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

研究启动奖为历史上黑人学院和大学的初级和中级职业教师提供支持，他们正在建立新的研究项目或重新定向和重建现有的研究项目。预计该奖项将有助于进一步提高教师的研究能力和效率，改善其所在机构的研究和教学，并使本科生参与研究经验。授予中央州立大学的奖项在许多领域具有潜在的更广泛的影响。该项目旨在采用图形处理单元（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。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补救中，通常需要使用具有有限数据的地下流体流动模型来预测诸如产出流体中的石油分数、二氧化碳浓度或污染物浓度的量。在这项工作中，贝叶斯框架的图形处理单元预测流量将采用。该项目的主要目标是：在现有的贝叶斯框架中扩展地下表征，该框架采用GPU上的流量模拟器;使用贝叶斯统计框架改进实验数据拟合;并扩展改进后的框架，以包括GPU上的三维流量模拟器。为了改善表征，将采用吞吐技术，该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，将使用集合卡尔曼滤波器，在不同的空间尺度上整合渗透率和孔隙度数据，以重建渗透率和孔隙度的精细尺度空间分布。通过与工业界的合作，拟议的贝叶斯框架可用于检测配备传感器的含水层中的污染物。

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.