Multi-Attribute Machine Learning Analysis for Weak BSR Detection

EAGE Houston Chapter

Julian Chenin shares his findings using an unsupervised machine learning multi-attribute analysis on 2D seismic data in the Pegasus Basin

Speaker: Julian Chenin, Geophysical Data Analyst, Bluware Inc.

Julian received a BS with Honors in Geology from the University of Houston in 2018 and a MSc in Geophysics from University of Oklahoma in 2020. His research interests use Machine Learning for seismic interpretation and characterization. He is now working as Geophysical Data Analyst at Bluware, where he optimizes machine learning algorithms for geoscience applications.
Abstract:
Gas hydrates that exist in the subsurface are often difficult to detect with reflection seismic data if the seismic data lack a strong bottom simulating reflection (BSR). High-amplitude BSRs are caused by a sharp decrease in acoustic impedance in the rocks as the hydrates transition from their solid form, to a free gas form due to changing pressure and temperature conditions with depth beneath the seafloor. Two key reasons for weak BSRs include: 1) insufficient free gas below the hydrate to create the needed impedance contrast, and 2) stratigraphy-parallel BSRs that are subtle and can only be identified with advanced seismic analysis. In these cases, the imaging and detection of the gas hydrate stability zone (GHSZ) becomes particularly difficult, as hydrate detection relies heavily on contextual indicators such as the BSR, gas chimneys, or pockmarks on the seafloor. There are several motivations for better quantifying gas hydrate volumes in the subsurface as they can serve as an economic resource and as a geohazard. To address and improve upon these imaging complications, an unsupervised machine learning multi-attribute analysis is performed on 2D seismic data in the Pegasus Basin in New Zealand where the BSR is not continuously or clearly imaged.

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Multi-Attribute Machine Learning Analysis for Weak BSR Detection

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