Low Saturation Gas Reservoirs Discrimination - An Integrated Approach

Liaw, Alfred

doi:10.3997/2214-4609.20149178

Cited by 1 publication

(4 citation statements)

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“…When looking at the SOM results, this is not readily apparent, especially in the case of the unoptimized Scarborough SOM result (Figures 9C, 11C and 13C). However, seal failure analysis has shown promising results for further characterization of LSG reservoirs, and future work would incorporate a detailed seal analysis of the discussed reservoirs [7,16,22].…”

Section: Discussionmentioning

confidence: 99%

“…Despite decades of research from primarily the rock physics discipline, minimal improvement in identifying gas saturation variations from seismic data has been achieved [3,[5][6][7]23,24]. A robust method of identifying and discriminating potential LSG occurrences remains unsolved, resulting in an additional factor in hydrocarbon exploration risk.…”

Section: Methods and Workflowmentioning

confidence: 99%

“…This bulk modulus similarity between lowand high-gas saturation is the crux of the LSG gas differentiation problem. There have been several amplitude and rock physics analyses performed in the past to better characterize LSG in the subsurface with limited success under specific conditions that cannot be applied in a typical exploration scenario [3,[5][6][7]. In recent years, machine learning techniques have gained a significant foothold within the geoscience community.…”

Section: Figurementioning

confidence: 99%

“…They are deepwater sand reservoirs that were deposited in a minibasin from a northerly source controlled by underlying salt bodies [3]. While the King Kong Prospect is a structural fourway closure, the Lisa Anne Prospect is positioned on the hanging wall of a large normal There have been several amplitude and rock physics analyses performed in the past to better characterize LSG in the subsurface with limited success under specific conditions that cannot be applied in a typical exploration scenario [3,[5][6][7]. In recent years, machine learning techniques have gained a significant foothold within the geoscience community.…”

Section: King Kong and Lisa Anne Prospects Offshore Gulf Of Mexicomentioning

confidence: 99%

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

Chenin

Bedle

2022

Geosciences

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

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Section: Discussionmentioning

confidence: 99%