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: 1546553
• 负责人: Mary Wheeler
• 金额: $ 82.33万
• 依托单位: University of Texas at Austin
• 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=1546553&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/

天然裂缝是地下的主要异质性，控制着地下流体和化学物质的流动和传输。它们的重要性不可低估，因为它们的透射率可能会导致二氧化碳地质封存过程中发生意外的迁移，它们强烈控制地热储层的热回收，并且可能由于流体注入地下而导致诱发地震。先进的计算方法对于设计裂缝介质中的地下过程以实现成功的环境和能源应用至关重要。该项目将解决以下关键的大数据和计算机科学挑战：（1）裂缝介质中地震波传播的计算； (2) 大数据分析，推断断裂特征； (3) 裂缝介质流动与输运的高性能计算； (4) 整合不同来源的数据以进行风险评估和决策。这将使技术设计能够解决关键的社会问题，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。该项目将为研究生和博士后团队提供跨学科培训。 通过计划举办的研讨会向高中教师和少数族裔进行宣传，将激发人们对环境绿色工程、数学和计算科学的兴趣。 许多应用将从这项研究中受益，包括计算机和信息科学与工程（CISE）、地球科学（GEO）以及数学和物理科学（MPS）。拟议的研究将强调使用大数据来表征裂缝的高性能计算（HPC）方法地下地震数据集、用于从地震反演结果和长期动态数据中提取裂缝相关信息的大数据分析，以及用于对裂缝性地下系统中的流动、传输和地质力学进行建模的高级计算方法。具体目标是： 使用高效的计算方案，开发一种有效的地震波在裂缝介质中传播的正演建模算法。使用在 GPU 上实现的高效计算方案（例如模拟有限差分）来计算裂隙介质中的流动和传输。对裂缝介质中的流动和地质力学进行高效的多物理场模拟。使用新颖的统计方案集成来自时移地震反演和流动/输运模拟的信息。使用有效的计算方案联合反演地震和流体流动数据以及不确定性量化。开发和部署基于可扩展混合分段的基板，该基板可以使用基于分段的原位/在途方法支持目标工作流程。计算模拟对于设计成功的环境和能源应用的地下过程至关重要。项目网址：http://csm.ices.utexas.edu/current-projects/