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(1)

Th e V a lu e of I n for m a t ion

a n d su cce ss fa ct or s for a N D R

Source :

ht t p: / / www.agiweb.org/ ngdrs/ ndr5/ post conference/ present

at ions/ Tonst ad.ppt

Kj e t il Ton st a d

Ex plor a t ion M a n a ge r M iddle Ea st

St a t oil ASA

2

Content

•Short present at ion of St at oil

•The value of I nform at ion

•I nform at ion “ front end loading”

•The Whale – “ from am bition to action”

•The Diskos proj ect – a sim ple “ pay back” evaluation

(2)

This is Statoil

•A m aj or oil producer: one m illion barrels per day

•World’s third largest crude oil seller

•Markets t wo- t hirds of all Norwegian gas t o European cust om ers

•Largest retailer of oil products in Scandinavia

•A group with clear growth targets

4

(3)

5

Production of oil and gas is rising

0 200 400 600 800 1000 1200 1400 1600

1999 2000 2001 2002 2003 2004 target

2007 target Oil Gas

966 1 003 1 007

1 074 1 120

1 350 6% annual

prodn growth ( 2003: 1 080 000 boe per day)Oil and gas production

I ncrease in production

1 350 000 boe per day in 2007

NCS 91.8% I NT 8.2%

NCS 75% I NT 25% 1 000 boe/d

1 080

Th e va lu e of I n for m a t ion

(4)

Questions and answers

•Typical question

– Shall I acquire a 2D or 3D survey in this area?

– Should I shoot seism ic or j ust drill another well?

– Shall I acquire a core in this well?

– Should I by another study?

•Typical answers from your Manager

– What is the cost

– What is it wort h

– What is the ret urn on this invest m ent?

8

Value of I nform ation

•I t ’s the difference bet ween the proj ect value wit h the inform at ion and t he proj ect value without the inform at ion, m inus the cost of acquiring t he inform at ion

•These im plies t hat :

– there m ust be alternat ive out com e, ot herwise no inform ation could add value, which again is the sam e as t o say t hat t here m ust be uncert ainty

•I f there is uncertainty

(5)

9

I nform ation

•According to the I nform at ion Theory:

– I nform at ion is defined as - “ reduct ion of uncert aint y”

Dat a and I nform at ion will reduce uncert aint y in proj ects…!

9A NDR should provide Dat a and I nform at ion t o Governm ent and I ndustry and thereby contribute to proj ect risk m itigat ion and hence increased value creat ion!

10

Paras 1999

IT EFFORT COMPARED WITH SUCCESS

0 100 200 300 400 500 600 700

0 50 100 Support Staff 150 200 250 300

(Data Management and TAs)

Re

se

rv

e

s Ad

d

it

io

n

(6)

Te ch n ology M a n a ge m e n t I n n ova t ion Bu sin e ss Pr oce ss Ope r a t ion s Pe ople

Pr oa ct ive n e ss Sh a r in g Tr a n spa r e n cy Con t r ol For m a lit y I n t e g r it y

I n for m a t ion Se n sin g Pr oce ssin g M a in t a in in g Or ga n iz in g Colle ct in g

I nform at ion Orient at ion

Business Perform ance

M a r k e t Sh a r e Gr ow t hFin a n cia l Pe r for m a n ceLe ve l of I n nova t ionSu pe r ior Com pa n y

Re pu t a t ion

{

Breakthrough: The Information Orientation of an

organization is linked to performance

Source: Dr. Donald A. Marchand

(7)

13

I nform ation “ Front end loading” :

Cost

Gr a h a m Tile y Sh e ll Ex plor a t ion

14

(8)

I nform ation “ Front end loading” :

V a lu e

Gr a h a m Tile y Sh e ll Ex plor a t ion

16

Uncertainty – Choice – I nform ation - value

•I t is im port ant t o focus invest m ents t owards inform at ion and inform ation syst em s that support decisions at the value generation “ front - end” of the proj ect life cycle

•Governm ent s should m ake all relevant Dat a and I nform at ion available as front- end loading t o proj ects t o m it igat e risk and thereby m axim ise revenues from all invest m ents

•Dat a and I nform at ion not in use has absolut e no value to nobody..!

(9)

Th e N or w e gia n N D R

A success st ory

18

The Diskos proj ect

" e st a blish a j oin t , ce n t r a l da t a ba se for im por t a n t qu a lit y a ssu r e d ge o- da t a a n d m a k e t h e m e a sily a cce ssible for t h e u se r "

Re qu ir e m e n t spe cifica t ion for Ce n t r a l Ge o- D a t a St or a ge N PD 1 1 t h Ju n e 1 9 9 3

(10)

The Norwegian NDR Challenge

Bu ild a br idge be t w e e n t h e oil com pa n ie s n e e d t o st or e , m a n a ge a n d a cce ss r a w da t a , a n d t h e

gove r n m e n t n e e d t o bu ild a n d m a in t a in a n a t ion a l da t a ba se

20

Post stack seism ic without NDR

Processing contractor

Authorities

Partners

Operator

Archive

(11)

