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/

天然裂缝作为地下的主要非均质性，控制地下流体和化学物质的流动和运输。它们的重要性不能低估，因为它们的transmittance可能会导致不希望的迁移过程中的地质封存的二氧化碳，他们强烈地控制热回收从地热储层，他们可能会导致诱发地震活动由于流体注入地下。先进的计算方法是至关重要的，设计地下过程中成功的环境和能源应用的裂缝介质。该项目将解决以下关键的大数据和计算机科学挑战：（1）计算地震波在裂缝介质中的传播;（2）用于推断裂缝特征的大数据分析;（3）裂缝介质中流动和运输的高性能计算;以及（4）整合来自不同来源的数据进行风险评估和决策。这将有助于设计解决关键社会问题的技术，例如从地表安全提取能源，长期封存大量温室气体以及安全储存核废料。该项目将为一组研究生和博士后研究员提供跨学科培训。 通过计划中的研讨会向高中教师和少数民族进行宣传，将激发他们对环境绿色工程、数学和计算科学的兴趣。 许多应用将受益于这项研究，包括计算机和信息科学与工程（CISE），地球科学（GEO），数学和物理科学（MPS）。拟议的研究将强调高性能计算（HPC）方法，用于使用大型地下地震数据集表征裂缝，BIG数据分析，用于从地震反演结果和长时间动态数据中提取裂缝相关信息，以及用于模拟流动、运输和地下裂缝系统的地质力学。具体目标是：开发一个有效的正演模拟算法，地震波在裂缝介质中的传播，使用有效的计算方案。使用在GPU上实现的高效计算方案（如模拟有限差分）计算裂缝介质中的流动和输运。对裂隙介质中的流动和地质力学进行高效的多物理场模拟。使用新的统计方案整合时移地震反演和流动/输运模拟的信息。地震和流体流动数据的联合反演和使用有效计算方案的不确定性量化。开发和部署可扩展的基于混合分段的基底，该基底可支持使用基于分段的原位/在途方法的目标工作流。计算模拟对于设计成功的环境和能源应用的地下工艺至关重要。项目网址：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