The EP Technical Data Quality Metrics...

Presentation Title (acronym) ; Division – Name/OPU/HCU/BU (acronym) ; Name of Presenter ©Petroliam Nasional Berhad (PETRONAS) 2014 1

Presentation Title: DQ Metrics Architecture

Division – PETRONAS

Name of Presenter : Philip Lesslar

© 2014 PETROLIAM NASIONAL BERHAD (PETRONAS) All rights reserved. No part of this document may be reproduced, stored in a retrieval system or transmitted in any form or by any means (electronic, mechanical, photocopying, recording or otherwise) without the permission of the copyright owner.

The EP Technical Data Quality Metrics Initiative

Philip Lesslar, Technical Assurance, Technical Data Digital Energy Conference 13th October 2014 Impiana Hotel, Kuala Lumpur

Architectural Considerations for Global Governance








• Objectives • Requirements for global governance • Key supporting initiatives • Architecture elements • Global governance readiness • Dashboard designs • Concluding remarks

Contents








Contents









• Show the relevance of architecture in addressing business problems

• Explain PETRONAS’s Data Quality Metrics initiative within this context

Objectives








Data

collation

Project

set up

Workflow and

Standardization

Project

Database

Application Source / feeder

databases

Master

(Reference) Data

Data Quality

Metrics

Corporate

Database

Publishing

Re-use

Previous industry studies : Geologists spend up to 60% of their time looking for & quality checking data for their projects

Re-use

Value Loop

Value Loop

Corporate Data

Cost management

Process improvement

Optimization

Reduce the 39 days to 5 mins

for the 4 data types.

Standards & workflows

Bring to acceptable quality

Capture work done for re-use

‘One version of truth’ datasets

Re-use many times

1

1

1

2

2 2

2

3

3

3

Approved

& final

Feeding new projects

unique master source

of quality checked data

The project space is where

the big decisions are made

Reduction of time for data

collation and project setup

Step 1

Step 2

Step 3

The Role of Data Management in Value Creation

Recent PETRONAS study : Geologists spend 39 days looking for & quality checking basic data (well header, checkshots,

deviation and well logs) for their projects. This is an estimated 20% of their time.

Internal








Contents









• Central data quality team

• DQ tool with global reach

• Ownership & accountability framework

• Standardized application and database portfolio

Minimum Requirements for global governance








Contents









• Data standards program • Data type standards, databases, reference data etc

• Data quality metrics initiative

• Business rules for - Data type (completeness, integrity, workflow), projects (structure), databases (footprint compliance), standards (compliance)

• Governance dashboards for transparency

• Technical Data Framework (TDF) rollout

• Guidelines, roles & responsibilities, accountability structure

• Application standardization

• Technical Applications Portfolio Management

Key supporting initiatives








Contents









IQM - Architecture

Query engine in central

runs against central and

remote databases

Query engine writes metrics

back to central database

Web clients from anywhere on

the company network can

configure IQM and see results

IQM Web server and the IQM

database are centralized

Web Client

Batch engine

Database

Web forms

EP Data

Configuration/results DB

Web Client

Metrics Engine

IIS (Internet Information

Services) Web server

Configuration/reporting

Central Remote

User interface

Oracle Oracle Oracle Oracle Oracle Oracle

The query engine can

run once, as a

nominated global user,

or multiple times,

against each remote

site

Diagram courtesy of Exprodat Inc.








Data Quality Metrics Aggregation (concept)

Single Query

eg. select well_name from well_master;

PROJECT

DATABASE

TARGET

Single Query

Single Query

Single Query

Single Query

Query Set

PROJECT

DATABASE PROJECT

DATABASE

PROJECT

DATABASE

PROJECT

DATABASE

OTHER

DataBases OTHER

DataBases OTHER

DataBases

MULTIPLE QUERIES MULTIPLE TARGETS

Multiple

Query

Sets

Multiple Target Sets Single

Metric

Single Metric

Single Metric

A G

G R

E G

A T

I O

N

MULTIPLE QUERY SETS MULTIPLE TARGET SETS








Data Quality Metrics Aggregation (practice)

Databases

of Interest

A B C D E F etc.

Data

of Interest

Data Element

of Interest

Assets

of Interest

Regions

of Interest

The (Enterprise) Dashboard

Bu

sin

ess

Fo

cu

s

Data

Fo

cu

s

The (Enterprise) Data Quality Trend








Contents









Global governance - scenarios

Central office

IQM

Metrics

engine

Country without data

export restrictions

Country with data

export restrictions

Implement default

architecture

Implement partly

distributed architecture

Our EP databases (Oracle & SQL Server)

Wells Projects Logs Our EP databases (Oracle & SQL Server)

Wells Projects Logs

Our EP databases (Oracle & SQL Server)

Wells Projects Logs

1 2 3








IQM – Architecture – Partly distributed

Central query engine runs against

central (and optionally remote)

databases

Web clients from anywhere on

the company network can

configure IQM and see results

IQM Web server and the IQM

database are centralized

Web Client

Batch engine

Database

Web forms

EP Data

Configuration/results DB

Web Client

Metrics Engine

IIS Web server

Configuration/reporting

Central Remote

User interface

Oracle Oracle Oracle Oracle Oracle Oracle

The query engine runs

in each site, under local

control

Metrics Engine

All query engines

writes metrics

back to a single

central database

Local query engine

captures metrics from

local databases

No data leaves the

country, only results

Diagram courtesy of Exprodat Inc.








Contents









DQ Dashboard designs – 4 candidate structures

DB

By Database

DB

By Datatypes

DB DB

DB

By Projects

DB

By Workflows

DB DB

1

2

3 4

Examples:

FINDER

CDS

EDM

OPENWORKS

Examples:

Well Header

Deviation

Checkshots

Examples:

Petrel Studio

Work in progress








DQ Dashboard designs – Combination 1 & 2 By Database & Datatypes

Databases Datatypes

A B C D E F G H I J K L








DQ Dashboard designs – 3, By projects

May 2014

April 2014








Concluding remarks

• Architecture is an important aspect to consider in

any implementation

• A data quality metrics initiative needs to be flexible

and scalable

• Sustainability aspects of such an initiative must

not be underestimated

• A data quality metrics program is not short term,

therefore ensure there are short term deliverables

to maintain credibility





Thank you

The EP Technical Data Quality Metrics...

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