holistic data management strategy for large noc’sadvanced analytics integrated asset model field...

Copyright © 2018 Accenture-Halliburton. Confidential

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Holistic Data Management Strategy for Large NOC’s

Dale Blue

Senior Manager – Global Digital Services

Landmark

Sacha Abinader

Managing Director – Upstream Digital

Accenture


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Disclaimer

This presentation is made by Accenture (Co-presenter) and Halliburton**. The contents of this presentation are for informational purposes only. Halliburton** and Co-presenter make no representation or warranty about the accuracy or suitability of the information provided in this presentation and any related materials. Nothing in this presentation constitutes professional advice or consulting services. No contractual relationship with Halliburton or Co-presenter is established by attending or viewing this presentation. No rights to intellectual property of Halliburton or Co-presenter are granted through this presentation. The opinions expressed in this presentation are those of the author and do not necessarily represent the views of Halliburton or Co-presenter. **Halliburton means Halliburton Energy Services, Inc., Landmark Graphics Corporation, and their affiliates.


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The Alliance represents ‘combined’ credentialized capability to assist NOC’s and IOC’s in achieving their E&P Data Management journey

THE ACCENTURE – HALLIBURTON – LANDMARK ALLIANCE

E&P Industry Leader

Access to global network of petro-technical experts

Technical Expertise

Leader in integration of subsurface data to drive

operational decisions

Integration leadership

98 years of E&P industry experience in over 70 countries

Strategic Insight

Extensive experience delivering complex transformations

change management & digital transformation

Implementation Expertise

World’s leading service provider for digital capabilities

Digital Leadership

Extensive experience in addressing the strategic

challenges of E&P companies


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Digital Vision & Outcome in the NEW

Basin Analysis & Leads Mgmt

Discovery & Appraisal Mgmt

Dev Concept & Design

Development Execution

Surveillance OptimizationField

Management

Digital Leverage

Continuous

learning

Predictable

resultsMultiply productivity

Shrink Time

Manage Costs

Better fact-

based decisions Enable Efficiency

ProductionDevelopmentExploration

Harnessing the NEW requires

► Focus on business transformation, not technology ► New ways to enable the business

► Petro-technical + NEW IT savvy + business process focus + data-focus

Whatthe business

is

Howthe business

invests

The waythe business

interacts

..and the wayevery employee

works

The waythe business

operates

The NEW can transform the way E&P operates

► Cross discipline efficiency ► Comprehensive asset insight ► Future-proofed workforce

Provide full-lifecycle economics visibility

Eliminate waste between technical and business organizations

Jump the learning curve

Improve certainty of outcomes

The Accenture – Halliburton – landmark alliance


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Digitization Trends

Benchmarking and

Strategy

End-to-End Digital

Integrated Platform

Enterprise Data

Management

Engineering and

Advanced Analytics

Integrated Asset Model

Field Instrumentation

and IoT

Improved operational

performance and cost

take out

Enhanced data quality

and decision making

Reactive to predictive to

prescriptive insights and

actions

Real-time operations

visibility and enhanced

safety

Enhanced automation

and field cost reduction

End-to-end integrated

workflows for optimal

field maintenance

Business Outcomes

VisualizeModel and

OptimizeAct

Gather, integrate and align Data

Reservoirs

Drilling Wells

Production Operations

Integrated model for the

overall upstream value

chain

Predictive Maintenance

Production

Optimization

Advance Analytics

Landmark DecisionSpace Platform

GISGnG ERP Maintenance Production Real-Time Other

Agile Field

Development

Planning

Real-time

Drilling

Analytics

Joint Solutions (examples)

The Accenture – Halliburton – Landmark alliance

Targeted

Production

Enhancement

Data Management

Transformation

Leveraging Joint Capabilities to deliver value added Data Management Strategy

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‘Accelerated Commercialization’ & ‘Unit Cost’ Reduction pressures make robust Data Management capabilities a critical imperative for the organization

THE CASE FOR CHANGE

… REQUIRE DIFFERENTIATED

CAPABILITIES…

… UNDERPINNED BY AN

EFFECTIVE DM CAPABILITY

CLIENT’S SUCCESS

DRIVERS…

Unit Cost

Reduction

Accelerated

Commercialization

Integrated

Asset Model

Digital Operations Excellence –

linking technical decisions with

cost implications

Base Production Management –

for steep declining production

Integrated Reservoir

Characterization

Agile and interconnected Business & Operations

Planning

Integrated Factory Drilling

Real Time

Performance

Intelligence

Predictive

Analytics

Source: Accenture-Landmark analysis


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NOC operations contains heterogenous landscapes with various data silos and end-to-end cross platform integration challenges

