Harnessing the Power of Operational Intelligence Work ... · Harnessing the Power of Operational...

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www.xodusgroup.com

Harnessing the Power of Operational Intelligence

Work Smarter Not Harder

Australian Oil and Gas Exhibition and Conference

Feb 2017

Presented by Andy Jones, General Manager, Perth

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Across the lifecycle . Across disciplines . Across the world

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>

> A brief history of control systems and data acquisition

> What is operational intelligence?

> Putting the data to work

> Software options

> The future

Overview



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Andy Jones, General Manager, Perth Office

> Operational Intelligence Systems / Intelligent Monitoring

> Online monitoring package specification, development and commissioning

> Modelling package integration (OLGA Hysys, Hysys Pipesim etc.)

> Use of modelling packages to aid operations and commissioning

> Hysys dynamics specialist



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A Brief History of Control Systems and

Data Acquisition


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> 270BC First recorded automated feedback system on

Egyptian water clock

> 1960s Early Supervisory Control And Data Acquisition

(SCADA)

> 1960s Computer processing of seismic data

> 1968 First Programmable Logic Controller (PLC)

> 1970s Distributed Control System (DCS)

> 1980s -1990s Widespread adoption of SCADA and

standardisation

> Onwards to present There has been an explosion in the

amount of data we collect and store But how do we best

make use of that data?

A Brief History of Control Systems and Data Acquisition



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What is Operational Intelligence? In a nutshell


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What is Operational Intelligence?

> Operational intelligence (OI) is an increasingly popular and powerful way of improving asset

performance. It is a category of real-time dynamic, business analytics that delivers visibility and

insight into data, streaming events and business operations. Operational Intelligence solutions run

queries against streaming data feeds and event data to deliver real-time analytic results. It

provides organisations with the ability to make decisions and immediately act on these analytic

insights, through manual or automated actions. The purpose of OI is to monitor asset performance

and identify and detect situations relating to inefficiencies, opportunities, and threats and provide

operational solutions.

> Increasingly sophisticated levels of operational intelligence are becoming more and more

prevalent in the oil and gas industry.



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Putting the Data to Work Unlocking the value of data


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Putting Data to Work Levels of Complexity



Level 1

Visualisation

> Management

dashboards

> Facility health

overviews

> DCS Mimics

> Alarm

summaries

Level 2

Logic

>

> Alarms and trips

> Daily averages

> Period of

excursions from

normal operating

conditions

Level 3

Calculations

> KPIs

> Virtual metering

> Flare modelling

> Heat

exchanger

fouling

> System mass

balance

Level 4

Reactive

> Event based

reporting

> Operator alerts

> Email, text

alerts

> Daily / weekly

reports

> Fault

identification

> Troubleshooting

Level 5

Proactive

> Optimise performance

> Asset integrity

> Predictive maintenance

> Start up optimisation

> Shutdown reporting

> Advanced compressor monitoring

Level 6

Integration & Analytics

> Databases

> Online realtime

simulation models

> Predictive data

analytics

> Automatic

recommendation

> Closed loop

feedback

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> Displayed in onshore office

for easy reference by

engineering support staff

> Typically a multi asset

overview such as this is

displayed permanently on a

support group or

surveillance area

> Based on real-time

information from the data

historian

Level 1 Visualisation Example 1 - Management KPI Dashboards

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> Asset overview level

monitoring dashboards

> Traffic light system

health indicators

> Ability to drill down for

more detailed

information

> Based on real-time

information from the

data historian

> Ability to look back at

any time in history

Level 1 Visualisation Example 2 - Facility Health Overviews

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> Sometimes not easily

available onshore due

to lack of bandwidth / or

software licence costs

> Quick trends / statistics

for all values

Level 1 Visualisation Example 3 - DCS Mimics with Extras

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> Warnings based on

current and daily values

-a-

monitoring

> Traffic light system

applies to monitored

parameters

>

> Embedded trend enables

long term analysis

> Daily values from table

can be trended using

click and drag

functionality

Level 2 Logic Example - User Defined Logic (Separate to DCS Alarms)

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> Virtual meter on oil

rundown

> Extended to all control

valves to estimate

mass balance through

system

> Used when metering

exist

> Calibration vs historical

data allows good

accuracy

Level 3 Calculations Example 1 - Virtual Metering

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Level 3 Calculations Example 2 Flare Emissions Tool

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> Replaces physical metering

with calculation based

> Diverse sources

blowdown ROs, regulators,

control valves, etc.

> Robust to errors flags

input tag errors, reverse

flow, etc.

> Coded directly in BabelFish/

PI Tools, PlantView,

Uniformance etc.

