A Novel, Real-Time, Automated Identification System for Biostratigraphy to Inform Drill Steering in Shale Oil and Gas Exploration

页岩油气勘探是一个困难、昂贵且高风险的过程。它需要训练有素的地质学家/生物地层学家在钻探（录井）产生的岩心样品中识别关键的微化石物种。这些微化石的特征可用于验证地质模型，并指导钻井向含油气层方向进行。当前实践的首要问题是，它进展缓慢，严重依赖于数量少、数量少、费用高、训练有素的人才。我们提出的解决方案是使用一种名为Daisy的新型自动物种识别系统；将训练有素的人员从日常鉴定工作中解放出来。我们认为，采用基于Daisy的页岩油气行业自动识别技术具有以下几个优势：1)它将成为所有相关“专家知识”的储存库；2)它将简化勘探过程，降低成本和环境影响；3)它将使勘探目前被认为太小而无利可图的油田更加可行；4)它还可以帮助延长已经“生产”的油田的寿命。

Shale oil and gas exploration is a difficult, expensive and highly risky process. It requires highly trained geologists/biostratigraphers to identify key micro fossil species in the core samples produced by the drill (mud logging). The identity of these micro fossils is used to verify geological models, and to steer the drill towards the oil and gas bearing strata. The overarching problem with current practice is that it is slow and critically dependent on a small, dwindling number of expensive and highly trained individuals. Our proposed solution is to use a novel, automated species identification system called Daisy; to free highly trained personnel from the chore of routine identification. We believe the adoption of an automated identification technology for the shale oil and gas sector based on Daisy, has several advantages including; 1) it would act as a repository for all the relevant “expert knowledge”, 2) it would streamline the exploration process, reducing costs and environmental impact, 3) it will make it more viable to explore fields which are currently considered too small and therefore unprofitable, 4) it could also help extend the life of fields already “in production”.