Forecasting Maintenance Excellence

Transcription

1 SOFTWARE Forecasting Maintenance Excellence 4 Tips towards Maintenance Excellence Wednesday, 03 May SAFER, SMARTER, GREENER

2 Presenters 2

3 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

4 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. 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. 2

5 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. 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 2

6 A Case Study: EnQuest PM Optimisation Study Client EnQuest Ltd 3 assets for Oil production North Sea Challenge 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 Value to Client 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 3

7 Our vision: global impact for Our vision: global impact for safe and sustainable future a safe and sustainable future MARITIME OIL & GAS ENERGY BUSINESS ASSURANCE SOFTWARE RESEARCH & INNOVATION 4

8 The BIG Picture: Plant-Wide Reliability Plant-wide Reliability Reliability Equipment performance data (failure frequencies) System configuration Unit Costs/Revenue Product price Man-hour/spares costs Transport costs Discount rates Maintainability Preventative Maintenance Activities Maintenance manpower Shift constraints Mobilization delays Spare& resource constraints Production Efficiency Achieved production Production losses Criticality Contract shortfalls Delayed cargoes Availability Equipment/System uptime Operability Plant interdependencies Plant re-start times Production/demand rates Storage Size Tanker Fleet and Operations 5

9 4 Tips towards Maintenance Excellence Operational Expenditure Modelling Spare Parts management Inspection and Degradation Cost of Failure VS Cost of Inspection 6

10 The BIG Picture: Plant-Wide Reliability Plant-wide Reliability Reliability Equipment performance data (failure frequencies) System configuration Unit Costs/Revenue Product price Man-hour/spares costs Transport costs Discount rates Maintainability Maintenance intervals Maintenance resources Shift constraints Mobilization delays Spares constraints Production Efficiency Achieved production Production losses Criticality Contract shortfalls Delayed cargoes Availability Equipment/System uptime Operability Plant interdependencies Plant re-start times Production/demand rates Storage Size Tanker Fleet and Operations 7

11 Operational Expenditure Modelling 8

12 Operational Expenditure Modelling Operational Expenditure 9

13 Operational Expenditure Modelling Philosophy Operational Expenditure 9

14 Operational Expenditure Modelling Corrective Philosophy Preventive Operational Expenditure Opportune 9

15 Operational Expenditure Modelling Corrective Different Resources Philosophy Preventive Operational Expenditure Opportune 9

16 Operational Expenditure Modelling Crew Vessel Corrective Safety Spare part Different Resources Philosophy Preventive Accessory Operational Expenditure Opportune 9

17 Operational Expenditure Modelling Crew Vessel Corrective Safety Spare part Different Resources Philosophy Preventive Accessory Operational Expenditure Opportune Constraints 9

18 Operational Expenditure Modelling Crew Vessel Corrective Safety Spare part Different Resources Philosophy Preventive Accessory Operational Expenditure Opportune Available Number Shift Constraints Mobilization time Travel time 9

19 Operational Expenditure Modelling Crew Vessel Corrective Safety Spare part Different Resources Philosophy Preventive Accessory Operational Expenditure Opportune Locations Available Number Shift Constraints Mobilization time Travel time 9

20 Operational Expenditure Modelling Crew Vessel Corrective Safety Spare part Different Resources Philosophy Preventive Accessory Operational Expenditure Opportune Locations Available Number Shift Constraints Mobilization time Travel time 9

21 Production rates Operational Expenditure calculation Pump on Pump off Actual Volume Pump on Pump on Pump degraded Time 10

22 Production rates Operational Expenditure calculation Pump on Pump off Actual Volume Pump on Pump on Pump degraded Time Maintenance Expenditure 10

23 Production rates Operational Expenditure calculation Pump on Pump off Actual Volume Pump on Pump on Pump degraded Time Maintenance Expenditure Logistics delays The active repair time Restarts following the repair Preparation time - system must reach a state where it is maintainable (deinventory, cool down/warm up, spading etc.) Diagnosis - Failure finding or troubleshooting period Resource Availability - Logistics delays Once all resources are available, actual repair work starts There might a number of operational constraints after the repair is completed: reinventory, warm up/cool down etc. 10

24 Production rates Operational Expenditure calculation Actual Volume Pump on Pump off Pump on Pump on Pump degraded Time Lost Revenue Maintenance Expenditure Logistics delays The active repair time Restarts following the repair Preparation time - system must reach a state where it is maintainable (deinventory, cool down/warm up, spading etc.) Diagnosis - Failure finding or troubleshooting period Resource Availability - Logistics delays Once all resources are available, actual repair work starts There might a number of operational constraints after the repair is completed: reinventory, warm up/cool down etc. 10

