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

天然裂缝是地下主要的非均质性，控制着地下流体和化学物质的流动和输送。它们的重要性不可低估，因为它们的透过性可能导致地质封存CO2时的不希望的迁移，它们强烈地控制地热储层的热回收，并且它们可能由于流体注入地下而导致诱发地震活动。先进的计算方法对于设计压裂介质中成功的环境和能源应用的地下过程至关重要。本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)利用大数据分析推断裂缝特征；(3)裂缝介质流动输运的高性能计算；(4)整合不同来源的数据，用于风险评估和决策。这将有助于设计解决关键社会问题的技术，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。通过计划中的讲习班向高中教师和少数民族伸出援助之手，将激发人们对环境绿色工程、数学和计算科学的兴趣。许多应用将受益于这项研究，包括计算机与信息科学与工程（CISE），地球科学（GEO）和数学与物理科学（MPS）。该研究将强调高性能计算（HPC）方法，利用大型地下地震数据集来表征裂缝；大数据分析方法，从地震反演结果和长时间动态数据中提取裂缝相关信息；先进的计算方法，用于模拟裂缝地下系统的流动、运输和地质力学。具体目标是：利用高效的计算方案开发地震波在裂缝介质中传播的有效正演模拟算法。在gpu上采用模拟有限差分等高效计算方案，计算破碎介质中的流和传输。在压裂介质中进行高效的多物理场流体和地质力学模拟。利用新颖的统计方案整合时移地震反演和流动/输运模拟的信息。地震与流体流动数据联合反演及不确定性量化的高效计算方法。开发和部署可扩展的基于混合分段的基板，可以使用基于分段的原位/在途方法支持目标工作流。计算模拟对于设计成功的环境和能源应用的地下过程至关重要。项目网址：http://csm.ices.utexas.edu/current-projects/

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/