ID: 1504
Presenting Author: Alena Nazarova
Session: 660 - Using Digital Tools to Enhance Transparency & Collaboration during Impact Assessments
Status: approve
AI methods for environmental monitoring in oil and gas are explored, highlighting barriers and opportunities for predictive analytics, regulatory compliance, and sustainable industry practices.
This paper explores the potential of artificial intelligence (AI) technologies to enhance environmental monitoring in the oil and gas industry, with a focus on air emissions and greenhouse gases. Monitoring is a critical component of sustainability and regulatory compliance, yet AI adoption remains limited due to fragmented datasets, sensor constraints, and inadequate infrastructure.
The study draws on international research, corporate reporting, and regulatory frameworks, applying empirical, analytical, and econometric methods. Within the framework of an investment project, dedicated software is under development to integrate AI into monitoring systems. A Time-Lag Recurrent Network (TLRN) was selected as the neural architecture for its ability to process time-series data and improve predictive accuracy. Historical methane and propane datasets were used for training and validation.
The findings indicate that AI-based monitoring is resource- and data-intensive but can deliver long-term benefits, including higher accuracy, faster data processing, and seamless integration with existing industrial systems. These technologies also strengthen transparency, regulatory compliance, and environmental accountability, supporting companies in meeting national and international climate targets.
This work contributes to a relatively underexplored field of applying AI to ecological monitoring in oil and gas. Unlike studies focused primarily on operational optimization, it demonstrates AI as a foundation for predictive environmental management and long-term sustainable competitiveness.
Alena Nazarova is a PhD student at Tomsk Polytechnic University, with research interests in environmental monitoring and the use of digital and AI-based tools in the oil and gas sector.
Coauthor 1: Artur Nurgaliev