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。通常通过应用计算机模拟程序来完成地下水和化石燃料资源开发的最佳策略，用户需要输入储层岩石的一组流量和运输属性。估计水库岩石的流量和运输特性的传统方法包括使用确定性模型方程。这种方法经常导致碳酸盐储层中测量和模拟的多相和反应性传输过程之间的大分分歧。碳酸盐岩石的复杂异质孔微结构反映出基本岩石物理特性（例如孔隙率）与流动性能（例如渗透率和折磨）（例如，渗透率和折磨）之间的随机（随机）关系，碳酸盐岩石几乎是不可能通过确定性模型方程捕获的碳酸盐岩石。这项研究的目标是（1）对孔岩石岩石岩石的随机关系进行预测理解，以及（2）建立机器学习模型，以捕获捕获基本岩石物理和之间的随机关系碳酸盐岩的流量和运输特性。这项研究将通过启用对环境负责和最佳策略的设计来发展国家福利和繁荣，从而开发碳酸盐储层的地下水和化石燃料资源，并将为学生在使用人工智能的使用方面的教育（例如，机器学习）开发地下水和化石燃料资源的技术。这项研究的目标将通过使用一种新型方法来实现，该方法克服了相对较少数量的孔微结构以及相关的流量和运输特性，可以通过常规和特殊分析来实验确定。这种新颖的方法包括构建数千个3D孔微结构的随机孔连接性，以纪念从核磁共振（NMR）测量值获得的孔径分布曲线和从2D扫描电子显微镜（SEM）成像中获得的孔的几何形状。对数千个相同孔径分布和主要孔几何形状的3D孔微结构的流量和运输特性的直接孔尺度模拟，但是在实际碳酸盐岩石中发生的那样，不同的孔连接将使实现这一研究的目标，从而实现这项研究的目标。应用统计/随机原理和机器学习技术。重点将放在从密西西比石灰游戏和俄克拉荷马州和堪萨斯州的Arbuckle集团中收集的碳酸盐岩石上。该项目由水文科学计划（HS）共同资助和启发竞争性研究的既定计划（EPSCOR）。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估标准，认为值得支持的支持。。