Science Cultivation

Science Cultivation

The Role of Remote Sensing Technology in Flaring Assessment in Iran's Oil and Gas Industries

Document Type : Promotion Article

Authors
Elmira Asadi-Fard 1
Samereh Falahatkar 1
Mahdi Tanha Ziyarati 2, 3
Seyed Kazem Alavipanah 4
1 Department of Environmental Sciences, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran
2 Department of Health, Safety and Environment Engineering, Ferdous Rahjoyan Danesh Higher Education Institute, Borazjan, Bushehr, Iran
3 Department of Health, Safety and Environment, Pars Special Economic Energy Zone, Bushehr, Iran
4 Professor in Department of Remote Sensing and Geographic Information System, Faculty of Geography, University of Tehran,Iran. & Member of Unesco Chair in Interdisciplinary Studies in Diabetes.
Abstract
Abstract
The flaring process or burning unwanted gases with highly flammable during oil and gas extraction and processing has numerous environmental and public-health consequences. Therefore, comprehensive investigations in this field are important. According to a Global worldwide report, the annual flaring rate is approximately 170 billion cubic meters, which leads to the emission of 300–400 million tons of CO₂ and air pollutants such as SOₓ, NOₓ, and volatile organic compounds (VOCs), and particulate matter into the atmosphere. As a result, beyond exacerbating climate change and acid rain, these emissions adversely affect human health through respiratory and cardiovascular diseases and cancer. In this paper, we presented and investigated the various algorithms applied for detecting thermal anomalies from flares and estimating flared-gas volumes at both global and national scales based on remote-sensing data. In one of the industrial regions in southern Iran (the Pars Special Energy Economic Zone), the RXD and NHI algorithms demonstrated high accuracy in detecting the thermal anomalies due to flame if flare. For estimating the volumes of gas flare, we compared three multivariable linear regression, artificial neural network, and decision-tree models, using data from the radiance of several satellites including Landsat 8 (bands 6, 7, 10, and 11), Suomi-NPP /VIIRS (M10 band), and Sentinel-5P pollution products. The results showed that the neural-network model outperformed the others. We conclude that by applying appropriate methods such as machine learning in comprehensive remote-sensing information, it is possible to fill existing data gaps in this field and take effective steps toward improved management.
Keywords
Flaring
Iran
Detection
Algorithm
Remote Sensing
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Science Cultivation
Volume 15, Issue 2 - Serial Number 30
December 2025
Pages 159-165

  • Receive Date 06 October 2025
  • Revise Date 23 October 2025
  • Accept Date 06 November 2025