Scale-out Storage for Oil and Gas Exploration

By Nicole Hemsoth

November 6, 2012

Introduction

Successful oil and gas exploration today requires ever-faster upstream processing. To shorten the compute time needed to get actionable information, organizations need to reduce survey processing run times from months to weeks and be capable of scaling to handle the explosive data growth.

With growing competition to open new fields and get more out of existing wells, getting answers faster gives organizations an advantage in overall costs, time to market, competitive bidding processes and with time-sensitive projects. Removing IT infrastructure obstacles that can slow upstream processing will improve an organization’s chance for success.

The biggest hurdle to time-to-oil is massive increases in the amount of data used and generated in support of a single project.

What’s needed to be the most productive (i.e., run the most jobs in a given time and make decisions faster) is a storage solution that is highly scalable, and that can the handle mixed workloads – large, sequential bandwidth and small random I/O together – that are increasingly important in upstream projects. Such a solution would accelerate data access and time-to-results by supporting high speed ingest to the broad range of custom and commercial applications used in processing and modeling.

Evolving requirements

Over the past decade, the geographical size of an average study has increased tenfold, and advances in study techniques, new sensors, and the transition to 4D have raised the average study dataset size to gigabytes or even terabytes. In fact, it is not unusual for a completed project to end up in the hundreds of terabytes range.

Additionally, some companies have sought to increase production of existing oil wells using innovative techniques. For example, this year BP announced a method whereby salt is removed from sea water before pumping it into an oil field. Compared to older techniques, the company expects this desalination step added to the traditional “waterflooding” technique will allow it to extract an extra 42 million barrels in its St Clair Ridge oilfield west of Shetland, off the Scotland coast.

The constant development and implementation of new extraction procedures means organizations will need to reexamine raw seismic and probe data, re-running analyses and simulations. That means data will need to be cost-effectively stored for long periods, located when new analysis is needed, and placed on high-performance storage to ensure upstream processing is not slowed when this data is re-run.

Taking all of these factors into account will help define the required characteristics of a scale-out storage solution that speeds upstream processing.

The storage solution must be high performance. The ability to handle large sequential I/O is no longer enough on its own. With so much data in every phase of every project, effective storage solutions need to handle small random I/O with equal grace. In this way, massive amounts of data and metadata can be effectively moved and computed.

To be effective in upstream, a solution must offer massive scalability. New seismic processing techniques produce hundreds of terabytes data per project. Across multiple projects this regularly develops into a need to store, access, and manage multiple datasets. As such, a storage solution must be able to consolidate hundreds of terabytes to petabytes of data onto a single platform.

Given that the goal is to speed upstream processing, storage-related downtime must be avoided. A solution must offer a full set of high availability features such as redundant components and paths, multiple RAID levels, and failover across multiple nodes.

A solution must also provide organizations with the flexibility to store data for longer times on appropriate cost/performance devices, while offering data management tools to migrate and protect that data.

DDN as your technology partner

Traditional storage solutions can introduce major performance and management problems when scaled to meet today’s increased requirements for upstream proceeding for oil and gas exploration. This is why many of the leading exploration companies are partnering with DataDirect Networks (DDN).

DDN offers an array of storage solutions with different I/O and throughput capabilities to meet the cost/performance requirements of any upstream processing effort. The solutions are extremely scalable in capacity and density. Based on its Storage Fusion Architecture, the DDN SFA 12K line offers a number of firsts including up to 40 GB/s host throughput for reads AND writes, 3.6 PB per rack, and the ability to scale to more than 7.2 PB per system. Furthermore, DDN lets organizations control their cost and performance profile by mixing a variety of media in the same system – SSD, SAS, and SATA – to achieve the appropriate cost/performance mix for their applications.

By consolidating on DDN storage, organizations get fast, scalable storage that solves performance inconsistency issues and provides easy-to-manage long term data retention.

Additionally, DDN offers the industry’s leading storage appliances – GRIDScaler and EXAScaler – which integrate leading HPC parallel file systems with DDN’s SFA storage to eliminate performance bottlenecks, while simplify deployment and management.

The bottom line is that DDN offers storage solutions that are ideally suited to the needs of organizations that want to accelerate their upstream processing. 

For more information about DDN solutions for oil and gas exploration, visit www.ddn.com.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

DARPA Continues Investment in Post-Moore’s Technologies

July 24, 2017

The U.S. military long ago ceded dominance in electronics innovation to Silicon Valley, the DoD-backed powerhouse that has driven microelectronic generation for decades. With Moore's Law clearly running out of steam, the Read more…

By George Leopold

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in 2017 with scale-up production for enterprise datacenters and Read more…

By Tiffany Trader

Fine-Tuning Severe Hail Forecasting with Machine Learning

July 20, 2017

Depending on whether you’ve been caught outside during a severe hail storm, the sight of greenish tinted clouds on the horizon may cause serious knots in the pit of your stomach, or at least give you pause. There’s g Read more…

By Sean Thielen

Trinity Supercomputer’s Haswell and KNL Partitions Are Merged

July 19, 2017

Trinity supercomputer’s two partitions – one based on Intel Xeon Haswell processors and the other on Xeon Phi Knights Landing – have been fully integrated are now available for use on classified work in the Nationa Read more…

By HPCwire Staff

HPE Extreme Performance Solutions

HPE Servers Deliver High Performance Remote Visualization

Whether generating seismic simulations, locating new productive oil reservoirs, or constructing complex models of the earth’s subsurface, energy, oil, and gas (EO&G) is a highly data-driven industry. Read more…

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's output. The Japanese multinational has made a raft of HPC and A Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the computer we use most (hopefully) and understand least. This mon Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee of the House of Representatives voted to accept the recomme Read more…

By Alex R. Larzelere

Summer Reading: IEEE Spectrum’s Chip Hall of Fame

July 17, 2017

Take a trip down memory lane – the Mostek MK4096 4-kilobit DRAM, for instance. Perhaps processors are more to your liking. Remember the Sh-Boom processor (1988), created by Russell Fish and Chuck Moore, and named after Read more…

By John Russell

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Fine-Tuning Severe Hail Forecasting with Machine Learning

July 20, 2017

Depending on whether you’ve been caught outside during a severe hail storm, the sight of greenish tinted clouds on the horizon may cause serious knots in the Read more…

By Sean Thielen

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's out Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the com Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee Read more…

By Alex R. Larzelere

Women in HPC Luncheon Shines Light on Female-Friendly Hiring Practices

July 13, 2017

The second annual Women in HPC luncheon was held on June 20, 2017, during the International Supercomputing Conference in Frankfurt, Germany. The luncheon provid Read more…

By Tiffany Trader

Satellite Advances, NSF Computation Power Rapid Mapping of Earth’s Surface

July 13, 2017

New satellite technologies have completely changed the game in mapping and geographical data gathering, reducing costs and placing a new emphasis on time series Read more…

By Ken Chiacchia and Tiffany Jolley

Intel Skylake: Xeon Goes from Chip to Platform

July 13, 2017

With yesterday’s New York unveiling of the new “Skylake” Xeon Scalable processors, Intel made multiple runs at multiple competitive threats and strategic Read more…

By Doug Black

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This