oil and gas data analytics

8/9/2019 Oil and Gas Data Analytics

http://slidepdf.com/reader/full/oil-and-gas-data-analytics 1/8

Big Data analytics in oil and gas

Converting the promise into value

By Riccardo Bertocco and Vishy Padmanabhan



Riccardo Bertocco and Vishy Padmanabhan are partners in Bain & Company’sDallas ofce. Both work with the rm’s Global Oil & Gas practice, and Vishy isalso a member of the Global IT practice.

Copyright © 2014 Bain & Company, Inc. All rights reserved.



Big Data analytics in oil and gas: Converting the promise into value

1

Big Data and analytics may be new to some industries,but the oil and gas industry has long dealt with largequantities of data to make technical decisions. In their

quest to learn what lies below the surface and how tobring it out, energy companies have, for many years,invested in seismic software, visualization tools andother digital technologies.

Now, the rise of pervasive computing devices—afford-able sensors that collect and transmit data—as well asnew analytic tools and advanced storage capabilities areopening more possibilities every year. Oil producers cancapture more detailed data in real time at lower costsand from previously inaccessible areas, to improve oil-

eld and plant performance. For example, they can pairreal-time down-hole drilling data with production dataof nearby wells to help adapt their drilling strategy,especially in unconventional elds.

These analytic advantages could help oil and gas compa-nies improve production by 6% to 8%. Bain nds thatthese advantages are typical of those found for companiesacross industries. Our survey of more than 400 executivesin many sectors revealed that companies with better ana-lytics capabilities were twice as likely to be in the top quar-

tile of nancial performance in their industry, ve timesmore likely to make decisions faster than their peers andthree times more likely to execute decisions as planned.

Analytical leaders, however, are still the exception. Our sur-vey showed that only about 4% of companies across indus-tries have the capabilities to use advanced data analytics todeliver tangible business value. While some oil and gas com-panies have invested in their analytics capabilities, many strug-gle to get their arms around this powerful new opportunity.

We often find that senior executives understand theconcepts around Big Data and advanced analytics, buttheir teams have difculty dening the path to valuecreation and the implications for technology strategy,operating model and organization. Too often, companiesdelegate the task of capturing value from better analyticsto the IT department, as a technology project.

In practice, a business unit should lead the effort becauseit requires cross-functional ownership and participation.

Some companies commit to ambitious technologytransformations in search of analytic nirvana, but thesetransformations may feel like a 10-year march and often

fail to generate enough value along the way.

Our experience shows that developing the capability toproduce value from advanced data analytics is a C-levelagenda item, requiring the sustained focus of the seniormanagement team—not just the CIO or CTO. Three crit-ical questions should form the basis of an effectiveadvanced-data analytics strategy:

1. Which applications of Big Data will produce themost value for our company?

2. Which organizational model will help us achievethat value?

3. Do we have the right capabilities and talent to makethe most of our data?

Which applications of Big Data will produce themost value?

Not all data is Big Data, of course, and not all analyticsrequire the horsepower and organizational model thatBig Data applications typically require. Still, advancedanalytics can play an important role in improving pro-ductivity in unconventionals, conventionals and mid-stream operations in oil and gas.

Unconventionals. Because of the vast number of wellsrequired in unconventional production and the speedwith which producers construct them, data plays a criticalrole in the decisions that create value. Operators makedecisions every day in the eld, typically with limitedinvolvement by central functions. Analytic capabil-ities help producers collect and analyze data on sub-surface and geographic characteristics to improve theirability to characterize shale basins in detail, with lesstrial and error. We see three areas in particular whereadvanced analytics capabilities can help give producersan edge:

• Geology interpretation. Analyzing the geology belowthe surface and comparing it against well perfor-




has helped it schedule preventive maintenance of equip-ment for its midstream customers.

Which organizational model will help capture that value?

Knowing the value inherent in better analytics is justthe start. How should oil and gas executives dene anorganizational model—including the right structure,processes and decision rights—that will encouragetimely, cross-functional collaboration and put the rightdata in the hands of decision makers?

Many oil and gas companies rely on operating models

that focus on functional excellence, and they developclear handoffs from one function to the next. Thisworks well for predictable processes that follow moderateschedules. But the model breaks down when decisionsmust be made quickly—as in unconventional production.The handoffs for any given shale well can involve func-tions like geology, drilling, completions, construction,land, regulatory and production, and any of these maybe involved at different points of the well constructionprocess. Operators might have hundreds of wells activeor in development, putting the functional model under

signicant strain.

