Big Data For Oil & Gas

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1 Big Data For Oil & Gas Jay Hollingsworth - 郝灵杰 Industry Principal Oil & Gas Industry Business Unit 1

2 The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle s products remains at the sole discretion of Oracle. 2

3 Oracle America s Cup holder 3

4 Oracle 4

5 Oracle Corporation FY12 SCALE INNOVATION $37.1B in revenue in FY12* #1 in 50 product/industry categories 380,000 customers in 145 countries 20,000 partners 108,000 employees 14 million developers in Oracle online communities 32,000 developers and engineers 18,000 customer support specialists, speaking 27 languages 17,000 implementation consultants 1.5 million students supported annually 850 independent Oracle user groups with 800,000 members * GAAP revenue reported in USD as of May 31,

6 Investment in Innovation and Integration $4.5B $2.6B $2.6B $3.1B $1.9B $2.1B $1.3B $1.5B FY04 FY05 FY06 FY07 FY08 FY09 FY10 FY11 OVER $19B IN R&D SINCE

7 Oracle: Continuous Innovation Development and Acquisitions Middleware Platform and Management Business Intelligence Data Integration Identity & Access Management Performance Management Enterprise Content Management Operating Systems Systems Management Virtual Machines 7

8 Oracle s Leadership in Oil & Gas Commitment to Open Standards 8

9 Agenda What s Important About Big Data What Makes Data Big Data Oracle s Big Data Scenarios Case Study: Real-time analysis 9

10 Why Is Big Data Important? US HEALTH CARE MANUFACTURING GLOBAL PERSONAL LOCATION DATA EUROPE PUBLIC SECTOR ADMIN US RETAIL Increase industry value per year by $300 B Decrease dev., assembly costs by 50% Increase service provider revenue by $100 B Increase industry value per year by 250 B Increase net margin by 60+% In a big data world, a competitor that fails to sufficiently develop its capabilities will be left behind. McKinsey Global Institute Source: * McKinsey Global Institute: Big Data The next frontier for innovation, competition and productivity (May 2011) 10

11 What Makes Data Big Data? GEODATA TEXT SENSOR VOLUME VELOCITY VARIETY VALUE 11

12 Big Data is any data Unstructured, Semi-Structured and Structured There is internal structure, but it s not handled in a formal data model or the structure was intentionally unanticipated. Ex. - Social Media, RSS feeds, Videos, DOCs, PDFs, XLS, CSV, graphics Semi-Structured. May not conform to DB tables, but still contains tags or semantic elements, possibly embedded in comments. Ex. - s, log files, machine generated content Structured data. Data that is identifiable due to structure. Usually in data records with metadata. Ex. - XML data, RDBMS, DW, CRM, ERP, OLAP 12

13 Any Oil & Gas data could be Big Data Unstructured, Semi-Structured and Structured There is internal structure, but it s not handled in a formal data model or the structure was intentionally unanticipated. Ex. DLIS, LIS, SEGx, Videos, DOCs, PDFs, XLS, CSV, graphics Semi-Structured. May not conform to DB tables, but still contains tags or semantic elements, possibly embedded in comments. Ex. OGP Px, morning reports, observer sheets, survey notes, s Structured data. Data that is identifiable due to structure. Usually in data records with metadata. Ex. - XML data, RDBMS (MDM, interpretation systems) 13

14 Big Data is not new Data being stored long term since HDD became cheap Finance Data held for several years for audit/compliance reasons Manufacturing Machine logs created and held for life of machine Transport Vehicle telemetry stored Telco Call detail records captured Retail Sale transactions Gaming Everything is captured! Oil & Gas Seismic, logs, surveying Real-time measurements 14

15 But Big Data techniques are NoSQL databases store un-modelled data Hadoop distilling information from huge datasets HDFS massively parallel storage and analysis In-memory databases fast data - lowest latency queries Extreme analytics new languages like R cover petabyte datasets Complex Event Processing analyze in real-time w/o database Real-time Decision - where patterns are not known in advance Bayesian inference engine + decision management Information Discovery discover relationships across mixed datatypes where they are not known a priori 15

16 Big Data in Action DECIDE ANALYZE ACQUIRE ORGANIZE Make Better Decisions Using Big Data 16

17 Big Data in Action DECIDE ACQUIRE Acquire all available data ANALYZE ORGANIZE 17

18 Big Data in Action DECIDE ACQUIRE Organize and distill big data using massive parallelism ANALYZE ORGANIZE 18

19 Divided Solution Spectrum DISTILL Data Type NoSQL Unstructured Schema-less Distributed File Systems Transaction (Key- Value) Stores MapReduce Solutions OLTP Data Warehouse SQL Schema ETL Acquire Organize Analyze 19

20 Big Data in Action DECIDE ACQUIRE Analyze all your data, at once ANALYZE ORGANIZE 20

21 Oracle Integrated Software Solution Stack DISTILL Data Variety Low Density High Density Information Density HDFS Oracle NoSQL DB Oracle Database (OLTP) Acquire Hadoop Oracle Data Integrator Organize OLH Oracle Database (DW) In-DB Analytics R Mining Text Graph Spatial Oracle BI EE Analyze New products and capabilities 21

22 R Statistical Programming Language ANALYZE Open source language and environment Used for statistical computing and graphics Strength in easily producing publication-quality graphs Highly extensible 24

23 Big Data in Action DECIDE ANALYZE ACQUIRE ORGANIZE Oracle NoSQL Database Oracle Enterprise Manager Oracle Data Integrator Oracle Loader for Hadoop Oracle R Enterprise 25

24 Big Data in Action DECIDE ACQUIRE Decide based on real-time big data ORGANIZE 26

25 Industry Challenges Reservoir Characterization Exponential size increase in traditional data types seismic, logs Management of exploding interpretation and simulation results Need to integrate real-time data into the earth model on the rig and in the office Drilling Risks to personnel and the public need to be reduced Need to automate real-time decision-making Need to unlock value in real time data archive after drilling phase is over Production Detect well problems before they become serious slugging, WAG gas breakthrough Need to make more real time decisions with fewer engineers Rapid optimization required to maximize output Data Management Astronomical data volumes require Big Data techniques Enormous data volumes prevent real time analysis and after-the-fact analysis Preserving real-time data 33

26 Data Flow from Sensors to Oracle Solution The Oracle High Performance Data Management Solution leverages existing instrumentation and data acquisition infrastructure Sensor Sensor Sensor Sensor Sensor Sensor RTU RTU Analog to Digital MTU/ DCS/ SCADA Drilling Data Production Data Process Control or Data Acquisition System Rigsite Aggregator Historian WITSML Other Protocols (OPC-UA, WITS, etc.) PRODML Oracle High Performance Data Management Solution RTU = Remote Terminal Unit MTU = Master Terminal Unit DCS = Distributed Control System 34

27 High Performance Data Management Sensor High Performance Data Management Sensor Sensor Sensor Sensor Sensor RTU RTU MTU/ DCS/ SCADA Drilling Data Production Data Rigsite Aggregator OPC/UA, DA, WITS Historian Real Time Drilling Real Time Production WITSML Service PRODML Service Exalogic OEP RTD BAM 3 rd Party Apps Real Time Appliance PPDM + Data Model Exadata PPDM Appliance Spatial OAA Data Mining R Enterprise GoldenGate Exalytics Endeca Essbase Hyperion OBIEE TimesTen Analytics Appliance Big Data Appliance NoSQL HDFS Hadoop w/ Loader Cloudera DLIS Loader SQL Loader CEP = Complex Event Processing BAM = Business Activity Monitoring RTD = Real Time Decisions Logs ASCII Data 35

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