M A N A G I N G C O M P L E X D A T A | D E L I V E R I N G Q U A L I T Y S O L U T I O N S

K e i t h W i n n i n g , P i p e l i n e D a t a S o l u t i o n s

T h e D i g i t a l i s a t i o n C h a l l e n g e i n t h e O i l a n d G a s I n d u s t r y

‘One of the objectives of digitalisation is to break down data silos and make data more fungible.’

Malcolm Forbes-CableThe Era of Oil & Gas Digitalisation is Upon Us. Energy Voice June 2019

Objective

Value

Maximise Value in Oil & Gas

Financial Performance

Customer Value

Environmental & Societal Value

ROIC (pre-tax)

Affordability

Reliability

Trust

Satisfaction

Increased Safety

Sustainability

Employment

Operating Margin (EBIT)

Capital Efficiency

Accidents Reduced

Injuries Reduced

Emissions

Water Consumption

Impact on JobsWorld Economic Forum Digital Transformation Initiative Oil and Gas Industry Executive Summary January 2017

Projects

Reference Projects

Middle East

• Khazzan Phase 1 & 2

North Sea

• PODS OneMap Support

Caspian

• SCP Expansion (SCPX)

SCPX (Onshore Transmission Line)

Khazzan (Onshore Field Development)

UK North Sea (Subsea Regional Assets)

Project Metrics SCPX Khazzan North SeaProject phase Design & Construction Construction OperationProject type Transmission Field development OperationalAsset type New New New & LegacyData models issued 11 74 1Lineloops / pipelines 11 74 1045Total centreline length (km) 500 400 1970Total record count 0.5 Million 0.5 Million 1 Million +

Definition

Creation

Validation (third party)

Remediation

Inspection data loading

Project Metrics

Definition

Definition – Key Processes

Data Modelling Specification

Project Requirements

Data Modelling

Philosophy

Project Close Out Report

Approved Lessons Learned

Operator Processes

Data ModellingContractor Processes

• Schema

• Data dictionary

• Corporate/project classification

• Minimum requirements

• Management of change (requirements)

Definition – Data Modelling Specification

• Projections and datums

• Project level domain values

• Interfaces

• Delivery formats

Definition – Project Requirements

• Gap analysis of operator’s specifications and requirements

• Methodology• Modelling procedure• Data classification• Management of missing / incomplete data by classification• Model metrics

• Measurable KPIs

• Management of change (Process)

Definition – Data Modelling Philosophy

• Out turn metrics

• Review (scope, budget, schedule)

• Technical queries submitted

• Approved changes to schema

• Lessons learned for review

Definition – Project Close Out Report

Gap Analysis

Technical Queries

Submitted

Approved Changes

to Schema

Approved Lessons Learned

Revise Documents

Definition – Lessons Learned

Creation

Inconsistency across models / lines

• Follow modelling philosophy

• Schema changes approved

• Domain compliance

• Field dimension compliance

• Use of automated processes

• Use of checklists

Data Model Creation

Managing related models

Bad / missing data

Management of change

Managing related models

• Master external TagID database

• Linked stationing

• Use of automated processes

• Use of checklists

Data Model Creation

Inconsistency across models / lines

Bad / missing data

Management of change

Bad / missing data• Be pragmatic – not all data is equal

• Manage in accordance with philosophy

• Deviations from philosophy need approval

• Document at record level

Data Model Creation

Inconsistency across models / lines

Managing related models

Management of change

Management of change• Client approval

• Document deviations at record level

• Model specific documentation (metrics)

• Close out report

• Review lessons learned

Data Model Creation

Inconsistency across models / lines

Managing related models

Bad / missing data

Validation

Referential

Schema

Integrity

Geospatial

Third Party Data Model Validation

Referential

Schema

Integrity

Geospatial

Referential Integrity

• Top down approach

• Focus on core tables

• Automated processes:• Identify orphan records• Missing data• Offline data

Referential

Schema

Integrity

Geospatial

Schema Integrity

• Documented approved schema changes

• Automated processes

• Domain and dimension compliance

Referential

Schema

Integrity

Geospatial

Geospatial Integrity

• Automated processes• Measures and station values• Coordinates• Projections• Geometry definition

