Forecasting Maintenance Excellence - DNV GL - Forecasting...•Production/demand rates •Storage...

DNV GL © 2017 Wednesday, 03 May 2017 SAFER, SMARTER, GREENERDNV GL © 2017

Wednesday, 03 May 2017

SOFTWARE

Forecasting Maintenance Excellence

1

4 Tips towards Maintenance Excellence

DNV GL © 2017 Wednesday, 03 May 2017

Presenters

2


Presenters

Victor Borges is senior product manager in charge of the Asset

Performance Management products in DNV GL.

Responsible for DNV GL’s performance forecasting tools, Maros and

Taro, and asset integrity management solutions, Synergi Plant.

2


Presenters





2

Guy Cozon is Head of DNV GL Advisory's London Performance

Forecasting group.

12 years of experience in predicting asset performance and risk in

the upstream and downstream oil and gas, petrochemical, rail and

aviation sectors.


Presenters





2

Guy Cozon is Head of DNV GL Advisory's London Performance

Forecasting group.

12 years of experience in predicting asset performance and risk in

the upstream and downstream oil and gas, petrochemical, rail and

aviation sectors.

Andrea Monferini is a chartered engineer and Principal consultant

in DNV GL.

13 years of experience in Performance Forecasting and facilitating

SIL, FMEA and FMECA analysis. Currently the expertise leader

within DNV GL UK for Maintenance Optimization


A Case Study: EnQuest PM Optimisation Study

EnQuest Ltd

3 assets for Oil production

North Sea

3

Challenges with facilities being maintained sub-optimally as a result of legacy

Maintenance Systems

Aging assets (two with first oil in 1978)

Unsustainable maintenance OPEX at today’s oil price

Reduce costs associated with preventative maintenance without impacting asset reliability

Rationalise the preventative maintenance to avoid unnecessary tasks

11,000 Maintenance hours/year saved per asset (~55% of analysed current PM

hours/year)

£1.5 million costs savings per annum (based on today’s market)

A revised set of Job Plans, including rationalised maintenance instructions

Client

Challenge

Value to Client


Our vision: global impact for a safe and sustainable futureOur vision: global impact for a safe and sustainable future

RESEARCH & INNOVATION

BUSINESS

ASSURANCE

OIL & GASMARITIME SOFTWAREENERGY

4


Plant-wide Reliability

Unit Costs/Revenue

•Product price

•Man-hour/spares costs

•Transport costs

•Discount rates

Production Efficiency

•Achieved production

•Production losses

•Criticality

•Contract shortfalls

•Delayed cargoes

Operability

•Plant interdependencies

•Plant re-start times

•Production/demand rates

•Storage Size

•Tanker Fleet and Operations

Availability

•Equipment/System uptime

Maintainability

•Preventative Maintenance Activities

•Maintenance manpower

•Shift constraints

•Mobilization delays

•Spare& resource constraints

Reliability

•Equipment performance data (failure frequencies)

•System configuration

The BIG Picture: Plant-Wide Reliability

5


4 Tips towards Maintenance Excellence

6

Operational Expenditure Modelling

Spare Parts management

Inspection and Degradation

Cost of Failure VS Cost of Inspection


Plant-wide Reliability

Unit Costs/Revenue

•Product price

•Man-hour/spares costs

•Transport costs

•Discount rates

Production Efficiency

•Achieved production

•Production losses

•Criticality

•Contract shortfalls

•Delayed cargoes

Operability

•Plant interdependencies

•Plant re-start times

•Production/demand rates

•Storage Size

•Tanker Fleet and Operations

Availability

•Equipment/System uptime

Maintainability

•Maintenance intervals

•Maintenance resources

•Shift constraints

•Mobilization delays

•Spares constraints

Reliability

•Equipment performance data (failure frequencies)

