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POSC EP Information Management Conference - Sep
97 BHPP Technical Information Systems Strategy
(TISS) Ian Shaw BHP Petroleum
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Summary of Presentation

• TISS Strategy
• TISS Projects
• Data Management - principles
• Data Architectures - options
• Implementation Issues

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Historical BHPP Environment
EXPLORATION
PET ENGINEERING
Hardcopy and some digital data
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Current BHPP Environment
ASSET TEAM EXPLORATION PET ENGINEERING
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Problems

• Data was not reliable and not accessible
• Personal data stores were popular
• Applications were many and varied with
  duplication of function
• Movement of data between applications needed
  skilled manual intervention
• Few roles or responsibilities defined for Data
  Management
• Few documented procedures to ensure data quality
  was maintained
• Hardware was decentralised and mixed
• Minimal exchange of knowledge between Regional
  Offices
• Value of Data Support Staff was not recognised

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Effect of the Problems

• A reduction in staff productivity
• finding the data
• formatting and reformatting the data
• reconciling the data
• managing many application environments
• managing diverse hardware platforms
• duplication of effort
• An impact on decision quality
• cant find the data
• lack of data context at handover points
• dont know the quality of the data
• dont know if the data is complete

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Future Drivers

• Increased data volumes and data complexity
• 4D seismic
• well bore images
• operational data for HSE requirements
• Increased need for data integration to support
• multidisciplinary work processes
• reservoir characterisation
• visualisation
• portfolio comparisons

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BHPP TISS Vision

• To enable BHP Petroleum to enter the 21st
  Century with functionally rich, integrated
  technical systems capable of supporting our core
  activities in service of technical and management
  users.

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TISS Strategy
Reduced Cycle Time (increased efficiency)
Better Decisions (increased effectiveness)

• Use all available data
• Use quality data
• Use best processes and systems
• Do more iterations

• Faster access to data
• Faster technical processes and systems
• Lower overall system management costs

Increased opportunities at reduced risk (main
area of value add)

• Faster and better decisions

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TISS Management Structure
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TISS Projects

• Data Project 1 - Data Inventory and Processes
• Data Project 2 - Data Architecture Definition
• Applications Project 1 - PE Core Applications
• Applications Project 2 - EP Applications
• Integration
• Infrastructure Project - Computers Networks
• Training Project
• Work Processes

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DP1 - Data Inventory and Processes

• Took a stocktake of existing Data Bases and
  contents in each Region
• Mapped the Data flows for 21 key data types in
  each Region
• Proposed 71 general and global and 184
  Region-specific recommendations to improve DM
• Designated Data Coordinators in each Region with
  an overall Coordinator
• Next Steps
• Recommendations prioritised in each Region -
  quick wins completed
• Define procedures and roles for data management

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Data Project 1 - Data Flow
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DP2 - Data Architecture Definition

• Investigated options available for Corporate Data
  Stores (CDS)
• Developed a short list of CDS options
• Recommended a strategic direction and a preferred
  option for a CDS pilot implementation
• Next Steps
• Test maturity of preferred option
• Plan pilot implementation on one or more Assets
• Review organisation and data management
  processes to maintain the data architecture

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AP 1 - PE Applications

• Interviews, workshops and surveys to gather
  information on experiences and preferences
• Recommended applications in 8 categories
• Commenced projects to select global applications
  for economics, simulation and flow network
  modeling
• Confirmed need for increased integration with
  Geoscience applications
• Next Steps
• Implement new software (migration strategy)
• Investigate new applications and best practices
  in PE

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AP2 - EP Applications

• Reviewed industry directions, vendor strategies
  and plans
• Investigated key areas for improved
  integration/interoperability
• Identified the need for data links and potential
  linking technology options
• Defined application portfolio gaps
• Next Steps
• Initiate projects to build data links
• Evaluate new systems to cover portfolio Gaps
• Define implementation strategy for gaps and links
• Finalise Technical Software Evaluation Guidelines

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Infrastructure Project

• Reviewed industry directions, vendor strategies
  and plans
• Defined key areas for quick wins and for long
  term improvements
• Developed draft guidelines for Unix
  administration
• Enabled sharing of knowledge and BHPP best
  practices to support Technical Systems
• Next Steps
• Complete definition of global guidelines
• Ensure there is an effective on-going Technical
  Systems Forum in addition to formal management
  structures

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Training Project

• Took responsibility for ensuring Change
  Management included in all projects
• Identified need for better training in use of
  existing systems and their integration
  capabilities
• Identified need for improved long term training
• Next Steps
• Clarify how global training needs relate to local
  training processes
• Develop a project to assist in implementation of
  other projects to ensure long term improvement in
  skills

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Work Processes

• High level definition of technical processes and
  identification of key decision points (buy
  acreage, decide development method etc)
• Mapping of processes to current applications and
  data stores
• Next Steps
• Quantification of value of key decision points
• Match value measure with applications and data
  stores as a means of determining priority of TISS
  implementation
• Review potential for organisational improvements
  needed to support improved technical processes

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Why do Data Management?

