ICE PRESSURE RIDGE IMPACTS ON OIL SPILLS IN THE ALASKAN OCS

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1 Ice in the Environment: Proceedings of the 16th IAHR International Symposium on Ice Dunedin, New Zealand, 2nd 6th December 2002 International Association of Hydraulic Engineering and Research ICE PRESSURE RIDGE IMPACTS ON OIL SPILLS IN THE ALASKAN OCS F.G. Bercha 1 and M. Cerovšek 1 ABSTRACT Subsea facilities in near shore regions of the Arctic seas are subject to impacts of ice pressure ridge scour. A recent evaluation of oil spill risks in the Alaskan OCS included development of an oil spill risk model capable of considering impacts of ice pressure ridges of different characteristics on subsea pipeline spill frequencies and severities. This paper focuses on the interactions of pressure ridges with subsea oil pipelines, and presents quantitative analytical evidence of the impacts of the principal ridge parameters of scour depth, flux, orientation, and interaction severity on pipeline failure and oil spill risk. Ridge scour depth is found to be the principal contributor to spill risk, while increased pipeline burial depth is one of the main spill risk mitigation measures. Conclusions and recommendations for risk mitigation and more detailed assessments are also presented. INTRODUCTION The relationships between the kinematics and mechanics of ice pressure ridges and crude oil spill risks in the Alaskan Offshore Continental Shelf (OCS) are presented in this paper. In continuum mechanics, kinematics pertain to geometric and movement aspects of the system; mechanics to force, pressure, rheology, and stress aspects. To the best of the authors knowledge, this is the first time that a practical analysis of these relationships has been presented. First, a general description of the oil spill risk model for the Arctic offshore is given. This model is needed to estimate the effect of ridges together with all other spill causes on spill risks. The kinematic and dynamic mechanical interrelations between ice pressure ridges, seabed sediments, and subsea oil pipelines are outlined. Next, methods for probabilistic calculation of characteristics of pipeline failures or losses of containment and resultant spills are presented, including explicit or implicit consideration of: ocean bathymetry; pressure ridge characteristic distributions including kinematics, mechanical properties, and orientations; ocean bottom geotechnical characteristics; pipeline characteristics including depth of bury, diameter and wall thickness, and oil characteristics. The paper is based on a comprehensive analysis of Arctic offshore oil spills for the U.S. Department of the Interior, Minerals Management Service, Alaskan Region (Bercha 1 Bercha Group, 2926 Parkdale Boulevard NW, Calgary, Alberta, T2N 3S9, Canada

2 International, August 15, 2002), carried out under the direction of the principal authors of this paper. OIL SPILL RISK SIMULATOR An oil spill risk model that incorporates all relevant oil spill causes is required to assess the effect on spill risk of varying ridge properties. It is the relative magnitude of the incremental risk introduced by changes in ridge characteristics that needs to be considered in subsea pipeline design. This section describes such a model; the model is applied to generate the results in Table 3, the principal numerical results reported in this document. Oil spill risks were predicted for several expected future oil and gas development scenarios in the Beaufort and Chukchi Seas offshore continental shelf (OCS) lease sale regions (Bercha International, August 2002). Figure 1 shows a flow chart of the simulator used for the predictions. Because sufficient historical data on oil spills for these regions do not exist, a risk model based on fault tree methodology was developed and applied. Figure 2 shows a typical fault tree for analyzing large spill frequencies. Using the fault trees, base data from the Gulf of Mexico were modified and incremented to represent expected Arctic performance including effects of pressure ridges in terms of oil spillage. Three principal spill risk indicators were quantified, as follows: Annual spill frequency Annual spill frequency per barrel produced Spill index, the product of spill size and spill frequency These indicators were quantified for spill sizes under100 bbl (small), 100 to 1,000 bbl (medium), 1,000 to 10,000 bbl (large), and over 10,000 bbl (huge) for shallow (< 10 m), medium (10 30 m), and deep (30 60 m) water depth zones. Table 1 summarizes typical developments in these zones. Quantification was carried out for each projected develop- Development Scenarios Historical Data Analysis Fault Tree Analysis Hazard Scenarios Arctic Spill Risk Facility Spill Size Frequency and Cause Arctic Spill Frequency Annual Annual <10" Dia Small Spill bbl Shallow Water Depth 0-10 m Medium Water Depth m Deep Water Depth m Sale 1 Sale 2 Frequency Frequency per bbl Produced Pipeline Medium Spill bbl Large Spill bbl Huge Spill > bbl Sale 3 All Sales Spill Index >10" Dia Platform Wells All Sales Non Arctic Chukchi Sea Base Case Mid P Production Well Exploration Well Development Well Chukchi Sea High Case Mid P Chukchi Sea High Case Non-Arctic Figure 1: Flow Chart

