Session: 07-02-03 Geohazards and Mechanical Damage
Paper Number: 87093
87093 - Estimating Pipeline Probability of Failure Due to External Interference Damage Using Machine Learning Algorithms Trained on In-Line Inspection Data
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). 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 17,000 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 impact energies.
The following may all influence the likelihood and energy of external interference damage and are considered as predictor variables in the machine learning models:
* 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 estimates of the probability of failure due to external interface damage that use more accurate and justifiable impact frequency statistics generated using worldwide ILI data and additional influencing factors based on pipeline exposure, resistance and mitigations.
Presenting Author: James White ROSEN
Presenting Author Biography: James is a Data Scientist at ROSEN Group specializing in the design and automation of data pipelines and the development of machine learning models for integrity management. James’s background is in software development for low-powered computing systems where he also holds a master’s degree in Electrical and Electronic Engineering from Newcastle University UK.
Estimating Pipeline Probability of Failure Due to External Interference Damage Using Machine Learning Algorithms Trained on In-Line Inspection Data
Paper Type
Technical Paper Publication