Multi Criteria Decision Analysis framework for risk management of oil and gas pipelines

Transcription

1 Reliability, Risk ad Safety Ale, Papazoglou & Zio (eds) 2010 Taylor & Fracis Group, Lodo, ISBN Multi Criteria Decisio Aalysis framework for risk maagemet of oil ad gas pipelies Alex. W. Dawotola, P.H.A.J.M. va Gelder & J.K. Vrijlig Iterdiscipliary Research Ceter Safety Critical Systems, Uiversity of Oldeburg, Oldeburg, Germay ABSTRACT: Oil ad gas pipelies are subject to differet degrees of failure ad degradatio durig operatio. Commo pipelie failure mechaisms iclude corrosio, mechaical damage, third-party damage, ad desig imperfectios. Oe or a combiatio of these failure mechaisms could evetually lead to rupture, carryig huge huma, fiacial, ad evirometal loss. Hece, the eed for reliable ad cost effective risk maagemet processes becomes more imperative. This paper proposes a decisio based method for risk maagemet of oil ad gas pipelies. The method is based o a Multi Criteria Decisio Aalysis (MCDA) framework, utilizig a Aalytic Hierarchy Process (AHP) to prioritize oil ad gas pipelies for desig, costructio, ispectio ad maiteace. A case study applicatio o pipelies i Nigeria is used to demostrate the proposed methodology. The methodology is a improvemet i the existig qualitative risk assessmet of pipelies. Furthermore, with ehaced accuracy i risk assessmet, cosiderable cost savigs i the ispectio ad maiteace plaig of the pipelie may be achieved. 1 INTRODUCTION 1.1 Backgroud Itegrity maiteace of pipelies is a major challege of service compaies, especially those ivolved i the trasmissio of oil ad gas. Two major factors have bee the drivig force behid this challege. These are the eed to miimize costs of istallatio, service ad maiteace, ad secod is risk miimizatio. Safety aalysis (or risk assessmet) of pipelies etails the study of the probability of its failure ad ay associated cosequeces i terms of ecoomic loss, huma hazards, ad degradatio of the eviromet. Pipelie leakage or burst could be disastrous, havig catastrophic ifluece o huma ad marie lives ad huge ecoomic loss. Pipelie disasters have bee recorded i both developed ad developig coutries, icludig Veezuela, UK, Russia, Caada, Pakista, Nigeria, ad Idia (Dey et al & Al-Khalill et al. 2005), ecessitatig the developmet of more effective risk maagemet strategies. Ideally, most pipelie operators esure that durig the desig stage, safety provisios are created to provide a theoretical miimum failure rate for the life of the pipelie. While i operatio, operators ofte used subjective estimate to carry out their routie based maiteace. However, subjective risk estimate is proe to iaccuracies with sometimes a ureliable outcome. Trasmissio pipelies are complex i ature, ad their risk aalysis could be simplified by usig a hierarchical approach, (Huipeg Li 2007). However, little has bee achieved o hierarchical risk aalysis of petroleum pipelies, as a aid to decisio aalysis, which is required i makig ispectio ad maiteace decisios. Aalytic hierarchy process is a promisig method for this applicatio. AHP, developed by Saaty fudametally works by usig opiios of experts i developig priorities for alteratives ad the criteria used to judge the alteratives i a system, (Saaty 1980). The outcome is a relative scale which gives maagers a ratioal basis for decisio makig. It has foud applicatios i diverse idustries, such as agriculture, (Quresh ad Harriso 2003), oil ad gas, (Al-Khalill et al & Cago et al. 2000), ad the public sector, (Dey 2002). I this paper, a systematic risk-based approach to risk maagemet of oil ad gas pipelies is preseted. The method is based o a multi criteria decisio aalysis framework, utilizig a aalytical hierarchy process to prioritize operatig pipelie for desig, costructio, ispectio ad maiteace. The overall objective, sub-objectives, attributes ad decisio alteratives are represeted i a hierarchy. Three differet oil ad gas pipelies operated by the Nigeria Natioal Petroleum Compay (NNPC) have bee used as a case study. Their failure factors ad historical failure records were obtaied from literatures ad historical records from the compay. The failure factors are listed 307

