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An analysis of inaccuracy in piepline construction cost estimation


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An analysis of inaccuracy in piepline construction cost estimation

  1. 1. Int. J. Oil, Gas and Coal Technology, Vol. 5, No. 1, 2012 29An analysis of inaccuracy in pipeline constructioncost estimation Zhenhua Rui*, Paul A. Metz and Gang Chen Department of Mining and Geological Engineering, University of Alaska Fairbanks, Duckering Building 418, P.O. Box 750708, Fairbanks, Alaska, 99775, USA Fax: +1-907-474-6635 E-mail: E-mail: E-mail: *Corresponding author Abstract: The aim of this paper is to investigate cost overrun of pipeline projects. A total of 412 pipeline projects between 1992 and 2008 have been collected, including material cost, labour cost, miscellaneous cost, right of way (ROW) cost, total cost, pipeline diameter, pipeline length, pipeline’s location, and year of completion. Statistical methods are used to identify the distribution of the cost overrun and the causes for overruns. The overall average cost overrun rates of pipeline material, labour, miscellaneous, ROW and total costs are 4.9%, 22.4%, –0.9%, 9.1% and 6.5% respectively. The cost estimation of pipeline cost components are biased except for total cost. In addition, the cost error of underestimated pipeline construction components is generally larger than that of overestimated pipeline construction components except total cost. Results of analysis show that pipeline size, capacity, diameter, length, location, and year of completion have different impacts on cost overrun of construction cost components. [Received: May 26, 2011; Accepted: June 28, 2011] Keywords: pipeline cost; cost overrun; cost estimation. Reference to this paper should be made as follows: Rui, Z., Metz, P.A. and Chen, G. (2012) ‘An analysis of inaccuracy in pipeline construction cost estimation’, Int. J. Oil, Gas and Coal Technology, Vol. 5, No. 1, pp.29–46. Biographical notes: Zhenhua Rui is a PhD candidate in Energy Engineering Management and MBA student at the University of Alaska Fairbanks. He also received his Master’s degree in Petroleum Engineering from the same university, in addition to his Master’s degree in Geophysics from China University of Petroleum, Beijing. His current research is the Engineering Economics of the Alaska In-state Natural Gas Pipeline. Paul A. Metz is a Professor of Department of Mining and Geological Engineering at the University of Alaska Fairbanks. He received his PhD from Imperial College of Science Technology and Medicine. He also received his MS in Economic Geology and MBA from the University of Alaska. His research interest include: market and transportation analysis of mineral resources; analysis of transport systems; engineering geological mapping and site investigation; mineral and energy resource evaluation.Copyright © 2012 Inderscience Enterprises Ltd.
  2. 2. 30 Z. Rui et al. Gang Chen is a Professor of Department of Mining and Geological Engineering at the University of Alaska Fairbanks. He received PhD in Mining Engineering from Virginia Polytechnic Institute and State University. He also received his MS in Mining Engineering from the Colorado School of Mines. His research interest include: rock mechanics in mining and civil engineering; mine ground engineering; frozen ground engineering and GIS application in mining industry.1 IntroductionCost error is the tendency for actual costs to deviate from estimated cost. Bias is thetendency for that error to have a non-zero mean (Bertisen and Davis, 2008). Cost error orbias is common and a global phenomenon in cost estimating (Flyvbjerg et al., 2003). Costestimating error and bias in other types of projects were mentioned and studied in manypapers. Pohl and Mihaljek (1992) reviewed 1,015 World Bank projects from 1947to 1987 with 22% average cost overrun and 50% time overrun. Merrow (1998) foundthat 47 of 52 megaprojects have an average overrun of 88%, and large projects andmegaprojects appear to have more cost growth than smaller projects. Flyvbjerg et al.(2003) examined 258 transport infrastructure projects (rail, bridge and road) with average28% cost overrun. Bertisen and Davis (2008) reviewed 63 international mining projectswith an average of 14% higher than estimated cost in the feasibility study. The literaturereviewed also shows that cost overrun exists over time. Overall cost overrun rates of allIndiana departments of transportation projects was 4.5%, and 55% of all projectsexperienced cost overruns (Bordat et al., 2004). Jacoby (2001) found that 74 projects witha minimum cost of $10 million had 25% cost overruns. Many researchers also try toexplain the project cost overrun phenomenon. Some researchers proposed that optimismand deception are major reasons for causing cost overrun (Flyvbjerg et al., 2003).Some researchers believe that engineers and managers have incentive to underestimatecost (Bertisen and Davis, 2008). Flyvbjerg (2007) suggested