nr talk, info-plosion conference (tokyo, jan 2012)

© 2011 IBM Corporation

Transformation of Natural ResourcesIndustries through Smarter Data, Modeling, and Analytics

Matt Denesuk, IBM Research ([email protected])

“Beyond the Info-plosion, Tokyo UniversityJanuary 16-17, 2012


Natural Resources Transformation and Management

Outline

Background -- Natural Resources & IT

Geophysical examples

Equipment-health-based solutions– Description– Impact– Issues

Future needs & opportunities

Comment Natural Resources includes Oil & Gas, Mining, Water, Agriculture, Solar, Wind,

Geothermal, …

But for simplicity, this talk focuses on O&G and Mining.



Natural Resource industries are facing a perfect storm of demand growth, scarcity and rising costs that threaten their ability to deliver materials, fuel, and food to the world.

These heavily asset-intensive industries are increasingly employing deep analytics and dynamic models, and integrating across their business operations globally, to meet the world’s needs and enable continued global growth.

The current environment provides an opportunity for natural resource firms and their ecosystems to leapfrog current models, and deploy integrated physical-digital-analytical environments that achieve these global objectives and create enormous value.




O&G and Mining >$7 trillion (2011)(and NR is actually much broader)

Natural Resources: critical economically & geopolitically. But facing a crisis.

Increasing Costs and Restrictions

Public Focus on HSE

Regulatory and Reporting

Workforce Issues

Difficulty Finding and Extracting New Sources

Dig/drill deeper

Developing harsh/remote areas

Complexity of exploration

Non-traditional sources

Increasing Global Demand

Global economic growth

Exploding middle class in China and India



Natural Resources Lifecycle

Exploration Production

Prospecting Physical testing and assessment

Extraction Primary distribution

Process / Refine

Secondary Distribution

Plan for Supply

Refinery, smelter, ...

Trucking, retail, ..

Understand reserves, policy, ...

Mining 1% of cost

20% infrastructure set-up (2-4 yrs)80% extraction and primary processing (10-30 yrs)

O&G25% of cost~1000 wells drilled/yr, ~$30M/ea.

75% of cost~2500 wells drilled/yr~$20M/ea.

IT helps, informs

IT very imp. in O&G, but can do much more

IT vastly underutilized. Must replace OT & grow

IT can grow, but less unique.



“Most Asset-intensive Companies have Managed IT as a Distinct Area of the Business Separate from the ‘Production’ side that Produces the Revenue-generating Service or Product.”*

Need for IT/OT convergence

Vast data from instrumented equipment.

Siloed architectures & growing organizational complexity

Strategic shift to outcome-based and service-oriented models

*”Operational Technology Convergence With IT”, Gartner, 7/2010

Business Operations Mgmt

Physical Operations and Production Mgmt

Sensingand ControlK

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“OT governance resembles the state of IT systems more than 10 years ago.”

Transformative value can be achieved through deeper analytics and linking of IT & OT. Advanced Asset Management is the strategic place to start.

OT: Operational Technology

Asset Mgmt / Maint



Natural Resource Industries are Very “Physical,” and have Tended to Use IT Less than Other Industries. But This is Changing...

2009 – 2010

A 0.5pt increase in IT spend ratio would drive $31B in incremental IT spend.

2009

Operating Margin (%)

IT S

pe

nd

ing

/ R

ev

en

ue

(%

)

Industries where value is generated by moving and manipulating data have high IT-spend ratios (and high productivity growth)



Physical-digital integration is how we make highly physical industries all about moving and manipulating data.

INSTRUMENTEDmeters, sensors, actuators, IP enablement, ...

INTERCONNECTEDtransmitters, networks, taxonomies, ...

INTELLIGENTreporting, visualization, predictive analytics & modeling, decision mgmnt, closed-loop automation, ...

+

+=

3 key things:

Smarter Planet



National Oil Companies

Major Oil Companies

Data Collection

Independents

Partnerships, etc.

Rtyp~$250B Rtyp~$250BRtyp~$40B

Rtyp~$16B

Data reduction & image rendering

Typical D/C: 5PF, 5PB

Seismic Service Companies

100MB/sec raw data rate Navmerge, time stamp, pre-process Dump on 0.5PB tapes

Sources getting dramatically harder to find.

– Depths, Salt, Computation, Storage, ...

Can service companies keep up? IT requirements growing rapidly Growing need for real-time / adaptive E&P New models & infrastructure needed

Drilling Today

$300M to “discover” a field

$150M to appraise it

$400M to drill production holes

Exploration is Already a Data and Analytics Business with Multiple Customer Entry Points. Disruptions Expected.



