  Leak detection in gas pipelines is an important and persistent problem in the
Oil and Gas industry. This is particularly important as pipelines are the most
common way of transporting natural gas. This research aims to study the ability
of data-driven intelligent models to detect small leaks for a natural gas
pipeline using basic operational parameters and then compare the intelligent
models among themselves using existing performance metrics. This project
applies the observer design technique to detect leaks in natural gas pipelines
using a regressoclassification hierarchical model where an intelligent model
acts as a regressor and a modified logistic regression model acts as a
classifier. Five intelligent models (gradient boosting, decision trees, random
forest, support vector machine and artificial neural network) are studied in
this project using a pipeline data stream of four weeks. The results shows that
while support vector machine and artificial neural networks are better
regressors than the others, they do not provide the best results in leak
detection due to their internal complexities and the volume of data used. The
random forest and decision tree models are the most sensitive as they can
detect a leak of 0.1% of nominal flow in about 2 hours. All the intelligent
models had high reliability with zero false alarm rate in testing phase. The
average time to leak detection for all the intelligent models was compared to a
real time transient model in literature. The results show that intelligent
models perform relatively well in the problem of leak detection. This result
suggests that intelligent models could be used alongside a real time transient
model to significantly improve leak detection results.
