Chevron Information Technology - The Never-ending Story of Subsurface/ HPC Evolution and its Effect on our Business.
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Chevron
Information Technology
The Never-ending Story of Subsurface/
HPC Evolution and its Effect on our Business.
Peter Breunig
Chevron Corporation
March 2014
This document is intended only for use by Chevron for presentation at Rice University in March 2014,
inclusion in hand-outs to presentation attendees. No portion of this document may be copied, displayed,
distributed, reproduced, published, sold, licensed, downloaded, or used to create a derivative work, unless the
© 2014 Chevron U.S.A. Inc. All Rights Reserved use has been specifically authorized by Chevron in writing.
Summary or take-aways?
The drivers for the subsurface space vis-a-vis HPC have not changed that
much from 2005
– More resolution driving cycles, storage and memory
– The Earth is smarter than you…..
Bottlenecks will still be the same:
– Compute, People and Physics.
Sensing
– Advances will drive the acquisition side and hence new need for more cycles etc.
– Does the modeling paradigm change or get enhanced? Do we have hybrid
workflows?
Damn the torpedoes full speed ahead and check around you!
© 2014 Chevron U.S.A. Inc. All Rights Reserved 2
Stop Work Authority – Safety moment
Roger Boisjoly
– Tried to stop the space shuttle
in 1986.
– Boisjoly traveled to
engineering schools around
the world, speaking about
ethical decision-making and
sticking with data. "This is
what I was meant to do," he
told Roberta, "to have impact
on young people's lives.”
– Excerpt from the Story.
© 2014 Chevron U.S.A. Inc. All Rights Reserved 23
Agenda
Chevron
Predictions from 2005, 2008 and today
What does that mean?
Historical seismic challenges (HPC)
Art and science of subsurface
What was the driver?
Whack a mole
Imaging/Seismic methods
Sensing effects
Conservation of Complexity
© 2014 Chevron U.S.A. Inc. All Rights Reserved 4
Chevron
A global company operating on six continents
100+ countries in
which we operate
30+ countries with
exploration and
Chevron
production activities
Corporation
Headquarters 18 refineries and
asphalt plants
30 chemical
manufacturing
facilities
3 retail brands
(Chevron, Texaco and
Caltex)
Exploration & Production Refining Chemicals 22,000+ retail outlets
© 2014 Chevron U.S.A. Inc. All Rights Reserved 5
Chevron is one of the largest, integrated energy
companies in the world
2nd largest integrated
energy company in the
United States
8th largest company in
the world
62,000+ employees
worldwide (includes
service station
personnel)
2.61 net million barrels
of oil per day in 2012
$26.2 Billion Net
Income in 2012
$36.7 Billion Capital
and Exploratory budget
for 2013
© 2014 Chevron U.S.A. Inc. All Rights Reserved 6
The Energy Value Chain
Capital-intensive
with long-lived assets
Explore
Develop
Produce
Ship
Refine
Blend
Store
Pipe
Information-intensive Distribute
with wide time-scales Market
© 2014 Chevron U.S.A. Inc. All Rights Reserved 7
2005 Talk
What becomes critical to the digital technology part of
the energy business
Technology application is critical to adding value.
Remote operations will be critical in deepwater.
Remote operations may be critical in shelf and land environments.
Big service companies providing all the innovation is not going to
happen (margins are too small).
Improved resolution within the reservoir is critical because:
• Deepwater wells are costly,
• Fully exploiting existing assets is essential.
Integration opportunities become critical.
Innovation will come from the “fringes of technology”, improving
equations, reducing approximations and refinement of
measurement.
Workflow efforts will be critical to define business value.
© 2014 Chevron U.S.A. Inc. All Rights Reserved 8
2008 Talk
Energy Industry Drivers
Managing the base and capital business
The oil business has always been about managing the margins in both
the upstream and downstream segments.
Operational excellence in operations is necessary.
World class management of capital projects is mandatory.
Exploration opportunities will be high risk (e.g., deepwater).
Global procurement is here to stay.
