This document summarizes a webinar about data strategies for managing commodity cycles in the oil and gas industry. It discusses how commodity prices experience long-term supercycles of price increases and decreases. It then covers how different oil and gas companies approach business strategy and data strategy to both reduce costs in downturns and capture growth in upturns. Finally, it discusses how adopting logical and virtualized data architectures can provide oil and gas companies with more flexible, secure, and real-time access to diverse operational data sources to inform business decisions.

1. Data Strategies for
Managing the Cycles in
Oil and Gas
Webinar
May 2020
Quinn Lewis, Consulting Director

2. Agenda1. Commodity (Super) Cycles
2. Business Strategy in Oil and Gas
3. Data Strategy
4. Agile Data Architectures
5. Key Takeaways

3. 3
What is a Commodity Supercycle?
Long term trends in the price of commodities
“The price of commodities is well known to be
volatile with dramatic upswings and downswings.
Overproduction, war, shortages and other factors all
affect the supply and demand of these commodities.
However, aside from these types of short term events, a
larger pattern appears in the price of movements of
commodities; there are multi decade periods of price
ascent and decline.”
FROM https://www.visualcapitalist.com/what-is-a-commodity-super-cycle/

4. 4
Price Cycles Across Major Commodities
Percentage deviation from long-term trend
FROM https://www.visualcapitalist.com/what-is-a-commodity-super-cycle/

5. 5
Supercycles in Commodity Pricing
BPCI Rates, 1899-2016
FROM https://www.visualcapitalist.com/what-is-a-commodity-super-cycle/
“When tracking the macro trend,
Supercycles, or complete rises
and falls above and below the
median line become apparent.
There have been four of these
Supercycles since the beginning of
the 20th century.”

6. 6
ConocoPhillips - 2019 Analyst & Investor Meeting
FROM https://hrcpdocctr.conocophillips.com/Documents/AIM.pdf#page=5

7. 7
Independent - ConocoPhillips
FROM https://hrcpdocctr.conocophillips.com/Documents/AIM.pdf#page=6

8. 8
Supermajor - Shell
FROM https://www.shell.com/investors/shell-and-our-strategy/our-strategy.html

9. 9
NOC - Saudi Aramco
1. Maintain its position as the world’s leading crude oil producer
by production volume and the lowest cost producer, while
providing reliable, low carbon intensity crude oil supply to
customers
2. Capture value from further strategic integration and
diversification of its operations
3. Expand gas activities in the Kingdom and internationally
4. Expand global recognition of Saudi Aramco’s brands
5. Efficiently allocate capital and maintain a prudent and flexible
balance sheet
6. Operate sustainably by leveraging technology and innovation
7. Deliver sustainable and growing dividends through crude oil
price cycles
FROM Saudi Aramco Annual Report 2019 (Page 29)

10. 10
Data Strategy Driven by the Business
Company → Data Vision
Corporate → Data Strategy
Data
Management
Data
Governance
Data
Architecture
Data
Delivery
Principles

11. 11
Data Strategy Spectrum and Elements
“A company’s overall strategy and its industry, competitive, and regulatory environment will inform its
data strategy”
Risk and Regulatory
Cost and Efficiency
Profit and Growth

12. 12
DEFENSE
1. Improve IT infrastructure and reduce data-related
costs (number of databases, etc.)
2. Meet industry regulatory requirements
3. Reduce general operating expenses
4. Streamline back-office systems and processes
5. Mitigate operational risks such as data breaks, fraud,
etc.
6. Rationalize multiple sources of the same data and
information
7. Prevent cyber attacks and data breaches
8. Improve the quality of data
OFFENSE
1. Respond rapidly to competitors and market changes
2. Optimize existing strong bench of analysts and data
scientists
3. Use sophisticated, (near-)real-time analytics for business
4. Leverage new sources of data, internal or external
5. Improve revenue through cross-sell, pricing, and
expanded customer base
6. Monetize the value of the company's data; use internal
data as a product or service
7. Create new products and services
8. Generate return on investments in big data & analytics
infrastructure
Specific Data Strategies
FROM “WHAT IS YOUR DATA STRATEGY?” MAY – JUNE 2017, HBR.ORG

