BIGDATA: Collaborative Research: IA: F: Fractured Subsurface Characterization Using High Performance Computing & Guided by Big Data (Funded by NSF)

Natural fractures act as major heterogeneity in the subsurface that control 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. Successful completion of tasks addressing these challenges 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 in the subsurface. 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 the proposed research, including Computer and Information Science and Engineering (CISE), Geosciences (GEO), and Mathematical and Physical Sciences (MPS).

Overview of the three-tiered research plan for fractured subsurface characterization

Figure. Overview of the three-tiered research plan for fractured subsurface characterization (above)

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 implemented on graphics processor units (GPUs).
  • 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.