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Application of Linearization and Approximation

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

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Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

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Published on: February 25, 2015

基于声压岩石物理参数映射的钻探过程中自动石质识别方法的研究.

Wei Jiang^1,2, Qingfeng Wang^1,2,3, Baoyong Yan^1,2

¹China Coal Research Institute, Beijing, China.

|November 7, 2025

概括

这项研究引入了一种新方法,用于在采用声压和岩石物理参数进行钻探时自动识别石质. 这种方法增强了智能煤矿勘探中的水库预测和自动化.

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Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

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科学领域:

地质地质地质地质地质地
钻井工程钻井工程
数据科学数据科学数据科学

背景情况:

在钻井过程中识别石是智能煤矿勘探的关键.
目前的方法缺乏准确性和自动化,阻碍了水库预测.

研究的目的:

开发一种用于在钻井过程中自动识别石质学的新方法.
建立声压和岩石物理参数之间的映射关系.

主要方法:

收集的核心样本用于岩石样本制备 (均和分层).
设计和建造了一套全面的实验室钻井系统.
开发了一种定量合适模型和基于声压岩石物理参数映射的自动识别算法.

主要成果:

声压是石质学识别的有效特征.
该算法实现了不同类型岩石的不同识别精度 (例如,花岩的71%).
这种方法对穿孔过渡区具有强度.

结论:

拟议的方法为钻探期间实时石质识别提供了一种新的方法.
这一进步对于智能煤矿勘探和自动化至关重要.