(ControlPoints)• Site features

Remediation

Identify

Analyse

Document

Propose Solution

Approval

Procedure

Processes

Identify Issues – Develop Solution

Perform

Focus on Key Issues

Error Index

Automated Processes

Validation

Model Metrics

Document

Execute – Document

Maintenance

• New and divested assets

• Changes to supporting systems

• Links to external document systems

• Schema revisions

• Lessons learned

Change Management

• Incorporation of new project data

• ILI and inspection data

• Repairs

• Diversions

• Third party crossings

Data Loading

mac16_09-000062120326110999041949663511120000917502900000000-831-031-251-0340768-0340189-0360120-141700039-117900025-099800017-087600006-075000035-070400025-065700031-060700003-058100028-056100015-0550-0002-0542-0010-0514-0029-0497-0018-0463-0028-0431-0029-0393-0032-0364-0029-0333-0032-0309-0032-0281-0034-0262-0037-0245-0028-0207-0023-0185-0025-0156-0024-0135-0025-0125-0033-0108-6000-0103-6000-0103-6000-0101-6000-0100-6000-0104-6000-0157-0032-0096-6000-0080-6000-0145-0031-011500005-0101-0021-0085-0018-0075-0018-0073-6000-0072-6000-0070-6000-0069-6000-0075-0014-0064-0018-0064-0015-0058-0015-0051-0011-0049-0012-004300001-003700006-003200009-003000019-002300024-001300028-0004000290000000030000090002900018000240002300021000320001600055-600000059-6000-002100021-001700025-00070002400000000300000600028000120002600067-600000069-6000000170002500074-600000064-001700075-600000078-6000000240002200082-600000070-001800092-600000097-600000099-600000101-600000103-600000104-600000103-600000109-600000105-003200065-001700086-001800121-002900147-003300181-003900195-003900216-003900245-003900269-004000302-003800329-003800369-003800402-003700452-003700498-004000530-003100552-002000574-001500606-0006006160000000711-002400827-003600903-003800951-002801081-003601150-00290119800167012070010401278-0019

Legacy Inspection Data

Header Code mac16_09KP -000062Time 120326Date 110999Easting 0419496Northing 6351112ROV Gyro 0Reference Depth 91.75Depth of Burial 0.29Spare 0Raw Depth 0Seabed left X -8.31Seabed left Y -0.31Trench left X -2.51Trench left Y -0.34Seabed right X 7.68Seabed right Y -0.34Trench right X 1.89Trench right Y -0.36Points 120

Legacy Data Transformation

120 pairs of cross profile coordinatesrelative to the TOP

Inspection Data – Is This An Issue?

Summary

Definition

Creation

Validation

Remediation

Care

Summary

• Data Modelling Specification

• Project Requirements

• Data Modelling Philosophy

• Data Modelling Procedure

• Project Close Out Report

• Approved Lessons Learned

Definition

Creation

Validation

Remediation

Care

Summary

• Follow data modelling philosophy

• Schema changes approved

• Domain compliance

• Field dimension compliance

• Use of automated processes

• Use of checklists

Definition

Creation

Validation

Remediation

Care

Summary

• Referential Integrity• Top down approach• Focus on core tables

• Schema Integrity• Documented approved changes• Domain and dimension

compliance

• Geospatial Integrity• Measures & station values• Coordinates & Projections• Geometry definition

Definition

Creation

Validation

Remediation

Care

Summary

• Identify• Analyse• Agree solution• Document procedure &

processes

• Perform• Automated processes• Validate model• Report model metrics

Definition

Creation

Validation

Remediation

Care

Summary

• Change Management• Incorporation of new project data• ILI and inspection data• Repairs & diversions• Third party crossings• New and divested assets• Changes to supporting systems• Links to external document

systems• Schema revisions• Lessons learned

Closing Thought

‘Today, the Oil and Gas sector has the opportunity to redefine its boundaries through digitalization.

After a period of falling crude prices, frequent budget and schedule overruns, greater demands of climate change accountability, and difficulties in attracting

talent, Oil and Gas companies can provide practical solutions.In the short term, digitalization can act as an enabler to tackle these challenges

and, in the long term, provide value to all of the industry’s stakeholders.’

Bob DudleyGroup Chief Executive, BP

Digital Transformation Initiative - Oil and Gas IndustryWorld Economic Forum, January 2017.

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