Testing, appraisal

Living reservoir models + platform data streams

Production

Rough seismic analysis + living basin models

Adaptive Prospecting

“Living Models”Integrated Analytics

+ +

HPC and algorithmic innovation

Increasing reservoir recovery rate on existing reserves by 1%

valued at $1T globally

Improving hit rate by 5pts saves $100M per field

Improving appraisal by 20% saves $30M per field. Could increase

recovery for much larger impact.

Rough seismic analysis + living basin and emerging reservoir models

O&G firms are spending heavily on geophysical analytics & models to transform E&P. IT-based innovation needed to do successfully.

Huge Impact, but help needed on IT side.



11

“Equipment-health-based Solutions” will solve critical problems being faced by the industry.

Leading firms moving from

Business performance (production, costs) of NR firms is extremely sensitive to utilization and effective application of their heavy

equipment assets.

“Fix on Failure” or

Scheduled Maintenance

Condition-based Maintenanceor

Predictive Maintenance

But, expected benefits have not been realized because of: Short alert lead times – insufficient to take effective actions Poor specificity –targeted information on what needs to be fixed/done Insufficient accuracy – many false positives Lack of controllability –information on what action to take to change

the health state of equipment, and truly optimize production Hard to scale to multiple equipment types, sites, and firms Absence of links to production management

• Poor data access & mgmnt.

• Needs CPS innovation!!



Example: Equipment Use Options drive Operations Optimization

Mining Scenario

Several transport trucks at a Western Australia iron ore mine reportedly need maintenance.

But a substantial order of x-grade iron ore is due in Suzhou in 11 days.

The system probes for possible options, including local operational changes, alternative WW sources, and order flexibility.

Concludes that best option is to load trucks at 60% capacity and drive them at 75% speed (reduces 10-day failure probability to <1%).

Oil & Gas Scenario

Gas compressor showing signs of trouble 3 months before a scheduled turnaround.

The system indicates that lowering pressure by 30% will extend health enough to delay turnaround.

–But then production levels will not be sufficient to fulfill scheduled shipment.

The system identifies that another platform can be run for 30 days at 115% throughput without significant risk before its next scheduled turnaround.

Coordinated actions taken, and $40M production loss avoided.



Many capabilities must be brought together to dynamically optimize natural resource production

Equipment Data

Business Data

Environment Data

Instrumentation & Data Integration

Deep Understanding of Equipment “Health”

Understanding of Production / Operational Processes

Logistics Physics

Business Goals / Commitments

Output Requirements

Dynamically- Optimized Processes

Management Capability



Why focus on NR? The potential value is staggering…

Maintenance / Asset Mgmt

Inventory & related SC

optimization

Operational optimization

CbM+, integration, basic analytics

“Prescriptive” modeling more integration, more analytics

Large expense. Can be reduced substantially.

Maintenance optimization drives lower inventory costs...

Higher asset utilization & integration w/business apps enables e2e optimization.

AM + CbM+

Value to single $30B NR firm*

$0.2B$0.7B

$2B

~$3B Annually!

*From joint IBM client studies



If it’s so valuable, why do we not see more of it?

15

Scheduled Maintenance“Fix on Failure”

Value

To

Predictive MaintenanceCondition-based Maintenance

Increased Specificity & AccuracyIncreased Lead TimeScale to multiple equipment types, sites

Controllability: Equipment use options affect availability

Operational Optimization basedon equipment use options

Emerging Technology

Current approaches

Because it’s really, really hard!(…but in areas where IT-based innovation is needed)

most companies here..

leaders moving here…

$Bs/yr (per company)

$100Ms/yr (per company)



Data needs Instrumented & connected equipment/structures Effective semantic data models Data quality assurance (missing data, sensor verification, …) Larger, higher-quality, more comprehensive data sets

– High volume/density: fine-grained, long-duration, time series equipment and environmental data

– Homogeneity: Narrow equipment diversity operating in diverse environments/conditions– Links to “outcomes” (maintenance, output, etc.)

Equipment DataBusiness Data

Environment Data

Electronic (sensor) time-series Vibrations, temperature, pressure, “speed”

variables (rotational, translational, …), etc.

Positional and/or motion data.

“Point process” / event data – e.g., when an accelerator is triggered; when gear is shifted, …

“Alerts” or other existing equipment health outputs

Offline data Fluids analysis, etc.

Financial & Customer Order/commitment pipeline

Asset Management Basic asset info (locations, model, age, etc.) Maintenance-related outcomes Inspection &

Repair history (incl. false positives).