© 2014 Chevron U.S.A. Inc. All Rights Reserved 9
Many technology trends are also emerging to present
compelling value creation opportunities for energy
companies.
Seismic Acquisition and
Inter & Intra-Vehicle Networks
Processing
Voice over IP (VOIP)
Video Conferencing Subsalt Imaging
Web 2.0 Basin Analysis
Broadband over Power Lines
Seismic Interpretation
WiFi / WiMAX / WiRAN and Visualization
3G / Mobile WiMAX
Free Space Optical Broadband
Communication
GPS
and Mobility Exploration Reservoir Geology and
Virtualized IT Infrastructure
Characterization
Reservoir Simulation
Predictive Analytics
Applied
Rock and Fluid Property
Artificial Intelligence Technology Measurements
Integrated Production Loss
Management Trends
Large-Scale Data Warehouses
Information Reservoir
Closed Loop BI
Workplace Management
Knowledge Management
Real time Database
Process Modeling / Linear
Digital Oil Field of the Future
Programming
Digital Refinery
Resource Assay and
Speciation
Product Speciation and
Material and Corrosion Blending
Management Operations and Hydrocarbon New Hydroprocessing
Water Solids and Power Reliability Optimization Processes
Management
Source: Industry Expert Interviews; Team Analysis
© 2014 Chevron U.S.A. Inc. All Rights Reserved 10Technology Summary
Andy Bechtolsheim talk from 2010,
found at James Hamilton’s blog: Perspectives
• Moore’s Law will continue for at least 10 Years
• Transistors per area will double ~ every 2 year
• 128X increase in density by 2022
• Frequency Gains are more difficult
• Power increases super-linear with clock rate
• Must exploit parallelism with more cores
• Need to increase memory and I/O bandwidth
• Need to scale with throughput
• Need a factor of 128X by 2020
• Most promising technology is memory stacks and Flash
• Supports lots of channels to scale bandwidth
• Very high bandwidth and transaction rates appears feasible
© 2014 Chevron U.S.A. Inc. All Rights Reserved 11Moore’s Law continues… © 2014 Chevron U.S.A. Inc. All Rights Reserved 12
Technology Trends – Computing Hardware
Exponential performance trend of computers continues
through new innovations:
Dramatic reduction in flash memory price
allowing affordable solid-state memory for PC’s
and datacenters
Chip design evolves - Intel just announced a 1
Teraflop chip design with 50 CPU cores
IBM – optical data links on conventional size
silicon achieves data rates of 25 gigabits/sec
European Union and Japan partnering to
develop optical network capable of 100
gigabits/sec Figure: Plot showing historical performance of world’s
fastest supercomputer as measured by TOP500
Statistics about the current top supercomputer Organization since 1993. Vertical axis is log scale.
– China’s Tianhe-2 – 17.6 petaflops/sec
– 16,000 server nodes - 3.12 million cores
– 2x faster than Oak Ridge Titan #1 Nov,
2012
– All components other than Intel processors
produced in China
Technology advances are available to enterprise
customers
© 2014 Chevron U.S.A. Inc. All Rights Reserved 13Trends in storage
• IETF voted down SATA-4 • Adoption of cloud storage
o RIP IDE, I will not miss you! make home consumer
drives a rarity
• Hybrid drives, band aid for a
• Mobile computing going all
problem we do not have SSD
• Object stores eroding the • New classes of drives
world of files systems designed for the BigData
• SSD at capacity not going to problems are emerging
be reality in my (useful) • New types of areal density
lifetime are troubling
Per Brashers, Founder
per@yttibrium.com
© 2014 Chevron U.S.A. Inc. All Rights Reserved 14What do Storage trends
mean to applications?