that cost underestimationand overestimation of transport infrastructure appear to be intentional by projectpromoters. Information asymmetries were also suggested as a reason for cost overrun(Pindyck and Rubinfeld, 1995). Rowland (1981) mentioned that large projects increasethe likelihood of a high number of change orders. Jahren and Ashe (1990) suggested thatlarge projects have large cost overruns due to complexity, but also mentioned thatmanagers of large projects try to keep cost overrun rates from growing excessively large.Large projects can lead to savings in unit cost, but it will limit the number of companieswho are able to carry out projects. Therefore, there is a trade-off between economies ofscale and competitive bidding practices (Bordat et al., 2004). Odeck (2004) indicated thatlarge projects have better management than small projects. Soil, drainage, climate andweather conditions have an impact on design standard and cost of materials for road andrail projects, and location influences construction and material cost due to varyingdistance from supplies (RGL Forensics, 2009). An Australian study shows that public-private partnership projects perform better than traditionally procured projects, While aEuropean study shows public-private partnerships exhibit higher costs than traditionallyprocured infrastructure (Infrastructure Partnerships Australia, 2008; RGL Forensics,2009). Flyvbjerg (2007) suggested that more research on the role of ownership in causing
  3. 3. An analysis of inaccuracy in pipeline construction cost estimation 31efficiency difference between projects needs to be conducted. He also used technical,psychological and political-economic factors to explain cost overruns. Although many studies have been conducted on projects’ cost overrun, there arelimited available references about pipeline project cost overrun. With available pipelinedata, this paper will focus on the cost estimation error of pipeline constructioncomponents, and will investigate and identify the frequency of occurrence of costoverruns as well as the magnitude of the differences between estimated cost and actualcost in pipeline projects. In addition, cost overrun in terms of pipeline project size,pipeline capacity, diameter, length, location, and year of completion are also investigated.2 Data sourceIn this study, the pipelines are selected on the basis of data availability. The Oil and GasJournal pipeline cost data are collected from Federal Energy Regulatory Commissionfilings from gas transmission companies, which are published by the Oil and Gas Journalannual data book (Penn Well Corp, 1992–2009). Due to limited offshore pipeline data,the pipeline data set in this paper contains only onshore pipeline data, and the pipelinecost in this paper does not include compressor station cost. The pipeline data set provides location and year, pipeline diameter and length.Pipelines in the data set were distributed in all states in the USA except Alaska andHawaii. It also contains the cost information of 15 Canadian pipelines. The pipelineswere completed between 1992 and 2008. Unfortunately, the data does not show theconstruction period of the pipelines. Therefore, cost is defined as real, accounted costsdetermined at the time of completion. The entire data set has 412 onshore pipelines. Thedata include estimated and actual cost of five cost components. The estimated costs aredefined as budget, or forecast, costs at the time of decision to build the pipeline. Theactual costs are defined as real accounted costs determined at the time of completingpipelines (Flyvbjerg et al., 2003). The five cost components are material, labour,miscellaneous, right of way (ROW) and total costs. Material cost covers cost of line pipe,pipeline coating and cathodic protection. Labour costs consist of the cost of pipelineconstruction labour. Miscellaneous cost is a composite of the costs of surveying,engineering, supervision, contingencies, telecommunications equipment, freight, taxes,allowances for funds used during construction, administration and overheads, andregulatory filing fees. ROW cost contains the cost of ROW and allowance for damages.The total cost is the sum of material cost, labour cost, miscellaneous cost and ROW cost(Penn Well Corp, 1992–2009). The location information for U.S pipelines was provided in a state format. A total of48 states were referred to, except for Alaska and Hawaii. The US Energy InformationAdministration (EIA) breaks down the US Natural Gas pipelines network into sixregions: Northeast, Southeast, Midwest, Southwest, Central and Western. The stategrouping is defined based on 10 federal regions of the US Bureau of Labour Statistics(EIA, 2010). The map of regional definitions is shown in Figure 1. These regionaldefinitions are used to analyse geographic difference. In this paper, the USA pipeline dataare divided in to six regions according to the EIA definition. In addition, there are 15Canadian pipelines, but reports did not show a specific province in Canada.