HETEROGENOUS LANDSCAPE

SAP Corp Master OW EDM Petrel Geolog OSI-PI

Petrotechnical

Enterprise Data Federation

Platform Services

AFE PRA

Back Office

Real-Time

Business

Planning &

Economics

ExplorationIntegrated

Well Delivery

Facilities

Management

Gas

Operations &

Maintenance

Production

and Revenue

AccountingBu

sin

ess

Fu

nctio

ns

Co

re

Syste

ms

Da

ta

So

urc

es

Core

Functions

Sales,

Marketing & IT

Supply Chain

Management

Finance & HR

Corporate


Operational Financial

Analytics & Visualization

ILLUSTRATIVE


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Answers

Why is strengthening the Data Management & Integration

function critical to Org success?

What is the vision for Data Management & Integration, in

order to effectively support the Org business?

What are the current Data Management & Integration

capabilities and pain points?

What actions are required to reach the desired end state in

the short, medium, and long term?

Questions

?

?

?

?

The assessment of current Organization’s DM practices & capabilities, defines ‘Desired State ‘ & necessary steps to achieve it



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Business capabilities will be underpinned by developing a holistic, all encompassing data management framework . . .

ENTERPRISE DATA MANAGEMENT FRAMEWORK


Data Architecture

Enterprise Data Model

Technology Architecture

Integration Architecture

Taxonomies

Warehousing & BI

(Data Analytics)

Data Marts/Data Lakes

Data Dashboards

Descriptive, Predictive &Prescriptive Analytics

Data Quality &Meta-data Mgmt.

Data Monitoring

Data Improvement

Data Harmonization

Metadata Definitions

Data Security &Data Development

Data Privacy

Data Retention

Data Requirements

Data Modeling

Data Governance

Organization

Roles & Responsibilities

Policies and Processes

Standards & Metrics

Data Operations &Master Data

Database Operations

Structured &Unstructured

“Gold Record” Standards

Enterprise Data

Management

Te

ch

no

log

yP

eo

ple

Pro

ces

s

Define and Manage Security Requirements

Define and Manage Data Requirements

Compliance with Corporate Policies and Standards

Data Mgmt. Executive

Data Stewards & Owners

Data Architects

Corporate IT Security

Security Management Tools

Data Modeling Tools

Model Management Tools

Automate/Optimize Data Workflows

Integrate Structured &Unstructured Data

Establish “Gold Standard” Systems of Reference



Data Architects

Corporate IT Security

Database Systems

Document and Content Management Systems

Data Quality Tools

BPM Tools

Data Integration

Metadata Definitions

Data Quality Standards

Data Quality Reporting and Improvement

Data Harmonization Across Systems



Data Managers / Analysts

MetadataRepository / Technology

Data Quality Tools

Data Integration

Build/Sustain Architecture

Integrate and Optimize Data Access

Monitor & Tune Data Marts

Define and Implement Analytics Solutions

Chief Digital Officer

Data Architects

Data Owners

Data Scientists

Database Systems

Data Integration

Data Visualization

Analytics Platform and Applications

Understand Requirements

Build/sustain technology and integration architecture

Build/sustain Enterprise DM

Define Taxonomies and Namespaces


Data Architect


Data Modeling Tools

Design / CASE Tools

Enterprise Architecture artifact Repositories

Data Policies

Data Standards

Business Data Ownership

Data Workflow

Approval & Issues




Governance Artifacts Repository

Metrics Dashboards


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The objective is to drive towards key success factors in order to have a sound Data Management and Integration Strategy

KEY SUCCESS FACTORS

Data Management & Integration approach can be re-used, adapted and scaled for multiple business functions,

assets & solutions

Data Integration approach is extensible to support future solution growth

Adoption for

Growth

Organizational

Capability

Sustainability &

Governance

Structured processes, guidelines and competencies to deliver and support data integration

Stakeholders understand & employ best practices

Clarity of roles and responsibilities at all levels

Program is flexible to adapt and support changing business needs (first gas and long-term adaptability)

Better transparency & traceability of Data Quality from identified System of Records to Functional Solutions

Clear processes in place to govern competing priorities across organization business functions and assets

