An effective G&G exploration strategy inspired by a wolfpack · An effective G&G exploration...

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An effective G&G exploration strategy inspired by a wolfpack N.M. Hernandez, F.Baillard, I. Panganiban, K.G. Maver 20 September 2019, Stavanger FORCE Machine Learning Conference

Transcript of An effective G&G exploration strategy inspired by a wolfpack · An effective G&G exploration...

Page 1: An effective G&G exploration strategy inspired by a wolfpack · An effective G&G exploration strategy inspired by a wolfpack N.M. Hernandez, F.Baillard, I. Panganiban, K.G. Maver

An effective G&G exploration strategy inspired by a wolfpack

N.M. Hernandez, F.Baillard, I. Panganiban, K.G. Maver

20 September 2019, StavangerFORCE Machine Learning Conference

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Outline

• ElasticDocs Technology Review

• Current Developments & Practical Applications

• Strategy for Breaking Cognitive Barriers in Exploration• Knowledge Network

• Native Language Understanding

• Conclusions

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Four Pillars

• Optical Character Recognition• Natural Language Processing• Deep Neural Networks • Geolocation & ElasticSearch

for Processing Unstructured Geoscience Data

So, last year

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40,000DOCUMENTS

500,000IMAGES EXTRACTED

100 yearsHISTORY

5

TeraBytes

100+

DOCUMENTS CATEGORIES

ELASTICDOCS IN NUMBERS

1M+ PAGES PROCESSED

1,000WELLS

7DISCIPLINES 20

BASINS

GeologyGeophysicsPetrophysicsGeomechanicsReservoir EngineerDrillingProduction

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Addressing the 4Vs of Big Data

Basin

Field

ReservoirWell

Grain

Frame

• Volume • Veracity • Variety • Velocity

High Resolution Basin Insight

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Data

Data

Data

Data

Knowledge Building in

New Ventures

Exploration

Transverse

Management of

Opportunities Secure Knowledge

Sharing

Discovery

P r i v a t e , S e c u r e E x p l o r a t i o n D a t a b a se

C o l l e c t i v e

A w a r e n e s s

Things we learned

o Technology evolution: Cyborg Data + Platform

o Exploration starts at your own (data) backyard

o New ventures can be data-driven, and need not be data-deprived

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NEW EXPLORATION STORY

Allow for easy understanding on:

• Risks associated to seal and migration

• Quality of the reservoir

• Probability of success

• Basin parameters as input to petrophysics, geophysics etc

Clear applications we’ve seen

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Access big data at our fingertips

Cognitive Challenge in Volume

Cognitive ability to digest

Native language

Information criticality

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Alpha

Pack

Scouts

Sole spirits

Visualization of Knowledge Networks

Node = Well

Network Thickness = Strength of Relationship

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Visualization of Knowledge Networks

What this image shows:

• 50 years of drilling history

Intra-relationship among wells

Inter-relationship between basins

Isolation of information – needs lookback?

Successful vs dry wells

Non-existing relationship

Step-out exploration

failures, vintage

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Visualization of Knowledge Networks

Paucity of network

Richness of network

What we can do:

• Add more data

• Add more understanding in areas with network paucity

• Could be opportunities for low-hanging fruits

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What are the wells “gossiping” about

SHARED LOGISTICS

SHARED NEIGHBORS

SHARED RESERVOIRS

SHARED GDE

…Top of the Latrobe level that is shared by the C21A well

…Begin tow from Bellatrix-1 to Calliance-3..

…A comprehensive set of offset wells for Dufresne-1B (includes) Abalone-1

…These wells (250 km & basins apart)… are of paramount importance for the extension of the producing plays discovered in the Paleogene…

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Cognitive Challenge: Language

Auto-machine translate

breaks down language barrier in exploration

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Cognitive Challenge: Language

New feature allows:

❑ Batch translate content in any language

❑ Simultaneous Query & Translate feature allows document to be searched in English, dives thru content in (any) language, and returns answer back in English

Hide OriginalORIGINAL TEXT

Las buenas porosidades y las areniscas limpias

en bloques caracterizan la Formación Plover en

Bodacious-1A. Estas arenas de buena calidad

están entrelazadas con arcilla. No se registraron

muestras significativas de hidrocarburos durante

la perforación de este pozo.

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Cognitive Challenge: Variety• Data Sources

private, internal corporate database

• Formatpdf, excel, word, powerpoint, gis, las, segy

• Disciplinesgeology, geophysics, petrophysics, reservoir engineering, drilling,

production

anydata

databaseML powered

data ingestion Cognitive

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Conclusion

• We started to move from solving analytical (technical) problems to improving (human) cognitive capabilities

• We are moving from machine learning experimentation to focused exploration problem-solving

• Evolving approach to data relationships to software platforms

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Thank you