19. okt. 2006Slide 1 Computas AS

AKSIOSemantic Support for Experience Transfer in Integrated Operations

Roar Fjellheim, Computas AS Roar Fjellheim, Computas AS

19. okt. 2006Slide 2 Computas AS

AKSIO

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result: equipment could not be withdrawn.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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AKSIO

19. okt. 2006Slide 6 Computas AS

AKSIO

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

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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AKSIO

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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Screening and annotating - GUI

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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