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

天然裂缝是地下的主要非均质性，控制着地下流体和化学物质的流动和运输。它们的重要性不可低估，因为它们的透射率可能导致二氧化碳在地质封存过程中不受欢迎的迁移，它们强烈地控制着地热储的热回收，并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裂隙介质中的地下工艺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)计算地震波在裂隙介质中的传播；(2)用于推断裂缝特征的大数据分析；(3)裂隙介质中流动和传输的高性能计算；以及(4)整合来自不同来源的数据以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，如从地表安全地提取能源、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研究员团队提供跨学科培训。通过计划中的研讨会向高中教师和少数族裔伸出援手，将激发人们对环境绿色工程、数学和计算科学的兴趣。这项研究将使许多应用受益，包括计算机与信息科学与工程(CEISE)、地球科学(GEO)和数学与物理科学(MPS)。拟议的研究将强调利用大型地下地震数据集表征裂缝的高性能计算(HPC)方法，从地震反演结果和长期动态数据中提取与裂缝相关的信息的大数据分析方法，以及用于模拟裂隙地下系统中的流动、运移和地质力学的高级计算方法。具体目标是：利用高效的计算方案，开发一种有效的地震波在裂隙介质中传播的正演模拟算法。使用在GPU上实现的高效计算方案(例如模拟有限差分)计算裂隙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有效的多物理模拟。使用新的统计方案整合来自时移地震反演和流动/输送模拟的信息。地震和流体流动数据的联合反演和使用高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暂存的基板，该基板可以使用基于暂存的就地/在途方法支持目标工作流。计算模拟是设计成功的环境和能源应用的地下工艺的关键。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/