Oxys Digital Transformation Occidental Petroleum Corporation - - PowerPoint PPT Presentation
Oxys Digital Transformation Occidental Petroleum Corporation September 6, 2017 Jod Jody Elli Elliott Senior Vice President, President, Oxy Oil and Gas Yan anni Char Charalambous Vice President, CIO Information Technology Da David Bo
Occidental Petroleum Corporation September 6, 2017 Jod Jody Elli Elliott
Senior Vice President, President, Oxy Oil and Gas
Yan anni Char Charalambous
Vice President, CIO Information Technology
Da David Bo Bowlby
Vice President, Data Management and Analytics
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Forwa ward-Looking
ments nts
Portions of this presentation contain forward-looking statements and involve risks and uncertainties that could materially affect expected results of operations, liquidity, cash flows and business prospects. Actual results may differ from anticipated results, sometimes materially, and reported results should not be considered an indication of future performance. Factors that could cause results to differ include, but are not limited to: global commodity pricing fluctuations; supply and demand considerations for Occidental's products; higher-than-expected costs; the regulatory approval environment; not successfully completing, or any material delay of, field developments, expansion projects, capital expenditures, efficiency projects, acquisitions or dispositions; uncertainties about the estimated quantities of oil and natural gas reserves; lower-than-expected production from development projects or acquisitions; exploration risks; general economic slowdowns domestically or internationally; political conditions and events; liability under environmental regulations including remedial actions; litigation; disruption or interruption of production or manufacturing or facility damage due to accidents, chemical releases, labor unrest, weather, natural disasters, cyber attacks or insurgent activity; failure of risk management; changes in law or regulations; reorganization or restructuring of Occidental's operations; or changes in tax rates. Words such as “estimate,” “project,” “predict,” “will,” “would,” “should,” “could,” “may,” “might,” “anticipate,” “plan,” “intend,” “believe,” “expect,” “aim,” “goal,” “target,” “objective,” “likely” or similar expressions that convey the prospective nature of events or outcomes generally indicate forward-looking statements. You should not place undue reliance
undertake any obligation to update any forward looking statements, as a result of new information, future events or otherwise. Material risks that may affect Occidental’s results of operations and financial position appear in Part I, Item 1A “Risk Factors” of the 2016 Form 10-K.
Use of non-GAA AAP Fina nanci ncial Informa rmati tion
This presentation includes non-GAAP financial measures. You can find the reconciliations to comparable GAAP financial measures on the “Investors” section of our website.
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Permian
characterization
approach
technology advancements
1.4 5,150 124
1.1 19,310 145
Oxy y Permian an Tota
2.5MM MM 24,460 269 269
100 150 200 250 300 350 400
OXY CVX PXD APA CXO XOM XEC EOG DVN ECA EGN FANG COP PE LPI APC KMI SHERIDAN SHELL RSPP SINOCHEM BHP WPX PERM RES. ENDEAVOR QEP MTDR SM NBL LINN CPE LGCY EPE AREX SSUMY HESS CWEI REN CRZO
PERMIAN BASIN NET MBOEPD OPERATED PRODUCTION** Liquids Gas
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Net Operated ed 2016 Net Acres Wells ls* Produc uction ion Mboed
Oxy Permian Busine iness Overvie view
*Gross Oxy operated wells including producers and injectors, and idle wells. **Source: Wood Mackenzie 2016 production, 3/2/17, company NWI% production rates, operators shown represent ~85% of Permian Basin daily production
Permian n Basin n Industr ustry Product ctio ion
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Machine Learning
Technical Data Management Production Optimization Field Automation Consolidated ERP Systems Next Generation Production Optimization
and Tools
decision making
financial data
Data
2001 2003 2005 2008 2012
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A flexible system that flows, changes form in real time, and seeks the most natural path to its destination.
Current nt Innovat ation
ine Statis tistics cs and Results ults Oxy’s Innovat ation
ess
INNOVATE TE IN CORE RE FOLL LLOW FASTER ER THINK DIFFE FFERE RENTL TLY
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Map View Real al-time time Alar arms ms Route e – Main in Scree een Productio
en Tren end d Screen en
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Oxy & Indus ustr try y Experti tise se Data Management ent Data & Analyti tics cs Domain n Experti tise
Insight ight & Recommen mendation dations
Stati tist stica cal Met etho hods ds
Data Colle lect ctio ion n & Profilin iling
Simulation
Processing
Algorithms
Opti timiza zati tion Artificia ficial Intellige gence nce
Comp mputatio ional nal Metho hods ds
University Partnerships O&G Industry Research Outside Industry Research Commercially Viable Algorithms Vendors IT
Key Levers
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yield
lengths to reach target point
dogleg severity
Projection Distance Max DLS limit = 11 degrees Max DLS limit = 14 degrees Max DLS limit = 24 degrees
Planned Trajectory Actual Trajectory
(staying in producing zone)
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High Speed, Low Fidelity Reservoir Models
Historical/field data to calibrate and quantify uncertainty
Field decisions that
production
Maximize NPV honoring economic, operating, and well constraints by generating thousands of what-if scenarios
Observation Well Injection Well Vent Well Producing Well
Temp, Press Production Injection Production Well Data Injection Well Data
Optimizer Reservoir & Operational Facilities
Target=$100MM
data sources
computational costs
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recognition
mechanical engineering+applied mathematics
drive efficiencies with chemical treatments, safety, failure detection, etc.
in advance.
failures to optimize preventative maintenance.
Oxy’s proprietary lift platform (Oxylift)
Time Risk vs Cost/Complexity
Risk of Failure Risk of Total Losses Risk of Additional Cost
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Explorati ration n & Apprais aisal Field d Developme pment nt Drilling ng & Comp mpleti etion Trading ding & Distr stributi ution Work rkforce ce Manageme ment nt Producti uction
Reservoir Characterization Where to Drill Lease Management Analytics Resource Risk Management Rate of Penetration Optimization Production Allocation & Monitoring Well Profiling Production Forecasting Asset Maintenance Supply Chain Optimization Inventory Optimization Route & Fleet Optimization Pipeline Inspection Contract Optimization Risk Management Capital Optimization Prediction of Supply Requirements Prediction & Optimization of required Workforce Equipment Loss & Failure Subsurface Dysfunction Detection & Prevention Drilling Trajectory Optimization Minimize Environmental Risk Optimization Cost vs Productivity Production HES Job Performance Monitoring Identifying Key Players & Future Leaders Identifying Organization Issues Retention & Risk Management
Predict ictive Analysis is Drones Virtual ual & Augment nted d Realit lity Intern ernet t of Things gs Big Data and Analytics cs Edge ge Comp mputing ing Cognit itiv ive Service ces & Machine chine Learni ning ng Robotics ics Cloud d Comp mputin ing g & Mobilit ility