT for Practical Use-Reliabilityof Radiography and Ultrasonic Testing”. 5th Nordic NDT Symposium, Esbo,Finland 1990, 26-28 August, NORDTEST

[5] Cramer, Espen H., Hjelm, Morten H. and Wästberg, Stig: “LCC in Design andMaintenance of Deep Water Risers”. The 10th Underwater TechnologyConference, Bergen, Norway, 1998

[6] Hauge, Jan: “Risk Based Inspection (RBI) – a modern tool for inspectionmanagement of static process equipment”. Inspection and Maintenance Strategiesfor the Offshore Industry, New Orleans September 14-15 1998


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Inspection ProgrammeMethod, Frequency,

Coverage,Location, Cost

Consequence of FailureSafety, Production Loss

Probability of FailureMaterials/Environment

and Strength

Company goalsPerformance

indicators

Inspection PlanInspection details,planning, logistics

Inspection and testingExecution & Reporting

Inspection data evaluationAnalysis of results

Risk Ranking perequipment

Optimisation&

SafetyCompliance

Figure 1. Inspection Management

Figure 2. Scenarios for Consequence Calculation

Leakage-small-medium-large

Ignition?

Yes

No

Personnel Safety

Repair

Production Loss

Environment

Toxic release

Production Loss

Repair


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Figure 3. Qualitative Risk Matrix

5 VH4 M H3 L M2 VL

Like

lihoo

d of

failu

re

1

A B C D EConsequence of Failure

Economic Risk

5 VH4 M H3 L M2 VL

Like

lihoo

d of

failu

re

1

A B C D EConsequence of Failure

Safety Risk

Cost-benefitanalysis

AcceptanceCriteria

Risk reduction required:- inspection- monitoring- data collection/verification

Figure 4. Quantitative Risk Ranking Matrix


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The methodology and application of RBI

EQUIPMENT RISK RANKINGS:

Item Itemrisk

Consequence DamagePoF

Expressed as

an affected

area

PoF for eachdamagemechanisms

Cons * PoF

ThinningPoF

CUIPoF

CrackingPoF

PoF

Equipment Database containing the key data

Apply Limit State Analysis and Reliability Index methods to calculate theprobability of failure. Calculate inventories and

Consequences of failure

Analyse the data

ACTION PLANS:

InspectionPrioritize items based on Risk andspecify inspection methods basedon the driving factor i.e. thinning,CUI and the location based onequipment type / service etc.

Evaluation of inspection findings.Fitness-For-Service Assesment

Other follow-up:Prioritize actions based onconsequence.This is aimed at items where risk isdriven purely by consequence andnot by an active damage mechanism.Remedial actions may involveupgrading, design modifications andmitigations

Plan actions based on the results

Inspect: Other:

AffectedArea

Consequences

Figure 5. The Methodology and Application of DNV’s RBI Technology.


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Figure 1. Financial Risk in $/year

$763,332

$1,064,569

$152,548

$-

$250,000

$500,000

$750,000

$1,000,000

$1,250,000

1998 2002 without the1998 Inspection

2002 with the1998 Inspection

Fina

ncia

l Ris

k $/

year

Figure 6: Financial Risk in $/Year

Figure 7: Current Risk Matrix


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Figure 8. Financial Risk Exposure for 0.1%, 0.5% and 0.8% sulfurfeed, for plan period after inspection

$34,793

$46,846

$26,421

$15,000

$25,000

$35,000

$45,000

$55,000

$65,000

0.1% S 0.5% S 0.8% S

Sulfur content in the feed

Figure 8: Financial Risk Exposure After Inspection

Cost of Inspection by Sulfur Content

$0$50,000

$100,000$150,000$200,000$250,000$300,000$350,000

0.1% Sulfur 0.5% Sulfur 0.8% Sulfur

% Sulfur in Crude

Cos

t of I

nspe

ctio

n

Figure 9: Cost of Inspection by Sulphur Content


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Figure 10. Overall distribution of risk among all 8294 pipe tags analysed on theSleipner A platform. VH: Very high, H: High, M: Medium,L: Low and VL: Very low risk.

Annual Failure Probabil ity for Leakge

1.0E-08

1.0E-07

1.0E-06

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Year

Failu

re P

roba

bilit

y

Present info After inspection in 1999

Annual Failure Probabil ity for Burst

1.0E-08

1.0E-07

1.0E-06

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Year

Failu

re P

roba

bilit

y

Present info After inspection in 1999

Figure 11. Estimated annual failure probabilities for leakage and burst failureof subsea pipeline due to internal corrosion. The updated estimatedfailure probabilities after 1999 are based on the assumption that nounexpected increase in the level of degradation is observed at thisinspection.

Risk ranking for all 8294 pipe tags

L57.5%

VL39.8%

M1.9%

H0.7%

VH0.1%

VHHMLVL


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Type of Equipment Number of Equipment % of Total

Drums and Towers 66 10

Heat Exchangers 51 7

Piping Segments 571 83

TOTAL 688 100Table 1: Scope of the RBI Study

Number of Equipment taken outof Turnaround List due to their

low risk levels

Dollars Saved byAvoiding Vessel Entry

Cost of the study Overall Savings

30 $225,000 $50,000 $175,000

Table 2: Candidates for Avoiding Vessel Entry and Cost Benefits

Equipment Count (10 Year Plan)

Risk Ranking Current 0.1% S; 0.15TAN

0.5% S; 0.28TAN

0.8% S;0.67 TAN

High 3 4 7 14

Medium High 25 51 63 61

Medium 337 314 303 298

Low 125 121 117 117

Table 3: Risk Ranking versus Equipment for Crude Slates

Probability of failure 2551 1096 3482 1145 20 8294>10-1 39 5 33 9 0 86

10-2 – 10-1 62 39 61 23 0 18510-3 – 10-2 491 260 228 74 0 105310-4 – 10-3 1901 792 3099 1039 20 6851

<10-4 58 0 61 0 0 119C-Class A B C D E

ΣΣΣΣRisk Cost(US $)

<135 K 135 K -1.35 M 1.35 M – 4.7 M 4.7 M -13.5 M >13.5 M

Table 4. Consolidated results of RBI analysis of Sleipner A platform topsides[6] with regard to internal and external damage (corrosion). Thematrix shows the number of piping items in each risk category.

rbi

Documents

make cost benefit decisions

det norske veritas

onshore oil

expected risk costs

financial risk exposure

prioritises inspection activities

remaining service life

high risk items