Growth Data durability
• Don’t expect capacity to go up • New options may not add value
any time soon o Unless they are designed in to the
o Shingled media will be append-only app at the start
or slower than tape • Disaggregated RAID offers value like
• Lots of ‘flash’ in the pan options D.E.C.
will arise, APIs not mature to take o Rack and row layout need to be part
use of them of the system
o Work on standards for • RV resistance, and relaxing the bit-
populate/depopulate needs to error rate may help performance
o If the app corrects some bitwise
start
• BigData specific drives may be errors, and retries those it cannot fix,
our only cost avoidance play the drives could service more IOPS
o Lower durability will be the enemy Per Brashers, Founder
per@yttibrium.com
© 2014 Chevron U.S.A. Inc. All Rights Reserved 15Network/Controller Trends
• More powerful, and • DMA/RDMA settling into place
• ‘Teaming’ at device levels,
smaller
starting toward disaggregated
• 12Gb likely to be end- RAID
state • T10-diff and other
• SAS switching competing validation/security features
• Traditional, boring RAID cards still
with PCiE switching lead the revenue
• PHY add-ins for more • Network is going to change a-lot!
complex configurations o Back to glass
o SAS/PCiE/Silicon Photonics
• Chipset sold separately o OpenFlow vs. ‘Agnostic Networks’
Per Brashers, Founder
per@yttibrium.com
© 2014 Chevron U.S.A. Inc. All Rights Reserved 16What does this mean to applications?
Rapid Growth Data Durability
• New types of communication • Data will finally become mobile
channels o Non-hierarchical topologies will
o Open socket, insert stuff, close enable better bandwidth
socket will go away • Some durability tasks can be
• Real intelligence in the controllers pushed down
o Encryption, error handling, etc.
o Look to new drivers and application
to be able to take advantage • Converged networks will mean
more requirements for reserved
• Higher density solutions will save capacity
power and deliver IOPS o Far past standard QOS, new ideas
o Flash assisted applications will need to be created (dynamic
mask rotational delays routing?)
Per Brashers, Founder
per@yttibrium.com
© 2014 Chevron U.S.A. Inc. All Rights Reserved 17What do memory trends
mean to applications?
Growth Data Durability
• Much more memory available • Many more write cycles
on the mother board o Heat dissipation and recovery
o Great for in-memory DBs has been worked out
• ‘self healing’ firmware will aid
• Access times will rise
us in masking errors
o Not so good for the in o At the cost of latency
memory DB • Protecting host data from loss,
• Cost curve remains high and issues with stale data
o Fabs take a lot of $$ to build o The reboot/decommission
and do not last very long problems need attention
before the first security breech,
or cluster corruption
Per Brashers, Founder
per@yttibrium.com
© 2014 Chevron U.S.A. Inc. All Rights Reserved 18CPU Trends
• The frequency game has played
out
• Cores and offload games are
starting to heat up
• Libraries and other compile-time
assisters are becoming common
• Low-power driven by the mobile
market offers interesting
disaggregation options, imagine Source: ACM.org
components on a network
assembling for an application,
and freeing when that application
is done with them. Software
Defined Computer ® ;-) Per Brashers, Founder
per@yttibrium.com
© 2014 Chevron U.S.A. Inc. All Rights Reserved 19What do CPU trends mean
to applications?
Growth Data Durability
• Lots and lots of in-card • More threading, more
calculations cores, more fragmentation
o I/O to the card remains a o Take care to get those college
mystery students to be better at it
• Extreme density of power too…
o Not good for cooling • Disaggregation means more
• New libraries need to be error checking
examined for suitability o Offloading may help, but you
o Sadly they are often the ‘secret may want to examine the
sauce’ and cost too much methods closely.
