US Offshore Energy Model™
EQECAT's US Offshore Energy Model™ is a fully probabilistic risk model that quantifies prospective risk from hurricanes in the Gulf of Mexico. The model runs on EQECAT's RQE™ (Risk Quantification & Engineering) catastrophe modeling software platform. The Gulf of Mexico US Offshore Energy Model was developed specifically for the offshore oil and gas industry. The model was initially released in 2007 to evaluate catastrophe risk to offshore energy assets in the Gulf of Mexico. EQECAT developed the offshore energy model in response to concern over accurate upstream catastrophe risk management in the Gulf of Mexico. Since the initial release, the model has been enhanced and updated to incorporate improvements to vulnerability functions, wave modeling, exposure definition, data processing functionality, and the application of complex insurance policy structures.
Offshore Energy Asset Exposure in the Gulf of Mexico
Tropical cyclones passing through the Gulf of Mexico pose significant risk to the offshore oil and gas infrastructure, leaving them highly vulnerable to the effects of high waves and severe winds. Tropical cyclones Ivan (2004), Rita (2005), Katrina (2005) and Ike (2008) destroyed scores of offshore assets, with combined material damage and lost production of approximately $2bn, $7bn, $6bn, and $6bn respectively.* Although the re/insurance industry has responded by adapting its products to balance exposure and premium, insured risk remains high to offshore energy assets.
*Willis Energy Market Report, March 2009
The following image shows hurricane track, wave height, and destroyed and damaged oil platforms throughout the Gulf of Mexico as a result of Hurricane Ike in 2008.
Hurricane Ike track, wave height, and destroyed and damaged oil platforms in the Gulf of Mexico
Features of EQECAT's US Offshore Energy Model
US Offshore Energy Model features are outlined below:
Peril Definition/Geographic Coverage
Offshore
The model analyzes risk in Bureau of Ocean Energy Management (BOEM) planning regions, as well as in US lease waters.
Onshore
Onshore oil and gas delivery points and processing facilities are modeled for wind and storm surge damage (and for effects on shut-in oil and gas production).
Hazard Definition/Derivation
Wind
Gust and sustained wind speeds, as defined by the probabilistic event set.
Waves
A separate wave module calculates wave heights from complex hazard elements, including key hurricane wind parameters, water depth, sea-floor slope, wind duration, and ocean fetch.
Landslides and Sub-sea Currents
These perils threaten sub-surface equipment and pipelines. Hazard is derived from hurricane and wave parameters, sea-floor slope, and water depth. Regions of mudslide hazard to pipelines are defined.
Stochastic Event Set
EQECAT's North Atlantic Hurricane Model has approximately 110,000 hurricane events generated from the stochastic perturbation of hurricane parameters. These are derived from historical hurricane data, including major recorded events. Of these, approximately 20,000 events span the oil- and gas-producing areas of the Gulf of Mexico, and 33,000 cover the continental US. A negative binomial frequency distribution allows for the temporal clustering of events.
Clients can use an alternative frequency set to allow for the effects of climate change. This is based on a risk index that is positively correlated with the warm phase of the Atlantic Multi-decadal Oscillation. A correlated analysis of offshore energy and onshore US and Caribbean property exposures is enabled through the use of the single probabilistic event set of EQECAT's North Atlantic Hurricane Model. Alternatively, it is facilitated with a Landfall Series Report for ‘clashing’ events with third-party models.
Exposure Definition
The model assesses risk to platforms, mobile offshore drilling units (MODUs), pipelines, onshore facilities and loss of production. Key asset exposure information is included in the model. Data from BOEM has been supplemented by data from private and specialized sources. Replacement cost information has been obtained directly from EQECAT’s energy industry clients. Data includes asset name, location, structure type, age, air gap, wells, and water depth, which are critical for risk assessment. Important details and replacement cost values are contained in EQECAT’s US Offshore Energy Industry Exposure Database, which is used to improve and supplement exposure data. Oil and gas production data are used in a Network Analysis module of platforms and pipelines for the calculation of lost production following a hurricane. Current oil and gas prices can be incorporated to calculate loss of production income.
Vulnerability Derivation
Physical Damage - Fixed Platforms and MODUs
Vulnerability functions are founded on structural analysis techniques and the in-house engineering expertise of ABS Consulting, EQECAT’s engineering risk consultancy parent company that is active in the oil and gas industry. Vulnerabilities for 34 asset types depend also on configuration type, age, air gap, supported wells, and water depth.
Physical Damage - Pipelines
BOEM data is used to define the pipeline network. Pipeline diameter, water depth, and slope data define pipeline vulnerability.
Removal of Damaged Assets
Vulnerabilities for removing debris or platform wrecks are related to platform damage ratios and water depth. Fixed and sliding scale industry cost data is utilized.
Control of Damaged Wells
Vulnerabilities related to wells that must be controlled, redrilled or plugged and abandoned depend on physical damage to the operating asset and the number of wells it supports. A non-linear vulnerability relationship reflects the sharp increase in costs to control a well once the operating structure has been severely damaged.
Loss of Production Income
An embedded Network Analysis module calculates lost production for oil and gas following an event. Platform-pipeline network damage, oil and gas production rates, network connectivity and redundancy, and time to restore production to normal are key parameters.
Model Validation
Tests have been conducted for the hazard, damage and insured loss components of the product. Modeled footprints have been tested against recorded hazard data from major historical events to refine the accuracy of the hazard module. The wave sub-module has undergone extensive testing. Modeled site damage has shown significant correlation with published damage data and data obtained from platform operators. Finally, insurance claims data has been used to check the overall robustness of model output.
Model Specifications
Model specifications or US Offshore Energy include:
Import Resolution
Exposure is imported to capture complex offshore exposure and policy information, geocoded from the individual platform identifier, the BOEM block number, or the latitude and longitude.
Hazard Analysis Resolution
Wind and wave hazards captured at a resolution of 0.025 degrees (approx. 2.5km) offshore; onshore hazard modeling captures location-specific distance to coast, elevation, and frictional aspects.
Lines of Business
Offshore Energy
Structure Types
Coverage Types
Model Output
Risk metrics include OEP and AEP loss exceedance curves, AAL, TVAR, and simulations of historical events. In addition, RQE's Year Loss Table (YLT) uniquely features three dimensional output: simulation year, events, and sample outcomes. Instead of reporting mean losses with standard deviations, each loss in the YLT represents one possible outcome for the associated event. This allows users to retain the full distribution of uncertainty when using model output in dynamic financial analysis and capital modeling. Conventional event loss results and other risk metrics can be derived from the YLT with arithmetic or simple database queries. YLT and event loss results are supported at the portfolio level. Other risk metrics are supported at multiple levels of refinement: from total aggregate portfolio results, to detailed output by block, policy and site location and coverage level.
Financial Modeling
Complex coverage-related policy structures with limit priorities, combined single limits and assured interest are modeled. Standard reinsurance treaty structures may be applied.
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