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Automation In Emissions Detection: Avoiding The Pitfalls
Susan Nash
Director of Innovation & Emerging Science and Technology at AAPG
Discover the transformative insights of Susan Nash, the Director of Innovation, Emerging Science and Technology at AAPG, as she takes the stage at the Oil & Gas Automation and Digitalization Conference 2023. With an illustrious role in identifying innovative endeavors and facilitating knowledge exchange, Susan has driven the exploration of cutting-edge products, services, and collaborations. Through webinars, workshops, and the AAPG U-Pitch New Technology Showcase, she has nurtured the development of resilience and adaptability in the industry. Join us in this blog as we dive into the wealth of actionable insights she shared during the conference, exploring the forefront of automation and digitalization in the oil and gas sector.
The pursuit of environmental sustainability has driven industries to adopt automation as a pivotal tool in emissions detection. The promise of more accurate, efficient, and proactive monitoring is reshaping how businesses tackle their environmental impact. Two primary avenues have emerged: continuous data collection through strategically positioned sensors and periodic earth imaging surveys facilitated by drones and satellites. These approaches offer real-time insights and comprehensive perspectives, respectively, enhancing the ability to identify emissions sources and trends.
In addition, the challenge of sparse data is being addressed through predictive models. These models leverage machine learning to analyse historical emissions data and environmental variables, predicting emissions hotspots and maintenance needs in areas where real-time data may be scarce. While the potential benefits of automation are undeniable, avoiding pitfalls is essential. Rigorous calibration of sensors, ensuring the accuracy of aerial imaging, and maintaining and updating automated systems are critical. Balancing automation with human expertise remains vital in making informed decisions and responding effectively to emerging situations. In embracing automation while remaining attentive to potential challenges, industries can progress toward a more sustainable future with heightened emissions detection and mitigation capabilities.
The pursuit of environmental sustainability has driven industries to adopt automation as a pivotal tool in emissions detection. The promise of more accurate, efficient, and proactive monitoring is reshaping how businesses tackle their environmental impact. Two primary avenues have emerged: continuous data collection through strategically positioned sensors and periodic earth imaging surveys facilitated by drones and satellites. These approaches offer real-time insights and comprehensive perspectives, respectively, enhancing the ability to identify emissions sources and trends.
In addition, the challenge of sparse data is being addressed through predictive models. These models leverage machine learning to analyse historical emissions data and environmental variables, predicting emissions hotspots and maintenance needs in areas where real-time data may be scarce. While the potential benefits of automation are undeniable, avoiding pitfalls is essential. Rigorous calibration of sensors, ensuring the accuracy of aerial imaging, and maintaining and updating automated systems are critical. Balancing automation with human expertise remains vital in making informed decisions and responding effectively to emerging situations. In embracing automation while remaining attentive to potential challenges, industries can progress toward a more sustainable future with heightened emissions detection and mitigation capabilities.
- Automated systems based on continuous data collection through sensors
- Automated systems based on periodic earth imaging surveys (drones and satellites)
- Predictive models for pipeline maintenance and leaks in areas of sparse data
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