Emissions management remains an active and crucial research area in the oil and gas industry. It is a broad research category spanning several different directions. The papers of the past year demonstrate the rich diversity of ideas and analytical techniques used for tackling different research questions in this space. The three papers presented here highlight some of the principal directions in which the research is conducted.
A dominant research focus is hydrocarbon-emission-detection technologies and flare-management methods. Research topics include first-principles-based estimate of emissions and emission-rate estimation using different emission-monitoring sensors in conjunction with the use of artificial-intelligence techniques. An example of this is in paper SPE 220857, in which deep-learning models have been used for real-time segmenting and detection of flares and estimation of smoke density.
In upstream applications, CO2 sequestration and monitoring of CO2 captured underground is an active area of research and development. Paper SPE 221264 presents a case study in which CO2 produced in a Malaysian field is reinjected into the reservoir, both to sequester CO2 and to increase production rates. The authors use a compositional dynamic simulation model of the reservoir to run an optimization study to determine the optimal injection rate that maximizes net hydrocarbon production and CO2-storage capacity simultaneously.
Other research areas in emissions management include the use of alternative fuels, new intelligent low-power valves, alternative energy storage approaches, and new low-cost CO2 and methane separating polymers. Paper SPE 220107 explores the use of alternative fuels such as hydrogen and ammonia for gas-turbine operations. The paper discusses the benefits of using ammonia over hydrogen as a fuel and demonstrates how it helps reduce both CO2 and NOx emissions simultaneously.
I hope the readers will find the papers interesting and useful in their research.
Summarized Papers in This October 2025 Issue
SPE 220107 Ammonia-Enriched Fuel Gas Significantly Reduces Emissions by R. Téllez-Schmill, KBC, et al.
SPE 220857 Deep-Learning-Based Flare-Smoke Detection Allows Real-Time Application by Kratika Gupta, ExxonMobil, et al.
SPE 221264 Integrated Approach to Gasfield Development, Carbon Sequestration Minimizes Emissions by Ahmad Khanifar, Petronas, et al.
Recommended Additional Reading
SPE 222414 Path Planning Optimizer for Workover Rigs and Stimulation Vessels: Efficiency and Emissions Management by Rishika Narang, SLB, et al.
SPE 221317 Assessing the Application of Drone TDLAS Methane-Emissions-Monitoring Technology in the Intertropical Convergence Zone Using Machine by K.W. Dawson, SeekOps, et al.
SPE 222817 Carbon Dioxide Site Storage Monitoring Using Cosmic Ray Muons by C.A. Steer, Geoptic Infrastructure Investigations, et al.
Arjun Roy, SPE, is the director of data science at Bently Nevada (Baker Hughes). He holds a PhD degree in engineering science and mechanics from The Pennsylvania State University and has more than 12 years of industrial experience in the areas of machine learning, AI, emissions management, production optimization, additive manufacturing, and prognostics and health management. Roy is an associated editor of SPE Journal and is a technical reviewer of several journals, including Engineering Applications of AI. His research interests include hybrid physics-based AI models, emissions management, failure physics, vibrations, and nonlinear dynamics.