Abstract

Previous 3D visualization studies in seismic data have largely been focused on visualizing reservoir geometry. However, there has been less effort to visualize the vertical hydrocarbon migration pathways, which may provide charge to these reservoirs. Vertical hydrocarbon migration was recognized in normally processed seismic data as vertically aligned zones of chaotic low-amplitude seismic response called gas chimneys, blowout pipes, gas clouds, mud volcanoes, or hydrocarbon-related diagenetic zones based on their morphology, rock properties, and flow mechanism. Because of their diffuse character, they were often difficult to visualize in three dimensions. Thus, a method has been developed to detect these features using a supervised neural network. The result is a “chimney” probability volume. However, not all chimneys detected by this method will represent true hydrocarbon migration. Therefore, the neural network results must be validated by a set of criteria that include (1) pockmarked morphology, (2) tie to shallow direct hydrocarbon indicators, (3) origination from known or suspected source rock interval, (4) correlation with surface geochemical data, and (5) support by basin modeling or well data. Based on these criteria, reliable chimneys can be extracted from the seismic data as 3D geobodies. These chimney geobodies, which represent vertical hydrocarbon migration pathways, can then be superimposed on detected reservoir geobodies, which indicate possible lateral migration pathways and traps. The results can be used to assess hydrocarbon charge efficiency or risk, and top seal risk for identified traps. We investigated a case study from the Dutch North Sea in which chimney processing results exhibited vertical hydrocarbon pathways, originating in the Carboniferous age, which provided the charge to shallow Miocene gas sands and deep Triassic prospects.

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