2 as the sub-objective factors i the MCDA. They have bee grouped ad idetified as exteral iterferece, corrosio, operatioal error, structural defects ad other mior failures. Each subobjective factor is further divided ito attribute(s), as appropriate. For example, corrosio is a sub-objective factor which is further divided ito exteral ad iteral corrosio. The selected crude oil ad gas pipelies are the decisio alteratives which will be prioritized for desig, costructio, ispectio ad maiteace. I the methodology, AHP is used to estimate the probability of failure of pipelies by combiig historical failure data of the pipelie with pairwise compariso carried out by experts. The expected values of cosequeces of pipelie failures are obtaied from typical cost of failures. Risk is the estimated by the product of probability ad cosequeces. Web-HIPRE versio 1.22 (Mustajoki ad Hämäläie 2000), is used to aalyze the results ad to carry out a sesitivity aalysis. Scietifically, the approach will be valuable to oil ad gas compaies i prioritizig the ispectio ad maiteace activities of their oil ad gas pipelies. The methodology could also prove valuable i arrivig at a desig, redesig, costructio ad moitorig decisios. 2 RESEARCH METHODOLOGY 2.1 The aalytic hierarchy process Aalytic hierarchy process is used i the decisio makig to estimate the likelihood of a evet, by establishig relative importace of each cotributig factors. The aalytical hierarchy process cosists of the followig basic steps: 2.2 Procedures Problem formulatio The ultimate goal of the AHP is defied. I this paper, it is the determiatio of risk of failure of oil ad gas pipelies. After the goal defiitio, cotributig factors to the failure are the idetified. If applicable, these factors are further divided ito 1 or 2 sub factors Selectio of decisio alteratives Idetificatio of decisio alteratives is very importat i the AHP. The coclusio o the decisio alteratives is the outcome of the AHP. I this paper, three oil ad gas pipelies are selected as the decisio alteratives. The goal of the AHP therefore is to compare these pipelies risk wise, ad to rak them based o the level of risk expected Collectio of iformatio Required features for the pipelies are divided ito physical data, costructio data, operatioal data, ispectio data ad Failure history. This iformatio is documeted for the hierarchical aalysis Hierarchy The ext step is the developmet of a hierarchy (value tree), which cosists of the goal of the risk assessmet, the failure factors ad sub factors, if applicable ad the decisio variables Expert elicitatio I the last step of the aalytical hierarchy process, data of the pipelies are preseted to a umber of experts who will carry out a pairwise compariso of the pipelies with respect to each risk factor. The outcome of the compariso is a matrix that raks the pipelies i order of likelihood of failure. The experts were required to rak each factor agaist aother usig the Saaty scale 1 9. Table 1 below gives a explaatio of the Saaty scale. For example, if two criteria are judged to have the same level of risk, the pairwise compariso score will be 1. A score of 9 is give if oe criterio is assumed to be extremely stroger tha the other. Itermediate judgmets of 2, 4, 6 ad 8 are selected whe a coclusio caot be reached from the scores of 1, 3, 5 ad 7 as defied i Table Cosistecy check AHP provides the possibility of checkig the logical cosistecy of the pairwise matrix by calculatig the Cosistecy Ratio (CR). AHP judgmet is acceptable if CR is less tha 0.1. Give a weight vector, Table 1. Saaty scale of decisio preferece. Judgmet Explaatio Score Equally Two attributes cotribute 1 equally to the objective Moderately Slightly favour oe attribute 3 over aother Strogly Strogly favour oe attribute 5 over aother Very strogly Strogly favour oe attribute 7 with demostrated importace over aother Extremely Evidece favourig oe 9 attribute over aother is of the highest possible order of affirmatio Itermediate judgmet The itermediate values are used whe compromise is eeded 2, 4, 6, 8 308