Consider, for example, the new well delivery process,where performance metrics such as the time from spudto hookup or the dead time between steps require visi-bility into activity data from each function involved. Ifthe functions (including land, regulatory, pad construc-tion, drilling, completions and operations) run on dif-ferent systems and rely on differently constructed datamodels, it becomes very difcult to have a clear, integratedview of what is happening in the eld.

Some companies address this challenge by deploying anasset-based organizational model, rather than a functionalone. In this model, all the key functions are deployed inthe eld and report into one geographically based organi-zational structure. In our experience, the model workswell at the site or division level, but may not scale to thelevel of a large international organization.

mance can help companies improve their ability tocharacterize shale basins with less trial and error.

• New well delivery.Better analytics can improve theway companies manage the entire process ofdrilling and connecting a well, reducing lag timeand minimizing the number of wells in processat a time. For example, transmitting microseismic,3D imaging over fiber-optic cables can improvenew well delivery performance.

• Well and eld optimization. Collecting and analyzingmassive volumes of geologic, operational and per-formance data, each with many variables con-

stantly changing, can help companies improveand optimize drilling parameters, well spacingand completions techniques, especially as theydrill more wells and bring them online.

Conventionals. Fewer decisions and wells are involved,but producers can still improve performance withaccess to more data than they had before. They can alsomove beyond measurement into predictive tools with arange of pattern-recognition techniques that help themspot trends, intervene early and create repeatable solu-

tions with predictable outcomes. For example, sensorsdeep in the wells or on drilling equipment send a con-stant stream of information that can help producersunderstand if or when a piece of equipment might fail.As these sensors become less expensive, their numbersgrow into the thousands and beyond, generating largevolumes of information. Integrating this data into oper-ations improves calibration and visualization capabilities,reducing technical risks.

Midstream. Data analytics can help monitor pipelines and

equipment and allow a more predictable and preciseapproach to maintenance. For example, sensors can indicatewhen equipment comes under unusual stress, allowingoperators to perform preventive shutdowns or interventionsthat may avoid accidents or spills. By way of illustration, aleading compressor manufacturer developed custom sensormodels and used predictive analytic software to activelymonitor the readings provided from these sensors, which




3

This kind of fast-paced, decision-driven environmentrequires better planning, which is also a preconditionfor, and a benet of, better data and the advanced ana-lytics capabilities that can make sense of it. Compa-nies with more advanced analytics capabilities aremore efcient in managing the range of data (includ-ing seismic, drilling logs, operational parameters suchas drill bit RPMs and weight on bit, frack performancedata and production rates) that help optimize welldesign and production. Each function may have a lotof data, but unless the operating model can weave it

together and place a “single version of the truth” inthe right hands at the right time, it is difficult toimprove performance.

Regardless of which model a company operates under,executives need to create a pathway for collaboration amongfunctions, with better systems and processes that allow notonly for rapid and integrated sharing of data, but also fororganized decision making with clear lines of accountability.

Do we have the right capabilities and talent to makethe most of our data?

Companies that build better analytics capabilities con-centrate their efforts in three areas: technology architec-ture, interaction between IT and the business, and hir-ing and retaining strong analytic talent.

Technology architecture. As in other industries, manyoil and gas companies have complex, legacy IT systemsthat have evolved over decades, and which now containmany different islands of disparate data sets. Addingreal-time, unstructured, large volumes of data multipliesthe problem—but that’s where valuable insights arise.

In oil and gas, key understandings emerge from link-ing various types of data to a well or set of wells. The busi-ness side sometimes underestimates how difcult thiscan be. Many systems lack unique well identication

Figur 1: Traits that best deliver a superior analytic outcome

Project ownership• Close collaboration between the functional business owners and IT teams

• A non-hierarchical structure and culture, which empowers engineers from the functions and IT architects to take full ownership

Portfolio management• Willingness to invest in innovation and foundational technology, even if near-term ROI is not directly clear

• Clear decision rights across new and legacy technology investments

Demand intake and prioritization• Having a clear point of view around where we need world-class analytics and where “good enough” is just that

Development/engineering• Roles and responsibilities clearly spelled out, with new roles where appropriate

• Clear career models ensure retention of top talent

Test and release• Implementing continuous delivery principles to maximize speed and reduce rework

• Using automation tools to enable rapid testing

Resource management• Defining resource management across three stages—long-term planning, annual planning and staffing

• Appropriate centralization of the resource management function

Source: Bain & Company




Analytic talent. Analytic talent is a scarce resource andthe talent prole in demand is not typically found within

oil and gas IT functions. IT organizations historicallyhired talent that had product expertise in business intel-ligence (BI) tools, or they hired or outsourced talent toproduce periodic and mostly static BI reports and to runback-ofce systems. Typically, the geologists and engi-neers employed by the functions do not have expertisein the latest analytic tools.

