一个数据驱动的框架,通过微结构性表征和孔尺度模拟来预测天然多孔岩的透性
Jinlong Fu1, Min Wang2, Bin Chen3
1Zienkiewicz Institute for Modelling, Data and AI, Faculty of Science and Engineering, Swansea University, Swansea, SA1 8EN UK.
概括
本研究提出了一个数据驱动的框架,将多孔岩石微观结构与透性联系起来. 它使用先进的成像,模拟和机器学习来准确地从微观结构数据中预测岩石的透性.
科学领域:
- 地质科学 地质科学
- 计算科学 计算科学
- 材料科学 材料科学 材料科学
背景情况:
- 了解多孔介质中的微观结构属性关系对于从微观结构数据中推导宏观属性至关重要.
- 由于多孔结构的复杂性,随机性和异质性,建立明确的微观结构属性映射具有挑战性.
研究的目的:
- 开发一个数据驱动的计算框架,用于研究天然多孔岩石中的微结构-透性联系.
- 创建一个具有成本效益的替代模型,以高精度和概括能力来预测岩石透性.
主要方法:
- 使用显微镜成像和随机重建获取3D数字岩石.
- 使用各种形态描述符对孔微观结构的定量表征.
- 高保真格子博尔兹曼模拟用于孔尺度流体流动和透性确定.
- 以性能为导向的特征选择,以确定最佳的微结构描述符.
- 数据驱动的建模,以适应微观结构-透性映射的替代模型.
主要成果:
- 创建了一个大型的3D数字岩石数据集,具有不同孔隙度.
- 确定了一个最优的特征集,最能代表岩石的透性.
- 一个替代模型被开发出来,可以准确地预测四个数量级的透性.
- 与实证/分析公式相比,代用模型显示出更高的预测准确性和概括性.
结论:
- 数据驱动的框架有效地将多孔岩的微观结构与透性联系起来.
- 特性选择提高了模型的可解释性,为透性机制提供了洞察力.
- 开发的替代模型提供了一个强大的工具,可以从微观结构数据中预测岩石的透性.
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