3 w1 w w Obtaied from a decisio matrix, 2 w a a a A a a a a a a The cosistecy of the decisio matrix is calculated as follows: Multiply matrix A by the weight vector w to give vector, B B b 1 Aw. b 2 where, b b1 a 1 1 w1 a12w2 a1 w b a 1 w a w a w b2 2 1w b a 31 w a3 2 w a3w (1) Divide each elemet of vector, B with the correspodig elemet i the weight vector w to give a ew vector b1 w1 c1 c b c 2 w2 2 b w c (2) max is the average of the elemets of vector c : c i i1 1 max Cosistecy Idex is the calculated usig, (3) CI max (4) 1 where is order of the decisio matrix ad max is obtaied from equatio (3) above. Usig equatio (4), Cosistecy Ratio is calculated as, CR CI (5) RI where RI is the radom idex ad its value is obtaied from Table 2 below. Table 2. Radom idex table RI Other measures of cosistecy have bee defied. For example, (Mustajoki ad Hämäläie, 2000) give a Cosistecy Measure (CM) of betwee 0 to 1 usig the Multi Attribute Value Theory iheret i the Web-HIPRE software. A CM of 0.2 is cosidered acceptable. Cosistecy Measure is calculated usig, 2 r i j r( i, j ) CM ( 1) ( 1 r ( i, j))( 1 r(, i j)) i j (6) k {1,, } is where r i, j a(, ikak ) (, j ), the exteded boud of the compariso matrix elemet a(i, j), ad r(, i j) is the iverse of r (, i j). CM gives a idicatio of the size of the exteded regio formed by the set of local prefereces, whe w r i j w for all i, j {1,, }. i 3 CASE STUDY j 3.1 Backgroud iformatio The AHP methodology of Risk Maagemet has bee illustrated by a case study of oil ad gas pipelies i Nigeria. A summary of the characteristics of the pipelies is give i Table 3 below. The goal of the research is to coduct a risk assessmet of give pipelies usig the AHP methodology. This is achieved by determiig the relative cotributio of differet failure factors to the overall pipelie failure. The failure factors idetified for this study are: corrosio, exteral iterferece, structural defects, operatioal error ad others. We arrived at these factors based o literature review, the historical record of failures from compay database, ad feedback from compay experts. Table 3. Summary of the attributes of pipelies. Attribute Pipelie EL AB AZ Primary service Gas Crude oil Crude oil Year of commissio Type of coatig Cocrete Polykee Polykee Legth 340 km 4 km 18 km Diameter Desig pressure 100 bar 207 bar 207 bar Operatig temperature 26.8C 33.4C 33.4C Material Carbo Carbo Carbo steel steel steel Climate Tropical Tropical Tropical Age of coatig 21 yrs 25 yrs 25 yrs Flowrate 600MCFd 1380bbls 1080bbls 309

4 A total of six pipelie experts participated i the expert judgmet study o risk assessmet of the pipelies. The affiliatios of the experts are i the followig orgaisatios: Shell Iteratioal, Chevro Exploratio, BJ Services, Nigeria Petroleum Developmet Compay, Nigeria Natioal Petroleum Compay, ad SBM Offshore. Attributes of the pipelies ad a historical failure records sheet cotaiig defiig characteristics of the pipelies were made available to the experts with a questioaire. 3.2 Costructio of hierarchy A hierarchy tree of the three pipelies is costructed usig Web-HIPRE software, versio The tree (Figure 1) cotais iformatio o the goal (failure of pipelie), criteria (failure factors) ad sub-criteria (sub divisio of failure factors). The decisio alteratives are the three pipelies uder cosideratio. mea method, the group judgmet for the pairwise compariso is obtaied by takig the geometric mea of the idividual judgmets. GMM is particularly suitable for aggregatig group preferece especially where coflictig resposes could arise. For parties, the geometric mea of judgmets from etries for the matrices of pairwise compariso is calculated as: 1/ G N k aij a ij, i j,, k1 Where: G a ij : The group judgmet for criterio i with criterio j of issue. a k ij : Judgmet of idividual k (k 1,, N) for criterio i with criterio j of issue. O : Ordeig of all criteria of issue. The result of the pairwise compariso for the group is show i Table 4 below. 