  4. 4. 32 Z. Rui et al.Figure 1 US natural gas pipeline region map (EIA) (see online version for colours)Note: Alaska and Hawaii are not included in the US Natural pipeline network map.In order to take a comparative analysis, all costs are adjusted by the ChemicalEngineering Plant Cost Index to 2008 dollars. The Chemical Engineering Plant CostIndex is widely applied on process plants for adjusting construction cost. The ChemicalEngineering Plant Cost Index has 11 sub-indexes and a composite index, which is theweight average of the 11 sub-indexes (Chemical Engineering, 2011). Pipeline index andconstruction labour index is used to adjust pipeline material and labour cost. TheChemical Engineering Plant Index is applied to pipeline miscellaneous and ROW cost.3 Performance of individual pipeline construction component cost estimationThis section will evaluate performance of pipeline construction component costestimation. Several methods may be used to study the difference between the estimatedcost and the actual cost. In this study, the estimated cost and the actual cost are used tocalculate cost overrun rate as a measurement of cost overrun. The formula for costoverrun rate is: Cost overrun rate = (actual cost - estimated cost) / estimated costIf the cost overrun rate is positive, the cost is underestimated, otherwise it isoverestimated. In this paper, all cost overrun rates are calculated with the above formula. The histogram of the cost overrun rate for pipeline construction components areshown in Figure 2 to Figure 6. If the cost error is small, the histogram would be narrowlyconcentrated around zero. If underestimated cost is as common as overestimated cost, thehistogram would be symmetrically distributed around zero. It appears that five figuresexhibited non-symmetric distributions, and none of them satisfied the above mentioned
  5. 5. An analysis of inaccuracy in pipeline construction cost estimation 33assumption. For material cost, 172 (42.0% of total) pipelines were underestimated, and238 (58.0% of total) were overestimated. For labour cost, 273 (66.7% of total) pipelineswere underestimated, and 136 (33.3% of total) were overestimated. For miscellaneouscost, 166 (40.8% of total) pipelines were underestimated, and 241 (59.2% of total) wereoverestimated. For ROW cost, 174 (45.7% of total) pipelines were underestimated, and207 (54.3% of total) were overestimated. For total cost, 222 (54.0% of total) pipelineswere underestimated, and 189 (46.0% of total) were overestimated.Figure 2 Cost overrun rates of material cost (see online version for colours)Figure 3 Cost overrun rates of labour cost (see online version for colours)
  6. 6. 34 Z. Rui et al.Figure 4 Cost overrun rates of miscellaneous cost (see online version for colours)Figure 5 Cost overrun rates of ROW cost (see online version for colours)In summary, more pipelines were overestimated for material, miscellaneous and ROWcosts, while more pipelines were underestimated for labour and total cost. In general, thepercentage of overestimated pipelines implies that there are still a fairly good number ofpipelines being completed with costs less than the estimated cost. In addition, themajority of pipelines (87.1% for material cost, 72.3% for labour cost, 67.3% formiscellaneous cost, and 89% for total cost) have cost overrun rates between –0.4 and 0.4.However, only 49.0% of the pipelines for ROW cost have cost overrun rates between
  7. 7. An analysis of inaccuracy in pipeline construction cost estimation 35–0.4 and 0.4. It demonstrates that ROW cost overrun is more severe than costcomponents, which is also indicated by its standard deviation (SD) (Table 1).Figure 6 Cost overrun rates of total cost (see online version for colours)Table 1 Summaries of cost overrun of pipeline construction cost components Material Labour Miscellaneous ROW TotalSkewness 5.77 4.83 4.77 3.25 2.20Kurtosis 49.22 44.88 42.06 15.77 12.29Minimum –0.95 –0.94 –0.94 –1.00 –0.94Maximum 5.67 7.04 4.56 4.55 2.12Range 6.61 7.98 5.50 5.55 3.06Average 0.05 0.22 –0.01 0.09 0.07Standard deviation 0.55 0.62 0.56 0.81 0.34Total number of pipelines 410 409 407 381 411Number of underestimated pipelines 172 273 166 174 222Number of overestimated pipelines 238 136 241 207 189Furthermore, statistical summaries of cost overrun of individual pipeline constructioncomponents are shown in Table 1. Skewness is a quantitative way to measure symmetryof the distribution. Symmetrical distribution has a skewness of 0. Positive skewnessmeans that the right tail is ‘heavier’ than the left tail. Negative skewness means that theleft tail dominates distribution. Kurtosis is a quantitative method to evaluate whether theshape of the data distribution fits the normal distribution. A normal distribution has akurtosis of 0. Kurtosis of a flatter distribution is negative and that of a more peakeddistribution is positive (Hill et al., 2007). Values of skewness and kurtosis in Table 1show that none of the cost overrun of five components is symmetrical normaldistribution, which matches the implication from the histogram graphs. Some
