AKSIO - Semantic Support for Experience Transfer in ...
Transcript of AKSIO - Semantic Support for Experience Transfer in ...
19. okt. 2006Slide 1 Computas AS
AKSIOSemantic Support for Experience Transfer in Integrated Operations
Roar Fjellheim, Computas AS Roar Fjellheim, Computas AS
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Outline
North Sea oil production
AKSIO – Active knowledge support
Semantic technology in AKSIO
Preliminary results
Prospects
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North Sea oil exploration and production
Challenging environment
Complex technology
Large-scale operations
“99.99%”requirements
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The value of knowledge
Case 1 – Lost well• Field A used equipment not compatible with drilling fluid –
result: equipment could not be withdrawn. Well had to be abandoned without producing oil – a loss of at least $100 mill.
• Company had identical incident at Field B 3 months earlier
Case 2 – Stuck pipe (recurring)• Drilling operation has a typical daily cost of $0.5-1 mill. and a
total cost of $20 mill. A stuck pipe incident may stop the operation for 1-2 days, resulting in up to 10% additional cost
• Stuck pipe is a common event and can be avoided in most cases by careful application of existing knowledge
In both cases: Knowledge transfer failed!
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Drilling process, organization and discipline networks (CoP)
Develop RTD
Develop RTD
Developdrill program
Developdrill program
OperationOperation
Finalreporting
Finalreporting
CoP 1
CoP 2ExpertiseLearning
Organization
Typical cost: $0.5-1 mill./day
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AKSIO – Active Knowledge System for Integrated Operations
Develop, test and evaluate an active socio-technical system (work process supported by technology) for improved KM in integrated operations• Provide decision makers with the best available knowledge in a
task-relevant, timely, and contextual manner
• Provide feedback loops for capturing and integrating new knowledge (and deleting obsolete knowledge)
Hypothesis• The active knowledge system must be completely embedded in
main work processes and be part of ordinary work
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Actors involved in AKSIO
Supportsenter
Offshore rig
Operationscenter
Communities(>20 CoPs)
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Experience transfer via databases?
Drillingprojects
DBRReporting
“Supply”
Well planners
Search
“Demand”
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Nancy M. Dixon: “Does your organization have an asking problem?”, KM Review, May/June 2004
Experience transfer is a social process
”Facilitation”!
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ExperienceFeedbackFacilitation
DBR
Disciplinecommunities
Drillingprojects
Support center
“Supply”
Planners
“Demand”
Facilitated experience transfer - collaboration
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Use case #1 - Capture and qualifyoperational knowledge
Drilling Projects
RegisterexperienceRegister
experience
ProcessfacilitationProcess
facilitation
DBR Experience
Drillingontology
AnnotateexperienceAnnotate
experience
Knowledge Resource
Map
B&Bexperts
DBRExperience
Governingdocuments
Discipline Advisors/ Networks
Subsurface Support Center
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KRM - Knowledge Resource Map
Original experience report+ Discipline leaders’ comments
+ Rich ontology-driven categorization
+ References to other experiences
+ References to governing documents
+ References to experts and other relevant people
+ Recommended follow-up
Interlinked “Web of knowledge and expertise”
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Use case #2 - Supply relevant knowledge to well planning
Well Planning Team
Create drillprogram
Create drillprogram
Drillingontology
Get relevantexperience
Get relevantexperience
Drillprogram
Discipline Advisors/ -networks Knowledge
Resource Map
Work process includes
B&Bexperts
Governingdocuments
Subsurface Support Center
DBRExperience
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Semantic technology in AKSIO
Drilling ontology
Representing the KRM
Screening and annotation
Ontology/process-based retrieval
Compliance to W3C standards
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AKSIO drilling ontology
Using existing taxonomies and ontologies is difficult• Possibly too heavy-weight and complex for efficient
annotation and search
Question driven ontology scoping related to application• “Do we have experience with EQUIP-34 used for
CEMENTING-OP-962?”