3.3 Results of pairwise compariso Idividual expert compariso Idividual expert opiio o the pairwise compariso of factors resposible for pipelie failures are separately collected usig a questioaire that was made available to each expert. The outcome of the compariso is the pairwise matrix for the failure likelihood of the pipelies, based o the judgmet of each expert. As expected, the outcome varied from oe expert to aother, sice a cosesus vote does ot apply i this case Group judgmet The idividual expert compariso is combied group wise usig the geometric mea method (GMM), (Aczel ad Saaty 1983). I the geometric Figure 2. Distributio of factors resposible for pipelie failures. The figure shows exteral iterferece as the leadig cause of pipelie failure followed by corrosio, with relative likelihood of failure of ad respectively. Figure 1. Hierarchy tree for EL, AB ad AZ pipelie. Figure 3. Factors resposible for failures of EL, AB ad AZ pipelies. The figure shows EL as the most vulerable amog the three pipelies. The most proouced failure factor for each pipelie is also show. For example, for EL pipelie it ca be see that exteral corrosio is the ext most sigificat failure factor after exteral iterferece. While for AZ pipelie it is iteral corrosio. 310

5 Table 4. Pairwise rakig of failure criteria ad likelihood of failure of EL, AB ad AZ pipelies. Pipelie Factors Likelihood Sub-Factors Likelihood EL AB AZ Exteral iterferece Sabotage Mechaical damage Corrosio Exteral corrosio Iteral corrosio Structural defects Costructio defect Materials defects Operatioal error Equipmet failure Huma error Others Sesitivity aalysis The graphs below (Figures 4 8) show the sesitivity of each of the failure factors to the likelihood of failure of the pipelies. I the sesitivity aalysis, the ifluece of chages o the overall model is ivestigated. The fial priorities of the likelihood of failures of the pipelies deped greatly o the weights attached to the failure criteria. Similarly, the likelihood of failure of each pipelie is iflueced by the weights attributed to each failure criterio. Hece, some chages i the relative weights of the failure criteria could lead to sigificat chages i the fial rakig. Sesitivity aalysis therefore gives good iformatio o the robustess of the model. The likelihood of failure due to exteral iterferece is icreased by 20% to see how this affects the rakig of failures of EL, AB ad AZ pipelies. This chage results i about 3% icrease ad 10% decrease i the weights of EL ad AZ pipelie respectively. AB pipelie has a icrease of 2% i weight. The result is displayed i Figure 4. Similarly i Figure 5, a 70% decrease i the weight of operatioal errors leads to about 3% icrease i the likelihood of failure of EL pipelie ad a 5% decrease i the weight of AZ pipelie, with about 1% decrease i the weight of AB pipelie. I additio, the weight of corrosio ad structural defect is further icreased by 50% ad 60% respectively, ad the weight of mior failures (others) is decreased by 75% to capture the sesitivity. The outcomes are represeted i Figures 6 to 8 below. Figure 4. Sesitivity of pipelie failure to exteral iterferece whe the likelihood of failure due to exteral iterferece is icreased by 20%, from 0.61 to Figure 5. Sesitivity of pipelie failure to operatioal error whe the weight of operatioal error is decreased by 70%. 4 RISK MANAGEMENT OF PIPELINES 4.1 Ispectio ad maiteace strategy Part of the risk maagemet strategies is to formulate a appropriate ispectio ad maiteace strategy for the three pipelies. Table 5 gives some possible strategy (ies) for each failure factor. Figure 6. Sesitivity of pipelie failure for a 50% icrease i the likelihood of failure due to corrosio. 311

