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The rapid progress of technology such as bigdata and analytics, sensors, and control sys -tems o ers oil and gas companies the chanceto automate high-cost, dangerous, or error-prone tasks. Most oil and gas operators arestarting to capture these opportunities and

would do well to accelerate their e orts. Com- panies that successfully employ automationcan signi cantly improve their bottom line.

While automation o ers many potential bene ts in the upstream value chain ofexploration, development, and production,some of the biggest opportunities are in

production operations, such as reducingunplanned downtime (Exhibit 1). Given theoil and gas industrys substantial increasesin upstream capital investment, optimizingproduction e ciency 1 is essential. Automa-tion creates several opportunities to thatend: maximizing asset and well integrity(by which we mean optimizing production without compromising health, safety,and the environment), increasing eldrecovery, 2 and improving oil throughput. 3

With the substantial production volumes ofo shore production platforms, even smallimprovements in production e ciency willhave meaningful nancial impact, as addi -tional throughput translates directly into morerevenue. In the low-volume regimes of currentunconventional mature assetsoil sands, forexamplecarefully targeted automation stepscan cut costs and, more important, can alsoimprove the reliability of production equip -ment, leading to higher revenues that canextend an assets economic life.

Industry challenges

Our benchmarking analysis of North Seao shore platforms illustrates the e ciencychallenge that many oil and gas companiesface. Research shows that average produc- tion e ciency dropped in the past decade, while the performance gap between indus-try leaders and other companies widened,from 22 percentage points in 2000 toaround 40 percentage points in 2012.

Automat ion is an important answer to the indust rys upstream challenges.

Stefano Martinotti,

Jim Nolten, and

Jens Arne Steinsb

1 Production e ciency isthe ratio of actual produc-tion over the maximumproduction potential.

2 Field recovery is theamount of oil extracted asa ratio of original quantitycontained in the oil bed.

3 Oil throughput is theamount of oil outputfrom the asset in a giventime period.

A U G U S T 2 0 1 4

B U S I N E S S T E C H N O L O G Y O F F I C E

Digitizing oil and gas production

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Benchmarking data also illustrate the broadopportunity for improvement. Best-in-classplayers do not incur higher costs to improveproduction e ciency, and high performanceis not linked to a speci c asset type or thematurity of assets. Instead, companies withhigh production e ciency are often similarin their quality of operations, approach toeliminating equipment defects, equipmentchoices, and planning and execution ofshutdowns. While our benchmarking focuseson North Sea o shore platforms, we expectto see similar patterns in other regions.

Regardless of location, most oil and gascompanies also face issues that complicatee orts to achieve sustained production- e ciency improvements. We believe furtherautomation can play a major role in address-ing the following industry-wide challenges.

More complex operations. Increasing volume and complexity in hostile, remotelocations (for example, arctic, o shore,and deepwater) require reliable remoteand automated or semiautomated opera-tions, and logistics optimized for e ciency.Mature assets with declining productionneed very e cient maintenance schedulesto keep production pro table.

Zero tolerance for health, safety,and environmental incidents. This is anonnegotiable imperative. Recent industryexperience has shown that in the currenthighly regulated environment, such inci -dents can threaten not only pro tability but also the very existence of an operator. Automated production control, monitoringthe condition of the equipment, and pre-dictive shutdown systems are now basicrequirements to prevent or mitigate cata -

strophic events in geographically dispersedremote operations.

The talent and experience gap. Theindustry is in the most dramatic demo-graphic shift in its history, commonlyreferred to as the big crew change. Thou-sands of petro tech nical professionals will be retiring soon, resulting in a knowledgeand experience crisis for the industry.Retention and recruitment are unlikelyto ll the gap completely. This developmentdrives e orts to codify many routine ana ly- sis and decision-support processes and, where possible, to automate them.

The automation imperative

Automation is not without its own challenges.Todays intelligent oil eld is ush withdigitally enabled wired systems, equipment,and components. A typical o shore produc -tion platform can have more than 40,000data tags, not all connected or used. Convert -ing this complex ood of data into better business and operating decisions requiresnew, carefully designed capabilities for datamanipulation, analysis, and presentation,as well as tools to support decision making.

The impact of addressing these automationchallenges can be material. Judging by our benchmarking research, improving produc -tion e ciency by ten percentage points can

yield up to $220 million to $260 million bottom-line impact on a single brown eldasset. For declining assets, automation couldextend eld life in an economically viable way. The potential could be even moresigni cant for green eld assets, whererequired instrumentation can be included

Takeaways

New opportunities forautomation in the oil andgas industry are beingmade available by tech-

nologies such as big dataand analytics, sensors,and control systems.

