American Oil and Gas Reporter - November 2015 - 61

SpecialReport: Oil & Gas Computing
remarks. "SAAS applications tend to
focus on the essentials, avoiding feature
overload, proving a more efficient and
reliable solution."

Massive Parallelization
There are two critical factors for efficient HPC seismic processing, according
to Charles Sicking, Global Geophysical's
vice president of research and development. The first is turnaround time. In a
business where time is literally money,
he says operators place a premium on
the speed as well as the accuracy of
processed results. And that leads to the
second factor: quality.
"Quality increases dramatically when
clients participate earlier and more often
in the processing," says Sicking. "With
faster turnaround times, it becomes reasonable to increase the number of quality
reviews. Quality goes sky high when
clients get to look at the data in different
ways and do more tests over the course
of a project."
Massive parallelization has significantly
improved both of these factors, according
to Sicking. Parallelization enables simultaneous multinode computations and data
access to make processes extremely efficient and save weeks in turnaround time.
He says that highly parallelized disk systems enable two kinds of parallelism
schemes for seismic processing.
The simplest is course-grain parallelization, whereby each CPU on each
node runs the same software application
against different parts of the data. In
this method, there is no intercommunication between the CPUs, and they do
not share memory or compute power. A
dataset split across 1,000 CPUs can be
processed 1,000 times faster, calculates
Sicking.
The second kind is fine-grained parallelism, in which one application runs
on a node with multiple CPUs. The application processes one piece of the data
using all the CPUs on one node simultaneously. This capability is used extensively
for computationally-intensive processes
such as reverse-time migration, he notes.
Both kinds of parallelization can be
combined by putting a course-grained
wrapper around a fine-grained application,
Sicking says. Then, for example, a seismic
volume containing 50,000 shots can run
on 100 nodes with each node processing
500 shots in parallel.
Super highly parallelized disk systems
are key to effective parallelization, ac-

cording to Sicking. Disk storage systems
have inherent physical limitations on the
speed of data access. "To bypass this
limitation, highly parallelized disk systems
have many blades with trays holding
disks," he explains. "Each blade has a
computer, and all blades communicate
and interface with the dataset, which is
distributed across hundreds of hard drives.
Requests for data are executed in a way
that increases disk input/output up to
1,000 times compared with the serial access on single hard drives."
Data access is fast enough that even
datasets with many terabytes can be accessed efficiently, he notes. "When we
changed the parallelization of our ambient
seismic processing algorithm, the run
time went from 2,100 down to 40 equivalent node days on the first large dataset,"
Sicking reports. "That huge improvement
dramatically shortened turnaround time."
As another example, Global Geopyhsical's seismic imaging application for
horizontal transverse isotropy scanning
requires very large compute resources,
says Sicking. "Our system application
uses parallelization to break the compute
into small pieces, allowing hundreds of
segments to run in parallel. Using this
method, many parallel jobs can run simultaneously on hundreds of nodes, allowing for the timely delivery of advanced
processing products such as inversion
ready gathers," Sicking says.
A third form of parallelization is to
have the entire dataset loaded into memory
on many nodes and use all of the CPUs
of all nodes to process that dataset. "This

method is very useful for transposing
multidimensional datasets to change the
framework of the data structure. To run
effectively, the entire dataset must be accessible simultaneously, says Sicking.
"In a parallelized system, the algorithm
shuffles the data until they are completely
transposed in memory, and then outputs
to the disk system with the new data
structure," he concludes.
Big Data Analytics
"The oil and gas industry is working
hard to catch up to the advances in information technology," says Scott Oelfke,
product manager at LMKR, who notes
that big data analytics already are being
used successfully in financial, manufacturing and retail. One area where Oelfke
says he sees some early experimentation
with big data technology is in production
optimization in unconventional reservoirs.
"With tools such as the open-source
Hadoop and SAP's in-memory HANA
platform, the technology exists to leverage
big data analytics. If upstream operators
can figure out the right questions to ask
and what datasets to use, they can get
more value from their geological and
geophysical data."
Another area where Oelfke says he
sees advancement is managing large data
volumes on corporate networks. That is
where advanced seismic attribute tools
come in, generating high-quality attributes
out of huge 3-D volumes, says Oelfke.
"In the past, this process was very
time consuming. Today, attributes can be

Big data analytics and advanced graphics processing are among the emerging technologies that hold tremendous potential in interpreting, integrating and visualizing geological, geophysical and petrophysical data. Shown here is a comprehensive integrated
model developed with LMKR's GeoGraphix™ interpretation system.

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American Oil and Gas Reporter - November 2015

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