"Sometimes the traditional methods are way more effective or efficient in handling certain things. To succeed in this new paradigm, we need to build on our strong fundamentals and progress further."
Ivan Lim Chen Ning shares how data-driven methods are reshaping geophysics by challenging traditional workflows and opening new possibilities. He highlights the role of AI, machine learning, and fiber-optic sensing in improving seismic interpretation, imaging, and monitoring. His insights show how combining strong fundamentals with modern digital tools can help geophysicists solve problems more effectively.
Read the September issue of TLE about data-driven geophysics at https://library.seg.org/toc/leedff/44/9.
KEY TAKEAWAYS
> AI and data-driven tools open new paths. They help geophysicists move beyond traditional workflows to find faster and simpler solutions.
> Fiber-optic sensing changes monitoring. DAS provides continuous well data, replacing point sensors and revealing signals directly.
> Strong fundamentals still matter. Success comes from combining proven geophysical methods with modern digital skills.
GUEST BIO
Ivan Lim Chen Ning is an Earth Scientist – Fiber Optics at Chevron, where he analyzes Distributed Fiber Optic Sensing (DFOS) data and develops real-time algorithms for field applications. He applies deep learning and signal processing to improve DFOS workflows, advancing distributed acoustic sensing in the energy industry. A member of Chevron’s Emerging Leader 2024 cohort, Ivan is recognized for solving cross-disciplinary challenges and driving innovation to help secure energy for the future.
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"Sometimes the traditional methods are way more effective or efficient in handling certain things. To succeed in this new paradigm, we need to build on our strong fundamentals and progress further."
Ivan Lim Chen Ning shares how data-driven methods are reshaping geophysics by challenging traditional workflows and opening new possibilities. He highlights the role of AI, machine learning, and fiber-optic sensing in improving seismic interpretation, imaging, and monitoring. His insights show how combining strong fundamentals with modern digital tools can help geophysicists solve problems more effectively.
Read the September issue of TLE about data-driven geophysics at https://library.seg.org/toc/leedff/44/9.
KEY TAKEAWAYS
> AI and data-driven tools open new paths. They help geophysicists move beyond traditional workflows to find faster and simpler solutions.
> Fiber-optic sensing changes monitoring. DAS provides continuous well data, replacing point sensors and revealing signals directly.
> Strong fundamentals still matter. Success comes from combining proven geophysical methods with modern digital skills.
GUEST BIO
Ivan Lim Chen Ning is an Earth Scientist – Fiber Optics at Chevron, where he analyzes Distributed Fiber Optic Sensing (DFOS) data and develops real-time algorithms for field applications. He applies deep learning and signal processing to improve DFOS workflows, advancing distributed acoustic sensing in the energy industry. A member of Chevron’s Emerging Leader 2024 cohort, Ivan is recognized for solving cross-disciplinary challenges and driving innovation to help secure energy for the future.
From Differences to Inversion: A New Era for 4D Seismic
Seismic Soundoff
26 minutes 19 seconds
4 months ago
From Differences to Inversion: A New Era for 4D Seismic
“Repeatability is the biggest hurdle in time-lapse monitoring, but it’s also where the opportunity lies. 4D FWI can help address those repeatability challenges.”
On this episode, Andrew Geary sits down with Madhav Vyas and Kris Innanen, guest editors for July’s The Leading Edge special section on 4D full-waveform inversion. They explain how 4D FWI goes beyond traditional differencing by fully inverting wavefields, making time-lapse seismic more robust against survey inconsistencies and complex overburden. Listeners will learn why now is the perfect time to adopt 4D FWI, the main technical hurdles around repeatability and uncertainty, and the first steps to take for reservoir monitoring and survey design.
KEY TAKEAWAYS
> 4D FWI inverts the full wavefield - reflections, refractions, diving waves, multiples - to detect reservoir changes, making it more robust than conventional 4D processing.
> Repeatability of surveys and assessing uncertainty are the biggest challenges, but workflows like hypothesis testing, batch FWI, and null-space shuttling help mitigate inversion noise.
> High-quality data and accurate physics (elasticity, attenuation, anisotropy) are essential; advances in GPU-driven computation enable faster iterations and richer uncertainty analysis.
CALL TO ACTION
Read July's The Leading Edge special section on 4D FWI at https://library.seg.org/toc/leedff/44/7, then explore an open-source FWI toolbox.
LINKS
* Visit https://seg.org/podcasts/episode-265-from-differences-to-inversion-a-new-era-for-4d-seismic for the complete show notes.
Seismic Soundoff
"Sometimes the traditional methods are way more effective or efficient in handling certain things. To succeed in this new paradigm, we need to build on our strong fundamentals and progress further."
Ivan Lim Chen Ning shares how data-driven methods are reshaping geophysics by challenging traditional workflows and opening new possibilities. He highlights the role of AI, machine learning, and fiber-optic sensing in improving seismic interpretation, imaging, and monitoring. His insights show how combining strong fundamentals with modern digital tools can help geophysicists solve problems more effectively.
Read the September issue of TLE about data-driven geophysics at https://library.seg.org/toc/leedff/44/9.
KEY TAKEAWAYS
> AI and data-driven tools open new paths. They help geophysicists move beyond traditional workflows to find faster and simpler solutions.
> Fiber-optic sensing changes monitoring. DAS provides continuous well data, replacing point sensors and revealing signals directly.
> Strong fundamentals still matter. Success comes from combining proven geophysical methods with modern digital skills.
GUEST BIO
Ivan Lim Chen Ning is an Earth Scientist – Fiber Optics at Chevron, where he analyzes Distributed Fiber Optic Sensing (DFOS) data and develops real-time algorithms for field applications. He applies deep learning and signal processing to improve DFOS workflows, advancing distributed acoustic sensing in the energy industry. A member of Chevron’s Emerging Leader 2024 cohort, Ivan is recognized for solving cross-disciplinary challenges and driving innovation to help secure energy for the future.
