HOUSTON OSLO PALO ALTO Operationalizing Analytics in Oil ... · 3. Rapidly deploy advanced analytics Deliver data science that will turn your industrial data into actionable insights

HOUSTON │OSLO │ PALO ALTO

Operationalizing Analytics in Oil & Gas: Tales from the Trenches

Houston – 20 Apr 2017

Copyright © 2016. Arundo Analytics. All rights reserved2

What are we seeing?


Music that makes you dumb…


Why are the Facebook posts dropping?


Facebook can watch you fall in love…..

Not necessarily the Best Algorithm, but the Best Dataset that will give Best Outcome


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

https://dev2.arundo.com/liveq


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


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


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

Documents

HOUSTON OSLO PALO ALTO Operationalizing Analytics in Oil ... · 3. Rapidly deploy advanced analytics Deliver data science that will turn your industrial data into actionable insights