© Peter Kaznacheev

Presentation at the Society of Petroleum Engineers, London 25 September 2018

Peter KaznacheevSenior Research Fellow, King’s College London, European Centre for

Energy and Recourse Security (EUCERS)

OIL AND GAS ARTIFICIAL INTELLIGENCE – OGAI 2.0

 The Application of Geospatial Information and Big Data Analytics

© Peter Kaznacheev

The structure of this presentation

© Peter Kaznacheev

Part One. OGAI 1.0. Operational applications of AI - an overview

- Upstream - Midstream - Downstream

Part Two. OGAI 2.0. Market analysis and geospatial information

- Who is collecting geospatial information - Who is using geospatial information - Market analysis based on regional data - Market analysis based on leading indicators - Market analysis based on proxy data - Integrating various types of data

Conclusions

2

! Reservoir analysis ! Seismic data

acquisition ! Seismic data

processing ! 3-D seismic imagery

AI in Geological surveys AI in Exploratory

drillingAI in Appraisal

! Well planning ! Automation of

drilling rigs ! Monitoring of

drilling ! Real-time drilling

optimisation! Well testing using

intelligent sensors ! Prediction of

failure of equipment

! Management of geological and geophysical data

! Well log digitising ! Well log

interpretation ! Calculation of

appraisal drilling parameters

! Reservoir modeling ! Reservoir

characterisation

Operational applications of AI Upstream — exploration

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! Optimisation of production throughout its various stages

! Flow measurement in wellheads

! Automated production control

! Reservoir pressure maintenance

! Enhancing recovery ! Operational troubleshooting ! Safety control ! Forecasting behavior of a

layer depending on its current state

AI in field development AI in production

! Field surveillance ! Placement of well pads ! Optimisation of well

location and design ! Wells controlling ! Access to field equipment

and diagnostics !  Improving the rate of

penetration ! Automated Manufacturing

Execution System ! Wireless Sensor

Networks

Operational applications of AI Upstream — field development and production

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"  Determining the optimal path for pipeline construction

"  Determining locations of terminals and pipeline connections

AI

"  Compressor station automation

"  Early warning about malfunctions

"  Corrosion monitoring and diagnostics

"  Calculating the most cost effective route

"  Autopilot and autodocking systems "  Tanker monitoring

"  Analysing economic conditions and weather patterns to forecast demand

"  Storage optimisation

Operational applications of AI Midstream

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#  Predictive analytics and predictive control algorithms

#  Reduced costs of operating storage facilities

#  Efficient equipment maintenance

#  Automation and asset optimisation of refining processes

#  Use of sensors and micro-controllers for infrastructure maintenance and early warnings

#  Distant monitoring of storage facilities and equipment

#  Leakage detection

#  Detection of equipment deformation

Storage Management Petrochemical Refining Security and Environment

Cost reduction

Improved efficiency

Increased safety

Enhanced output

Environmental compliance

Operational applications of AI Downstream

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6

Who is collecting geospatial information Selected companies

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# Real-'medatafromland,seaandsatellitemonitors.PartoftheDailyMailTrust.

# Shippingandstoragedata.Specialfocus:drillingac'vityandrigcounts.

# Combiningcommodityshipmentsdatawithsatellitefeedsandgovernmentaldatabases.

#  Inventorydataderivedfromsatelliteimagesofexternalfloa'ngrooftanks(EFRT).

# Real'meshippingdata,includingtankers.Globalmari'memap.

# Satelliteimageryanalysisofmari'meac'vity.Forecas'ngtonnageavailability.

# Datafrompublicandcommercialgeospa'alimageryproviders.

# Wellmonitoring,crudestorageandrefineriesdatafromsatellitefeeds.

Maritime Portal

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ORBITALINSIGHT

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# Hedgefunds# Traders# Refining&marke'ng

