Fostering relationships and highlighting trends in HPC for the oil & gas industry

For more effective oil exploration, DDN empowers your researchers to perform analysis against much larger and complex data sets and deliver faster and more effective models for seismic processing, reservoir simulations and acquisition. Meet with our storage experts at the 2017 Rice Oil & Gas HPC Conference to learn how cutting-edge DDN solutions deliver faster time to oil with reduced risk and significantly lower operational costs. DDN will show you how to deliver faster return on investment and profitability:

  • Support the highest resolution capture methods
  • Process the most advanced algorithms at record speeds
  • Produce higher fidelity models to maximize reservoir performance

Join us for our Facilities, Infrastructure, and Networking Talk
Wednesday, March 15, 4:20pm
Facilities, Infrastructure & Networking: Achieving the Ultimate Efficiency for Seismic Analysis
James Coomer, DDN

The pressure to reduce both operating and capital costs in seismic data analysis drives an on-going demand for efficiency improvements in computational processing facilities. This is against a background of dramatically increasing seismic data volumes. Wide/Multi/Rich-azimuth methods using multi-sensor arrays and sophisticated acquisition techniques are producing higher-fidelity subsurface images, and modern analytics techniques are enabling continued advancement in the interpretation of seismic data for both newly acquired data and historical oil field data. As a result of the volume and scale of the seismic data required for modern HPC-based seismic processing and imaging, the performance of the associated storage subsystem can be a source of the greatest overall efficiency improvements.

Seismic analysis is particularly challenging for today’s file systems due to a tendency towards large random IO and share-file IO. Therefore, improving IO efficiencies for complex seismic workloads is key. The benefit of a true parallel file system is the very high single-client performance that can be delivered and sustained even when many hundreds of clients are working concurrently. This session will share the results experimental benchmarks to attain optimal IO rates for Paradigm’s Echos application workloads.


March 15 – 16, 2017
Rice University
BioScience Research Collaborative (BRC) Building
6500 Main St
Houston, TX 77030

Event Site: http://rice2017oghpc.rice.edu/