1. Analytics Predict Profits –
Is business leadership digitally aware ?
Andrew Moore
Exploration Data Systems Consultant

2. © Andrew Moore 2015
Outline
 Reduced margins globally and especially in Australia are driving cost cutting
 The innovation response is limited to the engineering comfort zone
 Advanced analytics, especially in Exploration, can both save and make money
 How to persuade a reluctant management to apply 21st Century thinking
 Visualisation can help get the message across and raise thinking horizons

3. $200Bn is being invested in Australian projects to find
and produce “clean, safe energy”
But cost over-runs and delays have deterred further
investment. Meanwhile the oil price has crashed and
current margins are uneconomic.
Sophisticated financial analytics are directing
investments and raising profitability in other industries.
So why is the upstream O&G business slow to adopt
E&P data analytics? & How can we turn this around?© Andrew Moore 2015

4. Costs have crippled the oil and gas business, especially in Australia
 An Aug 2014 report from Ernst & Young showed that, on average, 64% of
O&G “megaprojects” had exceeded budgets and 73% missed deadlines.
 On average, cost forecasts for 205 projects surveyed were 59% above initial
estimates, that’s US$500Bn over an initial cost estimate of US$1.2t
 Chevron estimate that Australian costs are 40% higher than US
© Andrew Moore 2015

5. © Reelwell AS
What techniques is the industry using to
reduce these huge costs and delays ?
 Improved planning, and cutting budgets
 Pad drilling & SIMOPS for onshore field
development, un-manned off-shore platforms
 Extended horizontal drilling and geosteering
 Improved stimulation and recovery for tight gas
These are mainly engineering responses, they do not
exploit insights gained from operational data.
Industry leaders proclaim ours is a technology industry, but
most E&P engineers think of “technology” as hardware.
Source: Ernst & Young
© Andrew Moore 2015

6. Where can information and data
analytics have the most impact ?
 Process improvements from better IM & KM
 Reducing unplanned downtime of rotating
equipment - “Traditional” predictive analytics
 Exploiting new data relationships as well as
the obvious E.g. frac design, microseismic,
fracture propagation and resulting production
 Real-time drilling analytics and synthetic logs
in HPHT zones – could save $Ms per well.
© Sekal AS
The prize ? In 2012, 1GJ of contracted sales gas was
worth around A$4*. A 2% improvement in Santos Cooper
Basin production of 66PJ would have delivered ~$5M p.a.
* Source Credit Suisse E. Australia Gas Prices 04/14, Santos annual report 2012
© 2014 MicroSeismic, Inc.
© Andrew Moore 2015

7. What is data ?:
Even with no margins, this makes no material difference. Santos was investing over
$400M/yr in the Cooper*. Does analytics in Exploration offer more real value ?
So, if oilfield analytics can reduce costs and,
more importantly, increase revenue:
Why are we slow to adopt and innovate?
 The focus is on improved cycle times or reduced down
time based on existing disciplines, processes and
technology – 20th Century thinking, closed mind-set.
Let’s look at the big picture - $5M ?
 Exploration data is already Big, but adoption of analytics within the established scientific and
engineering disciplines is slow. Perversely, science is holding back the data scientist.
What is required to bridge the culture gap between the data scientist and the geoscientist ? :
 Mutual comprehension, foresight, and the ability to persuade leadership.
* Source: Santos Cooper Gas Growth Program © Andrew Moore 2015

8. Oilfield Analytics can be learned. But do we have the
Foresight ? – Is the promise of data-driven E&P
compromised by a 20th century view of its potential ?
Let’s look at some analogues :-
 "I bought a JEEP” – brilliant ad – but the futile
act of a dying breed – 20th Century thinking
 Tesla – 21st Century – A sexy solution to win
hearts and open minds.
 Outlander PHEV – The perfect everyday car?
 Also BMW i8, i3, Nissan Leaf etc. etc. etc.
 Spend some time to think just a
little outside the box.
© Andrew Moore 2015

9. © Andrew Moore 2015
Find (or be) a champion with foresight,
exchange knowledge,
make it stick
 Through stories – win hearts and open minds
 Through reason – demonstrate in the real world – why wouldn’t you use it?
 Through example – find a “killer app” and exploit the app delivery culture !
Ability to Persuade
How can “IT” convince a sceptical subsurface
leadership that really using information as an
asset and relying on predictive data models can
revolutionise our industry as it has for banking,
retail and manufacturing ?