Site condition / operating environment May include ambient temperature,

adjust/particulate levels, humidity, … Terrain-type info (for mobile assets)

Example data types:



Example: How to Develop Deep Understanding of “Equipment “Health”?

Instrument the Equipment

Understand the Data and Ensure Quality

Develop Models Explore vast #s of model types & parameter spaces.

Instantiate running models

RT running models

Instantiate running models

Proxy models or rules

Test actuals against predictions

HPC

HPCNote: “Big Data / Big Analytics” may only be needed to develop the models. Initially, they may run in more traditional environments.



What types of models?

Multi-time-series segmentation and correlation

Long-memory, probabilistic event networks

Physical-model-seeded machine learning

Examples:

…

3 Basic Types of models*– Physical– Knowledge-based– Data-driven / ML-based

*e.g., see Peng, et al. (2010)



Scaling up: “Living Model Management”

Capability for managing and improving models

Challenge:• Models difficult and time-consuming to create• Different models often mutually inconsistent (along many dimensions) and

difficult to integrate/compose. • Difficult to keep track of large #s of models, and apply correct model at

correct place and correct time. • Accuracy often poor and drifting; too little real-life feedback.

Solution:• Continuously improve and extend equipment health

model accuracy while the models are in use• Enable scale by managing models as assets

• Reducing effort to create, manage and update

Enabled by: • Connection to large-scale equipment data streams &

asset data• Site operational and environmental data available• Model-creating tools, libraries, and composition means.



Generic solution architecture: a cartoon version

Data Integration & Event Processing

Modeling &Data Mining / Machine

Learning

Model Management

CustomerERP

Asset Mgmt

Partner CbM-

related Offerings

Customer Data Collection Systems

Portal / Visualization

Rules Mgmnt &

Proc.

KM / Social / Collab.

Mas

sive

-Sca

le A

nal

ytic

s C

apab

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y

Most advanced solutions are fitting into some subset of this generic architecture.



Enabling New Business Models: Strategic shift to outcome-based and service-oriented models

Uptime guarantees

Maintenance Outsourcing (incl.

inventory)

Outcome-based equipment as-a-

service

Outsourcing partial operations

Warranty and Maintenance

• Equipment Mgmt: Single Multi-vendor • Customer: Single Multiple • Remote: Data Capture Mgmt• Integration Level: Asset Cross-site/function• Models: Descriptive Prescriptive• Data: Equipment Comprehensive

Equipment vendors provide more value & get closer to their customers. Obtain superior understanding of

their equipment “in-use” Link original equipment, parts,

service, and use management.

Strategic shift to outcome-based and service-oriented models

Examples: Rolls-Royce, Joy Mining Machinery, etc.

Most heavy equip vendors are around here…



Issues & what’s needed

Getting good data is the biggest impediment– Need improved “instrument & connect” capabilities (easy-to-retrofit sensing &

connectivity technology).

Better relevant “canned” analytics & modeling capabilities– Advanced multi-time-series analytics (segmentation, delayed effects &

correlations, …)– Model/technique libraries for machine functional components

Better collaboration between equipment vendors, users, and machine-learning-based analytics & modeling experts.

– Model composition capabilities– Multi-firm experimentation labs

Openly shared high-quality, comprehensive test data sets



Summary

Natural Resources (NR) industries are facing enormous fundamental challenges with global economic and geopolitical impact.

Natural Resources (NR) industries present huge & growing potential for value creation by IT innovators

– Growing instrumentation and complexity create the urgent need for CPS-type solutions

NR firms and their vendors recognize this, but they don’t have the capabilities to address

– Need to work with IT / analytics innovators.

Advanced asset management is an especially opportune entry point of IT-based NR value creation

– Leading to business optimization and outcome-based models

Innovation urgently needed in – Physical instrumentation– Communication– Machine data management– Advanced modeling/analytics



Changing the world – applications across a full spectrum of human activity.

Multiple sensor data streams

Outcomes

Environmental data

Higher-order

“Events”

Probabilistic Models / Rule Mining Actionable

Rules & options

Management system• Maintenance optimization• Use / output optimizationPhysical Models

Example process:

Broad range of applications.

Bridges

Water Infrastructure

Railroads

Aircraft

Mining Equipment

Oil Pipelines

Oil Platforms

Steel manufacture

Trucking Mobile ComputersIT Infrastructure

Heavy Infrastructure Business Equipment / Consumer Products

Human Health?

Home AppliancesBuildings

(HVAC, Elevators, Lighting, …)

Photocopiers

Refrigeration

nr talk, info-plosion conference (tokyo, jan 2012)

Documents

broader natural resources

natural resource firms

management og

ibm research

yrs og

management outline background

continued global growth

global objectives