# Chiefeconomistsandstrategydivisionsofoilcompanies

# Shippingcompanies

# Shipbrokingservicefirms

# Governmentsofoilexpor'ngcountries

# Thinktanksandresearchcentres

# Interna'onalorganisa'ons

SHELL SCENARIOS

8 Who is using geospatial information

Market analysis based on regional data Monitoring tank farms and refineries

© Peter Kaznacheev

# Targetaudience:tradersanddownstreamanalysts.

# Casestudy:OrbitalInsightusessatelliteimagesofexternalfloa'ngrooftanks(EFRT).

# Thelevelofliquidcanbemeasuredbasedonthethicknessoftheshadowontheroof.

# UsingAIalgorithmstomeasurestocksinspecifictankfarms,regions,countriesorglobally.

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Satellite images of external floating roof tanks — Orbital Insight

Types of oil storage

Market analysis based on regional data Using inventory data as a proxy

© Peter Kaznacheev

#  Inventorydatacanbeusedforthepurposesofdownstreamcompe'torintelligence.

# Anotherapplica'on:iden'fytrendsincrudesupply.

# Changesinstocksserveasproxiesforoilproduc?ontrendsinkey“triggercountries”i.e.Venezuela,Iran,Iraqetc.

# Officialdatareflec'ngcrudeproduc'oninthosecountriesisunreliable.

Data source: OPEC

Crude production in thousand barrels per day

# Example:TheextentofVenezuela’scrashinoilproduc'onwaslargelyunforeseenbyanalysts.

# 0.5mlnb/dwastakenoffthemarketin6months.

# Oneofthemainfactorsbehindtheoilpricerisein2017-2018.

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Market analysis based on leading indicators Drilling activity, rig counts, car counts, construction

© Peter Kaznacheev

# Someexamplesofleadingindicators:

-  Offshorestorageintankers

-  Drillingac?vity(especiallyintheUSlower48)

-  Pipelinecapacityandthroughput(especiallyinthePermianbasin).

-  Construc?onac?vity-  Cellphonedata-  Carcountsaroundoilandgassites.

# Casestudy:carcountsdataisavaluabletoolforretailanaly'cs.

# Alsoaleadindicatorforoildemandonaregionalorgloballevel.

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Market analysis based on proxy data Renewables and EVs

© Peter Kaznacheev

# TrendsinrenewableenergyandEVscontributetopeakoildemand.

# Evensmallchangesinthosefactorsaretrendindicators.Theyhaveanimpactontoday’soilprice.

# Mostmodelsarecurrentlyrelianton3-dpartyforecastsofrenewablesandEVs.

# Needfordatasourcedfromrealobserva'ons(e.g.solarfarms).

# Example:Bloombergforecaststhatelectriccarswillmakeup35%ofnewvehiclessalesby2040.

# SeveralOECDcountriesareplanningtobanthesaleofnewpetrolanddieselcarsby2040.

# Thiscouldreduceglobaloildemandby8mlnb/d.

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FUTURE STEPS IN COMMODITY

MARKET ANALYSIS

SUPPLEMENTING ECONOMETRIC MODELS

•  3-d party expert forecasts

•  Price volatility (GARCH models)

•  Market sentiment data

RESEARCHING MARKET SENTIMENT

•  Semantic analysis of news events and other open sources

•  Commodity market sentiment and «appetite for risk»

ARTIFICIAL INTELLIGENCE METHODS

•  Neural networks, fuzzy logic etc.

•  Combining econometric models and AI

SCENARIO AND INTERVAL FORECASTING

•  Interval forecasting in addition to “point forecasts”.

•  Price movement scenarios

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Integrating various types of data Future steps. Working with various analytical tools

13

© Peter Kaznacheev

Conclusions

© Peter Kaznacheev

# OGAI1.0focusedonopera'onalapplica'ons:upstream(bothexplora'onandproduc'on),midstreamanddownstream.

# OGAI2.0willfocusonmarketresearch,corporatestrategyandplanning.

# Twoapproachesinmarketanaly'cs:nichedatavs.en'revaluechain.

# Subscribers:hedgefunds;traders;IOCsandNOCs;shipping/shipbrokingfirms;governmentresearchcentres;interna'onalorganisa'ons.

# Threemajoranaly?calroutes:1)Marketanalysisbasedonregionaldata2)Marketanalysisbasedonleadingindicators3)Marketanalysisbasedonproxydata.

# Integra?ngvarioustypesofdata:1)Mul'-factorforecas'ngmodels2)Mapsofpricedrivers.

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© Peter Kaznacheev 15

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