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August 1, 2024The Leading Edge6 citations

On-demand high-resolution areal scouting via unmanned aerial vehicles enables sustainable near-surface geoscience data acquisition

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ARAhmad RamdaniKing Abdullah Petroleum Studies and Research CenterPGPavel GolikovSaudi Aramco (Saudi Arabia)KAKhaled AlsoqairSaudi Aramco (Saudi Arabia)

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Abstract

An accurate data acquisition plan is critical for near-surface geoscience investigations. Detailed understanding of the terrain and land cover of the survey area is essential to designing a data acquisition layout that efficiently meets the objective while mapping potential obstacles. Using satellite-based data is the most affordable and straightforward method to obtain this information. However, near-surface geoscience investigations demand a higher-resolution map than commonly available satellite imagery to design a detailed acquisition plan. This paper presents a concept that utilizes unmanned aerial vehicles (UAVs) to successfully and sustainably perform on-demand high-resolution areal scouting to aid near-surface geoscience data acquisition campaigns and seismic drone deployment. We share the results of two field trial cases where the presented concept was evaluated. Case A is a 16 km 2 extensive outcrop investigation in an arid desert environment. The initial satellite-based acquisition plan was changed considerably when UAV data sets became available. This led to an approximately 60% improvement in data productivity and efficiency, effectively reducing the carbon footprint of conventional scouting operations. Case B utilizes high-resolution areal scouting to determine the safe landing zone for autonomous seismic drone swarms to further support sustainable geophysical data acquisition. The UAV data enable seismic drone swarms to land optimally on preprogrammed landing points while navigating subsatellite-resolution obstacles. These two successful cases suggest that analogous productivity and efficiency improvements may be achieved in industrial-scale land seismic data acquisition planning that utilizes the UAV data set.

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Ramdani et al. (2024) studied this question.

synapsesocial.com/papers/68e5dc5db6db64358757210ehttps://doi.org/10.1190/tle43080494.1
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