•System configuration

The BIG Picture: Plant-Wide Reliability

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8



9

Operational Expenditure



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Philosophy



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Philosophy

Corrective

Preventive

Opportune



9


Philosophy

Corrective

Preventive

Opportune

Different Resources



9


Philosophy

Corrective

Preventive

Opportune

Different Resources

Crew

Vessel

Safety

Spare part

Accessory



9


Philosophy

Corrective

Preventive

Opportune

Different Resources

Crew

Vessel

Safety

Spare part

Accessory

Constraints



9


Philosophy

Corrective

Preventive

Opportune

Different Resources

Crew

Vessel

Safety

Spare part

Accessory

Constraints

Available Number

Shift

Mobilization time

Travel time



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Philosophy

Corrective

Preventive

Opportune

Different Resources

Crew

Vessel

Safety

Spare part

Accessory

Constraints

Available Number

Shift

Mobilization time

Travel time

Locations


Operational Expenditure calculation

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Pump onPump off

Pump onPump degraded

Pump on

Actual

Volume

Time

Pro

du

ctio

n r

ate

s



Maintenance Expenditure

10

Pump onPump off


Pump on

Actual

Volume

Time

Pro

du

ctio

n r

ate

s




10

Pump onPump off


Pump on

Actual

Volume

Time

Pro

du

ctio

n r

ate

s

Logistics delays

•Preparation time - system must reach a state where it is maintainable (de-inventory, cool down/warm up, spading etc.)

•Diagnosis - Failure finding or troubleshooting period

•Resource Availability -Logistics delays

The active repair time

•Once all resources are available, actual repair work starts

Restarts following the repair

•There might a number of operational constraints after the repair is completed: re-inventory, warm up/cool down etc.




Lost Revenue

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Pump onPump off


Pump on

Actual

Volume

Time

Pro

du

ctio

n r

ate

s

Logistics delays










Maintenance Expenditure Lost Profit Opportunity

Lost Revenue

10

Pump onPump off


Pump on

Actual

Volume

Time

Pro

du

ctio

n r

ate

s

Logistics delays









OpEx Modelling

OpEx modelling should take into account:

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Man-hours

Spares

Support

Consumables

Transport

Mobilization and Demobilisation


Spare Optimisation

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Spare part

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Stock Level – number of spares available

Replenishment Level - Stock level at which it becomes time to restock.

Time to Restock Range - lead time (days) to replace the used spares in stock.

Cost per unit – main cost for each part

SPARE

PARTS


Spare Management – Your Insurance policy

The cost of spares storage normally

runs at approximately 20% of the

original purchase price per

annum

This in turn leads to a reduction in

capital tied up and significantly lower

consequential costs, since

unnecessary and obsolete spare parts

are not retained.

On the other hand, the central store

contains critical spare parts that are

needed less frequently.

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Vendor

•Non-critical spare parts

Central Stock

•Critical spare parts

Stock on site

•Spare parts critical for production


Spare results

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Nr.Unsched Job Delays

Bearings 1.3

Valve 0

Pump 3.9

Compressor 4.5

Electric motor 0

Heater 6.3

01234567

Restock Level

Number in Stock

Replenishment level

Mobilisation time

Cost of spare

Other required resources

Production Loss


Criticality-Based Maintenance

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Our approach: Criticality-Based Maintenance

Most CMMS packages have the ability to qualitatively identify equipment criticality

(typically scale of one to 10)

Our approach quantifies the total criticality for a given component or equipment

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Maintenance Optimisation

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Weibull distribution

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Inspection and Maintenance Challenge

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The purpose of Inspection

Detect defects, and

Carry out remedial maintenance in

order to avert the development of

these defects to failure

Work

ing

Envelo

pe

Time

Failure


An effective implementation of several

methodologies leads to higher reliability and a

better understanding of the risk level. However,

many have several weaknesses:

Qualitative

Slow & Expensive

Overly conservative

Long gap between updates

Inflexible to adjust to specifics of each operation

Inspection and Maintenance Challenge

20

The purpose of Inspection

Detect defects, and

Carry out remedial maintenance in

order to avert the development of

these defects to failure

Work

ing

Envelo

pe

Time

Failure


Maintenance Optimisation Framework – Striking the Balance

Lost Production

Labour

Material Costs

Frequency of Inspection (CMMS)

Effectiveness of Inspection (defect detection, CMMS)

Job Plan & Work Order content (PM Instructions

and Failure Reports)

Optimum Maintenance & Inspection Interval

Lost Production

Labour

Material Costs

Frequency of Failure (CMMS)

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Cost of

Inspection/Maintenance

Cost of

Failure


Workflow

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Analyse data extracted form CMMSPM / CM / Defects

