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Application of LiDAR for hydrocarbon exploration in logistically and geologically challenging environments: examples from the Papuan Fold and Thrust Belt, Papua New Guinea

Authors: Luke Mahoney*; Mitch Furnass; Pedro Restrepo-Pace;

Application of LiDAR for hydrocarbon exploration in logistically and geologically challenging environments: examples from the Papuan Fold and Thrust Belt, Papua New Guinea

Abstract

The Papuan Fold and Thrust Belt (PFTB) in Papua New Guinea hosts the prolific Papuan Basin petroleum system yet remains underexplored compared with other fold and thrust belts worldwide. The underexplored nature of the PFTB results from its remoteness, inhospitable karst topography and thick tropical vegetation which combine to create an exceedingly difficult and expensive area to explore. There are, however, significant rewards awaiting those able to overcome these challenges. The history of exploration success in the PFTB has been closely linked to technological advancements aimed at overcoming these challenges. The latest development is LiDAR (Light Detection and Ranging), a remote sensing technology that provides the ability to 'see' through the thick tropical vegetation covering the PFTB, thus revealing previously obscured geological outcrops and revolutionising surface operation planning. LiDAR data are used to create very high-resolution digital elevation models of the earth's surface that have a variety of exploration applications including geological mapping and 3D geological model building. LiDAR has facilitated a significant leap in our understanding of hydrocarbon trapping styles in the PFTB. In this paper we discuss some of the ways that LiDAR is being used to explore within one of the most challenging yet underexplored areas in the world.

Open-Access Online Publication: Feb 24, 2023

Keywords

remote sensing, LiDAR, structure, hydrocarbon exploration, Papuan Fold and Thrust Belt

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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