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Pore-scale machine-learning modeling of flow and transport properties of carbonate rocks

碳酸盐岩流动和输运特性的孔隙尺度机器学习建模

基本信息

批准号：
2041648
负责人：
Javier Vilcaez
金额：
$ 28.24万
依托单位：
Oklahoma State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-02-01 至 2025-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2041648&HistoricalAwards=false
关键词：
Pore scale machine learning modeling

项目摘要

Carbonate rocks constitute one of the most common type of groundwater and fossil fuel reservoirs in the USA. Design of optimum strategies for groundwater and fossil fuel resources development is generally done through the application of computer simulation programs where users need to input a set of flow and transport properties of the reservoir rock. The traditional approach to estimate flow and transport properties of reservoir rocks consists of using deterministic model equations. This approach frequently results in large disagreement between measured and simulated multiphase flow and reactive transport processes in carbonate reservoirs. The complex heterogeneous pore microstructure of carbonate rocks is reflected by stochastic (random) relationships between fundamental petrophysical properties (e.g., porosity) and flow and transport properties (e.g., permeability and tortuosity) of carbonate rocks that are practically impossible to capture by deterministic model equations. The goals of this research are (1) to develop a predictive understanding of the stochastic relationship between the pore microstructure and flow and transport properties of carbonate rocks, and (2) to establish machine learning models that capture the stochastic relationship between fundamental petrophysical properties and flow and transport properties of carbonate rocks. This research will advance national welfare and prosperity by enabling the design of environmentally responsible and optimum strategies to develop groundwater and fossil fuel resources from carbonate reservoirs, and it will contribute to the education of students on the use of artificial intelligence (e.g., machine learning) technologies to develop groundwater and fossil fuel resources. The goals of this research will be achieved by using a novel approach that overcomes limitations regarding the relatively small number of pore microstructures and associated flow and transport properties of carbonate rocks that can be experimentally determined by routine and special analyses. This novel approach consists of the construction of thousands of 3D pore microstructures of stochastic pore connectivity honoring pore size distribution curves obtained from nuclear magnetic resonance (NMR) measurements and pore geometries obtained from 2D scanning electron microscopy (SEM) imaging. Direct pore-scale simulations of flow and transport properties for thousands of 3D pore microstructures of the same pore size distribution and dominant pore geometry, but different pore connectivity, as it happens in real carbonate rocks, will make possible achieving the goals of this research by applying statistical/stochastic principles and machine learning technologies. The focus will be on carbonate rocks collected from the Mississippian Lime Play and Arbuckle Group in Oklahoma and Kansas. This project is jointly funded by the Hydrologic Sciences Program (HS) and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

碳酸盐岩是美国最常见的地下水和化石燃料储层之一。地下水和化石燃料资源开发的优化策略设计通常是通过应用计算机模拟程序来完成的，用户需要输入一组储层岩石的流动和输运特性。传统的储层流动输运性质估计方法是采用确定性模型方程。这种方法经常导致碳酸盐储层中多相流和反应输运过程的测量结果与模拟结果存在很大差异。碳酸盐岩复杂的非均质孔隙微观结构是由碳酸盐岩的基本岩石物理性质（如孔隙度）与流动和输运性质（如渗透率和弯曲度）之间的随机关系反映出来的，而这种关系实际上是不可能通过确定性模型方程来捕捉的。本研究的目标是：(1)对孔隙微观结构与碳酸盐岩流动输运性质之间的随机关系进行预测性理解；(2)建立机器学习模型，捕捉碳酸盐岩基本岩石物理性质与流动输运性质之间的随机关系。这项研究将通过设计对环境负责的最佳策略来开发碳酸盐岩储层中的地下水和化石燃料资源，从而促进国家福利和繁荣，并将有助于教育学生使用人工智能（如机器学习）技术来开发地下水和化石燃料资源。本研究的目标将通过使用一种新的方法来实现，该方法克服了相对较少的孔隙微观结构和相关的碳酸盐岩流动和输运特性的限制，这些限制可以通过常规和特殊分析实验确定。这种新方法包括构建数千个随机孔隙连通性的三维孔隙微观结构，并遵循核磁共振（NMR）测量获得的孔隙尺寸分布曲线和二维扫描电子显微镜（SEM）成像获得的孔隙几何形状。通过应用统计/随机原理和机器学习技术，对真实碳酸盐岩中数千种具有相同孔径分布和主要孔隙几何形状、但不同孔隙连通性的三维孔隙微观结构的流动和输运特性进行直接孔隙尺度模拟，将有可能实现本研究的目标。重点将放在俄克拉何马州和堪萨斯州的密西西比Lime Play和Arbuckle Group收集的碳酸盐岩上。该项目由水文科学计划（HS）和促进竞争性研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。