Article ID Journal Published Year Pages File Type
5484521 Journal of Petroleum Science and Engineering 2017 9 Pages PDF
Abstract
This pessimistic situation can be radically changed by augmenting human expertise using automated species identification tools based on artificial neural network technology. DAISY [Digital Automated Identification System], a proven system of this sort, could revolutionise commercial biostratigraphy operations by enabling microfossil identification to be undertaken by technicians. This would yield immediate benefits for the industry as it would permit routine work to be performed quickly and accurately by less skilful, cheaper and therefore more available staff; freeing biostratigraphers to concentrate on non-routine, more complex tasks. The feasibility study presented here indicates that DAISY can consistently identify microfossils to species, with repeatable, high levels of accuracy. Crucially, it can also act as a permanent repository for taxonomic knowledge, which is currently lost when experienced personnel retire. There might also be additional environmental and social benefits if this technology is widely adopted within the oil and gas sector: as DAISY technology is generic, it can easily be re-targeted to interpret seismic data or even to estimate the impact of upstream exploration activities on abutting ecosystems.
Related Topics
Physical Sciences and Engineering Earth and Planetary Sciences Economic Geology
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