  8. 8. 36 Z. Rui et al.transformation techniques (such as natural log transformation) are applied to cost overrunrate data for fitting them to normal distribution, but the data transformations areunsuccessful. Therefore, the non-parametric statistical test is used in the below sections.P-value will be produced by each test. In statistics, traditionally, p < 0.01 is consideredhighly significant, p < 0.05 is significant. This criterion is also adopted in this paper. Table 1 shows that the minimum cost overrun rate for individual cost components arebetween –94% (labour cost) and –100% (ROW cost). The maximum cost overrun rate forindividual cost components are between 212% and 704%. The value of minimum andmaximum indicates that cost performance for some pipelines is extremely bad. Thelabour cost overrun has the largest maximum-minimum range of 798%, while total costoverrun has the smallest range of 306%. The SD of individual cost components are fairlysignificant, between 34% and 81% of the estimated cost. The large maximum-minimumrange and SD indicate that performance of pipeline construction cost estimating is veryunstable. It is noteworthy that labour cost has the largest maximum-minimum rangeand second largest SD, and ROW cost has the largest SD. Therefore, it is difficult toestimate labour cost and ROW cost accurately. Total cost overrun has the smallestmaximum-minimum range and SD due to its aggregation of other components. Average cost overrun is a key parameter to measure cost estimation performance ofindividual pipeline construction cost components. The labour cost has the highest averagecost overrun of 22%, followed by ROW cost of 9%, total cost of 7%, material cost of 5%and miscellaneous cost of –1%. The material, labour, ROW and total costs show positiveaverage cost overrun, while the miscellaneous cost has negative average cost overrun.This result denotes that, in average, actual cost is larger than estimated cost for allpipeline construction cost components except the miscellaneous cost. As mentioned before, there are more pipelines with overestimated material,miscellaneous and ROW costs than those with underestimated pipelines, and there aremore pipelines with underestimated labour and total costs than those with overestimationof these two cost components. However, it is an interesting finding that the average costoverruns of material and ROW cost are still positive, even though there are morepipelines with overestimated material cost, and ROW cost. It appears that cost estimationof pipeline construction cost components is biased, and the underestimating error isgenerally greater than the overestimating error for some pipeline construction costcomponents. In this paper, two statistical tests are performed to investigate this inference. A binomial test is conducted to examine if the error of cost overestimating is ascommon as the error of cost underestimating. As shown in Table 2, the p-value of thebinomial test rejects the null hypothesis that the overestimating error is as common as theunderestimating error for material, labour, miscellaneous, and ROW cost estimation(p < 0.05, two sided test), but fails to reject that for total cost (p > 0.05, two sided test).Therefore, the cost estimations of all pipeline construction cost components are biasedexcept total cost. Furthermore, the non-parametric Mann-Whitney test is employedto test if the cost underestimating error is the same as the cost overestimating error. Thep-value shown in Table 2 shows that the error of underestimated pipelines’ cost overrunare much larger than the error of overestimated pipelines’ cost overrun for material,labour, miscellaneous and ROW cost (p < 5%, one sided test), but not for total cost (p >5%, two sided test). Hence, the underestimating error is significantly more common andlarger than the overestimating error for all pipeline cost components, but not for totalcost.
  9. 9. An analysis of inaccuracy in pipeline construction cost estimation 37Table 2 Statistical tests of cost overrun of pipeline construction cost components Material Labour Miscellaneous ROW TotalBinomial test 0.001 0 0 0 0.114Mann-Whitney test 0.047 0 0 0.039 0.082After analysing overall cost overrun of pipeline projects, it is more important to identifysignificant factors that influence pipeline projects’ cost overruns. The analyses of costoverruns in terms of pipeline project size, pipeline capacity, diameter, length, location,and completion time are carried out in the following sections.4 Cost overrun in terms of pipeline project sizeHere, the project size is measured by the pipeline’s actual total cost. The pipelinetotal cost ranges from $33,576 to $1,933,839,076. According to the pipeline actualtotal cost, the pipeline project size is classified into groups of small, medium and large.185 pipelines with a total actual cost less than $10,000,000 are classified as smallprojects, 192 pipelines with a total actual cost between $10,000,000 and $100,000,000are classified as medium projects, and 33 pipelines with a total actual cost larger than$100,000,000 are classified as large projects.Table 3 Average cost overrun rate for different project size groupsComponents Project size Average SD Skewness KurtosisMaterial Small 0.10 0.70 4.55 30.86 Medium 0.01 0.42 7.34 80.95 Large 0.04 0.13 1.22 4.19Labour Small 0.16 0.47 1.31 6.95 Medium 0.28 0.76 5.19 40.15 Large 0.13 0.41 -0.50 4.03Miscellaneous Small 0.01 0.46 1.08 5.68 Medium 0.01 0.46 0.98 4.01 Large -0.04 0.46 1.08 5.68ROW Small 0.18 1.20 2.87 13.30 Medium 0.30 1.39 3.28 15.00 Large 0.23 0.54 1.60 7.12Total Small 0.04 0.36 1.89 10.04 Medium 0.08 0.32 2.70 15.24 Large 0.12 0.24 2.50 11.58Descriptive statistical analysis of cost overrun in terms of project size is shown inTable 3. As seen in Table 3, for the material, labour, miscellaneous cost and ROW cost,there is no linear relationship between average cost overrun rates and project sizes.However, for total cost, the average cost overrun rate increases as the project’s sizeincreases. For the total cost, large projects have the highest cost overrun rates. Aplausible explanation is that a large pipeline project, normally bigger than 1 billiondollars, can cause a huge demand that influences market price, such as steel price, and
