ACCESS NUMBER: M11PC00013

STUDY TITLE: Updates to Fault Tree for Oil-Spill Occurrence Estimators 

REPORT TITLE: Updates to Fault Tree Methodology and Technology for Risk Analysis, Liberty Project

CONTRACT NUMBER: M11PC00013 

SPONSORING OCS REGION: Alaska 

APPLICABLE PLANNING AREA: Beaufort Sea 

FISCAL YEARS OF PROJECT FUNDING: FY 2012-2016 

COMPLETION DATE OF REPORT: 

COSTS BY FY: FY 2011: $229,840 

 FY 2016: $ 36,150 

CUMULATIVE PROJECT COST: $265,990 

PROJECT MANAGER: Dr. Frank G. Bercha, P.Eng. 

AFFILIATION: Bercha International Inc. 

ADDRESS: 2926 Parkdale Blvd., NW, Calgary, AB, Canada, T2N 3S9 

PRINCIPAL INVESTIGATOR(S)*: 

Frank G. Bercha 

Milan C. Cerovsek 

Wesley Abel 

KEY WORDS: 

Oil spill risk, spill occurrence indicators, South Beaufort Sea, Liberty prospect, offshore oil production, 

offshore oil drilling, subsea pipeline, pipe in pipe, Monte Carlo simulation. 

BACKGROUND: 

The United States Department of the Interior, Bureau of Ocean Energy Management (BOEM) Alaska 

Outer Continental Shelf (OCS) Region uses oil spill occurrence estimates for National Environmental 

Policy Act assessments for all parts of their area of assessment, ranging from nearshore through shallow 

water, to deeper water. Although land to 3 nautical miles is not within BOEM jurisdiction, it is included in 

the BOEM environmental impact analysis; hence it is also included in the study area here. In the present 

study, the latest methodologies based on fault tree analysis developed for the assessment of oil spill rates 

associated with exploration and production facilities and operations in OCS Arctic waters are applied to a 

specific Scenario; namely, the Liberty Development Project, hereinafter called the Project. 

OBJECTIVES: 

The specific objectives of this study are as follows: 

 Assimilate North Sea and U.S. OCS oil spill statistics, and evaluate their applicability to the Project. 

 Develop the fault trees for estimating oil spill occurrences from the proposed Project associated with 

spills of different size categories. 

 Using the fault tree approach in a Monte Carlo simulation, develop alternative oil spill indicators and 

assess their variability, including effect of variability of both the historical data and the future 

development scenario parameters. 

 Evaluate the variability of alternative oil spill indicators for a similar development scenario in a nonArctic location. 
ACCESS NUMBER: M11PC00013

DESCRIPTION: 

Oil spill occurrence estimates were generated for the proposed offshore drilling and production facility 

located on an artificial gravel island with a pipeline to shore at the Liberty prospect (the Project), located 

in the South Beaufort Sea in the landfast ice zone, inside the barrier islands, approximately 9 km (5 miles) 

offshore. Because sufficient historical data on offshore oil spills for this region do not exist, an oil spill 

occurrence model based on fault tree methodology was developed and applied. Using the fault trees, 

base data from the Gulf of Mexico and Pacific OCS and, for losses of well control also from the North 

Sea, including the variability of the data, were modified and augmented to represent expected Arctic 

offshore oil spillage frequencies. Because the proposed subsea pipeline is a pipe-in-pipe, for which 

historical data are not available, a reliability analysis was carried out to develop loss of containment 

frequencies applicable to pipeline. 

Three principal spill occurrence indicators, as follows, were quantified for each year of the principal 

project components and the project as a whole, as well as life of field averages: 

 Spill frequency per 1,000 years 

 Spill frequency per 109 barrels produced 

 Spill index, the product of spill size and spill frequency 

These indicators were quantified for the following spill size ranges: 

 Small (S): 50 - 99 bbl 

 Medium (M): 100 - 999 bbl 

 Large (L): 1,000 - 9,999 bbl 

 Huge (H): >= 10,000 bbl 

Quantification was carried out for each future year for the Project, in duration up to 25 years. In addition, a 

comparative scenario for non-Arctic locations was formulated and analyzed for oil spill occurrence. The 

computations were carried out using a Monte Carlo process to permit the inclusion of uncertainties in the 

base and scenario data and Arctic effects, and generation of result probability distributions. 

As required, this work included conduct of fault tree analysis and associated outputs as generally 

described above. Various improvements to fault tree methodology, and revisions in Arctic and GOM spill 

occurrence factors were also developed and provided. The work included the quantification of robustness 

of the statistics and predictions, (Confidence Limits on spill occurrence estimates), and precision or 

variance of estimates. Spill occurrence rates (occurrence rates) for multiple size ranges of crude and 

diesel spills (>50bbl) were developed. Results included the number of spills of at least 1,000 bbl that is 

projected to occur. Generation of life-of-field and year 15 spill occurrence estimators and spill occurrence 

estimators converted to number of spills per billion barrels produced over the life of the exploration and 

development scenario were also included. 

SIGNIFICANT CONCLUSIONS: 

How do spill indicators for the Project scenario and for its non-Arctic counterpart vary by spill size and 

source? Table 1 in the next section summarizes the Life of Field average spill indicator values by spill 

size and source. The following can be observed from Table1: 

 Spill frequency per 103 years and per 109 barrels produced decreases with increasing spill size 

for all Arctic and non-Arctic scenarios. 

 The spill index increases with spill size for all Arctic and non-Arctic scenarios. 

 All non-Arctic scenario spill indicators are greater than their Arctic counterparts. 
ACCESS NUMBER: M11PC00013

How do the spill indicators vary by facility type for representative scenarios? The contributions of spill 

indicators by facility have been summarized by representative scenario years in the final report. The 

following may be noted from Table 1 in the next section: 

 The island contributes the most (82%) to the two Arctic spill frequency indicators. 

