Parallel pipelines are frequently installed over long distances, due to the difficulty in creating or maintaining the required corridor. This implies that a release in one pipeline can seriously affect another one. The main risks associated with this domino effect are erosion by fluid-sand jets and the thermal action of jet fires. In this paper a survey has been performed on the accidents that have occurred, and the diverse associated domino sequences are analyzed. The probability of occurrence of domino effect is a function of the location of the hole, the jet direction and solid angle, the diameter of both pipelines and the distance between them. A mathematical model has been developed to estimate this probability. The model shows how the probability of domino effect decreases with the distance and diameter of the source pipe, and increases with the diameter of the target pipe. Its frequency can be estimated from this probability and from the frequency of the initiating pipe failure plus, in the case of jet fire impingement, the probability of ignition. The frequency of the target pipe failure thus calculated, always higher than its individual frequency, allows a more realistic risk analysis.

Underground pipelines have a space-dependent condition that arises from various soil properties surrounding the pipeline (e.g., moisture content, pH, aeration) and the efficiency of protection measures. Corrosion is one of the main threats for pipelines and is commonly monitored with in-line inspections (ILI) every 2 to 6 years. Preliminary characterizations of the surrounding soil allow pipeline operators to propose adequate protective measures to prevent any loss of containment (LOC) of the fluid being transported. This characterization usually requires detailed soil measurements, which could be unavailable or very costly. This paper implements categorical measurements of soil properties and defect depth measurements obtained from ILI to characterize the soil in the surroundings of a pipeline. This approach implements an independence test, a multiple correspondence analysis, and a clustering method with K-modes. The approach was applied to a real case study, showing that more severe defects are likely located in poorly drained soils with high acidity.