There are many challenges associated with the design and installation ofArctic subsea pipeline. A leading example is the Northstar developmentpipelines currently operating safely offshore the Alaskan North Slope. Uniqueoffshore Arctic environmental loading conditions, such as ice gouging, influence each pipeline design differently. Statistical distributions andprobabilistic assessments of ice gouge records can be used to predict designextreme gouge depths which can then be used to determine pipeline burial depthsrequired for protection against ice keels.

The Northstar subsea pipeline project used the statistical ice gougeanalysis method described by Lanan et al. (1986), based on the exponentialprobability distribution, to select design pipeline burial depths forprotection against ice gouging. This method was applied to publicly availabledata and site-specific survey data collected prior to the pipeline installationin 2000, to predict the design extreme ice gouge depths expected along thepipeline route. Each year since the pipeline installation, new ice gouge datahas been collected by BP Exploration (Alaska).

This paper reviews additional ice gouge data collected since installation ofthe Northstar pipelines and has assessed the use of alternate ice gougeanalysis methods to predict extreme ice gouge design depths for future pipelineinstallations in the Beaufort Sea, Data available from all Alaskan Beaufort Seaice gouge surveys in the Northstar pipeline area was also included in some ofthe statistical comparisons.

Results obtained using the exponential analysis method were compared toanalyses using alternate probability distribution functions (PDFs), such as theWeibull, gamma, and log-normal. Data thresholds have also been investigated forPDF fitting. Work by Caines (2009, 2011) has shown that alternate probabilitydistribution functions might be more appropriate for modeling ice gouge depthdata, compared to the traditional exponential method. A brief comparativeanalysis was conducted using known age Northstar pipeline route data toinvestigate the effects of using all available gouge depth data versus annualmaximums only.

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