  10. 10. 38 Z. Rui et al.further increases the cost of pipeline construction. Expectation of increased pipelineconstruction costs induced an increase in the current unit construction cost (Rui et al.,2011a). Suppliers would raise prices with expectation for more demand. In addition, alarge project limits the numbers of suppliers and contractors, reduces competitionand increases the cost (Bordat et al., 2004; RGL Forensics, 2009). However, for themiscellaneous cost, large projects have the lowest cost overrun. It is possible that largerprojects have better management systems which coordinate different departments,increase the efficiency of material utilisation and take advantage of economies of scale. In order to determine if there is a strong relationship between project sizes andcost overrun for different pipeline construction components, the non-parametricKruskal-Wallis (KW) test is used to test the null hypothesis that the project size has noeffect on cost overrun of pipelines. The KW test is chosen because the value of skewnessand kurtosis shows that the cost overrun of each diameter group is not a normaldistribution. Therefore, the KW test will be used in this part and following parts when thedata does not produce normal distributions. For total cost, the result of the KW test shows that cost overrun for different projectsize groups are significantly different (p < 0.05). However, such a significant differenceis not found for other cost components (p > 0.05). Therefore, it is concluded that theproject size significantly influences cost overrun for total cost, but not for otherindividual cost components.Table 4 Average cost overrun rate for different diameter groupsComponents Diameter groups Average SD Skewness Kurtosis Num. of pipelinesMaterial 4–20 inches 0.13 0.68 3.70 21.37 124 22–30 inches 0.03 0.42 5.62 51.28 131 34–48 inches 0.00 0.52 8.56 92.67 155Labour 4–20 inches 0.39 0.95 3.85 23.97 126 22–30 inches 0.21 0.42 1.14 5.79 131 34–48 inches 0.09 0.33 0.87 7.18 155Miscellaneous 4–20 inches 0.17 0.99 4.11 24.64 123 22–30 inches –0.16 0.35 0.93 4.39 131 34–48 inches 0.02 0.48 0.92 4.38 152ROW 4–20 inches 0.43 1.57 2.61 10.01 115 22–30 inches 0.24 1.38 3.31 15.53 122 34–48 inches 0.11 0.81 2.71 15.52 153Total 4–20 inches 0.17 0.48 1.72 6.96 124 22–30 inches 0.03 0.24 0.65 6.55 131 34–48 inches 0.02 0.23 1.39 9.59 1555 Cost overrun in terms of pipeline diameterThe range in pipeline diameter is between 4 and 48 inches. The pipeline projects arecategorised into three diameter groups: 4–20 inch, 22–30 inches and 34–48 inches.Pipeline construction component cost overruns for three different pipeline diametergroups are shown in Table 4.
  11. 11. An analysis of inaccuracy in pipeline construction cost estimation 39 For material, labour, ROW and total costs, 4–20 inch pipelines have the highestaverage cost overrun rate, followed by 22–30 inches pipelines and 34–48 inchespipelines. For miscellaneous cost, 4–20 inches pipelines have the highest average costoverrun, but 22–30 inches pipelines have the lowest average cost overrun of –16%. 4–20inches groups have the highest average cost overrun rate for all construction componentscosts. It appears that small diameter pipelines are prone to cost overrun. Therefore, the non-parametric KW test is used to test the null hypothesis that type ofpipeline diameter has no effect on cost overrun of pipelines construction component cost.For material, ROW and total cost, the result of the KW test shows the cost overrun is notsignificantly different for different diameter groups (p > 0.5). For labour andmiscellaneous costs, the result of the KW test shows the cost overrun for types ofdiameter groups is significantly different (p < 0.01). Therefore, it is concluded that typesof diameter groups influence cost overrun of pipelines for labour and miscellaneous cost,and not for other components costs.6 Cost overrun in terms of pipeline lengthThe cost overrun in terms of pipeline length is tested in this section. The range of pipelinelength is between 0.1 and 713 miles. The length group is divided into two groups:0–20 miles and 20–713 miles. About 78% of pipelines are shorter than 20 miles, and therest of the pipelines are between 20 and 713 miles. Pipeline construction component costoverruns for two different pipeline length groups are shown in Table 5.Table 5 Average cost overrun rate for different length groupsComponents Length groups Average SD Skewness Kurtosis Num. of pipelinesMaterial 0–20 miles 0.05 0.56 5.25 44.21 321 20–713 miles 0.04 0.51 8.14 73.36 89Labour 0–20 miles 0.21 0.60 5.37 56.39 323 20–713 miles 0.26 0.70 3.46 20.41 89Miscellaneous 0–20 miles 0.17 0.72 4.71 38.60 319 20–713 miles –0.03 0.40 0.82 4.74 87ROW 0–20 miles 0.23 1.28 3.11 14.53 303 20–713 miles 0.30 1.18 3.89 21.62 87Total 0–20 miles 0.06 0.35 2.12 11.85 321 20–713 miles 0.10 0.30 2.80 14.57 89For material, miscellaneous and ROW costs, the 0–20 miles group has the highestaverage cost overrun rate, followed by the 20–713 miles inches pipelines. For labour costand total cost, the 20–713 miles pipelines have incurred the highest average cost overrun,followed by 0–20 miles pipelines. It appears that different construction component costshave different cost overrun rate patterns. The KW test is used to test the null hypothesis that type of pipeline length has noeffect on cost overrun. For all construction cost components, the results of the KW testsshow the cost overrun rate difference between types of length groups are not significantat the 5% significance level (p > 0.1). Therefore, cost overrun rates of all constructioncost components are not significantly influenced by types of pipeline length.
