© 2014 IBM Corporation Predictive Analytics at Work: Oil and Gas Exploration using Watson and Data Streaming IBM GBS March 2015

© 2014 IBM Corporation 2 Numerical models use facts e.g. traditional supply & demand Tell most consumers or in fact most analysts the price of oil would go from $110 to $50 but not justify Why it would not be trusted The first shale oil extraction patent was granted in But it was only written about and not included in the models until recently What if the models could learn beyond classic numerical machine learning If the models could understand the text to know that shale oil is going to have a critical causal relationship on the price of gasoline That would change the What is predicted but more importantly explain with the Why Price of Gas What and Why*

© 2014 IBM Corporation 3 1.High Frequency data on planned pipeline capacity 2.Streaming data on current demand 1.High Frequency data on planned pipeline capacity 2.Streaming data on current demand Price inflection points on supply 1.It becomes economic to uncap hard to recover oil wells 2.Profitable for shale oil extraction 3.Heavy oil / oil sands are affordable Price inflection points on supply 1.It becomes economic to uncap hard to recover oil wells 2.Profitable for shale oil extraction 3.Heavy oil / oil sands are affordable 1.Proven reserves 2.Refining capacity for gasoline versus diesel 1.Proven reserves 2.Refining capacity for gasoline versus diesel Ratio of the price of gas to substitutes (e,g. natural gas, alternative energy, diesel, etc.) Ratio of the price of gas to substitutes (e,g. natural gas, alternative energy, diesel, etc.) Connectors Watson Numerical elements to computer predicting gas prices

© 2014 IBM Corporation 4 However you cannot easily get the required rest of the model elements because people only written in text: Find new reserves Improve reserve management Improve recovery rates Improve operation efficiency Extend resource life Improve oil & gas recovery Manage and optimize injections Chemical and thermal recovery design In situ combustion optimization Weather impacting demand Political problems Venezuela If OPEC decides to restrict supply Slow down in Growth Geography (BRIC) countries GDP Alaskan intervention in drilling or pipelines build out Discussion on well completions (bringing new oil online)

© 2014 IBM Corporation 5 Weather (the average winter temp in NE down by 2) Political (Strikes by PDVSA oil workers paralyze oil production … has doubled its workforce since the strike of 2003 even though oil production has stagnated at well below pre-strike levels.) Supply (OPEC Reference Basket slipped heavily from October’s record peak, sliding $6.41 or 14%) Geography (In 2007 China’s economy expanded by an eye-popping 14.2% The IMF now reckons China will grow by just 7.8%) Extraction can find, structure and apply content from text: Legend Terms in red can be extracted and put into numerical models (e.g. temp down by 2) Terms in green can be extracted and looked up in a reference domain model and then applied to numerical models (e.g. OPEC = oil supplier, or NE = US East Coast) Words in black are throw away words

© 2014 IBM Corporation 6 Model with only numerical elements Supply Proven reserves * Refining capacity Pipeline capacity Current demand Historical Demand for the period Ratio of the price of gas to substitutes Everyone cares about the price of gasoline

© 2014 IBM Corporation 7 Model with numerical & unstructured elements Supply Proven reserves * Refining capacity * OPEC & Venezuela factor * Shale Oil * Pipeline capacity New Regulations * New completion rates Current demand Historical Demand for the period * Weather * China demand * Environmentalism Ratio of the price of gas to substitutes Everyone cares about the price of gasoline

© 2014 IBM Corporation 8 There is a lot of information, some of it streaming: True vertical depth True vertical depth MWD / LWD Data MWD / LWD Data Rock Mechanics Data Fluids Data Lithological Data Lithological Data Surface Equipment Data Surface Equipment Data Streaming Data Unstructured Data Structured Data BHA Dynamics Data Vibration Data Mud weight Data Mapping

© 2014 IBM Corporation 9 There is a lot of unused unstructured information: Regulations Well plans Best Practices Best Practices Manufact- urer’s FAQ Manufact- urer’s FAQ Streaming Data Unstructured Data Structured Data Shift End / Morning reports Shift End / Morning reports Driller's Network Driller's Network Data Mapping

© 2014 IBM Corporation 10 Model with numerical & unstructured elements Classical numerical model for Written assessment of material in the predicting circulation problems * Material in the shakers, Drill plan notes on fractures or have high permeability, etc. Probability of mud circulation problems is now much more complete

© 2014 IBM Corporation 11 Where else can your firm Merge Numerical and Unstructured Other parts of drillingPlant maintenanceExplorationWell planningEnvironmentHealth, Safety, Security and EnvironmentMajor Capital ProjectsTrading advisoretc.