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DRIVING VALUE FROM UPSTREAM DATA.How to measure and shape data into a high-return investment for the future.
An Infosys Consulting Perspectiveby James Soos, Sachin Padhye and S. Ganesh
[email protected] | InfosysConsultingInsights.com
Driving value from upstream data. | © 2020 Infosys Consulting Page 2
For many organizations, data is the new gold, but its value is not as easy to
ascertain. Especially within the oil and gas sector, lack of standardization and
a number of dimensions make valuation difficult. This paper reviews this
problem in detail, outlines a method for valuation and recommends ways to
drive valuation efforts in organizations.
INTRODUCTION
Driving value from upstream data. | © 2020 Infosys Consulting
Page 3Driving value from upstream data. | © 2020 Infosys Consulting
Every business decision, whether by a human or artificial intelligence (AI),
is based on available data.
Organizations tend to overlook the cost benefits and the value that
quality data itself contributes to decision making. Within upstream oil
and gas, the health of the data plays an important role across several
workflows such as reservoir management, drilling and completions,
facilities engineering and base business and operations.
Several use cases are cited where data can help increase speed to first
oil, enhance production, lower costs and unproductive time, and reduce
health, safety and environmental risks. Yet, there is no standard way to
determine the worth of the data itself that helps increase business
value.
As data appreciates in value, organizations must start treating it like
an asset.
The Increasing Importance of Data
While we measure the results of decisions made through data, we rarely calculate the additional benefits of the direct outcomes, that may result directly from improved quality of data.
Page 4Driving value from upstream data. | © 2020 Infosys Consulting
Like an asset, data can be owned and stored; it needs to be curated and maintained; it can be stolen, bought and sold; it can generate future benefits. Although data is not valued on the balance sheet, it needs to be guarded against misuse. The value of real assets increases as they are processed through a value chain. Acreage becomes more valuable as discoveries are made and appreciates as producing assets are developed. As data is processed through the value chain its business value increases too. Assets are valued for various reasons. Three main reasons are to ensure a fair price for acquiring or developing, to analyze its investment potential, and to plan and prioritize investments for acquiring or developing more assets. Similarly, data needs to be acquired at an appropriate price, maintained at a suitable cost and prioritized to align with business objectives. For these reasons, data needs to be valued just like assets.
Drawing a parallel between data and asset is the first step towards
determining its value.
Asset versus Data
Data can be stored, change ownership and has potential for investment and appreciation, therefore, it needs a systematic valuation, just like an asset.
Geological and
Geophysical
Reservoir
Engineering
Drilling &
Completions
Production
VALUE
Maintain
Store
Use
Acquire
Risk Management
Page 5Driving value from upstream data. | © 2020 Infosys Consulting
There are several approaches to calculating the value of data. These
range from assessing its market value – income generated by selling or
renting it, to assessing the cost of replacement. We use a grounds-up
approach while calculating the value of upstream data:
First, we identify data elements that constitute a workflow.
Second, we determine the value of each data element. For instance, we
identify AFE costs, definitive directional survey, real time completion,
real time drilling and well header as data elements that constitute the
drill and complete well workflow and then calculate the value of each
element in using the following equation.
A grounds-up approach helps to identify data elements of a workflow
followed by determining the value of each.
Valuation of Upstream Data
Workflow Data Element
Drill and Complete Well AFE Costs (Estimated Vs Actual)
Real Time Completion
Real Time Drilling
Well Header
Definitive Directional Survey
Decline Curve Analysis Decline Curve
Production Forecast
Well Header
Surveillance and Monitoring Drilling Jobs and Events
Production
Well Header
Knowing the value of individual elements enables organizations to bring about change and arrive at an ideal workflow.
Page 6Driving value from upstream data. | © 2020 Infosys Consulting
The terms in the above equation are explained as follows:
RevenuesData elements contribute to generating revenue. The contribution is
considered in proportion to the cost of acquiring that data element.
For instance, if the acquisition cost of a data element is 0.5% of the
drilling and completion costs, the contribution of that data element
to production (or revenue) is 0.5%.
Revenues are of three types:
Time Revenue: Contribution of revenue from data is high when acquired,
goes down in the medium term, increases in the long term. The time
revenue of data varies depending on when the data element is most
required.
Performance Revenue: The more groups use a data element, the more
it contributes to revenue.
Decision Revenue: If a data element influences all three levers of
production – increase in reserves, developing assets and increase in
production, then the contribution to revenue is greater.
Function
Workflow Group
Workflow
Data Element
BOE - Value
Data Valuation Model
Revenues
Risk Management
Process People Technology
Reve
nue
Cos
t/Ex
pen
se
Explore
Characterize
Prioritize
Develop
Execute
Operate
Wells
Risk
Business Drivers
Time RevenuePerformance RevenueDecision Revenue
AcquireUseReplaceMaintain
EH&SRegulatorySecurityProdcution
Data AssetValue
Calculating business value of dataValue of data = Revenues generated by data element + Risk Avoidance – Expenses incurred for data element
Expenses
Page 7Driving value from upstream data. | © 2020 Infosys Consulting
Risk AvoidanceRisk management is a top priority across the entire value chain and
data is used to make decisions that help. Risk events are identified,
likelihood and consequences are considered and a monetary value is
assigned for the risk that is mitigated given the health of the data. The
improvement in likelihood and consequences are evaluated based on
the health of the data element. The monetary worth of this improve-
ment is determined and used to calculate the value of data.
ExpensesData elements need to be procured, collated and managed for user
access and consumption. The expenses incurred to facilitate access and
consumption are classified as follows.