 Pipelines are second in relative contribution to spill frequencies (15%). 

 Wells are the lowest contributors to frequency indicators (4%) but highest contributors to spill 

index (83%). 

 It can be concluded that the island is likely to have the most, but smaller spills, while wells will 

have the least number but larger spills. Pipelines will be in between, with more spills than wells. 

A Monte Carlo analysis of the Project annual and Life of Field average spill indicators was conducted to 

evaluate the effects of input uncertainties on these indicators. The Cumulative Distribution Functions 

(CDF) presented in the report contain extensive information on the statistical properties of the spill 

indicators. Generally, the following can be observed from the analysis: 

 The variance of the frequency spill indicators (frequency and frequency per barrel produced) 

decreases as spill size increases for pipelines and the island. This is indicated by the decrease in 

the CDF slopes as spill size increases. 

 The opposite occurs for well frequency spill indicators, where larger spills show greater variance 

(lower CDF slopes) than smaller ones. 

 For all facilities, the frequency spill indicators for small and medium spills show significantly more 

variability than those for large and huge spills. 

 The variability of the pipeline spill index shows a greater variance for large and huge spills than 

for small and medium spills. The opposite occurs for the island; small and medium spills are more 

variable than larger spills. 

 For wells, the variance in spill index of huge spills dominates. There is very little variance for the 

large, medium, and small spills. 

 For all facilities, the spill index for huge spills is also the most variable. 

STUDY RESULTS: 

To provide statistical basis for the inputs, because sufficient historical data on offshore oil spills for this 

region do not exist, an oil spill occurrence model based on fault tree methodology was developed and 

applied. Using the fault trees, base data from the Gulf of Mexico and Pacific OCS and, for losses of well 

control also from the North Sea, including the variability of the data, were modified and augmented to 

represent expected Arctic offshore oil spillage frequencies. Three principal spill occurrence indicators, as 

follows, were quantified for each year of each scenario, as well as scenario life of field averages: 

 Spill frequency per 1,000 years 

 Spill frequency per 109 barrels produced 

 Spill index, the product of spill size and spill frequency 

These indicators were quantified for the following spill sizes: 

 Small (S): 50 - 99 bbl 

 Medium (M): 100 - 999 bbl 

 Large (L): 1,000 - 9,999 bbl 

 Huge (H): >= 10,000 bbl 

Quantification was carried out for each future year for the Project scenario, with a range of development 

parameters, in duration up to 25 years. In addition, a comparative scenario for non-Arctic locations was 
ACCESS NUMBER: M11PC00013

formulated and analyzed for oil spill occurrence. Generally, it was found that the non-Arctic spill indicators 

were likely to be higher than those for similar scenarios in the Arctic. The computations were carried out 

using a Monte Carlo process to permit the inclusion of uncertainties in the base and scenario data and 

Arctic effects. A wide range of details for each scenario was generated, including the following: 

 Expected time history of spill occurrences over the scenario life. 

 Spill occurrence variations by spill volumes in the above spill size ranges (Table 1). 

 Spill occurrence variation by spill cause such as work boat anchoring or ice gouging. 

 Spill occurrence contribution from each main facility type, including pipelines, the island, and 

wells (Table 1). 

 Comparison of spill occurrence predictions between Arctic and non-Arctic scenarios. 

 Life of field averages of spill occurrence estimators (Table 1). 

 The variability in the results due to uncertainties in the inputs was expressed as cumulative 

distribution functions and statistical measures. 

Table 1 

Summary of Life of Project Field Average Spill Indicators by Spill Source and Size 

Liberty Liberty Non Arctic 

Spill Indicators 

LOF Average 

Spill 

Frequency 

per 103 years 

Spill 

Frequency 

per 109 bbl 

produced 

Spill 

Index 

(bbl) 

Spill 

Frequency 

per 103

years 

Spill 

Frequency 

per 109 bbl 

produced 

Spill Index 

(bbl) 

Small and Medium Spills 1.947 0.397 0.867 3.397 0.692 1.512 

50-999 bbl 87% 87% 6% 89% 89% 8% 

Large Spills 0.160 0.033 0.859 0.253 0.052 1.353 

1,000-9,999 bbl 7% 7% 6% 7% 7% 7% 

Huge Spills 0.123 0.025 12.133 0.178 0.036 15.447 

=>10,000 bbl 6% 6% 88% 5% 5% 84% 

Substantial Spills 0.283 0.058 12.992 0.431 0.088 16.799 

=>1,000 bbl 13% 13% 94% 11% 11% 92% 

2.230 0.454 13.859 3.827 0.780 18.311 All Spills 100% 100% 100% 100% 100% 100% 

0.331 0.067 0.830 0.640 0.130 1.537 Pipeline Spills 15% 15% 6% 17% 17% 8% 

1.818 0.370 1.457 3.087 0.629 2.297 Island Spills 82% 82% 11% 81% 81% 13% 

0.080 0.016 11.572 0.100 0.020 14.476 Well Spills 4% 4% 83% 3% 3% 79% 

1.898 0.387 13.029 3.187 0.649 16.773 Island and Well Spills 85% 85% 94% 83% 83% 92% 

2.230 0.454 13.859 3.827 0.780 18.311 All Spills 100% 100% 100% 100% 100% 100% 

STUDY PRODUCTS: 

Bercha International Inc., “Updates to Fault Tree Methodology and Technology for Risk Analysis, Liberty 

Project.” BOEM Contract #M11PC00013, Final Report. OCS Study BOEM 2016-052, May 2016, 113pp. 

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