Unsupervised Machine Learning, Multi-Attribute Analysis for Identifying Low Saturation Gas Reservoirs within the Deepwater Gulf of Mexico, and Offshore Australia

نویسندگان

چکیده

An effective method of identifying and discriminating undersaturated gas accumulations remains unresolved, resulting in uncertainty hydrocarbon exploration. To address this problem, an unsupervised machine learning multi-attribute analysis is performed on 3D post-stack seismic data over several blocks within the deepwater Gulf Mexico Carnarvon Basin, offshore Australia. Results reveal that low-saturation (LSG) reservoirs can be discriminated from high-saturation (HSG) by using a combination instantaneous attributes are sensitive to small amplitude, frequency, phase anomalies with self-organizing maps (SOMs). This methodology shows promise for de-risking prospects, even if it not quantitative, particularly frontier exploration basins where wells may exist or very limited. However, only proved successful yielded limited results Basin. difference most likely due Basin having different amplitude response burial history fluid saturations when compared Mexico. Therefore, non-transferrable, needed other LSG-prone basins.

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ژورنال

عنوان ژورنال: Geosciences

سال: 2022

ISSN: ['2076-3263']

DOI: https://doi.org/10.3390/geosciences12030132