3 Note: Component relating to ice ridges. Figure 2: Large Spill Size Frequency Fault Tree Showing All Subsea Pipeline Failure Causes as well as Ice Ridge Effects

4 WATER DEPTH (m) EXPLORATION PRODUCTION TRANSPORT Table 1: Classification of Development Scenarios SHALLOW Artificial island - Drill barge - Ice island - Artificial island - Caisson island - Subsea pipeline MEDIUM Artificial island - Drill ship - Caisson - Caisson island - Gravity Base Structure (GBS) - Subsea pipeline DEEP Drill ship - Semisubmersible - Caisson island - Gravity Base Structure (GBS) - Subsea pipeline - Storage & tankers VERY DEEP Drill ship - Semisubmersible - New design structure - Submarine habitat - Subsea storage - Icebreaking & submarine tankers Chukchi Sea-High Case Spill Frequency per 10^3 years Non Arctic Spills Sale All Spill Frequency 60 Arctic Spills 55 62% 20% Spill Frequency per 1000 years % P/L Platforms Wells Figure 3: Chukchi Sea High Case Spill Frequency per 10 3 Years Year Figure 4: Annual Variation in Arctic and Non-Arctic Comparative Spill Risk Indicators ment year for different scenarios, ranging in duration up to 38 years, and for Chukchi Sea scenarios of 10-year duration. In addition, comparative scenarios for non-arctic locations were formulated and analyzed for oil spill risk. That is, the same scenarios (e.g., a 10-km subsea pipeline in shallow water) located both the Arctic setting and a non-arctic setting such as the Gulf of Mexico were analyzed. Generally, it was found that the non-arctic spill frequency indicators were likely to be approximately 40 % higher than those for similar scenarios in the Arctic. Spill indices were estimated to be 8 % higher. Figure 3 shows the distribution of predicted oil spills among the principal facility types for a given year (2010), while Figure 4 shows the expected evolution of spills over the Arctic project life to year 2038, as well as spills for a hypothetical non-arctic project. The computations were carried out using a Monte Carlo process to permit the inclusion of estimated uncertainties in the Arctic effects. A wide range of details for each scenario was generated, including the following: Expected time history of spill risks over the scenario life.

5 Spill risk variations by spill volumes corresponding to four spill size classes. Spill risk variation by spill cause such as boat anchoring or pressure ridge impacts. Spill risk contribution from each main facility type. Comparative spill risk between Arctic and non-arctic scenarios. The variability in the results due to uncertainties. IMPACT OF RIDGES ON SUBSEA PIPELINE OIL SPILL RISK Ridge-Pipeline Interaction Analysis Ice gouging, such as ridge scouring, occurs when a moving ice feature contacts the sea bottom and penetrates into it, generally as it moves against a positive sea bottom slope. The ice feature can be a multiyear ridge, a hummock, or ice rafting. Various studies have been conducted on the frequency and depth distribution of ridge gouging (Bercha and Associates, 1986; Hnatuik and Brown, 1983; Lanan and Ennis, 2001; Leidersdorf et al., 2001; O Connor and Associates, 1984; Weeks et al., 1983, 1982), as have a number of assessments of the likelihood of resultant subsea pipeline failure (Bercha and Associates, 1986; O Connor, 1984). Pipeline failure frequencies at different water depth regimes as a result of ice ridge action in this study have been estimated on the basis of the historical ice gouge characteristics (Hnatiuk and Brown, 1983; Lanan and Ennis, 2001) together with an analytical assessment (Bercha and Associates, 1986; Weeks et al., 1982) of their likelihood to damage a pipeline. According to Weeks (1983, 1982), a relationship between the expected probability of pipeline failure from ice gouging and ridge characteristics is as follows: where: -kx N = e H F T L sinφ (1) S N = Number of pipeline failures at burial depth of cover x (meters) k = Inverse of mean scour depth (m 1 ) H S = Probability of pipeline failure given ice gouge impact or hit F = Scour flux per km-yr L P = Length of pipeline (km) Ф = Gouge orientation (degrees) form pipeline centerline P For the Northstar project, according to Lanan and Ennis (2001), the mean scour depth is 0.4 m giving a k factor of 2.5, and scour flux is 4 gouges / km-yr. Using an average pipeline depth of cover of 2.5 m, a directional angle of 45, and a conditional failure probability (H S ) of 0.5, Equation 1 gives a frequency of / km-yr. The above ridge parameters are associated with characteristic geographical locations, geotechnical sea bottom characteristics and pipeline installation types. Due to limitations of space in this paper, a detailed description of the geographical variability of the pipeline failure frequency, N, is not given. The conditional probability of pipeline failure, H S, is a direct function of the geotechnical properties of the sea bottom, pipeline wall thickness and mechanics, and ridge keel characteristics, as described by O Connor (1984) and Bercha and Associates (1986). For the purposes of this analysis, this frequency must be distributed among different spill size consequences (Bercha International, August 15, 2002). Due to the difficulty of

