Deep Learning Portends ‘Sea Change’ for Oil and Gas Sector

By Tiffany Trader

February 1, 2018

The billowing compute and data demands that spurred the oil and gas industry to be the largest commercial users of high-performance computing are now propelling the competitive sector to deploy the latest AI technologies. Beyond the requirement for accurate and speedy seismic and reservoir simulation, oil and gas operations face torrents of sensor, geolocation, weather, drilling and seismic data. Just the sensor data alone from one off-shore rig can accrue to hundreds of terabytes of data annually, however most of this remains unanalyzed, dark data.

A collaboration between Nvidia and Baker Hughes, a GE company (BHGE) — one of the world’s largest oil field services companies — kicked off this week to address these big data challenges by applying deep learning and advanced analytics to improve efficiency and reduce the cost of energy exploration and distribution. The partnership leverages accelerated computing solutions from Nvidia, including DGX-1 servers, DGX Station and Jetson, combined with BHGE’s fullstream analytics software and digital twins to target end-to-end oil and gas operations.

Source: Nvidia

“It makes sense if you think about the nature of the operations, many remote sites, often in difficult locations,” said Binu Mathew, vice president of digital development at Baker Hughes. “But also when you look at it from an industry standpoint, there’s a ton of data being generated, a lot of information, and you often have it in two windows: you have an operator who will have multiple streams of data coming in, but relatively little actual information, because you’ve got to use your own experience to figure out what to do.

“On the flip side you have a lot of very smart, very capable engineers who are very good at building physics models, geological models, who often take weeks or months to fill out these models and run simulations, so they operate in that kind of timeframe. In between you’ve got a big challenge of not being able to have enough actual data crossing silos into a system that can analyze this data that you can take operational action from. This is the area that we at Baker Hughes Digital plan to address. We plan to do it because the technologies are now available in the industry: the rise of computational power and the rise of analytical techniques.”

Mathew’s account of the magnitude of data being generated by the industry leaves little doubt that this is an exascale-class challenge that requires new approaches and efficiencies.

“Even if you don’t talk about things like imaging data — which adds a whole order of magnitude to it — but, just in terms of what you’d call semi-structured data, essentially data coming up from various sensors, it’s in the hundreds of petabytes annually,” Mathew said. “And if you take a deep water rig you’re talking about in the region of a terabyte of data coming in per day. To analyze that kind of data at that kind of scale, the computational power will run into the exaflops and potentially well beyond.”

Source: Nvidia; BHGE

Like an increasing number of groups across academia and industry, Baker Hughes is tackling this extreme-scale challenge using a combination of physics-based and probabilistic models.

“You cannot analyze all that data without something like AI,” said Mathew. “If you go back to the practical models, the oil and gas industry has been very good at coming up with physics based models, and they will still be absolutely key at the core for modeling seismic phenomenon. But to scale those models requires combining physics models with the pattern matching capabilities that you get with AI. That’s the sea change we’ve seen in the last several years. If you look at image recognition and so on, deep learning techniques are now matching or exceeding human capabilities. So if you combine those things together you get into something that’s a step change from what’s been possible before.”

Nvidia is positioning its GPU technologies to fuel this transformation by powering accelerated analytics and deep learning across the spectrum of oil and gas operations.

“With GPU-accelerated analytics, well operators can visualize and analyze massive volumes of production and sensor data such as pump pressures, flow rates and temperatures,” stated Nvidia’s Tony Paikeday in a blog post. “This can give them better insight into costly issues, such as predicting which equipment might fail and how these failures could affect wider systems.

“Using deep learning and machine learning algorithms, oil and gas companies can determine the best way to optimize their operations as conditions change,” Paikeday continued. “For example, they can turn large volumes of seismic data images into 3D maps to improve the accuracy of reservoir predictions. More generally, they can use deep learning to train models to predict and improve the efficiency, reliability and safety of expensive drilling and production operations.”

The collaboration with BHGE will leverage Nvidia’s DGX-1 servers for training models in the datacenter; the smaller DGX Station for computing deskside or in remote, bandwidth-challenged sites; and the Nvidia Jetson for powering real-time inferencing at the edge.

Jim McHugh, Nvidia vice president and general manager, said in an interview that Nvidia excels at bringing together this raw processing power with an entire ecosystem: “Not only our own technology, like CUDA, Nvidia drivers, but we also bring all the leading frameworks together. So when people are going about doing deep learning and AI, and then the training aspect of it, the most optimized frameworks run on DGX, and are available via our NGC [Nvidia GPU cloud] as well.”

Cloud connectivity is a key enabler of the end-to-end platform. “One of the things that allows us to access that dark data is the concept of edge to cloud,” said Mathew. “So you’ve got the Jetsons out at the edge streaming into the clouds, where we can do the training of these models because training is much heavier and using DGX-1 boxes helps enormously with that task and running the actual models in production.”

Baker Hughes says it will work closely with customers to provide them with a turnkey solution. “The oil and gas industry isn’t homogeneous, so we can come out with a model that largely fits their needs but with enough flexibility to tweak,” said Mathew. “And some of that comes inherently from the capabilities you have in these techniques, they can auto-train themselves, the models will calibrate and train to the data that’s coming in. And we can also tweak the models themselves.”

For Nvidia, partnering with BHGE is part of a broader strategy to work with leading companies to bring AI into every industry. The self-proclaimed AI computing company believes technologies like deep learning will effect a strong virtuous cycle.

“The thing about AI is when you start leveraging the algorithms in deep neural networks, you end up developing an insatiable desire for data because it allows you to get new discoveries and connections and correlations that weren’t possible. We are coming from a time when people suffered from a data deluge; now we’re in something new where more data can come, that’s great,” said McHugh.

Doug Black, managing editor of EnterpriseTech, contributed to this report.

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!

Nvidia Debuts Turing Architecture, Focusing on Real-Time Ray Tracing

August 16, 2018

From the SIGGRAPH professional graphics conference in Vancouver this week, Nvidia CEO Jensen Huang unveiled Turing, the company's next-gen GPU platform that introduces new RT Cores to accelerate ray tracing and new Tenso Read more…

By Tiffany Trader

HPC Coding: The Power of L(o)osing Control

August 16, 2018

Exascale roadmaps, exascale projects and exascale lobbyists ask, on-again-off-again, for a fundamental rewrite of major code building blocks. Otherwise, so they claim, codes will not scale up. Naturally, some exascale pr Read more…

By Tobias Weinzierl

STAQ(ing) the Quantum Computing Deck

August 16, 2018

Quantum computers – at least for now – remain noisy. That’s another way of saying unreliable and in diverse ways that often depend on the specific quantum technology used. One idea is to mitigate noisiness and perh Read more…

By John Russell

HPE Extreme Performance Solutions

Introducing the First Integrated System Management Software for HPC Clusters from HPE

How do you manage your complex, growing cluster environments? Answer that big challenge with the new HPC cluster management solution: HPE Performance Cluster Manager. Read more…

IBM Accelerated Insights

Super Problem Solving

You might think that tackling the world’s toughest problems is a job only for superheroes, but at special places such as the Oak Ridge National Laboratory, supercomputers are the real heroes. Read more…

NREL ‘Eagle’ Supercomputer to Advance Energy Tech R&D

August 14, 2018

The U.S. Department of Energy (DOE) National Renewable Energy Laboratory (NREL) has contracted with Hewlett Packard Enterprise (HPE) for a new 8-petaflops (peak) supercomputer that will be used to advance early-stage R&a Read more…

By Tiffany Trader

STAQ(ing) the Quantum Computing Deck

August 16, 2018

Quantum computers – at least for now – remain noisy. That’s another way of saying unreliable and in diverse ways that often depend on the specific quantum Read more…

By John Russell

NREL ‘Eagle’ Supercomputer to Advance Energy Tech R&D

August 14, 2018

The U.S. Department of Energy (DOE) National Renewable Energy Laboratory (NREL) has contracted with Hewlett Packard Enterprise (HPE) for a new 8-petaflops (peak Read more…

By Tiffany Trader

CERN Project Sees Orders-of-Magnitude Speedup with AI Approach

August 14, 2018

An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learni Read more…

By Rob Farber

Intel Announces Cooper Lake, Advances AI Strategy

August 9, 2018

Intel's chief datacenter exec Navin Shenoy kicked off the company's Data-Centric Innovation Summit Wednesday, the day-long program devoted to Intel's datacenter Read more…

By Tiffany Trader

SLATE Update: Making Math Libraries Exascale-ready

August 9, 2018

Practically-speaking, achieving exascale computing requires enabling HPC software to effectively use accelerators – mostly GPUs at present – and that remain Read more…

By John Russell

Summertime in Washington: Some Unexpected Advanced Computing News

August 8, 2018

Summertime in Washington DC is known for its heat and humidity. That is why most people get away to either the mountains or the seashore and things slow down. H Read more…

By Alex R. Larzelere

NSF Invests $15 Million in Quantum STAQ

August 7, 2018

Quantum computing development is in full ascent as global backers aim to transcend the limitations of classical computing by leveraging the magical-seeming prop Read more…

By Tiffany Trader

By the Numbers: Cray Would Like Exascale to Be the Icing on the Cake

August 1, 2018

On its earnings call held for investors yesterday, Cray gave an accounting for its latest quarterly financials, offered future guidance and provided an update o Read more…

By Tiffany Trader

Leading Solution Providers

SC17 Booth Video Tours Playlist

Altair @ SC17

Altair

AMD @ SC17

AMD

ASRock Rack @ SC17

ASRock Rack

CEJN @ SC17

CEJN

DDN Storage @ SC17

DDN Storage

Huawei @ SC17

Huawei

IBM @ SC17

IBM

IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17

Intel

Lenovo @ SC17

Lenovo

Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17

Microsoft

Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17

Supericro

Tyan @ SC17

Tyan

Univa @ SC17

Univa

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This