REINVENTING ENERGY THE DATA FOUNDATION IMPERATIVE · 2020. 9. 4. · REINVENTION ENERGY: THE DATA FOUNDATION IMPERATIVE 2 For decades, the energy industry ran on the assumption that

REINVENTING ENERGY THE DATA FOUNDATION IMPERATIVE The journey to an intelligent,analytics-centric enterprise starts here

REINVENTION ENERGY: THE DATA FOUNDATION IMPERATIVE 2

For decades, the energy industry ran on the assumption that demand for oil and gas would steadily tick upward and the industry could get by with doing what it had always done, just incrementally better over time.

Today, there are a new set of priorities. These are driven largely by the looming global shift from fossil fuel-based to renewable energy sources. Future growth amid this energy transition requires a total transformation of cost structures with new customer-centric operating models, the maximization of every molecule’s value across the value chain, and the use of data as an engine of growth.

To their credit, industry leaders are starting to invest in new data capabilities, artificial intelligence (AI) solutions and cloud infrastructures to help them gather, process and use the information at their disposal. But thus far, they’ve captured just a fraction of their data’s value.

To enable better, faster decisions at this critical juncture, energy companies must embark on (or accelerate) their intelligent enterprise data journeys. With the energy transition fast approaching, there’s not a moment—or data-driven insight—to waste.

Data is trapped everywhere. So is value.


Data imperatives are business imperatives

Using data insights more effectively is a critical enabler of the energy industry’s reinvention. Holistic intelligent enterprise data models are needed to help leaders achieve three imperatives:

Enhancing Agility and Resilience The industry must build more flexible operations to deal with persistent volatility and cyclicality. That means instituting a lower (and more variable) cost structure, relying less on on-site physical assets and leveraging the supply chain network to help absorb market shocks.

Boosting Competitiveness The industry needs to maximize returns by making better decisions across the value chain and removing latency, waste and costs from operations. There are several ways they can accomplish this—from reducing field development and increasing speed to first oil in upstream operations to creating a more flexible pricing/product mix in downstream and retail operations.

Enabling Sustainability As the world continues to embrace non-fossil fuel energy sources and reward companies that take a strong stand on sustainability, the industry must rethink its approach to detecting, preventing and curbing the carbon footprint across its portfolio and subsectors.

Figure 1. The data and analytics environment is growing more complex

Data is fast becoming energy companies’ most valued asset. With data volumes of a typical energy company doubling every 18 months, cloud technologies rapidly advancing, and AI investments growing by 40 percent each year, the opportunity for oil and gas companies to make better use of data is tremendous.

PAST FUTUREBusiness Priorities before:Demand “certainty” limited the need to optimize cost structure/well performance• Increase reserves• Increase production• Reduce operating costs

Business Priorities now:

Data and Analytics before:• Energy sector data-heavy• Focus on reservoir and subsurface• Large volume of data generated

but not used• Asset-centric, remote

environments creating data silos


D&C

ReservoirCharacterization

Production Operations

Midstream

Refining

Retail

Upstream

Retail

Refining

Midstream

Upstream

Drilling and Completions

ReservoirCharacterization

Production Operations

Oil & Gas Industry

• Energy transition requires customer-centric, low carbon system

• Cost structure needs to be transformed through smart sourcing and operating model shift

• Industry needs to maximize value of every molecule across value chain

Data and Analytics now:• Data volume growing 2x every

12 to 18 months• Centralization of data in the

cloud• Energy industry needs to

maximize value out of data investments

• Remote control and applied intelligence are priority with current cost pressuresSource: Accenture Analysis

Data generated Data used Volume of data

The energy industry’s data ecosystem is complex. Millions of sensors are used to measure, monitor and control vast numbers of wells, rigs and other assets. However, most data analyses to date have been narrowly focused on gathering insights that inform reservoir modeling or describe sub-surface conditions. Complicating matters is the fact that most companies run multiple business systems that are asset-or function-specific. Data silos with disjointed processes, databases and platforms are ubiquitous and impede visibility and collaboration across areas such as engineering, maintenance, production operations and finance. As a result, a large percentage of data that could be integrated to gain an end-to-end view of operations is not used at all.

