Abstract
External interference damage is one of the main causes of pipeline failure reported in publicly available industry statistics from agencies such as the Canada Energy Regulator (CER) and the United States Pipeline and Hazardous Materials Safety Administration (PHMSA). Thus, failures due to external interference are often the most significant contributors to pipeline probability of failure in risk assessments and can play a significant role in operator decisions regarding risk-control expenditures, for example when it comes to the installation of additional impact protection, pipeline diversion or pressure restrictions.
The probability of failure due to external interference damage can be estimated by combining the probability that damage occurs (i.e. that the pipeline is hit), the probability that the impact is sufficient to cause instant failure and the probability of degradation to failure, given that damage has occurred. Degradation to failure is assessed using industry standard engineering models (such as the limit state functions given in Annex O of CSA Z662-19 [1]). However, the key challenge is predicting where, when, and with what energy the external interference damage may happen.
The prediction of a “hit rate,” or impact frequency, can often be subjective or based on statistics, which may not always be applicable or accurate for use on the pipeline under assessment. Top-of-line (TOL) deformation damage (dents) reported by in-line inspection (ILI) are a clear indicator of past external interference, which could have been introduced by third parties, contractors or the operator themselves. ILI data from ROSEN’s Integrity Data Warehouse (IDW) — which at the time of writing contains results from over 18,000 inspections — has been used to train machine learning models to estimate the frequency of external interference damage (per km-year). The distribution of dent sizes combined with pipe parameters is used to estimate a distribution of dent force.
The following may all influence the likelihood and energy of external interference damage and may be considered as predictor variables in a machine learning model:
• Local population density
• Land use
• Excavator types (typical bucket dimensions)
• Frequency of crossings (road, rail, other services)
• Pipeline burial depth
• Additional impact protection
• Pipeline markers and warning tape
• Patrol and surveillance frequency
• Operational control activities
• Pipeline material properties
This paper presents an approach to estimate the probability of failure due to external interface damage that use more accurate and justifiable impact frequency statistics, which are generated using worldwide ILI data and additional influencing factors based on pipeline exposure, resistance and mitigations.
