Optimizing Geospatial Analysis with GPU Acceleration

Today’s energy, oil, and gas (EO&G) companies are constantly challenged to operate better, faster, and more cost-effectively. Although new sources of oil seem increasingly hard to find, a recent study found that the United States has up to 264 billion barrels of undiscovered oil between existing fields, new discoveries, and yet-to-be-discovered fields. To capitalize on this valuable opportunity, EO&G companies are pushing the limits of their technology in order to improve planning, exploration, and production.

Analyzing the properties and characteristics of geographical locations is one of the ways EO&G companies quickly uncover new reserves, streamline field management, and make more informed decisions about where to drill. This technique, known as geospatial analysis, helps EO&G companies use spatial information about their sites through every stage of the extraction lifecycle, from opportunity analysis, to exploration and production, to abandonment. In order to fully leverage this data, organizations are employing powerful high performance computing (HPC) solutions to maintain, store, distribute, and analyze growing volumes of geospatial data.

However, the industry is encountering many IT challenges as geospatial data grows quickly in both scope and complexity. Traditional computing architectures are falling short when it comes to supporting the visualization and analysis of such large datasets, and they can’t ingest, store, and process this data quickly enough to help exploration teams make quick and accurate decisions in terms of where to drill. It is becoming clear that the only way to make sense of and extract intelligence from these growing data volumes is to leverage massively parallel processing.

GPU computing is providing the opportunity to resolve many of the current challenges with traditional compute infrastructures. When integrated with HPC systems, these technologies can not only deliver the storage, performance, and flexibility to rapidly process such massive datasets, but also allow remote exploration teams to collaborate more efficiently and offer a highly immersive graphics experience for users.

Multi-GPU systems like those from NVIDIA can help EO&G companies transform their operations in a variety of ways:

  • Visualize and analyze petabytes of geospatial data in milliseconds, dramatically reduce model processing times, and provide for more granular analysis of geospatial datasets.
  • Leverage advanced algorithms to locate issues with underground structures, monitor pipeline data and fluid dynamics, and appraise sites with less drilling.
  • Explore evolving techniques like deep learning to accelerate exploration activities and quickly uncover issues with specific geological locations.

HPC server platforms are the backbone for utilizing these groundbreaking technologies. Robust solutions from Hewlett Packard Enterprise (HPE) provide the tools and confidence to deliver HPC innovation, leveraging a dense form factor with a high ratio of NVIDIA GPUs to CPUs for transforming even the most massive datasets into intelligence. By leveraging the capabilities of HPC and GPU acceleration, EO&G companies can quickly process and analyze large volumes of geospatial data to make more informed, data-driven decisions, improve exploration and production, and mitigate the risk associated with expensive drilling activities.

HPE and NVIDIA are delivering technologies that empower EO&G companies to become more agile and innovative in the ways that they operate. To learn more about the potential of HPC and GPUs for a variety of industries, please follow me on Twitter at @Bill_Mannel. You can also check out @HPE_HPC and @NVIDIADC for more news and updates.

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