10. A Good Story
Repsol started the Kaleidoscope Project in
2007. The aim: compete with and out perform
the majors in deep water Gulf of Mexico plays.
 They partnered with IBM to build a
high performance computing platform
 They partnered with Stanford
University to develop new seismic
reverse time migration algorithms.
 They processed seismic 6 times faster
and improved imaging beneath salt
domes, raising exploration success for
Repsol GOM JV projects from the
industry norm of 20% to 50%.
http://www.repsol.com/es_en/corporacion/prensa/galeriamultimedia/transcripcion_video_francisco_ortigosa.aspx
http://www-07.ibm.com/innovation/au/shapingourfuture/downloads/repsol_case_study_2010.pdf
“We realized that for a project like Kaleidoscope, which was aiming for a clear shift in
our exploration model, we needed out-of-the-box thinking in every dimension.”
Francisco Ortigosa
© Andrew Moore 2015

11. What is data ?: The basis of reasoning ?
This innovative thinking lead to outstanding success
in Repsol deep water projects in GOM (Buckskin
2009), Brazil and West Africa with various partners.
 By 2013 Repsol was able to report:
“The proven reserve replacement ratio was 275%, one of
the highest in the industry worldwide and Repsol's all-time
high.
During 2013, Repsol continued its track record of success,
with nine finds in Brazil, Alaska, Algeria, Russia, Colombia
and Libya.”
The world’s highest reserve
replacement ratio in 2013
Source: Repsol Annual Report 2013
And now Repsol has bought Talisman. Big thinking
at board level has transformed Exploration – and
the company – by changing the mind-set.
Result :–
© Andrew Moore 2015

12. Necessity is the mother of invention - and NOW is the time !
 A combination of solvents and microwaves “melt” the bitumen in oil sands.
Tests suggest ESEIEH could slash SAGD energy costs by 80%.
 If there was ever a time for
innovation it is now.
 Alberta companies are testing
enhanced solvent extraction
incorporating electromagnetic
heating (ESEIEH) - an example of
innovative thinking (now 5 years
old) but more important than ever
at $50 oil..
* Source: Calgary Globe and Mail Jun 2012 © Andrew Moore 2015

13.  Definitions of Data – Google: “About 263,000,000 results (0.30 seconds)”
“the quantities, characters, or symbols on which operations are performed by computer” ?
“Things known or assumed as facts, making the basis of reasoning or calculation” ?
“Factual information, especially information organized for analysis or used to reason or
make decisions.” ?
 Exploration data has already cost $billions, but it is only valuable if used to
make decisions with a commercial outcome. Could our decisions be better ?
 Think of data management and analytics as decision support and then ask:
 Why does Exploration data sit idle whilst drilling data is left with the contractor ?
Why do old trends and new unexploited data relationships not inform decisions ?
 “The data is talking to us but we are not listening !” -
Why not ? Is this is the biggest waste of money in history ?
Data: The Basis of Reasoning
© Andrew Moore 2015

14.  The industry is cutting costs, but is this the right time to cut IT ?
 We can see, and learn about, how analytics can both save money and make money.
 We should be investing in monetizing our data, not reducing our innovative capability
 Google “Analytics in Oil and Gas Upstream”: About 372,000 results – this isn’t vapourware!
 Reason this: IT solutions are repeatable for a fraction of the initial cost.
 I said $5M p.a. was not “material”. But that’s just for Santos in the Cooper Basin.
If these techniques were applied to all fields we could easily be talking $50M p.a.
 1 saved stuck pipe and 1 HPHT incident avoided could easily add another $50M p.a..
 $100M p.a. is very material. This could turn IT into a profit centre.
 Reason this: Google “Analytics in Accountancy”: About 922,000 results !
 Accounts should be correct, yes ? So if data = money - Why not databases ?
Focus on the business, “monetize” the data
© Andrew Moore 2015

15.  Traditionally, data management has been a service assisting “the business”
 DM is not recognised as a profession or given credence, but it is now critical.
 Today, the service customer makes all the decisions – DM has little authority.
 Meanwhile the Digital Oilfield is driving more and more new data delivery
 Some form of data scientist must now exist to filter, interpret and analyse – gaining
credence & trust, influencing decisions. This role is critical and requires formal recognition.
 Is this a problem ? What is a geophysicist anyway, if not a data scientist ?
 Geoscience must connect to data science and adapt to new data processes
 This means collaborative workflows and standards are imperative to integrate data, and
 Data workers must adopt new techniques and automate to cope with data volumes
Support the Evolution of a New Breed
© Andrew Moore 2015