• “Which pressure-related problems are most frequent in this type of geological formation?”
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A simple drilling ontology (top-level)
State
Event
FieldSection
Formation
part-of
part-of
part-ofhas-state
causes
Equipment
Resource
Material
is-a is-a
AreaWell
part-of
prod
uces
owns
causes
usesOrganizat
ionOperationperforms
Plan Engineering
produces
guides performs
has-state causes
Concepts
Relations
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RDF representation of the KRM
Explicit semantics by representing metadata and annotations in RDF
Federated approach to source systems
KRM stored in triple-store
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Screening and annotation
Semi-automated screening based on metadata and ontology
Semantic annotation by discipline advisors• GUI driven by the ontology
• Editors create relations between KR and ontology and free-text annotation
Possible future extension: semi-automated annotation
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Context-driven knowledge activation
Well planning groupCollaborative decision making
Drillin
ExperienceReports
withSemantics
Task-relevant, timely, and contextualized
information
Search
metadata
Work
proce
ss
Produksjonspakning
Anker med hydraulisk
kommunikasjon
Well d
ata
User
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Process- and ontology-based retrieval
Ranking results based on ontology expansion tactics, occurrence, and annotation
Generating links to other experience reports and human experts
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Technical platformSemantic technology standards • OWL for classes and property definitions
• RDF for instances
• SPARQL for federation of queries
Infrastructure• DBR legacy application
• Web services integration
• Oracle 10.2g
• Microsoft SharePoint: collaboration, tasks and metadata
Tools• Protégé & plug-ins
• Jena 2.3
• MYSQL as Jena backend
Architecture
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How does the business see AKSIO?
DBRExperience ++
Ongoing drilling operations
Established experience
1. Quality assuranceof new experience reports
AKSIO-supportedprocesses
2. Search and activation of relevant experience
Integrated with normal work
processes and IT tools
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Preliminary experimental results
Process 1. Screen and annotate knowledge• Screening: Suppress up to 60% of experience reports as
irrelevant, too specific, etc.
• Annotation (qualitative assessment): Significantly increased understandability and reuse potential
Process 2. Search and activate knowledge• Work in progress
• Expected benefits: Increased and more systematic reuse of knowledge, timely and relevant information
• Challenge finding the required contextual info
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Ontology-enabled active search
Normal key word search• Search for experience on
Cementing
Query expansion tactics• Concept specialization and generalization:
e.g. • Expanding Bridge Blug Retainer to Cementing
• Concept relationse.g
• “Do we have experience with Gyro equipmentwhen used for Wireline operation”
• “Do we have experiences with Pack-off causing Stuck Pipe?”
• “Which pressure related problems have we met in Tare formation ?”
State
Event
FieldSection
Formation
part-of
part-of
part-ofhas-state
causes
Equipment
Resource
Material
is-ais-a
AreaWell
part-of
prod
uces
owns
causes
usesOrganiza
tionOperation
performs
Plan Engineering
produces
guides performs
has-state causes
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Ongoing tasks
Semi-automated annotation
Using other domain ontologies
Visualization and navigation of search results
Integration with commercial search tools (FAST)
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Prospects - Integrated operations G1 and G2
3-4% increased oil recovery
5-10% accelerated production
20-30% lower operational cost
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Prospects – Collaborative decisionmaking
Dynamic decision model
Real-time well operations data
Collaboration infrastructure
Collaborative decision making
Decision implementation
Operators Service providersExperts
Ontologies Well operations knowledge base
Decisions formaximal value
Real-time
Automation
Ontologiesenablecollaboration
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Summary
AKSIO focuses on knowledge management in integrated oil drilling operations
Uses semantic technology to retrieve knowledge in contextual and timely manner
The project designs, implements and verifies real scenarios using semantic technology
Preliminary results are encouraging and the project will next address decision support