By harnessing thesetechnologies and apply-ing them to support real-time decisions and actions,companies can improvethe productivity of theirassets and help preventhealth, safety, andenvironmental incidents.

To successfully implement

automation, oil and gascompanies should buildmultidisciplinary teamsand differentiate their auto-mation efforts for greenfieldand brownfield assets.

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from the start as part of the design. How-ever, operators and drilling contractors

are not the only players looking for a shareof the production-e ciency gains in oiland gas. (See sidebar, Automation ischanging the competitive landscape.)

We expect industry leaders to increasinglyadopt automation in upstream productionoperations, leading to improved e ciency.

As a result, the performance gap withindustry laggards could widen further.To illustrate how oil and gas companies

can unlock the value of automation, weanalyzed production maintenance, where

the opportunity is particularly attractive.

Applying automationto maintenance

There are many ways in which automatingmaintenance can improve productione ciency. For example, radio-frequency-identi cation tagging of equipment, along

with the use of other sensors, can help track

The highest-impact automation opportunities in upstreamlie in production operations.

Source: Expert interviews; McKinsey analysis

Upstream

Exploration Development Production

Medium-impact opportunity High-impact opportunity

Analysis andmodeling Seismic analysis

Drilling (eg, resource dispatching, well planning,eld-development planning)

Production optimization(eg, production-ow analytics)

Reservoir modeling (eg, real-time feedbackto optimize production)

Drilling andwells

Process-control automation (eg,automation of continuous processes)

Businessoperations

Reliability and preventive maintenance (eg, equipment-conditionmonitoring, maintenance-operations planning)Maintenance

Supply chain (eg, planning analytics, automating process steps in day-to-day operations)Supplychain

Capital productivity (eg, planning execution, documentation of work-ow automation)Capitalproductivity

Exhibit 1

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activity. Tracking, in turn, enables applica -tions that can monitor the condition ofequipment and support predictive mainte -nance and automated operations shutdowns.These applications minimize risk of cata -strophic failures and process disruptions, while maximizing equipment reliabilityand production e ciency.

But unlocking the value of automation inmaintenance isnt only about having lotsof data. Some companies struggle tomaintain data quality across their IT

networks. Others are not good enough ataggregating data and conducting meaningfulanalyses. Yet others experience challengesin turning analysis into action. Thats whymany oil and gas operators need to identifythe information shortfalls or leakages thatoccur when capturing data from processes,systems, and data stores and move themto where operational and business deci-sions are made (Exhibit 2). Having identi-

ed the leakages, they must then addressthem by improving the automation oftheir data ows.

Automation is changing the competitive landscapeTo capture the value that automation canbring to oil and gas production, operators

and drilling contractors must move fast,because well-positioned equipment manu-facturers are also seeking to win a substan-tial piece of the value at stake.

These providers are increasingly making theshift to combining equipment manufac turingwith service provisioning. This is made possibleby integrating condition- and per formance-monitoring technology in their equipment andcombining this with a service offering basedon big data processing and advanced analyticscapabilities. Examples can be observed forequipment in different parts of the productionsystem, including topside, subsea, anddown-hole operations. A benefit for equipmentmanufacturers is that revenue increasinglymoves from lumpy equipment sales to moreannuity-like services streams.

Similar transitions have occurred in otherindustries, such as aircraft manufacturing.Here, engine manufacturers no longer just

sell the engines and spare parts but also offerservice contracts that fix the maintenance

costs for flight operators. Predictive modelingof engine performance helps create value forcustomers by reducing downtime and thusimproving asset productivity. It also allowsthe engine manufacturer to better understandcustomer needs and capture more value fromthe services provided. Potential drawbacksfor operators are that these capabilities aredifficult for others to replicate, thus limitingcompetitiveness in this field of work.

For asset owners in the oil and gas industry,it is important to stay ahead of such devel-opments. If they dont, they risk becomingtoo dependent on technology providedby equipment manufacturers, which mightmake it difficult to get an integrated viewof how all their assets are automated andinterconnected. Additionally, they face therisk that improved asset productivity, thesingle biggest value lever of automation,would be pocketed to a large extent bythe equipment providers.

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Many forms of leakage impair automationin maintenance. One example is having onlyisolated data availability from individualequipment components, as opposed tomore network-based availability. Anotheris having only equipment-level pro les ofcomponents at risk, as opposed to compre-hensive coverage at the asset level. A thirdexample is to only catalog critical equipmentfailures rather than conduct extensiveroot-cause analysis of them.

Automated support of real-time decisionsand actions to reduce planned andunplanned downtime require the follow- ing elements.