16. Traditionally, the management of (mainly static) data has been a service.
Today, the service customer (decision maker) is always right – Education is required.
DM is rarely recognised as a profession or given credence, its importance is down-played.
Meanwhile the “Digital Oilfield” is driving more and more new data delivery
Some form of data scientist is required to filter, interpret and analyse – gaining credence &
trust, influencing decisions. This role is critical and requires formal recognition.
Is this a problem ? What is a Geophysicist anyway, if not a data scientist ?
Data science must constantly adapt to monitor new data streams.
This means collaborative workflows and standards are imperative to integrate data
And data workers must adopt new techniques and automate to cope with data volumes
 We don’t have enough eye balls ! How long before we evolve ?
Automate or grow more eyes
© Andrew Moore 2015

17. A Science ?!Can’t wait that long ? – Get Collaborating !
© Andrew Moore 2015

18.  Find the right sponsor and establish an agile development project
 Look for a seasoned risk taker who can balance the investment with potential benefit
 Engage with up-and-coming professionals recently trained in probability theory
 Look for “Explorationists” who can see beyond their own silo, who support collaboration
 Recruit good statisticians, preferably from within the company or industry
 Deep understanding of stochastic methodologies, i.e. a masters in statistics, is required
 But more so, industry knowledge, to bridge the gap with geoscientists and engineers.
 Your database platform and data integration may be an issue
 In-memory database platforms are better suited to analytics, is your platform suitable ?
 Look for discrete data-sets to assemble into a pilot analytics data mart.
e.g. seismic attributes to identify HPHT zones not visible in seismic sections
A Science ?!Can’t wait that long ? – Get Collaborating !
© Andrew Moore 2015

19. There’s an app for that …
© Andrew Moore 2015

20. There’s an app for that …
 Find a simple data relationship with obvious impact and create demand
 Safety improvements are always supportable – cite the Shell example post Macondo
 Don’t ignore mobile field-based technology – LDAR for example, and visualise in the office
 Exploit business intelligence tools like Spotfire to visualise data relationships
 Target specific user groups, control access through conditions of use
 Create demand through word of mouth, support utilisation, plan for it to go viral !
Examples:
 Self Organising Maps for multivariate analyses – e.g. reservoir characterisation
 Mapping seismic attributes to reservoir properties e.g. wide / multi azimuth & fractures
 Estimated Ultimate Recovery – Scenarios with well spacing, type curves & fluid dynamics
© Andrew Moore 2015

21. Visualisation is key to innovative thinking
 In the Quality vs. Quantity debate, both are right
 Visualisation is critical to expose both the correctness and completeness of data
 Any lack of quality or quantity may be embarrassing but will drive rapid improvements
 Improved access to data of a known quality – good or bad – informs better decisions
 Revealing “expert” data to “non-experts” encourages new insights
 Visualisation is a keystone of discipline integration, collaboration and innovation
 The juxtaposition of “expert” data from different disciplines encourages new thinking
 Expect entrenched views to present impediments to progress
The following slides are courtesy of Marathon and iStore, whose PetroTrek
portal is being released in Santos after many political hurdles were crossed
© Andrew Moore 2015

22.  Connection to
multiple data
stores
 Great QC / QA
tool to raise
quality
 No additional
database
 Quick + easy
access to data

27.  Focus on Safety to
sell your case

29.  Within a few years a geoscientist with data training will be promoted to a
position where this can happen – or maybe an accountant will !
 Until then, the old crew will ignore the evidence, saying IT costs the Earth.
21st Century companies know that IT is 2.5 times cheaper, in $50 boe terms,
than it was in 1990* plus it has the potential to significantly enhance ROI.
 Or possibly, the incumbent software providers will get there without you.
 More likely, major financial systems providers (IBM, Oracle, SAP), currently
spending $Ms to enter the market, will recoup their costs via your CEO.
 Explorationists are smart risk takers. Sooner or later someone will risk it
and use analytics to manage data volumes and reduce uncertainty.
”Professional curiosity will become an industry imperative” Keith Holdaway
Or Wait for The Big Crew Change
* Source Paradigm::-1990 industry IT cost $0.25 per a boe @ $20. Today it’s still $0.25 @ $50 boe. © Andrew Moore 2015

30. © Andy Moore, Exploration Data Systems Consultant
andrew.moore@dataco.co.uk
“Big Data” is a 21st Century issue.
21st Century thinking and volition is required
to apply new scientific methods to realise
orders of magnitude more benefit.
Thank You
Smart E&P requires
smart thinking