Detection of obstructions in oil and gas pipelines: machine learning techniques for hydrate classification

TL;DR

This paper applies machine learning algorithms like decision trees, k-NN, and Naive Bayes to classify hydrate formation in oil pipelines, achieving high accuracy and aiding in flow assurance.

Contribution

It introduces a machine learning-based method for detecting hydrate formation in pipelines, utilizing publicly available data and standard classifiers to improve flow assurance strategies.

Findings

Decision tree achieved 99.99% accuracy.

Machine learning effectively classifies hydrate formation.

Method enhances pipeline flow assurance.

Abstract

Oil and gas reserves are vital resources for the global economy, serving as key components in transportation, energy production, and industrial processes. However, oil and gas extraction and production operations may encounter several challenges, such as pipeline and production line blockages, caused by factors including sediment accumulation, wax deposition, mineral scaling, and corrosion. This study addresses these challenges by employing supervised machine learning techniques, specifically decision trees, the k-Nearest Neighbors (k-NN) algorithm (k-NN), and the Naive Bayes classifier method, to detect and mitigate flow assurance challenges, ensuring efficient fluid transport. The primary focus is on preventing gas hydrate formation in oil production systems. To achieve this, data preprocessing and cleaning were conducted to ensure the quality and consistency of the dataset, which was sourced from Petrobras publicly available 3W project repository on GitHub. The scikit-learn Python library, a widely recognized open-source tool for supervised machine learning techniques, was utilized for classification tasks due to its robustness and versatility. The results demonstrate that the proposed methodology effectively classifies hydrate formation under operational conditions, with the decision tree algorithm exhibiting the highest predictive accuracy (99.99 percent). Consequently, this approach provides a reliable solution for optimizing production efficiency.