Acquisition Expense: Cost of acquiring data either from external
sources or various internal efforts
Use Expense: Cost to use the data element. Use expense is inversely
proportional to the health of the data element. This depends upon
accuracy, completeness and noise. The use expense is low if complete-
ness and accuracy are high and noise is low, and vice versa.
Replacement Expense: Money spent to re-acquire the data, if lost.
Maintenance Expense: Cost of resources and infrastructure required
to maintain the data element.
Page 8Driving value from upstream data. | © 2020 Infosys Consulting
Business value of well headerBy adjusting different levers, organizations can arrive at the ideal state of data.
The difference between the ideal state and the current state of data
maturity can have massive cost implications.
Opportunity Cost of Data Maturity
The current value of data is based on its state. The target value is simulated by changing the current levers of
revenue, risk and expenses. The difference between the target and the current value provides the opportunity
cost of data. Along with calculating the value of data, it is equally important that an organization manage the
change for treating data as an asset.
Score: 0 to 10Score: 11 to 20Score: 21 to 30Score: 31 to 40Score: 41 to 50Score: 51 to 60Score: 61 to 70Score: 71 to 80Score: 81 to 90Score: 91 to 100
Select Data Maturity ScoreData Maturity Score
0 100
65
65
$184M$184M $380M$380M
$184M$176M $6M$6M
$184M$184M $13M$13M
$50 $50 $6M$6M
Time RevenueTime Revenue
Current ScenarioInput Panel Target Scenario
Aquisition ExpenseAquisition Expense
Business Value Business Value
Performance RevenuePerformance Revenue Usage ExpenseUsage Expense
Decision RevenueDecision Revenue Maintenance ExpenseMaintenance Expense
Risk AvoidanceRisk Avoidance
Current Business Values by Category Target Business Values by CategoryBusiness Value – Current & Target
Replacement ExpenseReplacement Expense
1. Real Time2. Long Term3. Medium Term
3-Medium term Not Sunk Cost-Consider
Not Sunk Cost-Consider
2-Medium 1-High
1-High 2-Medium
3-Low 3-Low
1. Black-out2. High3. Low
1. High2. Medium3. Low
1. High2. Medium3. Low
1. High2. Medium3. Low
1. High2. Medium3. Low
1. High2. Medium3. Low
$189M $197M
Select Workflow
Drill & Complete Well
Select CDE
Well Header
Avg well life (years)
30
Horizon (years)
20
Price per barrel
$50.00
Number of wells
47500
Production (BOEPD)
2931001
Annual decline rate
5.00
Revenue RevenueCost CostCurrent
$0M
$50M $50M
$50M
$100M$405M $405M
$544M $552M
$150M
TargetRisk Risk
Page 9Driving value from upstream data. | © 2020 Infosys Consulting
Just as the custody and value of a piece of equipment is tracked at
every step (such as assembly, manufacturing, packaging, shipping
and installation), so should the health of data be assessed at every
stage, change of custody and movement. Another parallel between
real assets and data is that storing, maintaining and guarding data
incurs a cost. If data is valued, then data management efforts can be
prioritized. Concepts such as charging various groups within an
organization to use data or creating an internal data marketplace
where groups can buy, sell and trade data can also help to reinforce
the notion and importance of data as an asset. The latter concept may
find takers especially in a federated system where individual business
units fund their own data management and improvement efforts.
Organizations must start treating data as an asset of value. Focused
efforts are required to change attitudes.
Changing Mindsets for Quality Data
Data valuation can help better align its needs and improvement efforts with business objectives.
Data movement and custody must be tracked to assess its health and better data management practices can be prioritized as per business needs.
Page 10Driving value from upstream data. | © 2020 Infosys Consulting
Data is an asset with an appreciating value. Efforts must be made to standardize
its valuation and to arrive at methods of doing so. Important aspects such
as types of revenue, risk management and different expenses must be
taken into account while determining its worth. The oil and gas sector must
pay special attention to changing organizational mindset from being data-
outcome oriented to data-health oriented. This can be achieved by tracking
its movement and custody and focused data management practices as per
business needs.
Conclusion
Page 11Driving value from upstream data. | © 2020 Infosys Consulting
MEET THE EXPERTS
Sachin works with large oil and gas companies in the upstream, midstream and down-
stream areas to frame their digital strategy across customer and employee experiences.
He helps his clients quantify value beginning with industry opportunities and ending
with decisions built with big data, analytical tools and visualizations and narratives. His
current focus is digital data monetization, where he helps companies put a monetary
value to the data that is used to execute their digital strategy. Sachin has an MBA from
the University of Michigan.
SACHIN PADHYE
Sr. Principal, Energy & Utilities practice (U.S.)
S. Ganesh has been with Infosys Consulting since 2011 and has led a number of digital
transformation projects for our key clients. During this period he has worked with
clients in a range of industries such as oil and gas, telecom, information services, media
and retail. His expertise involves solution design, business process optimization, value
articulation and program management. Ganesh also specializes in data insights and
visualization.
S. GANESH
Principal, Energy & Utilities practice (India)
Jim is a partner in our energy and utilities practice. He brings over 25 years of consulting
experience and possesses a highly-successful suite of work with clients spanning the
upstream value chain. Jim joined Infosys Consulting as part of the Noah Consulting
acquisition in 2015. Since then, he has been instrumental in the growth of the company
and has continued to lead one of the practice’s largest accounts. Overall, his strength
and expertise are anchored around program management for large-scale data manage-
ment projects, master data management strategy, application portfolio strategies, and
ERP solutions for clients across the oil and gas space.
JAMES SOOS
Partner, Energy & Utilities practice (U.S.)
InfosysConsultingInsights.com
LinkedIn: /company/infosysconsulting/
Twitter: @infosysconsltng
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