  12. 12. 40 Z. Rui et al.7 Cost overrun in terms of pipeline capacityIn this paper, the pipeline volume (capacity) is calculated with formula (Zhao, 2000): V = S *L 2 ⎛D⎞where S = π ⎜ ⎟ , V is the pipeline volume (ft3), S is the pipeline cross-sectional area ⎝2⎠(ft2), L is the pipeline length (ft), and D is the pipeline diameter (ft). In the data set forthis study, the smallest pipeline capacity is 92 ft3, and the largest is 36,220,080 ft3. In thissection, all pipelines are divided into three different groups of pipeline capacities to testwhether cost overrun rate is significantly different for different pipeline capacity.135 pipelines with a capacity between less than 75,000 ft3 are classified as small projects,136 pipelines with a capacity between 75, 000 ft3 and 284,768 ft3 are classified asmedium projects, and 139 pipelines with a capacity larger than 284,768 are classified aslarge projects. Descriptive statistical analysis of cost overrun in terms of pipeline capacity is shownin Table 6. A noticeable observation is that the small pipeline capacity group has thehighest average cost overrun rates for all construction cost components. Pipelines withsmall capacity appear to be particularly prone to cost overrun. Projects with largecapacity may take more advantage of economies of scale.Table 6 Average cost overrun rates for different capacity groups Components Capacity groups Average SD Skewness Kurtosis Num. of pipelines Material Small 0.19 0.80 3.81 22.63 135 Medium –0.03 0.24 0.97 4.47 136 Large –0.05 0.43 8.75 93.69 139 Labour Small 0.24 0.57 1.54 7.35 135 Medium 0.25 0.84 5.37 38.99 137 Large 0.16 0.38 0.47 5.10 140 Miscellaneous Small 0.13 0.97 4.14 25.36 133 Medium –0.08 0.43 1.11 4.25 135 Large –0.03 0.43 0.94 4.79 138 ROW Small 0.34 1.50 2.50 9.29 128 Medium 0.19 1.19 4.00 23.95 130 Large 0.20 1.05 3.54 19.34 132 Total Small 0.12 0.46 1.72 7.77 135 Medium 0.03 0.29 2.45 13.17 136 Large 0.05 0.21 0.95 7.96 139The KW test is used to verify that the pipeline capacity has no effect on cost overrun forconstruction cost components. For material cost, the result of KW test rejects the nullhypothesis (p < 0.001), indicating that pipeline capacity influences material cost overrun,and projects with small pipeline capacity have large positive cost overrun rates. Pipelineprojects with large capacity mean that more material is consumed. Therefore, projectswith a large capacity take advantage of economies of scale in purchasing materials,resulting in lower costs for materials as pipeline capacity increases. It may be that the
  13. 13. An analysis of inaccuracy in pipeline construction cost estimation 41estimator of pipeline projects did not estimate unit price with capacity changingaccurately or did not consider economies of scale in material cost. This may result inpipeline projects with small capacity that have large cost overrun. For labour,miscellaneous, ROW and total costs, the results fail to reject the null hypothesis thatpipeline capacity has no effects on the cost overrun rates (p > 0.05). Therefore, the costoverrun differences in the labour, miscellaneous, ROW and total costs are not statisticallysignificant for different types of pipeline capacities.Table 7 Average cost overrun rates for different regionsComponents Region groups Average SD Skewness Kurtosis Num. of pipelinesMaterial Midwest –0.02 0.29 0.03 4.81 55 Northeast –0.02 0.56 7.33 72.15 156 Southwest 0.02 0.37 0.32 5.35 30 Canada 0.18 0.26 0.80 2.75 14 Central 0.06 0.28 1.58 8.24 52 Southeast 0.26 0.92 3.63 15.69 55 Western 0.09 0.50 3.22 16.22 48Labour Midwest 0.12 0.38 1.19 8.36 55 Northeast 0.12 0.34 0.87 5.91 157 Southwest 0.28 0.60 1.04 3.30 30 Canada 0.02 0.33 –1.04 3.95 15 Central 0.20 0.49 0.31 2.38 52 Southeast 0.33 0.85 3.01 14.70 55 Western 0.55 1.14 4.20 23.28 48Miscellaneous Midwest –0.06 0.43 1.72 7.74 54 Northeast –0.07 0.45 1.14 5.97 155 Southwest 0.05 0.52 0.62 2.62 30 Canada 1.32 2.25 1.56 4.03 14 Central –0.02 0.37 0.84 3.97 51 Southeast 0.04 0.55 1.60 5.93 55 Western –0.08 0.54 1.73 6.28 47ROW Midwest 0.34 0.99 3.42 20.21 53 Northeast –0.10 0.76 2.31 12.10 150 Southwest 0.12 1.14 3.66 17.27 27 Canada 1.59 2.18 0.76 2.01 14 Central 0.26 1.11 2.74 12.51 50 Southeast –0.08 0.65 1.18 4.90 52 Western 1.01 2.35 1.84 5.14 44Total Midwest 0.01 0.24 –0.77 5.72 55 Northeast 0.00 0.26 1.72 10.97 155 Southwest 0.84 0.34 0.60 3.68 30 Canada 0.14 0.31 –0.86 4.11 15 Central 0.11 0.29 1.35 6.69 52 Southeast 0.13 0.45 1.93 6.80 55 Western 0.19 0.48 2.76 10.77 48
  14. 14. 42 Z. Rui et al.8 Cost overrun in terms of different regionsAs we know, pipeline cost is significantly different for different regions (Rui et al.,2011b). This section discusses whether cost overrun of pipeline construction costcomponents is different for different regions. As seen from Table 7, it is noticeable that cost overrun rate of the pipelines in theNortheast regions is the lowest in the USA as compared to the other regions, even thoughthe Northeast has a relatively high cost of living. In addition, the total cost overrun rate ofpipelines in the Northeast regions is a perfect 0. A possible explanation is that 155 out of412 pipelines in the data set are located in the Northeast regions, which provides morepractical experience and historical information for new pipeline cost estimating in thisregion. A