Oil and gas companies that fully leverage data analytics and related tools such as AI, cloud and IoT can boost their returns on capital employed (ROCE) by 15 percentage points.

Figure 2. Analytics can deliver a step change in performance across the value chain

Source: Accenture analysis.

In upstream operations, analytics can help companies optimize the allocation of capital across assets, well designs, and even help cap greenhouse gas emissions. In total, Accenture estimates that these and other actions could reduce breakeven costs by US$10 per barrel of oil equivalent (boe).

Real-time Visibility Resource Planning Process Efficiency System ApproachNetwork Optimization


Optimize field capital allocation

and velocity

Eliminate unplanned euipment downtime

Optimize integrity/corrosion

management

Optimize operational reliability and energy cost

Optimize turnaround efficiency

Optimize inputs-outputs

(crude/product slates)

Optimize fuels pricing

Optimize loyalty programs and new customer offerings

Optimize logistics and distribution

UPSTREAM MIDSTREAM REFINING

10$/boeBreakeven Reduction 10%

RevenueUplift

2$/boeMargin Uplift .5 pp

MarginUplift

RETAIL

15 pp ROCE Increase!

Optimize equipment and integrated supply

chain operations

Quantify and prevent GHG emissions in operations

Optimize gas pressure across

gathering network Cycle Time and ROI

OPEX

GHG Emissions

Asset Capacity

Asset Uptime

HSE Risk

OPEX

Asset Uptime

Product Revenue

Revenue from Fuels

‘New’ Revenue

Supply Chain Cost

Company Spotlight

One upstream company has set its vision for analytics with aspirational (but achievable) targets across the enterprise. It has prioritized the use cases it wants to address first across CAPEX, OPEX and emissions. It has identified what data will be needed. And it’s quantified the investment required to connect its data foundation to make it happen.

In midstream and downstream operations, most value from data and analytics will come from the prevention of unnecessary equipment failures, improvements to inspection programs, and the optimization of energy consumption. In total, Accenture estimates that these and other actions could improve margins by US$2/boe.

Figure 3. One upstream company’s analytics vision focuses on three areas of impact



Planning

Design & Execution

Supply Chain & Supplier Ecosystem

2XReduced

Cycle Time

CAPEX allocation recommendation

Advanced analytics application Volume of data

Full well & frac design recommendation

Logistics route optimization

Artificial Lift design & timing recommendation

Predicting artificial lift failures

Predictive materials & resource procurement

Processing &dipersion model

Energy management from compressors

Field operator route optimization

Optimize field capital allocation

Optimize equipment design & integrated

operations

Quantify and prevent GHG

emissions in operations

Perv

asiv

e us

e of

the

data

10-20%Improved

recovery factor

20-30%Logistics cost

reduction

10-15%Artificial lift

cost reduction

50-80%Deferment reduction

20-30%Supply chain

cost reduction

70-90%Methane emissions

reduction

15-25%Improved energy

efficiency

30-50%Improved people

efficiency

Sand Properties

Historical Completions

Surface Facilities

Coat Catalog Acreage & Land

Resevoir Characteristics

Well Design

Plan & Historical Well

Production Well Economics

AL Properties

Real-time Artificial Lift Data

Real-time Well &

Compressor Data

Drone & Camera Footage

Satellite Imagery

Gas Sensors

Operator Logs

Company Spotlight


A supermajor is using analytics to predict moisture content and its impact in order to optimize its corrosion under insulation inspection program. The capability allows the company to push out costly inspections without increasing risk. The result is a 10-20 percent reduction in inspection costs and an increase in asset life by up to 20 percent.

In the oilfield and equipment services (OFES) space, analytics is slowly being adopted to optimize internal supply chains and operations planning. However, the most meaningful value will come from digitizing products and services. In total, Accenture estimates that the market for upstream digital service providers will be US$10-15 billion by 2025.