AssignEquipment Type

PMClassification

PMCriticality

PM / CM / Defects Allocation

Optimised PM Intervals

PM Optimisation Review w/ Disciplines

FailureLikelihood

AssignExclusion Category

In-Scope Out of Scope


Workflow

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PMClassification

PMCriticality




FailureLikelihood



CRITICALITY


Workflow

22



PMClassification

PMCriticality




FailureLikelihood



CRITICALITY

PREPARATION


Workflow

22



PMClassification

PMCriticality




FailureLikelihood



CRITICALITY

PREPARATION

OPTIMISATION


Methodology (1) – Classification and Criticality Analysis

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Criticality AnalysisTo determine the PM Optimisationapproach to be adopted



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Criticality 1-3Non-critical equipment (1-3)

generally run to failure philosophy, where PM provides less benefit (30-40%)



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Criticality 1-3Non-critical equipment (1-3)

generally run to failure philosophy, where PM provides less benefit (30-40%)

Criticality 4-10Critical equipment PM Optimisation

• 8-10 PMs optimised individually (20%)

• 4-7 PMs optimised by class (40%-50%)


Methodology (2) – PM / CM / Defects Allocation

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For each PM, analyse maintenance history data, job plans and work orders to determine:

Planned maintenance activities

Location PM Number PM/CM Allocation Work Type ActFinish WO Number Description

E2013 R-O0022 R-O0022 PM 30/10/2004 2004-45191

OIL COOLER E2013 SEA WATER

VALVE OPERATION

E2013 R-O0022 R-O0022 PM 12/12/2004 2004-50591


VALVE OPERATION

E2013 NULL R-O0022 CM 27/08/2012 2012-2396

REPLACE BU5198 SW INLET VALVE

E2013

E2013 NULL R-O0022 CM 04/03/2013 2012-07317 REPLACE SEIZED BU5204

E2013 R-O0022 R-O0022 PM 09/01/2005 2005-01361


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 22/02/2005 2005-08471


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 22/03/2005 2005-12901


VALVE OPERATION

E2013 NULL R-O0022 CM 02/10/2004 2004-38001 VALVES PASSING

E2013 R-O0022 R-O0022 PM 28/04/2005 2005-17941


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 24/05/2005 2005-22751


VALVE OPERATION

E2013 R-O0022 R-O0022 Defect 27/06/2005 2005-28091


VALVE OPERATION

E2013 R-O0022 Excluded Excluded 15/11/2009 2009-5728


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 22/08/2005 2005-35061


VALVE OPERATION



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Previous corrective maintenance activities


E2013 R-O0022 R-O0022 PM 30/10/2004 2004-45191


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 12/12/2004 2004-50591


VALVE OPERATION

E2013 NULL R-O0022 CM 27/08/2012 2012-2396


E2013


E2013 R-O0022 R-O0022 PM 09/01/2005 2005-01361


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 22/02/2005 2005-08471


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 22/03/2005 2005-12901


VALVE OPERATION


E2013 R-O0022 R-O0022 PM 28/04/2005 2005-17941


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 24/05/2005 2005-22751


VALVE OPERATION

E2013 R-O0022 R-O0022 Defect 27/06/2005 2005-28091


VALVE OPERATION



VALVE OPERATION

E2013 R-O0022 R-O0022 PM 22/08/2005 2005-35061


VALVE OPERATION



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Historical rate of defect detection during the PMs (Effectiveness of maintenance and inspections)


E2013 R-O0022 R-O0022 PM 30/10/2004 2004-45191


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 12/12/2004 2004-50591


VALVE OPERATION

E2013 NULL R-O0022 CM 27/08/2012 2012-2396


E2013


E2013 R-O0022 R-O0022 PM 09/01/2005 2005-01361


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 22/02/2005 2005-08471


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 22/03/2005 2005-12901


VALVE OPERATION


E2013 R-O0022 R-O0022 PM 28/04/2005 2005-17941


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 24/05/2005 2005-22751


VALVE OPERATION

E2013 R-O0022 R-O0022 Defect 27/06/2005 2005-28091


VALVE OPERATION



VALVE OPERATION

E2013 R-O0022 R-O0022 PM 22/08/2005 2005-35061


VALVE OPERATION



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Historical rate of defect detection during the PMs (Effectiveness of maintenance and inspections)