13. 13
Defense or Offense?
Meet industry regulatory requirements
• In April of 2016, the U.S. Bureau of Safety and Environmental
Enforcement (BSEE) published the final Well Control Rule. Some key
additions to the regulations include requirements for operators to use
Real Time Monitoring (RTM) in their offshore operations.
• Beginning April 29, 2019, operators are required to use RTM during
well operations using subsea BOPs, surface BOPs on floating platforms,
and when operating in high-pressure and high-temperature (>15,000
psi, 350oF) environments.
• In the offshore oil and gas industry, RTM is a process that allows
personnel to remotely observe, review, and evaluate data for:
• Well control purposes
• Performance improvements
• Condition based maintenance
FROM https://www.bsee.gov/what-we-do/offshore-regulatory-programs/emerging-technologies/Real-Time-Monitoring-RTM

14. 14
Applied Data Strategies
Risk and Regulatory Cost and Efficiency Profit and Growth
OFFENSE
• Enable real-time
monitoring for well control
• Leverage drone imagery
for pipeline leak detection
• Generate lease road
breadcrumb usage for
safety and tax benefits
• Increase production
capability through
improved completion
design
• Track real-time field
personnel location for
optimal routing and safety
• Apply predictive analytics
thru increased resolution
of time and depth based
drilling data (e.g. stuck
pipe prevention)
• Improve basin play
analysis (using analogs)
DEFENSE
• Preservation of seismic
data for exploration
• Quality assurance for
reserves reporting
• Chemical use reporting for
frac jobs
• Reduce NPT operations
events
• Lower vendor spend
• Prevent loss of leases
• Standardized access to core
data
• Monitor competitor
activity
• Establish gold record well
log curves
• Ensure land data quality
for M&A activity

15. 15
Enabling Data Architecture

16. 16
Rise of Logical Architectures
The evolution of Analytical Architectures: Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs, Gartner April 2018

17. 17
Business – IT Dilemma
IT architecture is unmanageable and brittle because…
IT focuses on
Data Collection
& Storage
Business focuses
on Visualization
& Analysis
Business Needs All of the Data, Now
– So IT creates 100s to 1000s of brittle direct connections and
replicates large volumes of data
Inventory System
(MySQL)
Product Catalog
(Web Service -SOAP)
BI / Reporting
JDBC, ODBC,
ADO .NET
Web / Mobile
WS – REST JSON,
XML, HTML, RSSLog files
(.txt/.log files)
ERP
(Oracle)
Billing System
(Web Service - Rest)
ETL
Portals
JSR168 / 286,
MS Web Parts
SOA, Middleware,
Enterprise Apps
WS – SOAP
Java API
External Vendor
(Internet, Unstruc)

18. 18
Agility and Flexibility through Data Virtualization
ETL
Data Warehouse
Kafka
Physical Data Lake
ML/AI
SQL
interface
Logical Data Layer
Streaming
Analytics
Distributed Storage
Files
ETL
Data Warehouse
Streaming
Physical Data Lake
ML/AI
SQL
Interface
Streaming
Analytics
Distributed Storage
Files
IT Storage and Processing
BI & Reporting
Mobile
Applications
Predictive Analytics
AI/ML
Real-time Dashboards
Consuming Tools
QueryEngine
BusinessDelivery
SourceAbstraction
Business Catalog
Security & Governance
IoT/Sensor
Data
Flexible IT
Architecture
Agile Business
Insights

19. 19
Data Virtualization for Upstream Digitalization
 Asset Tracking & Maintenance
 Competitor Intelligence
 Production Optimization
 Risk/Cost Analysis
 Optimization in Drilling
FROM https://www2.deloitte.com/us/en/insights/industry/oil-and-gas/digital-transformation-upstream-oil-and-gas.htm

20. 20
Takeaways
1. Data strategies are driven by business value, tied to the organization, and enabled
by architecture
2. Logical designs can increase real-time, secure, performant, and shared data access
across Upstream data sources (lakes and warehouses included)
3. Flexibility and control in exploiting your data assets can be achieved by separating
data access from data storage as a fundamental principle
4. Data virtualization has been used in analytics use cases from surface to
subsurface including facilities, exploration, drilling, production and operations

21. 21
Next Steps
Access Denodo Platform in the Cloud!
Take a Test Drive today!
www.denodo.com/TestDrive
GET STARTED TODAY

23. Thanks!
www.denodo.com info@denodo.com
© Copyright Denodo Technologies. All rights reserved
Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm,
without prior the written authorization from Denodo Technologies.