Predicting pigging operations in oil pipelines

TL;DR

This paper introduces a machine learning approach using vibroacoustic data and decision tree regression to predict pigging operations in oil pipelines, enhancing maintenance efficiency and pipeline condition monitoring.

Contribution

The study develops a novel predictive maintenance method leveraging long-term vibroacoustic measurements and decision trees for automated pigging operation prediction.

Findings

High prediction accuracy demonstrated by RMSE metrics

Effective detection of acoustic signals from pigging operations

Pipeline condition monitoring at individual segment level

Abstract

This paper presents an innovative machine learning methodology that leverages on long-term vibroacoustic measurements to perform automated predictions of the needed pigging operations in crude oil trunklines. Historical pressure signals have been collected by Eni (e-vpms monitoring system) for two years on discrete points at a relative distance of 30-35 km along an oil pipeline (100 km length, 16 inch diameter pipes) located in Northern Italy. In order to speed up the activity and to check the operation logs, a tool has been implemented to automatically highlight the historical pig operations performed on the line. Such a tool is capable of detecting, in the observed pressure measurements, the acoustic noise generated by the travelling pig. All the data sets have been reanalyzed and exploited by using field data validations to guide a decision tree regressor (DTR). Several statistical indicators, computed from pressure head loss between line segments, are fed to the DTR, which automatically outputs probability values indicating the possible need for pigging the pipeline. The procedure is applied to the vibroacoustic signals of each pair of consecutive monitoring stations, such that the proposed predictive maintenance strategy is capable of tracking the conditions of individual pipeline sections, thus determining which portion of the conduit is subject to the highest occlusion levels in order to optimize the clean-up operations. Prediction accuracy is assessed by evaluating the typical metrics used in statistical analysis of regression problems, such as the Root Mean Squared Error (RMSE).