Collaborative Research: ITR-(ASE+EVS)-(dmc+sim) Data Driven Simulation of the Subsurface: Optimization and Uncertainty Estimation

合作研究：ITR-(ASE EVS)-(dmc sim) 地下数据驱动模拟：优化和不确定性估计

• 批准号: 0426354
• 负责人: Manish Parashar
• 金额: $ 18.6万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-10-01 至 2008-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0426354&HistoricalAwards=false
• 关键词: Collaborative; Research; ITR; ASE+EVS; dmc+sim

Intellectual Merit. Remote sensing is employed in science and engineering problems to infer material properties when these properties can not be directly sampled. To better understand and manage our environment for safety and economic reasons, much progress has been made in imaging the subsurface and estimating physical properties based on remote sensing data. Repeated observations over targets for environmental remediation and reservoir production has become a recognized diagnostic tool for assisting management decisions. In addition, improved optimization techniques capable of responding to large, multi-resolution, disparate, dynamic datasets in a fault tolerant and adaptive fashion are a fundamental requirement for effectively estimating and minimizing the uncertainty in any data driven application. The integrated and e_ective treatment of these issues motivates the present project. The assembled research team proposes to advance the mathematical, engineering and computational foundations necessary to enhance our understanding and extend the predictive capabilities of the physical processes that govern the subsurface phenomena at multiple temporal and spatial scales. Target applications include management of aquifers for water resources, optimizing oil and gas production, and monitoring environmental risks e.g., at waste containment sites or arising from natural hazards.The intellectual merits of the project include: (1) development of the next generation of accurate, multi-scale, coupled chemical, uid, geomechanical, and geophysical simulators for modeling instrumented subsurface environments; (2) large scale optimization techniques (based on a hybridization of global and local approaches) to drive reliable decision-making and a dynamic symbiotic feedback between computation and data; (3) deployment of an autonomic Grid middleware for providing the adequate processing substrate and data management services for (1) and (2). The realization of the above contributions will result in the Data Driven Subsurface Simulation Framework (DDSSF).

智力优点。当无法直接采样这些属性时，遥感被采用在科学和工程问题中来推断材料特性。为了更好地理解和管理我们的环境出于安全和经济原因，在对地下和基于遥感数据估算物理属性的成像中取得了很多进展。 对环境修复目标和储层生产的目标的重复观察已成为公认的诊断工具，以协助管理决策。此外，改进的优化技术能够以容错和适应性的方式响应大型，多分辨率，不同的动态数据集，这是有效估计和最小化任何数据驱动应用程序的不确定性的基本要求。对这些问题的综合治疗促进了本项目。组装的研究团队提议提高所需的数学，工程和计算基础，以增强我们的理解并扩展在多个时间和空间尺度上控制地下现象的物理过程的预测能力。目标应用包括水资源的管理，优化石油和天然气的生产以及监测环境风险，例如在废物遏制地点或由自然危害产生。 （2）大规模优化技术（基于全球和本地方法的杂交），以推动可靠的决策和计算和数据之间的动态共生反馈； （3）部署自主网格中间件，用于为（1）和（2）提供足够的处理基板和数据管理服务。上述贡献的实现将导致数据驱动的地下模拟框架（DDSSF）。