Optimizing Gas Pipeline Operations with Machine Learning: A Case Study of A North American Energy Company

Abstract

Abstract The development of the economy is directly linked to energy consumption (Ozturk 2010). As the natural gas demand continues to grow globally, optimizing gas pipeline operations becomes a critical imperative for energy companies (Evans July 2005). This is mainly because transmission cost account for 30% of the total cost of production globally (Arash Bazyar 2021). To address this challenge, natural gas pipeline operators increasingly turn to advanced technologies such as machine learning (ML) to optimize their operations, improve efficiency, and reduce costs. This paper presents a compelling case study of a North American energy company that collaborated with a leading cloud service provider to leverage a business intelligence (BI) application backed by ML algorithms to analyze historical pipeline data and optimize gas pipeline operations while minimizing greenhouse gas (GHG) emissions. The objectives of this paper are multi-fold: first, to demonstrate the efficacy of a BI application powered by ML algorithms in optimizing gas pipeline operations. Second, to showcase the transformative journey of the North American energy company in leveraging cloud-enabled ML solutions to achieve substantial operational improvements. This case study offers valuable insights into how ML can revolutionize the traditional pipeline optimization process and deliver tangible business results. Third, to discuss the building blocks of the ML solution deployed. Furthermore, fourth, to educate our readers on potential areas for further research and advancement. We also discuss challenges and considerations the industry may face in the broad adoption of ML applications. To begin, this paper explores the capacity conundrum of industry leaders in the natural gas transportation sector. It sheds light on the existing challenges where operators spend considerable time analyzing data from various sources to assess the operational capabilities of their pipelines. By delving into these challenges, this study provides a comprehensive understanding of the need for innovative approaches such as ML to address these complexities. Following this paper, this paper explores the application of AI/ML solutions in pipeline optimization within the oil and gas sector, highlighting critical use cases and the potential benefits they bring. The paper features a prominent North American energy company that confronted similar challenges in pipeline operations. Through a strategic collaboration with a leading cloud service provider, the company embarked on a digital transformation journey to optimize its pipeline operations using ML technologies. This paper elucidates the methodologies, procedures, and processes involved in successfully implementing ML algorithms and a BI application tailored to the specific needs of the energy company. The results of this case study demonstrate the remarkable outcomes achieved through the integration of ML algorithms and the BI application. The application optimizes gas throughput daily by leveraging historical pipeline data and operator knowledge, enhancing overall operational capability. Statistical models employed in the application enable anomaly detection and system optimization and provide a unified user experience. The successful deployment of this ML-driven solution has empowered operational planners to share critical data with gas control teams and field operations, ultimately optimizing maintenance schedules and maximizing asset utilization. The tangible benefits realized by the energy company include a significant increase in daily natural gas throughput volume while simultaneously achieving substantial cost savings. Lastly, we will talk about Future Directions and Potential Challenges. Specifically, how future research in optimizing gas pipeline operations with ML should explore advanced algorithms, integration with emerging technologies, and explainable models. Moreover, understand why challenges in data quality, system integration, workforce skills, and regulatory compliance must be overcome for broader industry adoption of ML in the gas pipeline sector.