使用AI/ML快速有效地探索和开发

A. Aming
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引用次数: 0

摘要

了解如何将训练有素的人工智能(AI) /机器学习(ML)技术应用于3D地震数据卷,从而快速对整个卷或企业地震数据库进行无偏见的数据驱动评估。无论是使用现场硬件还是基于云的大型集群进行分析,这种自动化方法都为任何数据集的解释和前景分析提供了无与伦比的见解。人工智能(AI) /机器学习(ML)技术使用无监督遗传算法创建称为地质种群的波形族,用于导出振幅,结构(时间或深度取决于输入的3D地震体积)和新的地震适应度属性。我们将展示如何使用适应度来解释三维地震体的每个峰、谷和零交叉的古地貌和相图。通过构造、振幅和适应度属性图,勘探与生产(E&P)团队可以利用整个三维地震数据量快速评估和降低与石油系统相关的地质和地球物理(G&G)风险和不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using AI/ML to Explore & Develop Quickly and Efficiently
See how application of a fully trained Artificial Intelligence (AI) / Machine Learning (ML) technology applied to 3D seismic data volumes delivers an unbiased data driven assessment of entire volumes or corporate seismic data libraries quickly. Whether the analysis is undertaken using onsite hardware or a cloud based mega cluster, this automated approach provides unparalleled insights for the interpretation and prospectivity analysis of any dataset. The Artificial Intelligence (AI) / Machine Learning (ML) technology uses unsupervised genetics algorithms to create families of waveforms, called GeoPopulations, that are used to derive Amplitude, Structure (time or depth depending on the input 3D seismic volume) and the new seismic Fitness attribute. We will show how Fitness is used to interpret paleo geomorphology and facies maps for every peak, trough and zero crossing of the 3D seismic volume. Using the Structure, Amplitude and Fitness attribute maps created for every peak, trough and zero crossing the Exploration and Production (E&P) team can evaluate and mitigate Geological and Geophysical (G&G) risks and uncertainty associated with their petroleum systems quickly using the entire 3D seismic data volume.
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