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> Calculating compressor performance relative to curves

> In the event of a trip or alarm, report automatically generated and sent to relevant parties

> Alerts can be emails or text messages

> All relevant data immediately in the hands of those who need it

> Fast response to minimise downtime

Level 4 Reactive Example - Troubleshooting Shutdowns

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> ESP lifetime statistics

> Production data / well

test comparison

> Pumps fluid level

indicator

> Weekly / monthly

automated reports

> Comparison across all

pump to allow load

biasing

> Comparison with

historic failures

> Alerts if predicted to be

close to failure

Level 5 Proactive Example 1 Real Time and Historical ESP Monitoring

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Level 5 Proactive Example 2 Advanced Compressor Monitoring

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Level 5 Proactive Example 3 Start Up Sequence Visualisation and Optimisation

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> Ability to track duration of

each start-up activity

> Determine where the start-

up benchmark is not being

met

> Display key start-up status

indicators from around the

process and utilities

systems

> Facilitates proactive

monitoring of the asset

start-up sequences by the

surveillance team

> Determine how much

downtime is associated with

a particular activity

Co

mm

on

Tra

in 1

Tra

in 2

Essential Power

Instrument Air

Essential Services Water

Diesel Pump

Main Power

Nitrogen

Tempered Water

Seawater

Integrated Services Water

Flare

Production Separator Pressurised

Wet Fuel Gas

Hot Oil System

Produced Water

Gas Turbine Fired

Glycol Contactor

Glycol Circulation

Dry Fuel Gas

Corrosion Inhibitor

Gas Export

Condensate Export

System

Time: 00:00:00

Co

mm

on

Tra

in 1

Tra

in 2

Time: 04:32:16

Co

mm

on

Tra

in 1

Tra

in 2

Time: 14:03:12

Co

mm

on

Tra

in 1

Tra

in 2

Time: 23:45:40

Co

mm

on

Tra

in 1

Tra

in 2

Time: 28:33:11

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Online transient / dynamic

models

> Linked to process data

> Real-time

>

> Predictive

Primary functions

> Liquids management

> Hydrocarbon accountancy

> Process visualisation

Level 6 Integration Example 1 Online Real Time Modelling (Dense Phase)

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Online transient / dynamic

models

> Multiphase pipeline

> Online linked single and

multiphase models

> 2 instances of the model

Real time

Primary functions

> Liquids management

> H2S / CO2 management

Level 6 Integration Example 2 Online Real Time Modelling (Multiphase Large Network)

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Level 6 Integration Example 3 Predictive Data Analytics

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Work Smarter Not Harder Pictured: Shaun Gregory, senior vice-president strategy, science and technology, Woodside

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Software Agnostic


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Software and Architecture

PZT TT VT AT FZT

Yokogawa DCS

ABB DCS

Emerson DCS

Siemens PCS

Honeywell PHD

Yokogawa Exaquantum

Aspentech IP21

OSI PI System

Third Party software

to interrogate the

data historian, e.g.

P2 Products

OSIsoft PI Tools

Yokogawa

FASTTOOLS

Xodus XPLORE

PHD Uniformance

Aspen Plantview

Data Historian

Plant

Instrumentation

DCS / PCS

Data Visualisation

Intelligent Engineering

Simulation

Examples include:

DCS mimics

KPI dashboards

Live PFDs (e.g. subsea valve alignment visualisation)

Traffic light health indicators

Alarm management / visibility

Examples include:

Virtual metering

Compressor performance and degradation monitoring

Heat exchanger monitoring

Shutdown / blowdown valve reporting

Facility start-up monitoring

Column performance monitoring

Examples include:

Linking of dynamic process models, e.g. HYSYS,

Unisim.

Linking of dynamic multiphase flow simulators, e.g.

OLGA, Ledaflow, COMPAS.

Faster than real time modelling for forecasting, or what-

if scenarios.

Leveraging real-time data.

Xodus Intelligent Monitoring Operator Domain



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The Future


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Example Data Structure for Predictive Data Analytics

PZT TT VT AT FZT

Data Lake

Data Historian

Plant

Instrumentation

DCS / PCS

Procurement

Maintenance

Production

Accounting

Process

Simulation

Excel Calculation

Visualisation Software

Data Analytics tools (e.g. Tableau, IBM

Watson) used for visualising:

Historical process data

Simulated process data

SAP / Oracle data

Market data

Financial data

Etc.

Tools can be BI or OI tools, depending

on the end user requirements.

Benefit of such a structure is that all

data is accessible by any visualisation /

data analytics software.

Not tied to specific software vendors

for visualising / interrogating data from

different sources.



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> Remote operations

Drones

Unmanned subsea surveillance

Robotics for inspection and maintenance

> More sensors

Internet of things


Work Smarter Not Harder Some images courtesy of Accenture

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Xodus has delivered intelligent monitoring solutions for a number of major

Experience Global and Local



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Xodus have developed operational intelligence tools for these Australian based clients

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