Classification of Digital Rocks by Machine Learning to Discover Micro-to-Macro Relationships and Quantify Their Uncertainty

通过机器学习对数字岩石进行分类，以发现微观到宏观的关系并量化其不确定性

• 批准号: NE/H002804/1
• 负责人: Jingsheng Ma
• 金额: $ 13.52万
• 依托单位: Heriot-Watt University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FH002804%2F1
• 关键词: Classification; Digital; Rocks; Machine; Learning

Recent advances in high-resolution imaging of porous materials have led to a dramatic increase in the collection of digital subsurface rock samples and have stimulated the development of a capability to model the rock microstructures and to calculate macro-scale transport, mechanical and acoustic properties by numerical simulations. These lead to a modelling approach that offers the potential for greatly expanding our database of material properties, without relying on expensive, or in some cases, impossible, laboratory measurements. It is envisaged by many that this approach, when augmented with validation lab measurements and micro-scale physics of concern, can be extended to discover predictive relationships between micro-scale arrangements of voids/solids and macro-scale properties, along with quantification of their uncertainty. This approach offers a potential solution to many applications where the properties of key types of rocks must be estimated from few samples. Waste disposal, CO2 storage and hydrate exploration in the subsurface are good examples within the NERC remit. In those applications, fine-grained rocks are of key concern, since they are assumed to function as barriers preventing substances from escaping into the atmosphere/biosphere. Unfortunately, such materials are expensive to sample and extremely difficult and costly to measure in the laboratory. Hence, an ability to predict fine-grained rock properties reliably and robustly would enable better modelling of macro-scale physical behaviours, assessment of the uncertainty of the behaviours, and understanding of the impacts of such applications to environment and public health. A micro-to-macro predictive relationship is expected to be highly non-linear when the physics becomes complex. Our preliminary investigations [1] on 3D micro images shows that even a single-phase flow property, like permeability, shows a strong non-linear correlation with the geometric and topological features (fig.1). Moreover, a robust non-linear relationship has to be identified from a large collection of samples and validated against new samples. Machine Learning (ML) provides a framework to carry out an automated process in which the knowledge of non-linear relationships can be learnt progressively from the growing collection of samples in a self-supervised manner. Such a process suits this purpose but must be underpinned by a set of smart and efficient tools for data search and retrieval, data-analysis, and data-mining. A basis on which all these tools are based is the ability to classify digital rock samples according to the diverse features of their microstructures as well as measured and/or calculated properties. The objective of this project is to explore the feasibility of constructing a suite of feature-based, content-aware and self-supervised ML classification techniques for digital rocks, within the NERC topic of environmental informatics. This will produce a ML system capable of classifying digital rock samples and macro-scale properties according to pre-defined controlling features. Ultimately, knowledge and experience gained from this pilot project will enable PIs to make fuller proposals to develop a suite of ML-based technologies for identifying predictive relationships between micro- and macro-scale features and predicting macro-scale properties. There is a scope for extending the technologies to other types of natural porous media and impacting across industries and research communities to address engineering and scientific questions about the physical properties of porous materials.

多孔材料高分辨率成像的最新进展导致数字地下岩石样本的收集急剧增加，并刺激了岩石微观结构建模和通过以下方式计算宏观尺度传输、机械和声学特性的能力的发展：数值模拟。这些导致了一种建模方法，该方法可以极大地扩展我们的材料特性数据库，而无需依赖昂贵的或在某些情况下不可能的实验室测量。许多人设想，当这种方法通过验证实验室测量和关注的微观物理进行增强时，可以扩展到发现空隙/固体的微观排列和宏观属性之间的预测关系，以及它们的量化不确定。这种方法为许多应用提供了潜在的解决方案，在这些应用中，必须从少量样本中估计关键类型岩石的特性。废物处理、二氧化碳封存和地下水合物勘探是 NERC 职责范围内的很好的例子。在这些应用中，细粒岩石是关键问题，因为它们被认为充当防止物质逃逸到大气/生物圈的屏障。不幸的是，此类材料的采样成本昂贵，并且在实验室中测量极其困难且成本高昂。因此，可靠且稳健地预测细粒岩石特性的能力将能够更好地对宏观物理行为进行建模，评估行为的不确定性，并了解此类应用对环境和公共健康的影响。当物理变得复杂时，微观到宏观的预测关系预计将是高度非线性的。我们对 3D 显微图像的初步研究 [1] 表明，即使是单相流动特性，如渗透率，也显示出与几何和拓扑特征的强烈非线性相关性（图 1）。此外，必须从大量样本中识别出稳健的非线性关系，并针对新样本进行验证。机器学习 (ML) 提供了一个执行自动化过程的框架，在该过程中，可以以自我监督的方式从不断增长的样本集合中逐步学习非线性关系的知识。这样的过程适合此目的，但必须以一组用于数据搜索和检索、数据分析和数据挖掘的智能且高效的工具为基础。所有这些工具的基础是能够根据数字岩石样本的微观结构的不同特征以及测量和/或计算的属性对数字岩石样本进行分类。该项目的目标是探索在 NERC 环境信息学主题范围内为数字岩石构建一套基于特征、内容感知和自监督的 ML 分类技术的可行性。这将产生一个能够根据预定义的控制特征对数字岩石样本和宏观特性进行分类的机器学习系统。最终，从该试点项目中获得的知识和经验将使 PI 能够提出更全面的建议，开发一套基于 ML 的技术，用于识别微观和宏观尺度特征之间的预测关系并预测宏观尺度属性。这些技术还有很大的空间可以扩展到其他类型的天然多孔介质，并影响整个行业和研究界，以解决有关多孔材料物理特性的工程和科学问题。

项目成果

Classification of Digital Rocks by Machine Learning

通过机器学习对数字岩石进行分类

• 发表时间: 2012
• 期刊: ECMOR XIII-13th European Conference on the Mathematics of Oil Recovery. 2012.
• 作者: Ma; J Et Al
• 通讯作者: J Et Al

SHIFT: an implementation for lattice Boltzmann simulation in low-porosity porous media.

SHIFT：低孔隙率多孔介质中晶格玻尔兹曼模拟的实现。

• DOI: 10.1103/physreve.81.056702
• 发表时间: 2010-05-06
• 期刊: Physical review. E, Statistical, nonlinear, and soft matter physics
• 作者: Jingsheng Ma;K. Wu;Zeyun Jiang;G. Couples
• 通讯作者: G. Couples

Assessing Impact of Shale Gas Adsorption on Free-Gas Permeability via a Pore Network Flow Model

通过孔隙网络流动模型评估页岩气吸附对游离气渗透率的影响

• DOI: http://dx.10.2118/178552-ms
• 发表时间: 2015
• 作者: Ma J

Unconventional Oil and Gas Resources Handbook

非常规油气资源手册

• DOI: http://dx.10.1016/b978-0-12-802238-2.00004-3
• 发表时间: 2016
• 作者: Ma J

The Impact of Pore Size and Pore Connectivity on Single-Phase Fluid Flow in Porous Media

孔径和孔隙连通性对多孔介质中单相流体流动的影响

• DOI: http://dx.10.1002/adem.201000255
• 发表时间: 2010
• 期刊: Advanced Engineering Materials
• 影响因子: 3.6
• 作者: Jiang Z