few negative cost overrun rates also appear in some regions for differentconstruction component cost. The result of KW tests shows that the cost overruns of difference for different regionsare highly significant for all construction cost components (p < 0.001). Weathercondition, soil property, population density, cost of living, terrain condition, and distancefrom supplies are variable for different regions and make pipeline project cost estimationmore difficult (Rui et al., 2011b; Zhao, 2000). More detailed information on pipelineroutes is needed to explain cost overrun difference among different regions. Therefore, it is concluded that the cost overrun rates of all cost components showsignificant difference for different regions and the pipeline’s location matters to costoverrun in all cost components.9 Cost overrun over timeForty-seven megaprojects between the mid 1960s and 1984 were reported with a costoverrun rate of 88% (Merrow, 1998). Over 1,000 World Bank projects between 1947 and1987 had cost estimated errors (Pohl and Mihaljek, 1992). 55% of all Indiana Departmentof Transportation’s projects between 1996 and 2001 experienced cost overruns (Bordatet al., 2004). Cost overrun is constant for more than a 70-year period between 1910 and1998 for 208 transportation projects in 14 nations on five continents (Flyvbjerg et al.,2003). All the literature show that the cost estimated error persists over time in manydifferent types of projects. But is there any improvement in pipeline compressor stationprojects over time? This section tries to discover whether the cost estimating performancehas improved over time. Improved performance of cost estimating is normally expected.The average cost overrun rate of compressor station construction components between1992 and 2008 are displayed in Figure 7. The cost overrun rate of ROW cost fluctuateswidely with a declining trend. The cost overrun rate of labour cost shows a decreasebefore 2004 and then a significant increase after 2004. But cost overrun rate of material,miscellaneous and total costs change more gradually over time. The length of the construction phase influences cost overrun rate. Therefore, it isbetter to use the year of planning to build as the time measurement (Flyvbjerg et al.,2003), but the available data does not provide the year of building and constructionperiod. Therefore, the year of completion is used to as a measure of the time, which maycause bias. The non-parametric Nptrend test is conducted to test whether there is a trendof cost overrun rate over years. All results of the Nptrend test show only cost overrunrate of ROW decreases over time (p < 0.05). Therefore, based on available data, it is
  15. 15. An analysis of inaccuracy in pipeline construction cost estimation 43concluded that cost estimating of ROW cost has improved over time, not for othercomponents.Figure 7 Annual average cost overrun rate of pipeline construction cost components (see online version for colours)10 Conclusions and future workThis paper statistically analyses the cost estimating performance of individual pipelineconstruction cost components by using 412 pipeline projects. The trend and distributionof all 412 pipelines construction cost components cost estimation over the 1992–2008periods are analysed. Overall average cost overrun rates of the material cost, labour cost,miscellaneous cost, ROW cost and total cost are 4.9% (SD = 54.8%), 22.4%(SD = 61.8%), –0.9% (SD = 56.2%), 9.1% (SD = 80.9%) and 6.5% (SD = 33.5%)respectively. The labour and ROW costs have the largest cost overrun compared to theother cost components. The statistical test results show that cost estimating in all costcomponents is biased except in the total cost. And the magnitude of cost underestimatingerror is generally larger than the overestimating error except for total cost. Furthermore,cost overrun rates of pipeline construction cost components are analysed in terms ofpipeline project size groups, capacity groups, diameter groups, length groups, locationgroups, and the year of completion to investigate the relationship between cost overrunsand different groups. The cost overrun rate for the total cost shows a significantdifference for different project size groups, and the cost overrun rate increases withproject size. An expected large demand, limited supplies and contractors for large-sizeprojects cause big cost overruns (Bordat et al., 2004; RGL Forensics, 2009; Rui et al.,2011a). The cost overrun rates of the labour and miscellaneous costs show significantdifferences for diameter groups, and the small diameter group has the highest averagecost overrun rate. The cost overrun rates of all construction cost components are notsignificantly influenced by pipeline length. The cost overrun rate of the material cost issignificant for different pipeline capacity groups, and projects with small pipelinecapacities appear to be particularly prone to cost overrun. Large capacity pipelineprojects may purchase material at low price due to large scale. The planners or estimator