25 Production rates Operational Expenditure calculation Actual Volume Pump on Pump off Pump on Pump on Pump degraded Time Lost Revenue Maintenance Expenditure Lost Profit Opportunity Logistics delays Preparation time - system must reach a state where it is maintainable (deinventory, 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: reinventory, warm up/cool down etc. 10

26 OpEx Modelling OpEx modelling should take into account: Man-hours Spares Support Consumables Transport Mobilization and Demobilisation 11

27 OpEx Modelling OpEx modelling should take into account: Man-hours Spares Support Consumables Transport Mobilization and Demobilisation 11

28 Spare Optimisation 12

29 Spare part Stock Level number of spares available SPARE PARTS 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 13

30 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. Vendor Non-critical spare parts Central Stock Critical spare parts Stock on site Spare parts critical for production 14

31 Spare results Restock Level Number in Stock Replenishment level Mobilisation time Cost of spare Other required resources Nr.Unsched Job Delays Bearings 1.3 Valve 0 Pump 3.9 Compressor 4.5 Electric motor 0 Heater 6.3 Production Loss 15

32 Criticality-Based Maintenance 16

33 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 17

34 Maintenance Optimisation 18

35 Weibull distribution 19

36 Working Envelope Inspection and Maintenance Challenge The purpose of Inspection Detect defects, and Carry out remedial maintenance in order to avert the development of these defects to failure Failure Time 20

37 Working Envelope Inspection and Maintenance Challenge The purpose of Inspection Detect defects, and Carry out remedial maintenance in order to avert the development of these defects to failure Failure Time 20

38 Working Envelope Inspection and Maintenance Challenge The purpose of Inspection Detect defects, and Carry out remedial maintenance in order to avert the development of these defects to 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 Failure Time 20

39 Maintenance Optimisation Framework Striking the Balance Cost of Inspection/Maintenance Cost of Failure Lost Production Labour Material Costs Frequency of Inspection (CMMS) Lost Production Labour Material Costs Frequency of Failure (CMMS) Effectiveness of Inspection (defect detection, CMMS) Job Plan & Work Order content (PM Instructions and Failure Reports) Optimum Maintenance & Inspection Interval 21

40 Workflow Analyse data extracted form CMMS PM / CM / Defects In-Scope Assign Equipment Type Out of Scope Assign Exclusion Category PM Classification Failure Likelihood PM Criticality PM / CM / Defects Allocation Optimised PM Intervals PM Optimisation Review w/ Disciplines 22

41 Workflow Analyse data extracted form CMMS PM / CM / Defects In-Scope Assign Equipment Type Out of Scope Assign Exclusion Category Failure Likelihood PM Classification PM Criticality CRITICALITY PM / CM / Defects Allocation Optimised PM Intervals PM Optimisation Review w/ Disciplines 22

42 Workflow Analyse data extracted form CMMS PM / CM / Defects In-Scope Assign Equipment Type Out of Scope Assign Exclusion Category Failure Likelihood PM Classification PM Criticality CRITICALITY PM / CM / Defects Allocation PREPARATION Optimised PM Intervals PM Optimisation Review w/ Disciplines 22

43 Workflow Analyse data extracted form CMMS PM / CM / Defects In-Scope Assign Equipment Type Out of Scope Assign Exclusion Category Failure Likelihood PM Classification PM Criticality CRITICALITY PM / CM / Defects Allocation PREPARATION Optimised PM Intervals OPTIMISATION PM Optimisation Review w/ Disciplines 22

44 Methodology (1) Classification and Criticality Analysis Criticality Analysis To determine the PM Optimisation approach to be adopted 23

45 Methodology (1) Classification and Criticality Analysis Criticality Analysis To determine the PM Optimisation approach to be adopted Criticality 1-3 Non-critical equipment (1-3) generally run to failure philosophy, where PM provides less benefit (30-40%) 23

46 Methodology (1) Classification and Criticality Analysis Criticality Analysis To determine the PM Optimisation approach to be adopted Criticality 4-10 Critical equipment PM Optimisation 8-10 PMs optimised individually (20%) 4-7 PMs optimised by class (40%-50%) Criticality 1-3 Non-critical equipment (1-3) generally run to failure philosophy, where PM provides less benefit (30-40%) 23