6 Figure 7. Sesitivity of pipelie failure due to structural defect whe the weight is icreased by 60%. Figure 8. Sesitivity of pipelie failure for a 75% decrease i the weight of failure for other mior failures. Table 5. Maiteace strategy for pipelie failures. Sub-Factors Sabotage Mechaical damage Exteral corrosio Iteral corrosio Costructio defect Materials defects Equipmet failure Huma error Maiteace strategy Patrollig Pipelie Markig/Improved Right of Way (ROW) Pipe coatig Itelliget piggig survey Recostructio/ Replacemet Replacemet of pipelies Replacemet of faulty equipmets Operator traiig also be explaied by the poor coatig coditio of the pipelie. The result preseted i Figure 9 shows EL pipelie as the most vulerable pipelie amog the three pipelies. To reduce the likelihood to exteral iterferece more patrollig is suggested, while efficiet pipe coatig is recommeded i order to reduce exteral corrosio. The study reveals that AB pipelie is more liable to exteral iterferece tha AZ pipelie, but ot as liable as EL pipelie. Also, o iteral corrosio, AB seems to be more liable tha EL pipelie, but better tha AZ pipelie. O Materials defect, the three pipelies geerally have the same level of failure expectatio. The similarity o the expected failure due to materials defect is expected because the three pipelies are all made of the same material, carbo steel. For AB pipelie, a itelliget piggig survey (IPS) is recommeded to reduce its high likelihood to iteral corrosio, ad regular patrollig is also recommeded to reduce its likelihood to exteral iterferece. AZ pipelie has the highest likelihood of iteral corrosio, equipmet failure ad huma error. The ivestigatio from the pipelie operator cofirmed that the pipelie has suffered previously from iteral corrosio ad huma failure. It is however iterestig to ote that if all the failure factors are cosidered, the likelihood of failure of pipelie AZ is much better tha that of EL ad AB pipelies. Therefore, AZ pipelie ca be cosidered the least vulerable amog the three pipelies, as represeted i Figure 9 ad Table 4. This is due partially to the least likelihood of AZ pipelie to exteral iterferece, which has very high cosequeces. To reduce its operatioal error, immediate replacemet of faulty equipmets ad retraiig of operators is recommeded. Itelliget piggig survey is also recommeded to mitigate iteral corrosio. 4.2 Expected failure cost For the three pipelies, the severity of failure was estimated from historical failure costs available from database of the pipelie compay. I additio, EL pipelie is udoubtedly the most vulerable amog the 3 pipelies, havig the highest likelihood of failure. The expected vulerability of EL pipelie to exteral iterferece ad exteral corrosio is much higher tha expected for AB ad AZ pipelies. EL pipelie is located i the Niger-Delta regio of Nigeria, with a high likelihood of third party iterferece ad sabotage due to the restive ature of the regio. The high tedecy to exteral corrosio could Figure 9. Rakig of pipelies accordig to likelihood of failure. Likelihood: EL 0.488, AB 0.317, ad AZ

7 Table 6. Ispectio ad maiteace strategy for failure factors. EL pipelie AB pipelie AZ pipelie Sub-Factors Severity of failure ( $000 m) Severity of failure ( $000 m) Likelihood Likelihood Likelihood Severity of failure ( $000 m) Exteral iterferece Sabotage , ,000 Mechaical damage , Corrosio Exteral corrosio Iteral corrosio Structural defects Costructio defect Materials defects Operatioal error Equipmet failure Huma error Others Likelihood of o failure Expected failure cost $689,560 $167,095 $99,145 pipelie experts that participated i the AHP were asked to utilize their previous experiece to estimate the severity of failure based o the uique characteristics of each pipelie. I Table 6, the likelihood of failure obtaied from the AHP result i Table 4 is combied with the severity of failure to calculate the expected failure cost for each pipelie. It ca be cocluded from the results that the expected failure cost of EL pipelie is much higher tha for AB ad AZ pipelie. Moreover, AB pipelie has a higher likelihood of failure as compared to AZ pipelie. The expected failure cost calculatio further idicates that allocatig equal maiteace resources to the three pipelies will be a wrog ad ieffective maiteace strategy. Therefore, i the allocatio of maiteace resources, EL pipelie; with the highest expected failure cost should receive more attetio tha the other two pipelies. I additio, AB pipelie will require more maiteace resources tha AZ pipelie. 