6 containing spills under ice, one can expect that the majority of spills would be in the large and huge categories. However, huge spills would be limited by segment length. Thus, a conditional probability (given a spill) of 50 % has been assigned to large spills, and one of 14 % to huge spills. Least likely are small spills, and accordingly they have been given a probability of 13 %. The remaining probability of 23 % has been assigned to medium sized spills. The resultant distribution of spill sizes associated with ridge impacts is given in Table 2. Table 2: Base Pipeline Failure Frequency Component Attributable to Ridge Impacts Spill Size (bbl) Small < 100 Medium Large 1,000 9,999 Huge 10,000 Frequency Increment per 10 5 km-yr Medium (10 30 m) Shallow (0 10 m) Deep (30 m +) Comment Note: Ice gouge failure rate calculated using Equation 1 for 2.5-m cover, 0.2-m average gouge depth, 4 gouges per km-yr flux, and 0.5 H S. Impact of Ridge Characteristics on Pipeline Failure Rate The principal ridge characteristics affecting pipeline failure rate are the mean scour depth, the ridge scour flux, and the ridge keel integrity and sea bottom properties, as reflected in the conditional probability of pipeline failure. Naturally, the impact of these characteristics will vary for different pipeline burial depths. The ridge-pipeline interaction model was applied to reasonable ranges of the above principal parameters to estimate their impact on the pipeline failure probability. Specifically, a base case with a burial depth of 2.5 m, a mean scour depth of 0.2 m, a flux of 4 scours / km-yr, and a conditional probability of failure of 0.5, based on the North Star data (Lanan and Ennis, 2001) was utilized. Figure 5 shows the results of the sensitivity analyses for each of the three ridge characteristic variables and the pipeline burial depth for realistic ranges of the variables. Impact of Ridge Characteristics on Oil Spill Risk Now, variation of ridge characteristics will be considered, together with all other spill causes shown in the fault tree in Figure 2. This is accomplished by using the oil spill risk model described earlier. The impact of ridge characteristics on oil spill risk was investigated by applying expected variations over the spectrum of ridge characteristics to the modeling of large (1,000 to 10,000 bbl) spills. Use was made of the fault tree model subsystem of the oil spill risk model to calculate large diameter pipeline large spill frequencies for feasible variations of the ridge parameters and pipeline burial depth. Details of the calculations are given in the technical report (Bercha International, August 15, 2002). Table 3 summarizes the results of these calculations. Here, one can see the impacts of the depth-related characteristics,

7 Burial Depth Mean Scour Depth 1.E+01 1.E+01 P/L Failure Rate [N/km-yr] 1.E-01 1.E-03 1.E-05 1.E-07 1.E-09 P/L Failure Rate [N/km-yr] 1.E-01 1.E-03 1.E-05 1.E-07 1.E-09 1.E E Burial Depth [m] Mean Scour Depth [m] Ridge Scour Flux Conditional Probability of P/L Failure 1.E-04 1.E-04 P/L Failure Rate [N/km-yr] 1.E-05 1.E-06 1.E Ridge Scour Flux [N/km-yr] P/L Failure Rate [N/km-yr] 1.E-05 1.E Conditional Probability of P/L Failure Figure 5: Sensitivity of Pipeline Failure Rate to Ridge Characteristics and Pipeline Burial Depth and the low impacts of the flux and conditional failure probabilities. In particular, it can be seen from Table 3 that as mean scour depth increases over its full range from 0.1 m to 1.0 m, the large spill rate rises exponentially by a factor of over 1,000, from a value of 2 in 100,000 at 0.1 m to a value of 0.58 at 1.0 m. The opposite is true for burial depth, with the spill rate at 0-m burial depth taking on a value of 0.7, and dropping down to roughly 2 in 100,000 at a burial depth of 5 m. Both ridge scour flux and conditional probability of pipeline failure show a very weak dependence, indicating that these parameters need not be of primary concern. CONCLUSIONS AND RECOMMENDATIONS The principal conclusion from the work is that total (all causes) oil spill risk is very sensitive to ridge mean scour depth. This should not be confused with the ancillary, but trivial, conclusion that oil spill risk from ridging (only) has a similar but much stronger pattern (as it is not diluted by other spill causes). The following are specific conclusions regarding total spill risk: The key ridge parameters affecting both total spill frequency and risk are those relating to the depth of the pipeline and depth of the scours. For a given mean scour depth, the ridge scour flux has a weak effect. For a given ridge scour depth and burial depth, the conditional probability of pipeline failure, a function of pipeline, sea bottom geotechnical, and ridge keel integrity characteristics, also has a weak effect.