21

Post stack seism ic with NDR

Processing contractor

Authorities

Partners

Operator

NDR

Tapes

Netw

ork

22

Evaluation of the DI SKOS

proj ect, an exam ple

Process Time used

in 1999 Time used in1992

Logging in 2 min Selecting data 5-10 min Processing a

request (1000km/3D survey)

10 min/2hours

Network transfer (13-14 Gb/hour)

10 min/5hours (8bit data)

Total 1 day 10 days

(12)

Data downloaded from the Diskos data base from 1999 to mid 2003

0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 2 5 0 0

1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3

Giga byte

Source: PetroData a/s

Accumulated download 1999 – 2003 = 57 Terabyte Total data volume in database = 60 Terabyte

24

Evaluation of the DI SKOS proj ect

zTangible benefit s

–reduced act ivit y in G&G archive –dat a version cont rol

–reduced storage cost –reform at t ing

–internal and external distribut ion –workst at ion form at t ing

–reduced SW and HW invest m ent –reduced disk space needs –data select ion and overview –spec data selection

–effect ive & safe dat a t rade –NPD updates ( culture data)

zI ntangible benefits

–faster proj ect cycle tim e –m ore dat a accessible

–qualit y assured dat a ( im proved user confidence)

–m ore effect ive dat a flow from cont ractors to Statoil

–use of standards –easy report ing t o NPD

–easy report ing of dat a in license

Benefits

VannesRennesNantes

Saint-Brieuc

Quimper

Saint LoCaenRouen

Paris Orléans Châlons-surMarne Metz Strasbourg Besaçon Dijon Lyon Marseille Montpellier Toulouse Bordeaux Clermont-Ferrand Limoges Poitiers PauTarbes Foix PerpignanCarcassonne Auch Mont-de-MarsanAgenMontauban Rodez CahorsPérigueux Tulle AurillacMendeLe PuyPrivas Valence AvignonNice Toulon DigneGap GrenobleChambéry Nimes Annecy Bourg Lons-leSaunier Lons-leSaunierMâcon NeversBourges Saint Étienne Châteauroux Guéret AngoulêmeLa RochelleNiort La Roche-sur-Yon ToursBloisLe Mans LavalChartres AlençonEvryMelunAuxerreTroyes ChaumontVesoulEpinalColmacBelfort Nancy Bar-le-Duc Charleville-MézièresLaon LilleArrasAmiensBeauvais Evreux Versailles Ajacciio Bastia Angers Albi

" The maps are not beeing more correct if you put them into a screen"

(13)

25

Evaluation of the DI SKOS proj ect

The tangible benefits exeeds the cost!

Cost reduction in 1999

94 95 96 97 98 99 00

Cost ac 2,1 4,8 9,1 13,9 19,2 26,2 33,5

Benerfit ac 0 0 0 5 15 42,4 69,8

26

1

st

, 2

nd

, 3

rd

and 4

t h

levels of effects

•1st level: I nform at ion “ front - end loading” and risk m it igat ion

•2ndlevel: Reporting and distribution of dat a t o governm ent and

partners

•3rdlevel: More efficient St at oil int ernal access t o dat a

•4t h level: Overall cost reduct ion for St at oil in handling and m aint aining

(14)

Norwegian NDR – success factors

• A NDR is not a m useum , nor an archive …but it should be a living data base…

• A NDR and the governm ent release policy are strongly conected

• High focus on get t ing dat a in, quality assure t he dat a and get t ing t he dat a out of the system and used by governm ent and indust ry

• Data quality ( next slide)

• Assure support/ buy in from oil com panies and contractor com panies

• Governm ent reporting should be in line with com panies internal specs

• Governm ent reporting from oil com panies m ust be t im ely..

• Secure use ( t echnical, operat ional, st aff …build in t rust ..)

• Operational business m odel …the right incentives

• Clear on all legal and cont ractual aspects..predictable future..

• Organisational – all stakeholders should be included

28

Success factors - Data quality

• Reliable data quality is essential for success.

• Knowledge about t he st at us of quality is m ore im port ant t han always having highest qualit y.

• Consistency in quality is im portant

– exam ple: High Quality Log Data in Norway,

( not highest possible quality but consist ent!)

• I n the first phase of the Norwegian NDR a lot of effort was required to load seism ic according to agreed standard to the NDR.

• Difficult balance between high quality and required effort.

– Data quality will increase over tim e in case data is used frequently

(15)

29

The balance bet ween “ Cooperation and

Com petition”

• I n Norway it was decided to coope r a t e on DM and com pe t e on

interpretation.

• Good clim ate for cooperation in Norway.

– culture

– “ appreciated” by the authorities

• Norwegian authorities give “ strong” advices, but are also willing to cooperate ( good balance) .

• Trust is essential.

• Make sure t hat t he syst em is secure

– but don’t get “ hysteric” about security.

• Com panies strive to create I nform ation asym m et ries in the m arked t o gain com pet it ive advant ages

30

Mot ivat ion

(16)

I nform ation and the Multiplier Effect

Cost Savings for

Efficiency Creat ing Value for Effectiveness Com petitive

Necessity

Com petitive Advant age Technology

Orientation I nform at ion Orientation Technology

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