5 CONCLUSIONS A decisio based model has bee preseted for risk maagemet of oil ad gas pipelies. The model uses available data ad structured expert judgmet to predict the probability of failure ad severity of failure of oil ad gas pipelies. The work has made a uique cotributio to the applicatio of Aalytic hierarchy process i prioritizig oil ad gas pipelies for maiteace. The geometric mea method is applied to arrive at a group cosesus by combiig idividual resposes of experts o pipelie failures. The software, Web-HIPRE used was foud suitable to model the case study, ad to express the sesitivity of the failure factors. The case study of petroleum pipelies i Nigeria revealed some iterestig coclusios, which shows that locatio plays a sigificat role i pipelie itegrity. Similar works (Dey et al & Al-Khalill et al. 2005) have cocluded that corrosio is the most sigificat failure criterio of petroleum pipelies i Idia ad Saudi Arabia. However, for the Nigeria case study, exteral iterferece is foud to be the most importat failure criterio, represetig 60% of the etire failure criteria. The high likelihood of failure by exteral iterferece obtaied is due to the ifluece of sabotage acts o the petroleum pipelies. Therefore, icreasig security aroud the pipelies would help to improve their reliability. Cocetratig o most relevat failure factors is cost efficiet as it helps the cocetratio of maiteace resources o most relevat failure factors. The maagemet will also fid this approach to be beeficial i formulatig a ispectio ad maiteace policy for the compay s assets. For the pipelies, the outcome of the decisio aalysis could also prove useful i formulatig idividual ad societal risk acceptace criteria (Vrijlig et al. 2004). The participatio of experts with workig kowledge of the pipelies reduces the subjective ature of the AHP method, although subjectivity has ot bee totally elimiated. I future work, a structured expert calibratio techique will be applied to further reduce subjectivity. Also, the accuracy of the severity of failure estimated could be further improved with more data from the pipelie operator. ACKNOWLEDGMENT The authors would like to ackowledge the maagemet of the Nigeria Natioal Petroleum 313

8 Compay (NNPC) ad Natioal Petroleum Developmet Compay (NPDC) for their geerous supply of data used i this study. All the experts that participated i this research are also appreciated for their useful cotributios. REFERENCES Aczel, J. ad Saaty, T.L 1983, Procedures for sythesizig ratio judgmets, Joural of Mathematical Psychology 27, pp Al-Khalil1, M. et al. 2005, Risk-Based Maiteace Plaig of Cross-Coutry Pipelies, J of Performace of Costructed Facilities. Vol. 19 No. 2 pp Cago, E., Caro, F., Macii, M. ad Ruggeri, F Usig AHP i determiig the prior distributios o gas pipelie failures i a robust Bayesia approach. Reliab Eg Syst Safety; 67: pp Dey, P.K. 2002, Bechmarkig project maagemet practices of Caribbea orgaizatios usig aalytic hierarchy process, Bechmarkig: A iteratioal joural, Vol. 9 No. 4. Dey, P.K. et al. 2004, Risk-based maiteace model for offshore oil ad gas pipelies: a case study, Joural of Quality i Maiteace Egieerig, Vol. 10 Number 3 pp Hämäläie, R.P. 2010, Itroductio to Value theory aalysis, System Aalysis Laboratory, Helsiki Uiversity of Techology, Filad. Huipeg, Li. 2007, Hierrachial Risk Assessmet of water supply systems, PhD thesis, Lougborough Uiversity, UK. Mustajoki, J. ad Hämäläie, R.P. 2000, Web-HIPRE: Global decisio support by value tree ad AHP aalysis, INFOR, Vol. 38, o. 3, pp Quresh, M.E. ad Harriso, S.R. 2003, Applicatio of the Aalytical Hierarchy process to Riparia Revegetatio Policy optios, Small-scale Forest Ecoomics, Mgt. ad Policy 2(3). Saaty, T. 1980, The Aalytic Hierarchy Process, McGrawHill, New York. Vrijlig, J.K. et al. 2004, A framework for risk criteria for critical ifrastructures: fudametals ad case studies i the Netherlads, Joural of Risk Research 7 (6), pp