Chevron Information Technology - 2014 Oil & Gas HPC...

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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

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!

2


Roger Boisjoly

– Tried to stop the space shuttlein 1986.

– Boisjoly traveled to

engineering schools aroundthe world, speaking aboutethical decision-making and

sticking with data. "This iswhat I was meant to do," he

told Roberta, "to have impacton young people's lives.”

– Excerpt from the Story.

2

Stop Work Authority – Safety moment

3


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

4


Chevron

A global company operating on six continents

5

100+ countries in

which we operate

30+ countries with

exploration and

production activities

18 refineries and

asphalt plants

30 chemical

manufacturing

facilities

3 retail brands

(Chevron, Texaco and

Caltex)

22,000+ retail outletsExploration & Production Refining Chemicals

Chevron

Corporation

Headquarters


Chevron is one of the largest, integrated energy

companies in the world

6

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


The Energy Value Chain

7

Produce

Ship

Distribute

Market

ExploreDevelop

RefineBlend

StorePipe

Capital-intensive

with long-lived assets

Information-intensive

with wide time-scales


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.

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.

9


Many technology trends are also emerging to present

compelling value creation opportunities for energy

companies.

10

Inter & Intra-Vehicle Networks

Voice over IP (VOIP)

Video Conferencing

Web 2.0

Broadband over Power Lines

WiFi / WiMAX / WiRAN

3G / Mobile WiMAX

Free Space Optical Broadband

GPS

Virtualized IT Infrastructure

Applied

Technology

Trends

Operations and

Reliability

Hydrocarbon

Optimization

Exploration

Information

Workplace

Communication

and Mobility

Reservoir

Management

Predictive Analytics

Artificial Intelligence

Integrated Production Loss

Management

Large-Scale Data Warehouses

Closed Loop BI

Knowledge Management

Real time Database

Digital Oil Field of the Future

Digital Refinery

Material and Corrosion

Management

Water Solids and Power

Management

Process Modeling / Linear

Programming

Resource Assay and

Speciation

Product Speciation and

Blending

New Hydroprocessing

Processes

Seismic Acquisition and

Processing

Subsalt Imaging

Basin Analysis

Seismic Interpretation

and Visualization

Reservoir Geology and

Characterization

Reservoir Simulation

Rock and Fluid Property

Measurements

Source: Industry Expert Interviews; Team Analysis


••

••

••

••

Technology SummaryAndy 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

11


Moore’s Law continues…

12


Technology Trends – Computing Hardware

13

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

Statistics about the current top supercomputer

– 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

Exponential performance trend of computers continues

through new innovations:

Figure: Plot showing historical performance of world’s

fastest supercomputer as measured by TOP500

Organization since 1993. Vertical axis is log scale.


• Adoption of cloud storagemake home consumerdrives a rarity

• Mobile computing going allSSD

• New classes of drivesdesigned for the BigDataproblems are emerging

• New types of areal densityare troubling

Trends in storage

• IETF voted down SATA-4

o RIP IDE, I will not miss you!

• Hybrid drives, band aid for a

problem we do not have

• Object stores eroding the

world of files systems

• SSD at capacity not going to

be reality in my (useful)

lifetime

Per Brashers, [email protected]

14


Growth

• Don’t expect capacity to go up

any time soon

o Shingled media will be append-only

or slower than tape

• Lots of ‘flash’ in the pan options

will arise, APIs not mature to take

use of them

o Work on standards for

populate/depopulate needs to

start

• BigData specific drives may be

our only cost avoidance play

o Lower durability will be the enemy

Data durability

•

•

•

New options may not add value

o Unless they are designed in to the

app at the start

Disaggregated RAID offers value like

D.E.C.

o Rack and row layout need to be part

of the system

RV resistance, and relaxing the bit-

error rate may help performance

o If the app corrects some bitwise

errors, and retries those it cannot fix,

the drives could service more IOPS

What do Storage trendsmean to applications?


15


Network/Controller Trends

• More powerful, and

smaller

• 12Gb likely to be end-

state

• SAS switching competing

with PCiE switching

• PHY add-ins for more

complex configurations

• Chipset sold separately

• DMA/RDMA settling into place

• ‘Teaming’ at device levels,

starting toward disaggregated

RAID

• T10-diff and other

validation/security features

• Traditional, boring RAID cards still

lead the revenue

• Network is going to change a-lot!o Back to glass

o SAS/PCiE/Silicon Photonics

o OpenFlow vs. ‘Agnostic Networks’


16


Rapid Growth• New types of communication

channelso Open socket, insert stuff, close

socket will go away

• Real intelligence in the controllerso Look to new drivers and application

to be able to take advantage

• Higher density solutions will savepower and deliver IOPSo Flash assisted applications will

mask rotational delays

Data Durability• Data will finally become mobile

o Non-hierarchical topologies willenable better bandwidth

• Some durability tasks can bepushed downo Encryption, error handling, etc.

