Driving Value from Upstream Data - Infosys Consulting · UPSTREAM DATA. How to measure and shape data into a high-return ... Although data is not valued on the balance sheet, it needs

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.


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


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.


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


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.


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





$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


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.


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


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.)

[email protected]

InfosysConsultingInsights.com

LinkedIn: /company/infosysconsulting/

Twitter: @infosysconsltng

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Driving Value from Upstream Data - Infosys Consulting · UPSTREAM DATA. How to measure and shape data into a high-return ... Although data is not valued on the balance sheet, it needs