HOUSTON │OSLO │ PALO ALTO
Operationalizing Analytics in Oil & Gas: Tales from the Trenches
Houston – 20 Apr 2017
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What are we seeing?
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Music that makes you dumb…
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Why are the Facebook posts dropping?
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Facebook can watch you fall in love…..
Not necessarily the Best Algorithm, but the Best Dataset that will give Best Outcome
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3 pictures will not give you enough data to write an
algorithm that separates dogs from cats
3 million photos of cats and dogs would allow you to fill in
the gaps and write an algorithm that would learn
Combined data sets from various rigs, operators,
platforms will enable greater use and understanding
Arundo End-to-End, from Data to Value
7
1. Access your dataMake your data accessible from anywhere by anyone who matters
2. Standardize your dataTurn your disparate data into a structured and unified format so that
you can compare apples to apples on an industry-wide basis
4. Collaborate & share dataShare and annotate data for rapid data-driven problem solving across
teams and companies
3. Rapidly deploy advanced analyticsDeliver data science that will turn your industrial data into
actionable insights
5. Capture value at scaleAccess a private and public market-place that allows you to meter and
monetize operationally intensive algorithms and applications
3rd Party Connectors, Azure availability, granular control
of your data
ETL, automated or prescriptive in tool of choice
In-application collaboration & social interaction
Auto-deploy a model from native DS tools, materialized
via reusable apps
Reuse models, monetize externally, and increase
accuracy through scale
Data & Things
Product Differentiator: Making it Simple for Customers
User
Interface
Orchestration
& Automation
Containers
& Micro services
Integrated
Extraction
Selective
Transformation
Centralized
Store
Native Data
Science Tools
Data Science
Sandbox
Auto-Deploy
Models
Use Cases, Portfolio Sample
1
Onshore support
Example case: Data
extraction, cleansing, and
analytics prep
2 Supply chain
Example case: logistics
forecasting, intelligent routing
3 Brown field asset integrity
Example case:
Predictive maintenance
7 Well construction
Example case:
Field Development
Planning Reservoir
management
Example case: Gas
breakthrough
prediction & PLT
augmentation
w/seismic
8
Details on following pages
4Green field
Example case: “Plato’s rig”
5
Manufacturer CBM, OEM
Example case: global pump
manufacturer
Process & Instrumentation
Example case: ML mining P&ID
repository to automate equipment to
processing mappings
6
Predictive Maintenance, Topside Equipment
Required Data SourcesCase
A large National Oil Company (NOC) was seeking to
reduce its yearly equipment maintenance costs by 10%.
Management sought to implement a data-driven, condition-
based maintenance approach where preventative
maintenance is prioritized based on real-time data
streaming from the equipment. This allows for extended
maintenance intervals through just-in-time scheduling. The
company selected Arundo through an RFP to deliver the
fully integrated CBM solution.
Real Time view
Signal data
● Historical signal data (typically
stored in OSIsoft PI-server,
SE Wonderware, Aspentech,
etc.
● Tag-lists (I/O lists) and access
to relevant P&ID if necessary
● Access to live streams of
signal data for implementation
of monitoring
Failure notifications and work orders
● Historical failure notifications
● Historical maintenance work
orders
Facility model / equipment hierarchy
● Break-down of equipment
hierarchy / facility model and
(if existing) mapping to tag-list
2009
# anomalies detected
Anomaly Detection, Blind Tests
Aug 2014
Compressor
re-bundling
Aug 2015
Thrust
bearing
replaced
Dec 2016
Vibration
sensors
calibrated
Arundo fully implemented a condition-based monitoring
solution for their most serviced equipment, gas
compressors, which were responsible for a majority of the
NPT. Arundo developed a machine-learning model, trained
it from historical data, and deployed it within the Arundo
platform. Prior to production roll-out, the model was
validated in a double blind test and successfully predicted
equipment failure with a 95% accuracy rate, as early as 8-
weeks in advance. The company has recognized a
reduction in routine maintenance by 75% while prioritizing
critical repairs to prevent downtime.
Impact
Asset Groupings and Clustering
Sensor readings from example compressor -
Subtle changes on individual signals difficult to pick up
Virtual sensor showing “deviation from
normal state” - Any deviation from any
normal operating mode will be visible
Cluster all sensors available on the compressor.
Calculate the distance of each point in time from the
center of its respective cluster.
Sample representation of 3 of the sensors colored by
their respective clusterEach compressor has upwards of 40 sensors, which
are not consistent in raising alarms
The distance-to-center starts to increase prior
to known failures on the compressor.
2. Test model1. Build model
Automated Tag Mapping, P&ID Mining
• Benefitso Digitization/ automation of
linkages between: Standard process flows and
equipment
Process and people
Equipment and people
o Identification of like equipment in event of safety recall/ required maintenance
o Dramatic acceleration and quality improvement in comparison to manual mapping
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Relational node map, auto-
created using ML (text
mining & image recognition)
Human vs. machine
mapping accuracy
Equipment Manufacturer CBM, OEM
Typical data sources involved
Signal data
● Historical vendor data from equipment testing and
maintenance
● Historical sensor data, typically stored in customer
historian (ex: OSIsoft PI, GE Proficy, Honeywell PhD,
Schneider Wonderware, etc.)
● Access to live streams of signal data for implementation
of monitoring
Failure notifications and work orders
● Historical failure notifications
● Historical maintenance work orders
Data Sharing
● Sharing of equipment data between operator and vendor
● Sharing of analytical insights and innovation requirements
ImpactCase
By digitally enabling equipment, Arundo
enables seamless sharing of equipment
insights between user and vendor.
Arundo signed an agreement with a global pump
manufacturer to deliver the digitized pumping
experience.
Arundo Enterprise is delivering analytic
capability, real-time equipment monitoring, and
just-in-time maintenance scheduling to
equipment manufacturer and user. The model
accuracy and predictive insights provided
exponentially increase with each asset brought
online with Arundo’s fleet learning models that
blend intelligence from all deployed assets.
End customers gain:
● Improved transparency into underlying
asset conditions, real-time visibility
● Condition based maintenance, reduced
downtime and costs by extending the
maintenance interval
Revenue
Cost / product
Improve competitive
position
Increase
maintenance
intervals / decrease
costs & downtime
Enable new business
models
Deliver value added
services
Accelerate product
innovation
Enable fleet visibility
/ control
Bridging the Surface / Subsurface Gap with Data Science
Surface data: equipment sensor signals
Classic Arundo DS application:
New application:
Well data: seismic, geological logs, etc.
Predictive Maintenance
Models
ProductionOptimization
Models
Fully integrated models
● All data in same place
● Unique in marketplace
● Massive value at scale
Seismic knowledge → exploration
$B/y budget for this customer
How do we gain insights from data?
Engineer’s approach:
• I expect flow to increase just before a seal failure in a compressor
• Monitor flow and raise an alarm when it goes over a threshold
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Data Scientist’s approach:
• I know failure happened at time t
• What can I infer from data far away from time t vs. just before time t
• Is it possible to model?
• Raise an alarm based on the model output
Underlying
System
Sensor
Data
Theory approach
Data driven approach