• Converged networks will meanmore requirements for reservedcapacityo Far past standard QOS, new ideas

need to be created (dynamicrouting?)

What does this mean to applications?


17


Growth

• Much more memory available

on the mother board

o Great for in-memory DBs

• Access times will rise

o Not so good for the in

memory DB

• Cost curve remains high

o Fabs take a lot of $$ to buildand do not last very long

Data Durability• Many more write cycles

o Heat dissipation and recovery

has been worked out

• ‘self healing’ firmware will aid

us in masking errorso At the cost of latency

• Protecting host data from loss,

and issues with stale datao The reboot/decommission

problems need attention

before the first security breech,

or cluster corruption

What do memory trendsmean to applications?


18


Source: ACM.org

CPU 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

components on a network

assembling for an application,

and freeing when that application

is done with them. Software

Defined Computer ® ;-) Per Brashers, [email protected]

19


Growth

• Lots and lots of in-card

calculationso I/O to the card remains a

mystery

• Extreme density of power

o Not good for cooling

• New libraries need to be

examined for suitabilityo Sadly they are often the ‘secret

sauce’ and cost too much

Data Durability

• More threading, morecores, more fragmentationo Take care to get those college

students to be better at ittoo…

• Disaggregation means moreerror checkingo Offloading may help, but you

may want to examine themethods closely.

What do CPU trends mean to applications?


20


Rise to the challenge• Data classification

• Reduced replicas, at the cost ofrapid restores

• Data durability challengeso given pressure to store forever, and

have unreliable equipment to do so

• Virtualize the data and datacenter, not just the server

• Leverage new technologies, evenif it means a partial re-write

Netting it all out

Influencers• Storage is flat lining

• Controllers do not know how to

add value

• Memory is forgetting

• CPU’s are forgoing bandwidth for

IOPS

• Motherboards are breaking the

monolithic barriers

• Datacenters are becoming cost

efficient, at the expense of added

failures


21


Top ten strategic technology trends for 2014Gartner; 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

22


Sensing in the 2010’s like microscope in 1700s?

23


Decision

Executive

Mobile

InternetSmartphone

Tablet

Wearable computing

SensorsM2M

Mesh

SmartPhones

Social NetworksSentiment

Crowdsourcing

Gaming

SaaS

PaaS

IaaS AnalyticsDashboards

Modeling

Prediction

“Big Data”Volume

Velocity

Variety

“Internet of Things” Grows

24


The goal of subsurface work, geologic view, draw this

to look like

25


The goal of subsurface work, geologic view, this!

26


Seismic method, Wikipedia.org

From THIS!

27


Reservoir 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.

28

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


An iterative view of the subsurface workflow

29

MappingReservoir

Characterization

Cross-sections

Petrophysics

Reservoir

Simulation

Seismic

Interpretation

Stratigraphic

Modeling

Well Planning &

Drilling Simulation


2007 Talk

The real goal, at acceptable earnings/barrel

30


2007 Talk

HPC Value: Chevron Cray 1985-1989

The Cray cost roughly $10mm

over 3 years.

$10,000/day.

Feed the beast was the mantra.

31


HPC Challenges

“Improving one component of the system pushes the

bottleneck to another component”…

32

Desktop

Visualization

Storage

Server

Cluster

Network

Software

Applications

Work expands to fit the resources available:

• Reservoir simulation -- less coarsely desampled

earth models

• Seismic imaging – more finely sampled field

experiments

• Reassessment of past assumptions and points

of estimation – past compute impossibilities

Pushing the bottleneck:

More finely sampled models

require higher performing, more

finely sampled visualization that

is 3 dimensional and spin-n-

rotate in real time, accessible

remotely -- which in turn

requires more compute, faster

graphics, innovation to across

the network capabilities

Pushing the bottleneck:

“Disk is cheap, keep more

information online” … thus

lots more space to expand

the size of the problem

Pushing the

bottleneck:

Expand compute

performance and

memory available, then

you will need to improve

effective storage

available and the

bandwidth to storage


HPC Challenges – whack a mole

33

Interconnect

Network

CPU

Data

Volume/

Storage


2007 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”….

34


2007 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)

35


HPC/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

36


Some 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

37


Sensors’ 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

38


Exploration : Microscope :

Info/context : Sensing?

39

19% 300md

8bit 40003 @ 1.5mm - 50Gb

38% 700md

16bit 40003 @ 1.8mm - 100Gb9% 0.01md

16bit 40003 @ 2.7mm - 100Gb

2.6mm 2.4mm 2.5mm

Pulsed illumination of a fruit. Background image added

MIT – Ramesh Raskar MIT Media Lab; Project Director


Conservation 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?

40


HPC 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.

41


Matrix inversion vs. parallel shots in seismic modeling

(large memory machine)

42

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Whole matrix in memory


Wave 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.

43


2010 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

44


Unconventionals 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

45


Big 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?

46


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!

47

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