What’s more, the IT talent issue in most oil and gas com-panies extends to the architecture group. As cloud andopen-source architectures become more popular, ITgroups may nd themselves less familiar with the latesttechnologies and, more important, may not feel empow-ered to make the changes that best-in-class companiesare embracing.

But oil and gas executives should avoid the temptationof trying to solve this issue just by throwing high-pricedanalytic labor at the problem. The right talent modelshould balance industry knowledge with analytic skillsin a model focused on solving specic problems andidentifying new opportunities. This capabilities upgradeshould include people who understand open-sourcemodels, cloud technologies, pervasive computing anditerative development methodologies. Executives shouldkeep in mind their goal of a model that enables bettercollaboration among domain experts, IT architects, toolspecialists and experienced analytic resources.

Oil and gas companies will need to improve their ana-lytics capabilities in order to compete in an industrywhere decisions are moving faster and the stakes aregrowing ever higher. Creating a world-class analyticscapability takes time and investment, and it can onlyhappen with a sustained focus by top management.Senior executives should avoid delegating this challengeto their IT function ; rather, they should work closelywith IT leaders to transform their companies into data-driven organizations. Now is the time to develop a planthat maps an organization’s ambitions against its capa-bilities—and describes the path toward a world-class,advanced analytics capability.

numbers, which are essential for tracking the meta-data on each well. It’s not always clear who should

assign the ID—the reservoir engineers, the regulatorydepartment or the land department.

In addition, many systems prioritize nancial trackingand reporting, and barriers may exist between these andthe operational systems that collect and manage impor-tant geologic and performance data. Wells need cleardesignations that are consistent across systems, but oftenthe data remains isolated in nancial and operationalsilos . In other words, most companies design thesesystems for the nancial needs of the center, rather thanthe operational needs of the eld.

But companies can become more nimble and effective bycarefully focusing their IT investments on the most criticalareas and saying no to requests that fall outside that focus.Some organizations with signicant legacy investmentstake a two-track approach that includes a long-term planto modernize the entire IT stack while allowing a well-funded (but well-bounded) separate program that movesquickly on the most important opportunities. For example,one leading international oil company implemented asophisticated Hadoop analytic platform on Amazon WebServices’ cloud infrastructure and limited the touchpointswith its legacy technology infrastructure—a strategy thathelped keep it cost-effective and agile.

Business-IT engagement. The IT functions in most oiland gas companies were designed to automate back-ofce support functions as cost-effectively as possible.To the extent that IT enabled the business, it did so insteady, well-scoped and largely predictable projects thatdidn’t require quick and cross-functional collaboration.

As that’s changing, IT should organize around decisionsrather than functionally siloed processes (se Figur 1).This relationship between IT and the business shouldevolve into a real-time collaboration with less friction.Clearly the solution is not to build a siloed unit focusedon the Big Data opportunity. For analytics to have a signif-icant effect on the company’s performance, it must be in-tegrated into day-to-day operations.



Share m i o , Tru Rs ts

Bain & Company is the management consulting rm that the world’s business leaders cometo when they want results.

Bain advises clients on strategy, operations, technology, organization, private equity and mergers and acquisitions.We develop practical, customized insights that clients act on and transfer skills that make change stick. Foundedin 1973, Bain has 50 ofces in 32 countries, and our deep expertise and client roster cross every industry andeconomic sector. Our clients have outperformed the stock market 4 to 1.

What sets us apart

We believe a consulting rm should be more than an adviser. So we put ourselves in our clients’ shoes, sellingoutcomes, not projects. We align our incentives with our clients’ by linking our fees to their results and collaborateto unlock the full potential of their business. Our Results Delivery ® process builds our clients’ capabilities, andour True North values mean we do the right thing for our clients, people and communities—always.



For more information, visit www.bain.com

Key contacts in Bain’s Information Technology and Oil & Gas practices:

Riccardo Bertoccoin Dallas ([email protected]) Vishy Padmanabhanin Dallas ([email protected]) Rudy Puryear in Chicago ([email protected]) Rasmus Wegenerin Atlanta([email protected])

oil and gas data analytics

Documents