Exclude failure modes that the PMs do not defend against


E2013 R-O0022 R-O0022 PM 30/10/2004 2004-45191


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 12/12/2004 2004-50591


VALVE OPERATION

E2013 NULL R-O0022 CM 27/08/2012 2012-2396


E2013


E2013 R-O0022 R-O0022 PM 09/01/2005 2005-01361


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 22/02/2005 2005-08471


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 22/03/2005 2005-12901


VALVE OPERATION


E2013 R-O0022 R-O0022 PM 28/04/2005 2005-17941


VALVE OPERATION

E2013 R-O0022 R-O0022 PM 24/05/2005 2005-22751


VALVE OPERATION

E2013 R-O0022 R-O0022 Defect 27/06/2005 2005-28091


VALVE OPERATION



VALVE OPERATION

E2013 R-O0022 R-O0022 PM 22/08/2005 2005-35061


VALVE OPERATION


Methodology (3) – Optimisation – Fixed Interval

25

An Optimised Interval is generated for

each PM (or for each class of PM), based

on Failure and Inspection costs

Ctot = Cf + Ci

depending on:

Frequency of previous inspections

Frequency of failures (list of CM

activities)

Effectiveness of inspections (list of

defects detected)

% of production loss, dependent on the

PM / CM durations (oil/gas price)

Labour time and costs (labour rate)

Material costs for inspections and repairs

Other costs (tools, reputational, etc.)

24 m

RTF

12 m

12 m

Current Interval

Optimised Interval

Current Interval

Optimised Interval


Methodology (4) – Optimisation – Probabilistic Approach

26

A distribution of the Optimised Interval

is generated for each PM (or for each class

of PM), based on Failure and Inspection

costs

Ctot = Cf + Ci

depending on:



activities)


defects detected)

% of production loss, dependent on the

PM / CM durations (oil/gas price)

Labour time and costs (labour rate)

Material costs for inspections and

repairs

Other costs (tools, reputational, etc.)


Methodology (4) – Optimisation – Probabilistic Approach

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Distributed Parameters:



activities)


defects detected)

Oil/gas price

Material costs for inspections and repairs

The Optimised Interval is based on defined

Criteria (i.e. risk acceptability):

Mean value

P50

P40 – P60

P30 – P70

RTF

12 m

12 m


Value to Customer

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Maintenance OPEX (and Backlog) reduction,

avoiding traditional over-conservative

approaches 20-40% reduction in PM

manhours for production-critical equipment

Very large reduction in PM manhours for non-production-critical equipment where a run to failure philosophy is appropriate

TYPICAL RESULTS


Value to Customer

28



approaches

Maintenance effort concentrated on areas that

most affect production, and reduced in areas

where it provides less benefit

20-40% reduction in PM manhours for production-critical equipment


TYPICAL RESULTS


Value to Customer

28



approaches




Generate an Optimum Inspection Interval for

each PM with a quantitative technique which

combines technical, operational, and

commercial considerations



TYPICAL RESULTS


Value to Customer

28



approaches








Comprehensive action plan for each optimised

PM, with associated revised Job Plans, including

rationalised maintenance instructions



TYPICAL RESULTS


Value to Customer

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approaches











Dynamic Analysis



TYPICAL RESULTS


Value to Customer

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approaches











Dynamic Analysis

Sensitivity analysis: test effect of changes in gas

price, labour rate, maintenance/repair costs,

etc., including evaluation of uncertainties



TYPICAL RESULTS


Value to Customer

28

The DNV GL approach is

compliant with the

requirements set by the

OIL&GASUK guideline

‘Maintenance

Optimisation Reviews:

Sharing Experience and

Learning’, 2016



approaches











Dynamic Analysis

Sensitivity analysis: test effect of changes in gas

price, labour rate, maintenance/repair costs,

etc., including evaluation of uncertainties



TYPICAL RESULTS


Have you got any question?

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SAFER, SMARTER, GREENER

www.dnvgl.com

Thank you!

30

DNV GL Oil and Gas, Performance Forecasting and Maintenance Optimisation

Guy COZON, Team Leader ([email protected])

Andrea MONFERINI, Principal Consultant ([email protected])

DNV GL Software, Asset Performance Management Products

Victor BORGES, Senior Product Manager ([email protected])

mailto:[email protected]



Forecasting Maintenance Excellence - DNV GL - Forecasting...•Production/demand rates •Storage...

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