Per Brashers, Founder
per@yttibrium.com
© 2014 Chevron U.S.A. Inc. All Rights Reserved 20Netting it all out
Influencers Rise to the challenge
• Storage is flat lining • Data classification
• Controllers do not know how to
• Reduced replicas, at the cost of
add value
rapid restores
• Memory is forgetting
• Data durability challenges
• CPU’s are forgoing bandwidth for o given pressure to store forever, and
IOPS have unreliable equipment to do so
• Motherboards are breaking the • Virtualize the data and data
monolithic barriers center, not just the server
• Datacenters are becoming cost • Leverage new technologies, even
efficient, at the expense of added if it means a partial re-write
failures
Per Brashers, Founder
per@yttibrium.com
© 2014 Chevron U.S.A. Inc. All Rights Reserved 21Top ten strategic technology trends for 2014
Gartner; David W. Cearley
1. Mobile device diversity and management
2. Mobile apps and applications
3. The Internet of Everything
4. Hybrid cloud and IT as service broker
5. Cloud/client
6. The era of personal cloud
7. Software-defined anything
8. Web-scale IT
9. Smart machines
10. 3D printing
© 2014 Chevron U.S.A. Inc. All Rights Reserved 22Sensing in the 2010’s like microscope in 1700s? © 2014 Chevron U.S.A. Inc. All Rights Reserved 23
“Internet of Things” Grows
“Big Data”
Volume
Velocity
Variety
Sensors
M2M
Mesh
SmartPhones
Decision
Executive
Mobile
Internet
Smartphone
Tablet
Wearable computing
SaaS
PaaS
Social Networks IaaS Analytics
Sentiment Dashboards
Crowdsourcing Modeling
Gaming Prediction
© 2014 Chevron U.S.A. Inc. All Rights Reserved 24The goal of subsurface work, geologic view, draw this
to look like
© 2014 Chevron U.S.A. Inc. All Rights Reserved 25The goal of subsurface work, geologic view, this! © 2014 Chevron U.S.A. Inc. All Rights Reserved 26
Seismic method, Wikipedia.org
From THIS!
© 2014 Chevron U.S.A. Inc. All Rights Reserved 27Reservoir Management Process
Engineer’s view
The reservoir management process integrates the following steps:
(1) acquisition of data;
(2) interpretation of each data type to obtain an interpretation model for
the data;
(3) integration of all available data interpretation models into a reservoir
model;
(4) calculation of the reservoir model behavior with a reservoir simulator;
(5) calibration of the reservoir simulator by history matching production
data;
(6) coupling the reservoir simulator with well and surface facility
simulators;
(7) using the coupled simulators to calculate reserves and predict
production for various development scenarios.
Evolution of Reservoir Management Techniques: From Independent Methods to an Integrated Methodology. Impact on Petroleum
Engineering Curriculum, Graduate Teaching and Competitive Advantage of Oil Companies
Authors Alain C. Gringarten, Imperial College of Science, 1998 Society of Petroleum Engineers
© 2014 Chevron U.S.A. Inc. All Rights Reserved 28An iterative view of the subsurface workflow
Reservoir
Mapping Characterization
Seismic
Interpretation Cross-sections
Petrophysics
Stratigraphic
Modeling
Reservoir
Well Planning & Simulation
Drilling Simulation
© 2014 Chevron U.S.A. Inc. All Rights Reserved 292007 Talk
The real goal, at acceptable earnings/barrel
© 2014 Chevron U.S.A. Inc. All Rights Reserved 302007 Talk
HPC Value: Chevron Cray 1985-1989
The Cray cost roughly $10mm
over 3 years.
$10,000/day.
Feed the beast was the mantra.
© 2014 Chevron U.S.A. Inc. All Rights Reserved 31HPC Challenges
“Improving one component of the system pushes the
bottleneck to another component”…
Work expands to fit the resources available:
Cluster • Reservoir simulation -- less coarsely desampled
earth models
Software • Seismic imaging – more finely sampled field
Applications experiments
Server • Reassessment of past assumptions and points
of estimation – past compute impossibilities
Visualization
Network
Pushing the
bottleneck:
Expand compute Pushing the bottleneck:
performance and More finely sampled models
memory available, then Desktop require higher performing, more
you will need to improve finely sampled visualization that
effective storage is 3 dimensional and spin-n-
available and the Pushing the bottleneck: rotate in real time, accessible
bandwidth to storage remotely -- which in turn
“Disk is cheap, keep more
requires more compute, faster
information online” … thus
Storage lots more space to expand
graphics, innovation to across
the network capabilities
the size of the problem
© 2014 Chevron U.S.A. Inc. All Rights Reserved 32HPC Challenges – whack a mole
Interconnect
CPU
Data
Volume/ Network
Storage
© 2014 Chevron U.S.A. Inc. All Rights Reserved 332007 Talk
Success can be a double edged sword
Internal imaging development and subsequent service was very successful over the
past 12 years. (mentioned in Daniel Yergin’s: The Quest)
We moved through the low oil era of 1998.