Internet

Cable

VLAN

Comms DWH User Clients

Management

Console

Data

Comms

Data Source

Data

Cable

Data Source

Comms

VLAN

Cable DWH User Clients

Firewall

DMZ

DMZ Production LAN

10/100 BaseT

Oracle

Inst

EDW

E2900

Domain 1

4 x UIV

64 GB

RAM

Apps Server 2

OLAP /

Business

Object

V890

4 x UIV

16 GB

RAM

16 ports FC Switch

SAN Storage

Production LAN 1000BaseT

Development Clients

Apps Server 1

ETL

V890

4 x UIV

16 GB

RAM

Web

Server

V240

2 x UIII

2 GB

RAM

Backup LAN 1000BaseT

L700

Tape Library

Oracle Inst

Staging,

ODS & FIN

ODS

E2900

Domain 1

4 x UIV

64 GB

RAM

Backup Media Server

V880

4 X UIII

8 GB RAM

Assumptions:

Reuse of existing E2900 server for production

Boxed Up items are assumed to be part of

the existing StarHub Infrastructure

Systems & Applications

Data

Business Process

Application Architecture

Time

Technical Architecture

Source Assessment

Business Requirements

Conceptual Data Model & Models of Analysis

Physical Data Model

Logical Data Model

KPIs and Dimensions

Financial and commercial Projects performance

Target Actual Trend Target Actual Trend

Relationship performance Service performance

Target Actual Trend Target Actual Trend

<= 2 E user: 1.8

<= 2 ISLT: 2.3

<= 2 GTOLT: 2.4

• Comment 1

• Comment 1

• Comment 1Service Changes -

Disputed0 56

Financial - volumes 100% 100% • Comment 1 Late Running Projects 0 2

• Comment 1

Open Disputes 5 2 • Comment 1

Financial - Invoicing 100% 82%

• Comment 1

Bus. Case Impacts 0 - ive • Comment 1

Commercial - changes TBD 24

• Comment 1

Service Changes -

Prioritised0 27 • Comment 1

Service Changes -

Transitional0 23

Behaviour change 3 1.2 • Comment 1 Critical SLA Failures 0 8

• Comment 1

Relationship 0 0 • Comment 1

Agility < 2 2.6

Customer Satisfaction • Comment 1

Standard SLA Failures 0 17 • Comment 1

• Comment 1

Service Improvement 0 0 • Comment 1

VIP Serivce

Improvement0 0

• Comment 1Service Desk

Performance80.0% 90.0%

• Commentary

• Commentary

Services Board Dashboard

Processes Metrics

DATA MANAGEMENT & GOVERNANCE

Information

Business Critical Information

ILLUSTRATIVE



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The critical areas & gaps for improvement between Data and Business functions,

are analyzed and prioritized

DATA MANAGEMENT MATURITY ASSESSMENT BY FUNCTION

Reservoir Eng

Drilling

Completions

Production

Finance

Exploration

Planning

Supply Chain

Bus. Function

Data Function Document& Content

Mgmt.

Data WH &BI Mgmt.

Reference &MDM

Data

Security

Mgmt.

DB Ops

Mgmt.Data Dev.

Architecture

Mgmt.

Meta-data

Mgmt.Governance

Data Quality

Mgmt.

LargeModerateSmall/ClosedMaturity Gap between As-Is and Org Best-in-Class

Source: Interviews with Client; Accenture-Landmark analysis

ILLUSTRATIVE


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MATURITY GAP PROGRESSION & POTENTIAL ORG CAPABILITIES DRIVEN BY DM

‘Shaping Performance’

‘Establishing Balance’

LargeModerateSmall/ClosedMaturity Gap between As-Is and Org Best-in-Class

Data Visibility &Transparency

Well Completions

Workflow Optimized

Predictive

Maintenance

Fully Integrated

Planning & Scheduling

Integrated Reservoir

Management

AI To Support

Decision Making

Digital Twin

Integrated Field

Management

Improved Decision Making

Discovery Phase

Findings

Data Management capabilities need to advance in a phased approach; starting with building foundational capabilities

Source: Accenture-Landmark analysisPotential Capabilities

‘Building the

Foundation’


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IDENTIFY BUSINESS REQUIREMENTS AND USE CASES – SAMPLE DELIVERABLE

1

3

2

SAMPLE USE CASES

1. Develop optimization

2. Surface facility management

3. Supply chain optimization

WELL

CONSTRUCTION

EXAMPLE CASE:Field Development

Planning

SUPPLY CHAIN

EXAMPLE CASE:Logistics forecasting, intelligent routing

BROWN FIELD ASSET INTEGRITY

EXAMPLE CASE:Predictive maintenance

Sample Analytics Use Cases

Collect business requirements and define Use Cases to drive future Analytics across the organization



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holistic data management strategy for large noc’sadvanced analytics integrated asset model field...

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