8 The following recommendations can be made from the work: In order to minimize total oil spill risk, it is recommended to route pipelines in an area where the mean scour depth of ridging is low even if scour flux is higher. In order to minimize risk of pipeline oil spills, it is recommended to bury the pipelines as deeply as possible, and preferably with a burial depth several times greater than the mean scour depth, again regardless of flux. The ridge-subsea pipeline interaction simulation carried out here is a first order approximation; more detailed assessments can be made using the model described for location-specific pipeline routes. Table 3: Variation in Pipeline Oil Spill Rate with Ridge Parameters Parameter P/L Failure Rate (N / km-yr) Large Spill Rate* (N/10 5 km-yr) Burial Depth (m) E E E Mean Scour Depth (m) E E E Ridge Scour Flux (N / km-yr) E E E Conditional Probability of P/L Failure E E E * Large Spill Rate given is for m water depth, large diameter (> 250 cm) subsea oil pipelines. REFERENCES Anderson, Cheryl McMahon and LaBelle, Robert P. Comparative Occurrence Rates for Offshore Oil Spills. (ISSN ). Spill Science & Technology Bulletin 1(2): (1994). Bercha International Inc. Alternative Oil Spill Occurrence Estimators for the Beaufort and Chukchi Seas Fault Tree Method. Final Report to United States Department of Interior, Minerals Management Service, Alaska Outer Continental Shelf Region (August 15, 2002). Bercha, F.G. Special Problems in Pipeline Risk Assessment. In Proceedings of IPC International Pipeline Conference. Calgary, AB (October 1-5, 2000). Bercha, F.G. and Cerovšek, M. Large Arctic Offshore Project Risk Analysis. In Proceedings of the Russian Arctic Offshore Conference. St. Petersburg, Russia (1997). Bercha, F.G. and Associates (Alberta) Limited. Ice Scour Methodology Study. Final

9 Report to Gulf Canada Resources, Calgary, AB (March 1986). Bercha, F.G. Probabilities of Blowouts in the Canadian Arctic, In Proceedings of the North Sea Offshore Conference, Stavanger, Norway (1978). Goff, R., J. Hammond and Nogueira, A.C. Northstar Sub Sea Pipeline Design of Metallurgy, Weldability, and Supporting Full Scale Bending Tests. In Proceedings 16th International Conference on Port and Ocean Engineering under Arctic Conditions (POAC), Vol. 1, Ottawa, ON (August 12-17, 2001). Hnatiuk, J. and Brown, K.D. Sea Bottom Scouring in the Canadian. In Proceedings of the 9 th Annual Offshore Technology Conference, Houston, TX (May 2-5, 1983). Lanan, G.A. and Ennis, J.O. Northstar Offshore Arctic Pipeline Project. In Proceedings 16th International Conference on Port and Ocean Engineering under Arctic Conditions (POAC), Vol. 1, Ottawa, ON (August 12-17, 2001). Leidersdorf, C.B., Hearon, G.E., Hollar, R.C., Gadd, P.E. and Sullivan, T.C. Ice Gouge and Strudel Scour Data for the Northstar Pipelines. In Proceedings 16th International Conference on Port and Ocean Engineering under Arctic Conditions (POAC), Vol. 1, Ottawa, ON (August 12-17, 2001). O'Connor, M.J and Associates Ltd. Preliminary Ice Keel/Seabed Interaction Study. Final Report to GCRI (March 1984). Weeks, W.F., Barnes, P.W., Rearic, D.M. and Reimnitz, E. Some Probabilistic Aspects of Ice Gouging on the Alaskan Shelf of the. US Army Cold Regions Research and Engineering Laboratory (June 7, 1983). Weeks, W.F., Barnes, P.W., Rearic, D.M. and Reimnitz, E. Statistical Aspects of Ice Gouging on the Alaskan Shelf of the. US Army Cold Regions Research and Engineering Laboratory (1982).