As the oil business rebounded, “prospects/opportunities” increased.
Exploration success increased.
Reservoir quantification increased.
We didn‘t increase the number of “developers” as fast as the service business grew.
The run business required support, and the future business could have been
compromised.
We didn’t increase the number of software engineers either.
Our biggest bottleneck is this one, the carbon based life forms.
Interesting observation: 1980s/90s -> many more developers, per compute power. I
believe it is related to BEAST feeding again. A Healthy Tension.
Interesting observation by an experienced seismic researcher “I liked it better when we
had the SGI’s because the book keeping was easier…”
– Remember “life is book keeping”….
© 2014 Chevron U.S.A. Inc. All Rights Reserved 342007 Talk
Present Day Methods
Historically and today, the challenge is “what can we throw out and
get a good image?”
Differential/Wave Imaging Methods
– 3D Reverse-Time Migration (Time extrapolation)
– 3D Wavefield Migration (Depth extrapolation)
Integral/Ray Imaging Methods
– 3D Kirchhoff / Gaussian Beam
3D Acoustic/PseudoAnisotropic Wavefield Modeling
2D Full Wavefield Inversion (proof of concept)
© 2014 Chevron U.S.A. Inc. All Rights Reserved 35HPC/Seismic Facts
Imaging/Modeling drives compute cycles
– 2002 – 1000 gflops/s – Kirchoff Migration
– 2004 – 10,000 gflops/s – wave equation migration
– 2010 – 150,000 gflops/s – reverse time migration
– 2014 – 1,500,000 gflops/s – acoustic full wavefield inversion (1.5 pflop/s)
Seismic Modeling, Imaging, Analysis – drives data volumes.
– Narrow Azimuth, traditional till the mid/late 2000s
– Wide azimuth, 2005’s roughly
– OBN, similar to Wide.
3D acoustic RTM is pushing above 60 Hz, not there yet with elastic. FWI
requires many iterations, so it is not run to the same high frequencies, and is
mainly acoustic. “Whatever process we do today “acoustic” will be done
“visco-aniso-elastic” in about 10 more years of Moore’s law” reliable
geophysicist
© 2014 Chevron U.S.A. Inc. All Rights Reserved 36Some Future Methods
3D Elastic Anisotropic Modeling
3D Elastic Anisotropic Reverse Time Migration & Imaging with
Multiples
3D Full Wavefield (constrained) Inversion - normal, elastic 5x, visco-
elastic 50x….
Iterative Wavefield Modeling for Stochastic Inversion
60’s Digital, 70’s Wave equation migration (post stack), 80’s
Dip Moveout, 90’s Pre stack depth migration, 00’s Anisotropy
Oz Yilmaz ~ 1999.
10’s Acquisition/Sensing
© 2014 Chevron U.S.A. Inc. All Rights Reserved 37Sensors’ effects
The availability and density of sensor data is increasing exponentially.
Most data is born digitally today.
There is a long-term unsatisfied desire to model integrated facilities and
reservoirs in near real-time, leveraging those sensors; HPC?
Companies want to be able to optimize investments across assets and to
explore many scenarios. We are only able to do this at an extremely granular
level: HPC?
There is a desire to integrate the detailed modeling with the large scale
investment optimization and “tweak the knobs” in real time in order to
understand large-scale company alternatives over the long-term: HPC?
New sensors, capable of producing terabytes of data per day, are planned to
be deployed in large numbers in remote locations. Due to the data volumes
and anticipated work processes, local processing of the data will be
required. This could require small, lower cost HPC capabilities which require
very little support in the field to be developed
© 2014 Chevron U.S.A. Inc. All Rights Reserved 38Exploration : Microscope :
Info/context : Sensing?
