Enabling End User Access to Big Data in the O&G Industry
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1 Enabling End User Access to Big Data in the O&G Industry Johan W. Klüwer (DNV) and Michael Schmidt (fluidops) 1 / 28
2 HELLENIC REPUBLIC National and Kapodistrian University of Athens 2 / 28
3 . Paradigm Shift for Data Access Budget: Running until November partners 5 countries 100 man years Images Siemens AG, Harald Pettersen/Statoil 3 / 28 FP7 Large-scale IP project
4 Plan. O&G Big Data challenges. The Optique mission. Use case at Statoil Exploration. O&G expert access to Big Data 4 / 28
5 The Optique Ecosystem R&D Early Adopters Partner Network 5 / 28
6 Big Data Challenges Velocity BIG DATA Volume Variety Complexity Gartner Inc. 6 / 28
7 O&G Information Challenges Complexity drivers:. Many disciplines. Many locations. Requirements standards, regulations, customer demands. Asset management lifecycle data Data storage and access mechanisms:. Proliferation of data formats and models. Application dependent data. Limited tools for end users 7 / 28
8 O&G Disciplines Administration Instrumentation/metering Quality management Procurement Marine operation Mechanical Civil/architect Inspection HSE Drilling Piping/layout Telecommunication Electrical Material technology Subsea Project control/economy Structural Weight control Geology Operation Reservoir HVAC Process Pipeline (NORSOK, 1996) 8 / 28
9 Top-down Perspective on O&G Corporate Data Promote uniformity across the enterprise.. Production of information. Use of information. Hand-over of information 9 / 28
10 O&G Data Models POSC Epicentre 1.0 PPDM 3.3 OPC Statoil (POSC 2.2) WITSML Seabed OPC-UA Technologies EXPRESS UML 1.0 XML / 28
11 Main Observation Experts are still struggling with spreadsheets and ad hoc filters/look-up tables. 11 / 28
12 The O&G Expert vs. SQL A quite common situation in several or all my projects so far, I ve had the need to check and cross check data which in our current tool was quite difficult, perhaps combine the data between tables and hopefully not having to do it in Excel all the time. However, the program had an option to do SQL based searches. As few or none of the engineers know SQL coding, I had the IT support or system support make me the SQL string that was needed in order to make a specific search, and I then copied it into a text field so that I could use it later or try to modify it. This is quite common for several of my engineers in similar positions as I had. Experts spend too much time collecting data 12 / 28
13 Bottom-up Perspective on O&G Corporate Data translation Information need Custom SQL query End user IT expert Sources. IT staff become critical engineering project resources 13 / 28
14 Use Case at Statoil: Stratigraphy In my area of interest, return the wellbores that penetrate chronostrat unit C1 and return information about the lithostratigraphy and the hydrocarbon content in the wellbore interval that penetrates the C1 unit. 14 / 28
15 From Prospects to Projects in a Data-driven Process 15 / 28
16 GIS Datasets from Corporate Archive... information need IT expert. query. ArcGIS. ArcGIS. db. Archive. Project DBs snapshot End user. Query development may take several days. Including other sources in queries is difficult/impossible 16 / 28
17 New Expert Friendly Interface Expert Optique Application Ontology queries. Automated Translation SQL queries Sources timely, complete, and correct results 17 / 28
18 Emerging Semantic Technologies POSC Epicentre 1.0 PPDM 3.3 OPC Statoil (POSC 2.2) WITSML Seabed OPC-UA Technologies EXPRESS UML 1.0 XML Linked Data proposal RDF, OWL 1, D2RQ SPARQL Optique R2RML, OWL / 28
19 Existing Systems and Sources 19 / 28
20 Ontology-based Data Access (OBDA) Ontology:. Knowledge of concepts and relationships. Understandable by humans & computers. Integrating multiple domains Mappings: End users query Ontology.. View data through the ontology mappings mappings 20 / 28
21 Ontologies at O&G Complexity are now Practical. Mature tools for building and verifying ontologies. Exponential improvement in reasoner efficiency Year O-size Complete Time (s) k No k Yes k Yes k Yes 5 (Horrocks 2012) 21 / 28
22 Optique Architecture Application results End-user Stream Adapter. Query Formulation query Ontology Query Transformation Query Planning Query Execution IT-expert Ontology & Mapping Management Mappings Query Execution Data models Std. ontologies cross-component optimization streaming data 22 / 28
23 SQL->RDF Mappings. Optique Architecture Zoom-in Optique Platform: Integrated via Information Workbench Answers visualisation Ans. visual.: Workbench Presentation Layer Query Formulation Interface Ontology and Mapping Management Interface Ontology editing Interface: Protégé Optique's Configuration Interface Infor. Workbench frontend API* Information Workbench frontend API* Information Workbench frontend API* Infor. Workbench frontend API* Workbench visualisation engine External visualisation engines Stream analytics mining log analysis Query Formulation Processing Components Query by Navig. Context Sens. Ed. Direct Editing 1-time Q 1-time Q 1-time Q Stream Q Stream Q Stream Q SPARQL SPARQL SPARQL Query Ans man Faceted search QDriven ont 1-time Q Stream Q construction SPARQL Export functional 1-time Q Stream Q ranking, abduction SPARQL provenance, etc. Feedback funct. Ontology and Mapping Manager's Processing Components Approximation Bootstrapper Analyser Simplification ontology mapping ontology mapping ontology mapping Ont/Mapp Evolution Engine Transformator bootsrappers ontology mapping ontology mapping Ont/Mapp matchers Ont/Mapp revision control, editing Configuration of modules LDAP authentification... Sesame Query Answering Component Ontology Processing Ontology reasoner 1 Ontology reasoner 2... Ontology modularization OWL API Query transformation Query rewriting Semantic QOpt. Syntactic QOpt 1-time Q Stream 1-time Q Stream SPARQL Q SPARQL Q Sem indexing Query Execution 1-time Q Stream SPARQL Q 1-time Q Stream Q JDBC, Stream API Distributed Query Execution 1-time Q Stream SPARQL Q Component Manager, Setup module Materialization module Federation module Metadata Sesame Shared triple store Sesame - ontology - mappings - configuration - queries - answers - history - lexical information - etc. Application Layer Federation module Q Planner 1-time Q Stream SQL Q Optimization 1-time Q Stream SQL Q Manager Shared database JDBC, Teiid Stream connector Cloud API Data, Resource Layer... RDBs, triple stores,... temporal DBs, etc. data streams Cloud (virtual resource pool) Components Component Front end: mainly Web-based Group of components API Colouring Convention Optique External solution solution Application receiving answers Types of Users Expert users End users * E.g., widget development, Java, REST 23 / 28
24 fluidops Platform Integration 24 / 28
25 Incremental Implementation. Build on the data you have today. Leverage processing power of existing systems. Engage people to develop knowledge and capabilities 25 / 28
26 Optique Partner Programme 26 / 28
27 Optique Benefits Optique introduces new technology and new ways of working.. More efficient enterprise knowledge workers. More efficient IT operations. More efficient enterprise information management 27 / 28
28 Johan W. Klüwer Michael Schmidt Web: / 28
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