Company Spotlight

A supermajor has implemented a cloud-enabled, centralized, real-time customer insight system to measure consumption of a variety of lubricants. The system makes it possible for the company to ensure supplies are available, based on consumption, margin optimization and production data.

Company Spotlight

An integrated OFES company began its digital journey by offering a predictive failure analytics suite for its artificial lift products. Now, it is establishing an ambitious strategy to digitize the entire production domain, from chemicals optimization to workover planning.

In retail operations, the greatest value from analytics lies in optimizing fuel pricing, improving the customer experience and reducing distribution costs. In total, Accenture estimates that these and other actions could increase revenue by 10 percent and margins by 0.5 percentage points.


Mind the Gap. Then close it.A small number of leading energy players are aggressively investing in what it takes to get their data houses in order. They are unlocking the potential of analytics by overcoming the data foundation maturity chasm. Yet, for most energy companies, the promise of clean, connected data and end-to-end AI-enabled business intelligence has not yet materialized. Why? Because companies’ data foundations and operating models have not yet achieved the maturity required.

Energy industry’s data foundation maturity

Figure 4. The existing data maturity gaps for energy companies are significant

MATURITYCAPABILITIES

Implicit From CEO down

Data Strategy

Complex and Siloed Integrated and modular

Data Platform and Connectivity

Undefined Governed across org

Data Governance

Limited focus End-to-end Journey Mgmt

Business Adoption

Basic Product mindset

Data Literacy and Culture

Energy sector current state

Target state required to overcome industry challenges



01

02

An integrated data strategy must be in place to support the new business strategy. In many cases, the data strategy and business strategy will be indistinguishable.

Analytics leaders are intently focused on identifying the business challenges that analytics can address and the data needed to overcome them. They do not pursue dozens of uses cases at once. Instead, they prioritize several high-value, end-to-end opportunities that can offer tangible benefits.

Data is generally not considered a key element of business strategy.

Data silos, complex tooling and monolithic architectures constrain the ability for energy companies to perform cross-functional analytics. For example, in the production domain, most energy companies’ maintenance systems, artificial lift systems, chemical management systems, production data and financial data are not linked. That makes it almost impossible for them to perform the integrated analyses needed to optimize production performance.

This is not just because of the complexity of data. It is also due to a lack of investment. Industry analysts estimate that only 1-2 percent of total industry spending goes to IT and digital enhancements. Energy companies also allocate 20 percent less of their IT budgets, on average, to digital transformations than companies in other industries. Too many companies continue to rely on legacy IT environments that are simply not up to the challenge the industry faces.

Analytics leaders act differently. They invest in an “intelligent data foundation” to overcome the platform and data engineering barriers. Underpinned by a modern, scalable architecture, an intelligent data foundation sets the stage from which energy companies can build data capabilities that can be used across the enterprise securely, on-demand and at pace.

Data platforms and connectivity are typically viewed as IT issues, not a business priority.

These maturity gaps are typically caused by five constraints. Analytics leaders are free from these:

ASK Does your business have an agenda for analytics linked to a compelling business case?

ASK How are you applying innovation in data platforms and engineering to scale our data initiatives?


The technology stack that underpins such an intelligent data foundation needs to enable data aggregation, classification, prioritization, visualization, governance and, of course, security. Specifically, it should enable energy companies to:

• Build the data ”pipelines” to swiftly and securely move the data from where it is generated to where it is consumed

• Classify and prioritize data elements across the enterprise to make them quickly accessible, shareable and easy to connect

• Automate data governance processes and implement business and data management rules at scale and with minimal manual work

• Establish a data controls framework that unifies data classification, enterprise security policies and cloud capabilities to deliver data security and privacy by design

Figure 5. There are six key attributes of an intelligent data foundation

PROVIDE RAPID ACCESS TO PRIORITY DATA SETSBring together the set of diverse, prioritized data sets that will unlock trapped value when blended together

