Underground cross-country pipelines are widely used in the Oil & Gas and Petrochemical Industries to transport raw materials and products, e.g. crude oil, natural gas and gasoline. The loss of mechanical integrity of such pipelines has occurred on numerous occasions world-wide, due to a variety of causes such as corrosion, external impact, defects, operational errors and natural hazards. With materials being transported at very high pressures, pipeline failures may result in major releases of hazardous materials. Hence, such failures present a risk to people (in the case of ignition of flammable materials) and the environment.
Sotera has developed a model to estimate the annual failure frequency of cross-country pipelines. The model uses historical databases, such as CONCAWE (liquid hydrocarbons) and EGIG (natural gas), and develops failure frequencies as a function of several parameters, including:
- pipeline diameter,
- type of material (steel),
- wall thickness,
- maximum allowable operating pressure (MAOP),
- depth of burial,
- external impact of protection, e.g. concrete slabbing,
- corrosion protection,
- intelligent pigging operations,
- location type (urban or rural),
- soil type,
- seismic activity.
Sotera has also developed a large database for the failures of piping and equipment in the onshore process equipment in the oil & gas, petrochemical and chemical industries.
Several equipment categories are described, namely:
- atmospheric storage tanks:
- expansion joints,
- flanges and gaskets,
- fired heaters,
- heat exchangers,
- instrument fittings,
- loading/offloading hoses and arms,
- pressure vessels,
- process piping,
- refrigerated storage tanks,
For each equipment category a detailed description is provided and the scope to which the frequency data relate, e.g. process piping does not include valves, flanges and instrument fittings. A compilation of frequency statistics is then provided in chronological order. From these statistics, an analysis is conducted and a summary of the data for use in risk assessment in given; alternatively, one can use other data provided in the statistics, which may be more relevant depending on the circumstances or specific environments.