BIGDATA: Collaborative Research: IA: F: Fractured Subsurface Characterization using High Performance Computing and Guided by Big Data

BIGDATA：协作研究：IA：F：使用高性能计算和大数据指导的断裂地下表征

• 批准号: 1546145
• 负责人: Ivan Rodero
• 金额: $ 31.46万
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546145&HistoricalAwards=false
• 关键词: BIGDATA; Collaborative; Research; IA; Fractured

Natural fractures act as major heterogeneities in the subsurface that controls flow and transport of subsurface fluids and chemical species. Their importance cannot be underestimated, because their transmissivity may result in undesired migration during geologic sequestration of CO2, they strongly control heat recovery from geothermal reservoirs, and they may lead to induced seismicity due to fluid injection into the subsurface. Advanced computational methods are critical to design subsurface processes in fractured media for successful environmental and energy applications. This project will address the following key BIG data and computer science challenges: (1) Computation of seismic wave propagation in fractured media; (2) BIG DATA analytics for inferring fracture characteristics; (3) High Performance Computation of flow and transport in fractured media; and (4) Integration of data from disparate sources for risk assessment and decision-making. This will enable design of technologies for addressing key societal issues such as safe energy extraction from the surface, long-term sequestration of large volumes of greenhouse gases, and safe storage of nuclear waste. The project will provide interdisciplinary training for a team of graduate students and postdoctoral fellows. Outreach to high schools teachers and minorities through a planned workshop will inspire interest in environmental green-engineering, mathematics, and computational science. Numerous applications will benefit from this research, including Computer and Information Science and Engineering (CISE), Geosciences (GEO), and Mathematical and Physical Sciences (MPS).The proposed research will emphasize high performance computation (HPC) approaches for characterizing fractures using large subsurface seismic data sets, BIG data analytics for extraction of fracture related information from seismic inversion results and long-duration dynamic data, and advanced computational approaches for modeling flow, transport, and geomechanics in fractured subsurface systems. The specific objectives are to: Develop an efficient forward modeling algorithm for seismic wave propagation in fractured media using efficient computational schemes. Compute flow and transport in fractured media using an efficient computational scheme implemented on GPUs such as mimetic finite differences. Perform efficient multiphysics simulation of flow and geomechanics in fractured media. Integrate information from time-lapse seismic inversion and flow/transport simulation using novel statistical schemes. Joint inversion of seismic and fluid flow data and uncertainty quantification using efficient computational schemes. Develop and deploy a scalable hybrid-staging based substrate that can support targeted workflows using staging-based in-situ/in-transit approaches. Computational simulation is critical to design subsurface processes for successful environmental and energy applications. Project URL: http://csm.ices.utexas.edu/current-projects/

自然裂缝起着控制地下流体和化学物种的流量和运输的地下的主要异质性。它们的重要性不能被低估，因为它们的透射率可能导致二氧化碳地质隔离期间不希望的迁移，它们强烈控制着地热储层的热量恢复，并且由于地下向地面注射流体，它们可能导致诱导的地震性。先进的计算方法对于在破裂的介质中设计地下过程至关重要，以成功地进行环境和能源应用。该项目将解决以下关键的大数据和计算机科学挑战：（1）破裂介质中地震波传播的计算； （2）用于推断断裂特征的大数据分析； （3）断裂培养基中流量和运输的高性能计算； （4）从不同来源的数据集成以进行风险评估和决策。这将实现技术的设计，以解决关键的社会问题，例如从表面上提取安全能量，长期隔离大量温室气体以及安全存储核废料。该项目将为研究生和博士后研究员团队提供跨学科培训。 通过计划的研讨会向高中教师和少数民族的宣讲将激发人们对环境绿色工程，数学和计算科学的兴趣。 Numerous applications will benefit from this research, including Computer and Information Science and Engineering (CISE), Geosciences (GEO), and Mathematical and Physical Sciences (MPS).The proposed research will emphasize high performance computation (HPC) approaches for characterizing fractures using large subsurface seismic data sets, BIG data analytics for extraction of fracture related information from seismic inversion results and long-duration dynamic data, and advanced computational裂缝地下系统中建模，传输和地质力学的建模方法。具体目标是：开发一种有效的培养基中地震波传播的有效的正向建模算法，使用有效的计算方案。使用在GPU上实现的有效计算方案（例如模拟有限差异），计算培养基中的流量和传输。在断裂的培养基中对流量和地质力学进行有效的多物理模拟。使用新型统计方案从延时地震反转和流量/转运模拟中整合信息。使用有效的计算方案对地震和流体流数据的关节反转以及不确定性定量。开发和部署基于可扩展的混合阶段的基板，该基材可以使用基于基于登台的原位/传输方法来支持有针对性的工作流程。计算模拟对于为成功的环境和能源应用设计地下过程至关重要。项目URL：http：//csm.ices.utexas.edu/current-projects/

项目成果

Persistent Data Staging Services for Data Intensive In-situ Scientific Workflows

适用于数据密集型原位科学工作流程的持久数据暂存服务

• DOI: 10.1145/2912152.2912157
• 发表时间: 2016
• 期刊: Proceedings of the ACM International Workshop on Data-Intensive Distributed Computing
• 作者: Romanus, Melissa;Klasky, Scott;Chang, Choong-Seock;Rodero, Ivan;Zhang, Fan;Jin, Tong;Sun, Qian;Bui, Hoang;Parashar, Manish;Choi, Jong
• 通讯作者: Choi, Jong