Pulsed illumination of a fruit. Background image added
MIT – Ramesh Raskar MIT Media Lab; Project Director
2.6mm 2.4mm 2.5mm
19% 300md 38% 700md 9% 0.01md
8bit 40003 @ 1.5mm - 50Gb 16bit 40003 @ 1.8mm - 100Gb 16bit 40003 @ 2.7mm - 100Gb
© 2014 Chevron U.S.A. Inc. All Rights Reserved 39Conservation of Complexity
Model vs. Data
– Complexity moves from the model to the data?
We spend time building models that represent the subsurface. As we
can sense more and more stuff do we move the complexity from the
model to the data?
– Acoustic Sensing, real time information, digital rocks?
© 2014 Chevron U.S.A. Inc. All Rights Reserved 40HPC directions and Conservation of Complexity
FWI:
Acoustic, Elastic, Visco-elastic, visco-aniso-elastic
– Moore’s Law, keep going, “dam the torpedoes full speed ahead”
What if imaging in complex domains is not a good inverse
problem? Physics bottleneck?
In forward modeling we are attempting to invert the matrix but are
actually transposing it, due to limitations (approximations) in computer
and illumination.
– What if the assumptions in the wave equation techniques fail at some
point due to the complexities.
• What if you could do partial images, and then data mine once you had
the wave-field propagator?
Large CPU, large memory, large data movement compute
problem.
© 2014 Chevron U.S.A. Inc. All Rights Reserved 41Matrix inversion vs. parallel shots in seismic modeling
(large memory machine)
4.5
4
3.5
3
2.5
2
1.5
1
Whole matrix in memory
0.5
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
© 2014 Chevron U.S.A. Inc. All Rights Reserved 42Wave bottlenecks are with us for awhile (2007 Talk).
Still true today…
With these new methods comes significant increases in data, and
cycles.
Whatever we add to our HPC system gets used. The cycle time
decrease mirrors the sampling increase.
A significant milestone might be when the sampling that we record at
is the sampling we process at.
– But then again, data, heat, power and people may prevent us from
reaching that too fast.
© 2014 Chevron U.S.A. Inc. All Rights Reserved 432010 Talk
HPC Value and Bottlenecks
The bottlenecks come in 3 types:
1. Computer bottlenecks will be with us for awhile, but will be
assuaged by faster CPUs, better interconnects, faster I/O.
– Different paradigms: FPGA, Cell, GPU, Co-processors will have their
place and should provide some relief above.
• These adversely effect the next bottleneck.
2. People bottlenecks will continue and I believe are something that
needs to be focused on.
3. Physics bottlenecks will be constrained by the computer
bottlenecks and the people bottlenecks.
• Could change with the onset of different paradigms
© 2014 Chevron U.S.A. Inc. All Rights Reserved 44Unconventionals and HPC?
Unconventional oil and gas is a
margin business. More
assembly line then the rest of
the Upstream business.
Sweet spot, rock mechanics and
rock property modeling become
the big opportunity.
– Horizontal length, frac length,
frac stages
© 2014 Chevron U.S.A. Inc. All Rights Reserved 45Big data and HPC/Seismic
1980 Big Data = Seismic Processing
Companies had seismic platforms
– OC grew around those, both interpreters (looking at and interpreting the
data) and connectors processing the data.
2014 Big data = every function.
– Sensing/real time drives boat loads of data for everyone.
– Platforms might be a reasonable opportunity for companies. (sentiment
data example)
– Kaggle
What is the role of HPC in this large platform environment?
© 2014 Chevron U.S.A. Inc. All Rights Reserved 46Summary or take-aways?
The drivers for the subsurface space vis-a-vis HPC have not changed that
much from 2005
– More resolution driving cycles, storage and memory
– The Earth is smarter than you…..
Bottlenecks will still be the same:
– Compute, People and Physics.
Sensing
– Advances will drive the acquisition side and hence new need for more cycles etc.
– Does the modeling paradigm change or get enhanced? Do we have hybrid
workflows?
Damn the torpedoes full speed ahead and check around you!
© 2014 Chevron U.S.A. Inc. All Rights Reserved 47You can also read
