机器学习与地球物理数据处理

Data analytics and machine learning(Read More)
Mike Davidson

Abstract: The explosion of data types and volumes we are experiencing in the oil field, particularly in unconventional reservoir development, is stretching the capabilities of traditional manual workflows. The complex interactions between dynamic reservoir properties and the many-faceted well completions process are governed by complex physics that may be only partially understood. Furthermore, our best attempts to perform controlled experiments can often produce highly varied results, suggesting the stochastic nature of production from unconventionals and that we may benefit from adding new tools to our toolkit of deterministic, physics-based analysis.
在油田勘探开发过程中,特别是在非常规油气勘探开发中,所采集的数据类型及数量已呈爆炸式增长,这远远超出了传统人工处理方法的处理能力。动态储层特性与多面井完井过程之间的复杂相互作用受复杂物理学的控制。此外,作者通过最佳尝试来展示可控实验导致了非常多样化的结果,展示了非常规油气生产能力的随机特性,并展示了将Machine Learning 用于数据分析的巨大优势。

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