Data capture. This involves automatedhardware sensors and manual data capture by engineers. Both should be deployed basedon a detailed analysis of use cases, which area method for gathering the functionalrequirements of applications. 4 Hardwaresensors should help ensure su cientcoverage of data, as well as provide redun -dancy (that is, data backups) for high-valuemeasurements of equipment-performancedata. High-precision hardware sensors areusually more costly than low-precisionsensors and should be used only in criticalcases. Manual data capture is useful whenparts of assets are not yet equipped tomonitor and measure performance or for

4 For more infor mationon use cases, see MichaelHuskins, James Kaplan,and Krish Krishnakanthan,

Enhancing the e ciencyand e ectiveness ofapplication development,

McKinsey on BusinessTechnology , August 2013,mckinsey.com.

Limiting data leakages can improve operationaland business decisions.

Funnel elements Information-leakage types

Data capture

Infrastructure

Data management

Analytics

Visualization

People and skills

Data integrity not maintained

Data not streamed/stored

Data not accessible

Data not analyzed

Data not communicated

Data not used in decision making

Operational and business decisions

Processes and systems

Exhibit 2

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inspections and failure analysis. Usingregulatory-certi ed handheld electronicdevices for manual data capture improvesdata accuracy, consistency, and availability.

Data infrastructure and data manage-ment. Data infrastructureranging fromtransactional data in relational (such asenterprise-resource-planning) databasesto data in Apache Hadoop or similar bigdata analytics platformsshould allowcompanies to couple data from disparatesources of equipment and manual captureto aid decision-support analytics. Streaming

of real-time data in situations requiringimmediate data availability also needsinfrastructure support. Technology choicesshould be made on an individual basis byevaluating the data ow and asking severalquestions: Is the volume of data high? Is itreal time or batch? Are the data unstruc- tured or structured? For example, forunstructured data processing, a solutionlike Apache Hadoop is more relevant thantraditional relational databases.

Data analytics. Industry leaders are usinganalytical models to predict failures of criticalequipment components. The next level ofsophistication includes connecting all theparts of the end-to-end production valuechain to optimize the balance betweenproduction and downstream stages, forexample, by adapting upstream productionlevels to account for expected future demandshifts in downstream retail. It also includesusing simulations to test failure scenariosin platform operations and employing textmining for analysis of unstructured inputfrom engineers and operators.

Data visualization and staff training. Software applications are needed that presentdata and insights in a way that is closely tiedto priority operational and business decisions.One example is knowledge systems that suggestactions to maintenance engineers who take intoaccount previous repair approaches as well asthe speci c history of the asset. Managers andsta must have adequate training to collabo -rate and work with these applications.

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Success factors forautomating oil andgas production

In our experience, companies that havesuccessfully pursued automation programsfor production e ciency have employedseveral e ective approaches.

Building multidisciplinary teams

Successful automation programs havesta with backgrounds ranging from pro- cess automation, process-domain expertise(for example, in maintenance), data manage-ment, and cybersecurity to interface design.These multidisciplinary teams includerepresentatives from every aspect of theorganizations IT function. Sometimes theteams also include equipment vendors.

Differentiating greenfieldand brownfield automation

In green eld automation programs, thedigital processes are built in during projectdevelopment to ready the technology forfuture advances, taking into account the

ve- to seven-year life cycle of these proj- ects. For brown eld programs, companiesdevelop overlays (for example, upgrades of

wireless and mobile) that pull the requireddata ow out of the platform to supportanalytics units. This approach helps to avoid being locked in by technology choices from

the past. Some companies use a library ofreusable software components to achieveeconomies of scale across o shore platforms.In our experience, this library approach ispreferred to mandating a single, centrallydeveloped overlay across platforms.

Thinking big, piloting small, scaling fast

Companies with successful programs thinkin terms of total life-cycle costs and eco-nomics. They build a digitization teamand make automation part of a corporatedigitization program. Their automationprograms are integrated with all aspectsof their complex organizations, workprocesses, and human behaviors. Industryexperience and prudent risk managementdictate that this level of complexity bethoroughly tested and proved in small-scalepilot implemen tations. Once the conceptis proved, rapid scaling is needed to securethe payo . Such a scale-up requires toolsand capabilities in technology-enabled trans-formation, change, and risk management.

. . .There is a clear competitive imperativefor increasing automation in oil andgas production. Companies that success-

fully implement big data and analytics,sensors, and other new technologies

will be well positioned to meet theirindustrys challenges.

The authors wish to thank Enrico Benni, Stuart Morstead, Don Painter, and Tor Jakob Ramsy for theircontributions to this article.

Stefano Martinotti is a principal in McKinseys Abu Dhabi ofce, Jim Nolten is an associate principal in the Amsterdam ofce, and Jens Arne Steinsb is an associate principal in the Oslo ofce. Copyright 2014McKinsey & Company. All rights reserved.