• The value of our company is based on its
  reserves, its barrels of oil equivalents. They
  are all virtual. We cannot count them, they
  are a projection of our data. What are really
  our assets - barrels of oil equivalent or data
  describing the barrels we think we have?
• Quiet Revolution CERA Report, Nov 96.

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Data is an Asset

• Data Management is part of core business
• Data including information should be treated like
  any other asset.
• 1) Data has an acquisition/generation cost
• 2) Data has a maintenance cost
• 3) Data has a disposal cost
• 4) Data has a value at any point in time
• (How is this value determined?)
• DM seeks to minimise 1, 2 and 3 and increase 4.
• Making data more accessible increases its value.

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The Benefits of Data Management

• Productivity Gains
• Industry estimates suggest that staff spend 20 to
  30of their time dealing with data - finding
  it - verifying it - reformatting it -
  correcting it - understanding the differences
  in standards
• Risk Management
• Users often dont use all available data because
  it takes too long to find or they are unaware of
  its existence

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The Components of a Data Management Solution

• Principles, Standards and Procedures
• Definition of Roles and Responsibilities
• Policies - Regions/Group
• Architecture, software, hardware
• Skilled staff
• Adequate funding
• Committed management
• Committed users

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Roles Responsibilities

• Identify Domain Experts- responsible for the
  meaning of the data item
• Identify Data Owners - responsible for the data
  value - current data - Asset Team -
  non-current data - Data Manager
• Identify data administrators responsible for
  loading, preserving and making the data available

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Ideal Data Architecture Requirements

• Corporate Data Store (CDS)
• Wide coverage of EP data types
• Standards based (POSC Epicentre)
• Vendor independent
• Easy migration from version to version
• No duplication of data
• Store versions of data with contextual
  information
• Easy data loading (exchange) between CDS and
  working databases
• Contains validated data of known quality
• Ability to provide an inventory of all data
  available about an item (eg well) from one logon
• Provides a separation between master/corporate
  data and working data

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BHPP Architecture
Elan/ Petroview
StratWorks
SynTool
Mapping
IRAP/ RMS
SeisWorks
Eclipse
Zmap
Reporting
Finder
In-house DBs
Project Data Stores
Corporate Data Store
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Two Options for Data Architecture

• Option 1
• Product with a relatively mature user interface
  and functionality using some proprietary data
  models but the vendor is committed to adopting
  the POSC standards
• Option 2
• Product is an Epicentre system with a less well
  developed user interface and functionality

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Data Architecture - Option 1
Data Browsing/Selection
Meta Data Registration
Data Loading
Build/Archive Project
Application Interaction
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Data Architecture - Option 2
Data Browsing/ Selection
Corporate Data Store
Data Loading
Build/Archive Project
Project DB 2
Project DB 1
Application Interaction
Application 3
Application 2
Application 1
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Implementation
Data Requirements (Data Growth, Data Complexity)
Option 1
Option 2
Value to BHPP
96 97 98 99 00 01 02 03 04
Time
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Implementation Lessons - Industry

• Plan in detail with review points - series of
  small projects (0.5 to 1 year) rather than one
  large one
• Buy-in is an ongoing requirement - best done by
  results from successful short projects
• Reduce low level debate (eg fonts available)
• Cultural change is not easy
• Manage (reduce) expectations - users and
  management - Things Take Time
• Develop by the Business (EP) with IT used as a
  resource
• Dedicated team - not a part time job
• Difficult to apply objective measures for the
  value added

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The Process of Change
It works!
Not a bad idea!
Taking time ??
Start
Results arent visible
Skeptical
Seeing payoffs
Is it worth it??
The Essence of Change, Liz Clarke
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Acknowledgments

• I gratefully acknowledge BHPP for allowing this
  material to be published and the input and ideas
  from my colleagues on the TISS projects.
• In addition I would like to acknowledge the
  contribution of the staff of the Oil Companies we
  have visited and spoken with.