AUGMENT DATA TRUST & USABILITYEnable additon of business context to data, boosting its usability, and provide visibility of its changes from point of origination to consumption, boosting trust in data

ENABLE DATA EXPLORATIONMake data searchable and provide the tooling and collaboration capabilities create cross-functional insight that unlocks trapped value

DELIVER A FAST-PATH FROM IDEATION TO DEPLOYMENT, AT SCALEDeploy a flexible ‘data supply chain’ architecture underpinned by modern platform architecture and engineering for a fast, scalable path to production

APPLY INNOVATIONS IN PLATFORMS, ENGINEERING AND AIContinuously innovate, imlementing a combination cloud platforms, modern data and machine learning engineering, and AI techniques to enable speed, automation and agility

IMPLEMENT DATA SECURITY AND PRIVACY BY DESIGNCombine risk plus value lenses to secure critical data, implementing data security and privacy controls without impacting data exploration


03

04

Most oil and gas companies still operate in a siloed manner. Enterprise-wide data practices and usage/ security protocols are quite rare. Where they are in place, they are typically maintained by understaffed teams relative to analytics leaders.

At the core of any Intelligent Data Foundation must sit a robust, clearly defined data governance framework. Operators would never be able to manage an offshore platform without a clear set of procedures, a dedicated organization and performance management system. Data management requires the same elements of governance. To build trust in (and usability of) data, it must be governed consistently across functions.

This means:

• Establishing policies for everything data-related, from data ownership, acquisition and capture to lineage and quality

• Setting up a central governance organization that manages performance to data-quality metrics

• Leveraging the latest machine learning-based tools to index data and automate enforcement of policies

Data governance is ill-defined or inconsistently applied.

New skills will be needed to align with these capabilities and governance. For example, in a recent Accenture study, we identified that approximately 50 percent of future production engineers’ work will be “digitally enhanced” with analytics.

Data literacy is low.

ASK How can you enable ownership and collaboration for data assets that cross functions?

ASK Do you know what data and analytical skills will be needed? Where are our gaps? How will we fill them?


Figure 6. Analytics will transform the work of the production engineer of the future

A long-term, sustainable talent model has two elements. The first involves hiring newly minted data scientists. The second involves developing ”citizen data scientists” by arming functional experts such as engineers with analytics skills. Since a large percentage of the oil and gas workforce have STEM (science, technology, engineering and mathematics) backgrounds, upskilling them in analytics is likely a more pragmatic option with quicker time to value.

There are several best practices oil and gas companies can apply to ensure they have the analytical skills they need. They can, for example:

• Tap into a broader ecosystem of learning providers to train staff, rather than building custom in-house training programs

• Create the environment that allows analytics to flourish by promoting leaders who continuously model these skills and make them a priority

• Leverage third-party analytics providers to outsource data science work

What activities are completed?What can happen

to the work?

Data analysis

Data monitoring

Engineering and solutioning

External stateholder managment

Reporting

Authoring procedures

Chemical injection monitoring

Chemical and landowner engagement

Continuous improvement

Engineering support for interventions

Frac hit monitoring

Maximizing economic production

On-going and ad-hoc reporting

Procuring labor and equipment for artifical lift

Reducing level of effort (OPEX)

Supporting business needs

Verifying and approving invoices

Well performance analysis

Well surveillance

Domain

Automated

Multi-skilling

Digitally enhanced


05Enterprise-wide adoption of analytics, AI and other data tools represents a major cultural change within an organization. How leaders think about data, the types of problems they can solve, and the innovative solutions they can scale are by-products of a truly data-driven organization.

Additionally, the true value of analytics can only be realized when the business adopts the new ways of working it introduces. The ability to adapt to highly automated and data-driven operations will be key.

Change management is under-appreciated. ASK Do you have a plan in place to ensure adoption of data governance and new ways of working across the organization?

With these foundational elements in place, an oil and gas company can enter the final stretch of its intelligent enterprise data journey. That’s when advanced AI solutions can be deployed to overcome previously insurmountable hurdles. That’s when entirely new ways of working can be implemented, based on new AI-driven insights. That’s when the organization finally realizes the full potential of its data.


