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 industry updates delivered to you every week!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire