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Oil companies are poised to benefit from a substantial
decrease in the time it takes to model oil reservoirs, following a
supercomputing breakthrough announced by Italian major Eni.
and US-based Stone Ridge Technology (SRT) revealed on 4th May a new
cooperative agreement designed to advance and accelerate the development
of ECHELON, SRT’s high-performance reservoir simulator.
companies will work together over the next three-and-a-half years to
enhance and promote next-generation simulation technology and workflows
that are enabled by ECHELON’s performance.
One of the significant
differentiating features of ECHELON is that it runs entirely on
Graphical Processing Units (GPUs) rather than CPUs, said Vincent Natoli,
the CEO of SRT.
A decade ago, NVIDIA, the primary GPU vendor,
opened up its chip architecture to go beyond just graphics to any sort
of general calculation.
“They created the CUDA development
environment that allows general programmers to access the incredible
power of their massively parallel chips,” said Natoli.
many have jumped at the opportunity to take applications that typically
run on CPUs and port them over to GPUs, experiencing tremendous speed
ups in some cases.
Oil and gas companies started working on GPUs
as early as 2008-2009, said Geetika Gupta, Principal Product Manager,
HPC & AI at NVIDIA. “They saw that the kind of problems that they
are trying to solve, fit the GPU architecture very well. Seismic
analysis and ResSim require a lot of parallel processing in converting
the data collected via radio signals into creating a model of the
sub-surface of the earth,” she said.
GPUs were primarily designed for graphics, where 1000s of cores are computing in parallel and the image gets displayed.
ResSim model comprises of millions of 3-d cubes called cells that
together create the model of the earth’s sub-surface. Solving all these
cells in parallel is well suited for the GPU’s inherent processing
architecture. The oil and gas companies realised this was great
architecture which fits their problems and workflows very well, so they
started adopting it,” said Gupta.
NVIDIA has since then
worked with several developers in the oil and gas community. “We’ve
taken their feedback and made it easier for customers in this area to
adopt GPUs via developer tools and libraries,” said Gupta.
claims ECHELON is the fastest, most scalable simulator in the world and
is built to run entirely on NVIDIA Tesla GPUs, the same high-performance
computing platform now powering the revolution in artificial
intelligence, machine learning and big data.
codes model the subsurface flow of hydrocarbons and water in a petroleum
reservoir. They allow energy companies to optimize recovery from their
assets by simulating numerous ‘what-if’ scenarios for well placement and
In the oil and gas industry, the first
applications to move over to GPUs were related to seismic imaging, said
Natoli. “Now almost every large oil company is using GPUs in one way or
another – most are addressing seismic imaging.”
Caption: Image of reservoir model showing horizontal transmissibility (red= high, blue=low).
Source: Stone Ridge Technology
The reason for this is all to do with performance; GPUs are
delivering more raw speed than CPUs and the tech trends are in their
favor. Where CPU performance has stagnated in recent years, GPUs have
kept advancing along the Moore’s law curve.
“Two things make
ECHELON exceptional, the GPU implementation and my team’s expert
approach to the applied math and numerics,” said Natoli. “First, the GPU
chip has more capability, both in the execution of calculations and the
movement of data. If you compare a modern GPU to a CPU, the GPU can
move data and do calculations roughly 10 times faster.”
our solver methodology is robust, it converges quickly and it uses
advanced and well adapted numerical approaches. It’s as much about
consideration of the numerics and applied math as it is about the
hardware. If you get the former wrong the hardware won’t save you.
That’s why we're getting the results we report,” he said.
software on GPU also reduces hardware footprints so it not only
calculates more quickly, it does so with less hardware and for less
money. As a result, oil companies can generate more information about
how to find and extract hydrocarbons from their billion dollar assets in
less time and at less cost.
A quantum leap
recent announcement using ECHELON to run a massive number of reservoir
models in record time on its HPC4 supercomputer (3,200 NVIDIA Tesla
GPUs) may represent a quantum leap in capability.
deployed in its Green Data Center facility, performed the breakthrough
calculation in early May, opening the path to a new era for reservoir
engineering numerical modeling. HPC4 executed 100,000 high-resolution
reservoir model simulation runs, taking into account geological
uncertainties, in a record time of 15 hours. In comparison, said Eni,
most reservoir engineers in the industry can run just one single
simulation in a few hours with CPU-based hardware and software.
high-resolution model of a deep-water reservoir, with 5.7 million active
cells, was executed with 100,000 different geological realizations,
each one running on a single GPU in an average time of 28 minutes to
simulate 15 years of production.
The Eni Green Data Center’s
hybrid supercomputers (HPC 3 and HPC4) have a peak performance capacity
of 22.4 PetaFlop/s and provide strategic support to the company’s
digital transformation process across the entire value chain, from the
exploration and development of oil and gas reservoirs, to the management
of the big data generated in the operational phase by all of the
“Eni has bought what is currently the most
powerful commercial supercomputing cluster in the world and they bought
it for a reason: they are going to run applications with these GPUs and
now that they have ECHELON, they can do reservoir simulation at a
capacity far beyond anyone else in the industry,” said Natoli.
promise is one of significant performance improvements for oil
companies willing to adopt GPU-based simulation software. Models that
have until now taken one or two days, are now completing in less than an
Explained Natoli: “The latest Volta GPUs can simulate model
sizes up to 12 million cells. For CPU-based codes to get the best speed
they have to use a lot of cores, in other words multiple CPU processors
in multiple boxes. It breaks up the reservoir into hundreds or
thousands of little pieces and spreads it over dozens of nodes. This is
inefficient because it incurs additional communication costs between
cores and nodes. So even the best speeds from CPU codes don’t come close
ECHELON can run on any machine with an NVIDIA GPU. In fact, one member of the SRT team is running it on his MS Surface laptop.
other oil majors have conducted trials, and smaller firms have licensed
the technology, Eni remains in the vanguard in terms of reservoir
modeling on GPUs. In the Italian company’s view, the path opened by
this technology is a key and integral part of its digital strategy and
Looking to the future, advocates expect the use of
GPUs to go beyond the upstream into the mid-stream and downstream areas
too. “The upstream side will be good for seismic imaging and
interpretation,” said Gupta. “With the emergence of AI, oil and gas
customers are looking at adopting deep learning techniques as part of
digital objectives to improve revenues and efficiencies. It’s a big new
opportunity: GPUs are well suited for deep learning and AI.”