Research Initiation Award: Uncertainty Quantification of Multi-Phase Porous Media Flows on GPUs

研究启动奖：GPU 上多相多孔介质流的不确定性量化

• 批准号: 1600818
• 负责人: Arunasalam Rahunanthan
• 金额: $ 29.92万
• 依托单位: Central State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1600818&HistoricalAwards=false
• 关键词: Research; Initiation; Award; Uncertainty; Quantification

Research Initiation Awards provide support for junior and mid-career faculty at Historically Black Colleges and Universities who are building new research programs or redirecting and rebuilding existing research programs. It is expected that the award helps to further the faculty member's research capability and effectiveness, improves research and teaching at his home institution, and involves undergraduate students in research experiences. The award to Central State University has potential broader impact in a number of areas. The project seeks to employ a Bayesian framework on Graphics Processing Units (GPUs) for forecasting flows of the spatial distribution of subsurface properties, such as permeability and porosity. Undergraduate students will be involved in the project and a new course in high performance computing will be developed. In oil recovery, carbon dioxide sequestration, or monitoring and remediation of aquifer contamination, it is often required to forecast quantities such as the fraction of oil in the produced fluid, carbon dioxide concentration, or concentration of contaminants, using subsurface fluid flow models with limited data. In this work, a Bayesian framework on Graphics Processing Units for forecasting flows will be employed. The main objectives of the project are to: expand the subsurface characterization in an existing Bayesian framework that employs a flow simulator on GPUs; improve experimental data fitting using the Bayesian statistical framework; and extend the improved framework to include a three-dimensional flow simulator on GPUs. To improve the characterization, a huff-puff technique will be employed, which consists of injecting a tracer for a short period of time into the subsurface during the huff phase, and monitoring the tracer during the puff phase. Additionally, an ensemble Kalman filter for integrating permeability and porosity data at different spatial scales for reconstructing fine-scale spatial distributions of permeability and porosity will be used. Through collaboration with industry, the proposed Bayesian framework can be used to detect contaminants in water aquifers equipped with sensors.

研究启动奖为历史悠久的黑人学院和大学的初级和职业生涯中期教师提供支持，这些教师正在建立新的研究计划或重新定向和重建现有的研究计划。预计该奖项有助于进一步提高教师的研究能力和有效性，改善其所在机构的研究和教学，并让本科生参与研究经验。授予中央州立大学的奖项可能会在许多领域产生更广泛的影响。该项目寻求使用图形处理单元(GPU)上的贝叶斯框架来预测地下特性(如渗透率和孔隙度)的空间分布的流动。本科生将参与该项目，并将开发一门新的高性能计算课程。在石油开采、二氧化碳封存或含水层污染的监测和修复中，经常需要使用数据有限的地下流体流动模型来预测采油流体中的石油比例、二氧化碳浓度或污染物浓度等数量。在这项工作中，将使用一个关于图形处理单元的贝叶斯框架来预测流量。该项目的主要目标是：在现有的在GPU上使用流动模拟器的贝叶斯框架中扩展地下表征；使用贝叶斯统计框架改进实验数据拟合；以及扩展改进的框架以包括在GPU上的三维流动模拟器。为了改善表征，将采用吞吐技术，包括在吞吐阶段将示踪剂短时间注入地下，并在吞吐阶段监测示踪剂。此外，还将使用集合卡尔曼滤波，以整合不同空间尺度上的渗透率和孔隙度数据，以重建渗透率和孔隙度的精细空间分布。通过与工业界的合作，建议的贝叶斯框架可以用于检测配备传感器的含水层中的污染物。