  16. 16. 44 Z. Rui et al.may not estimate the material unit price changing with scale accurately or may even failto consider the economies of scale factors. The cost overruns of all construction costcomponents are significantly different for different regions. The weather, soil, terrain,terrain condition, population density and experience are suggested as causes for making itdifficult to estimate cost accurately. Cost estimating accuracy of pipeline projects havenot improved over the 1992–2008 time period. Based on the analysis of historical pipeline cost estimated errors, Table 8 providessome proposed guidelines for project estimators conducting pipeline cost estimation. It isconsidered that individual cost components should receive varying degree of attentionunder different conditions in order to make cost estimation efficient and reliable. A fourlevel scale: maximum attention, moderate attention, less attention, and minimumattention, is applied for the amount of attention and effort paid to individual costcomponent cost analysis, depending on the project size, pipe diameter, pipeline length,pipeline capacity, and region of construction, as given in Table 8.Table 8 Proposed guidelines for pipeline cost estimatorsCategory Sub-category Material Labour Miscellaneous ROW TotalProject size Small C B D B D Medium D A D A C Large D B D A BDiameter 4–20 inches B A B A B 22–30 inches D A B A D 34–48 inches D C D B DLength 0–20 miles D A B A C 20–713 miles D A D A CCapacity Small B A B A B Medium D A C B D Large D B D B DRegion Midwest D B C A D Northeast D B C C D Southwest D A D B A Canada B D A A B Central D B D A B Southeast D A D C B Western B A C A BNotes: A = maximum attention; B = moderate attention; C = less attention; D = minimum attentionTo the best of the authors’ knowledge, this paper is the first in-depth analysis of pipelineconstruction component cost overruns. Suggested future work may include the followinglist:• Different reasons for cost overrun for other types of projects are proposed by different researchers, and many of the proposed causes did not have hard data support. The same situation applies to pipeline projects; lack of good quality projects data is a major difficulty in investigating more in-depth causes of pipeline cost overrun. Therefore, collecting more accurate information on the pipeline
  17. 17. An analysis of inaccuracy in pipeline construction cost estimation 45 construction period, the ownership of projects, the material of pipeline, and the thickness of pipeline wall is a major part of future work. Furthermore, quantity analysis of collecting data should be conducted to investigate the causes of cost overrun.• More application with analysis results will be used in the future pipeline projects, such as pipeline cost overrun distribution and average cost overrun rate.• Develop a set of recommendations to help mangers and engineers to better estimate pipeline project costs and minimise the cost estimating errors.ReferencesBertisen, J. and Davis, G.A. (2008) ‘Bias and error in mine project capital cost estimation’, The Engineering Economist, Vol. 53, No. 2, pp.118–139.Bordat, C., McCullouch, B., Sinba, K. and Labi, S. (2004) An Analysis of Cost Overruns and Time Delays of INDOT Projects, Joint Transportation Research Program, Indiana Department of Transportation and Purdue University, West Lafayette, Indiana.Chemical Engineering (2011) Chemical Engineering’s Plant Cost Index, available at (accessed on May 2011).Energy Information Administration (EIA) (2010) U.S. Natural Gas Pipeline Regional Definitions Map, available at (accessed on July 2010).Flyvbjerg, B. (2007) ‘Policy and planning for large infrastructure projects: problems, causes, cures’, Environment and Planning B: Planning and Design, Vol. 34, No. 4, pp.578–597.Flyvbjerg, B., Skamris, M. and Buhl, S. (2003) ‘How common and how large are cost overrun in transport infrastructure projects?’, Transport Reviews, Vol. 23, No. 1, pp.71–88.Hill, R.C., Griffiths, W.E. and Lim, G.C. (2007) Principle of Econometrics, 3rd ed., John Wiley and Sons, Inc., New York.Infrastructure Partnerships Australia (2008) Available at (accessed on May 2011).Jacoby, C. (2001) Report on Supplemental Agreement Reasons, AASHTO-FHWA Project Cost Overrun Study, Federal Highway Administration, US Department of Transportation, Washington, D.C.Jahren, C. and Ashe, A. (1990) ‘Predictors of cost-overrun rates’, ASCE Journal of Construction Engineering and Management, Vol. 116, No. 3, pp.548–551, American Society of Civil Engineers, Reston, VA.Merrow, E.W. (1998) Understanding the Outcomes of Megaprojects: A Quantitative Analysis of Very Large Civilian Projects, R-3560-PSSP, Rand Corporation, CA.Odeck, J. (2004) ‘Cost overruns in road construction-what are their sizes and determinants?’, Transport Policy, Vol. 11, No. 1, pp.43–53.Penn Well Corporation (1992–2009) Oil & Gas Journal Databook, Tulsa, Oklahoma.Pindyck, R.S. and Rubinfeld, D.L. (1995) Microeconomics, 3rd ed., Prentice-Hall, Englewood Cliffs, N.J.Pohl, G. and Mihaljek, D. (1992) ‘Project evaluation and uncertainty in practice: a statistical analysis of rate-of-return divergences of 1015 world bank projects’, The World Bank Economic Review, Vol. 6, No. 2, pp.255–277.RGL Forensics (2009) Ex Post Evaluation of Cohesion Policy Programmes 2000–2006, Work Package 10, European Commission, Brussels, BE.Rowland, H. (1981) The Causes and Effects of Change Orders on the Construction Process, PhD Thesis, Georgia Institute of Technology, Atlanta, GA.
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