Abstract

The Yampi Shelf on Australia's North-West Shelf is highly prospective, with two discrete hydrocarbon sources producing dry gas and oil. To reduce exploration uncertainty relating to gas flushing and poor top seal capacity, a study was undertaken to characterise hydrocarbon migration in the area. It used a combination of seismic amplitude and structural data integrated with shipboard water column geochemical sniffer (WaSi) data, satellite Synthetic Aperture Radar or SAR data and aircraft-acquired Airborne Laser Fluorosensor (ALF) data. Data were acquired synchronously and in staged programs, to allow both direct comparison and time-series analysis of results. Massive natural dry gas and oil seepage was detected, though the relative abilities of WaSi, SAR and ALF to detect and characterise this seepage were markedly different. The spatial distribution, concentration, and relative composition of the detected seepage were controlled principally by the regional seal's thickness and capacity, rather than by the inherent composition and flux of the migrating hydrocarbons. WaSi preferentially identified gas seepage, often in basin-ward locations, because the high relative permeability of gas favoured its early leakage, even through thick seals. SAR preferentially identified oil seepage, which was episodic and largely restricted to the basin-margin at the regional zero-edge-of-seal, reflecting the low relative permeability of oil, even through thin seals (it leaked ‘late’). ALF principally detected low-level oil seepage from charged traps, and was hence most useful for trap ranking. The ability of these remote sensing tools, as well as that of seismic data itself, to detect hydrocarbons appears critically dependant upon interplays between the relative sensitivity of the assorted tools to detect various hydrocarbon phases and the capacity of the top seal itself. The study has demonstrated that the interactions between geology and hydrocarbon charge are predictable, and that understanding these interactions is crucial for the reliable interpretation of remote sensing data.

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