Data for speed. Data at speed.While the energy industry has lagged others on the journey to data maturity, energy companies now have the chance to close the gap. With the right focus and investment, they can leap forward with their data and analytics transformations and achieve in 12 to 18 months what would have taken more than five years in the past.

“ANALYTICS INFANCY” Initial POCs developed.

“DATA-AS-AN-ASSET” Up and runing data-driven transformation. It starts with a clear vision and value case for data and AI.

• Formal link between data/AI and value in overall business strategy• Growing connectivity driven by specific business-led AI use cases• Governance prevalent but still primarily IT led• Fit-for purpose, data-driven training programs in place• Measurements for adoption and value in place through sustainment still issue

• Data strategy link to value is implicit• Foundations exist only within business system (e.g. SAP) • Asset/function-centric governance• Limited business participation in POCs• Little to no investment in the change management required for adoption

“AI AT THE CORE OF THE ENTERPRISE” AI solving big rock problems for CXO. It enables predictive operations, drives customer and employee satisfaction, and is pervasive.

• Data and AI critical components of the busines strategy• Data integration at company level leveraging automated technology• Governance a partnership between business and IT• Pervasive data culture (e.g. proficiency tied to individual goals)• Agile change management in place driving sustained adoption and value

~12-18 months

~18-24 months

Technology

Consumer Goods

Oil and Gas

Figure 7. Energy companies can achieve data maturity in just two years

Your 90-day plan starts nowThere are three steps energy companies can start taking today to accelerate the transformation that is needed.


Develop an analytics vision containing use

cases with aspirational performance

improvement targets across the enterprise

Perform a holistic audit across the five key

capabilities—data strategy, connectivity, governance, literacy and adoption—to identify the most critical gaps to deliver the vision

Design an operating model that encourages the use of analytics and

new ways of working

Harnessing data and AI is not a luxury anymore. It is a matter of survival. The question is no longer “when should we start?” but “how fast can we get it done?”

Authors

Muqsit AshrafSenior Managing Director,Global Energy Sector Lead

Matt WaltersManaging Director, Strategy & Consulting, Energy

Sylvain VaquerSenior Manager, Strategy & Consulting, Energy

Neale Johnson Managing Director, Strategy & Consulting, Applied Intelligence

Pankaj SodhiManaging Director, Strategy & Consulting, Applied Intelligence

About Accenture

Accenture is a leading global professional services company, providing a broad range of services in strategy and consulting, interactive, technology and operations, with digital capabilities across all of these services. We combine unmatched experience and specialized capabilities across more than 40 industries—powered by the world’s largest network of Advanced Technology and Intelligent Operations centers. With 513,000 people serving clients in more than 120 countries, Accenture brings continuous innovation to help clients improve their performance and create lasting value across their enterprises. Visit us at www.accenture.com.

DISCLAIMER: This document is intended for general informational purposes only and does not take into account the reader’s specific circumstances and may not reflect the most current developments. Accenture disclaims, to the fullest extent permitted by applicable law, any and all liability for the accuracy and completeness of the information in this presentation and for any acts or omissions made based on such information. Accenture does not provide legal, regulatory, audit, or tax advice. Readers are responsible for obtaining such advice from their own legal counsel or other licensed professionals.

This document makes reference to marks owned by third parties. All such third-party marks are the property of their respective owners. No sponsorship, endorsement or approval of this content by the owners of such marks is intended, expressed or implied.

Copyright © 2020 Accenture All rights reserved.Accenture, its logo, and New Applied Now are trademarks of Accenture.

Jürgen Weichenberger Analytics Executive, Strategy & Consulting, Applied Intelligence

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REINVENTING ENERGY THE DATA FOUNDATION IMPERATIVE · 2020. 9. 4. · REINVENTION ENERGY: THE DATA FOUNDATION IMPERATIVE 2 For decades, the energy industry ran on the assumption that