47 Methodology (2) PM / CM / Defects Allocation Location PM Number PM/ CM Allocation Work Type ActFinish WO Number Description E2013 R-O0022 R-O0022 PM 30/ 10/ E2013 R-O0022 R-O0022 PM 12/ 12/ E2013 NULL R-O0022 CM 27/ 08/ REPLACE BU5198 SW INLET VALVE E2013 For each PM, analyse maintenance history data, job plans and work orders to determine: Planned maintenance activities E2013 NULL R-O0022 CM 04/ 03/ REPLACE SEIZED BU5204 E2013 R-O0022 R-O0022 PM 09/ 01/ E2013 R-O0022 R-O0022 PM 22/ 02/ E2013 R-O0022 R-O0022 PM 22/ 03/ E2013 NULL R-O0022 CM 02/ 10/ VALVES PASSING E2013 R-O0022 R-O0022 PM 28/ 04/ E2013 R-O0022 R-O0022 PM 24/ 05/ E2013 R-O0022 R-O0022 Defect 27/ 06/ E2013 R-O0022 Excluded Excluded 15/ 11/ E2013 R-O0022 R-O0022 PM 22/ 08/

48 Methodology (2) PM / CM / Defects Allocation Location PM Number PM/ CM Allocation Work Type ActFinish WO Number Description E2013 R-O0022 R-O0022 PM 30/ 10/ E2013 R-O0022 R-O0022 PM 12/ 12/ E2013 NULL R-O0022 CM 27/ 08/ REPLACE BU5198 SW INLET VALVE E2013 For each PM, analyse maintenance history data, job plans and work orders to determine: Planned maintenance activities Previous corrective maintenance activities E2013 NULL R-O0022 CM 04/ 03/ REPLACE SEIZED BU5204 E2013 R-O0022 R-O0022 PM 09/ 01/ E2013 R-O0022 R-O0022 PM 22/ 02/ E2013 R-O0022 R-O0022 PM 22/ 03/ E2013 NULL R-O0022 CM 02/ 10/ VALVES PASSING E2013 R-O0022 R-O0022 PM 28/ 04/ E2013 R-O0022 R-O0022 PM 24/ 05/ E2013 R-O0022 R-O0022 Defect 27/ 06/ E2013 R-O0022 Excluded Excluded 15/ 11/ E2013 R-O0022 R-O0022 PM 22/ 08/

49 Methodology (2) PM / CM / Defects Allocation Location PM Number PM/ CM Allocation Work Type ActFinish WO Number Description E2013 R-O0022 R-O0022 PM 30/ 10/ E2013 R-O0022 R-O0022 PM 12/ 12/ E2013 NULL R-O0022 CM 27/ 08/ REPLACE BU5198 SW INLET VALVE E2013 For each PM, analyse maintenance history data, job plans and work orders to determine: Planned maintenance activities Previous corrective maintenance activities Historical rate of defect detection during the PMs (Effectiveness of maintenance and inspections) E2013 NULL R-O0022 CM 04/ 03/ REPLACE SEIZED BU5204 E2013 R-O0022 R-O0022 PM 09/ 01/ E2013 R-O0022 R-O0022 PM 22/ 02/ E2013 R-O0022 R-O0022 PM 22/ 03/ E2013 NULL R-O0022 CM 02/ 10/ VALVES PASSING E2013 R-O0022 R-O0022 PM 28/ 04/ E2013 R-O0022 R-O0022 PM 24/ 05/ E2013 R-O0022 R-O0022 Defect 27/ 06/ E2013 R-O0022 Excluded Excluded 15/ 11/ E2013 R-O0022 R-O0022 PM 22/ 08/

50 Methodology (2) PM / CM / Defects Allocation Location PM Number PM/ CM Allocation Work Type ActFinish WO Number Description E2013 R-O0022 R-O0022 PM 30/ 10/ E2013 R-O0022 R-O0022 PM 12/ 12/ E2013 NULL R-O0022 CM 27/ 08/ REPLACE BU5198 SW INLET VALVE E2013 For each PM, analyse maintenance history data, job plans and work orders to determine: Planned maintenance activities Previous corrective maintenance activities Historical rate of defect detection during the PMs (Effectiveness of maintenance and inspections) Exclude failure modes that the PMs do not defend against E2013 NULL R-O0022 CM 04/ 03/ REPLACE SEIZED BU5204 E2013 R-O0022 R-O0022 PM 09/ 01/ E2013 R-O0022 R-O0022 PM 22/ 02/ E2013 R-O0022 R-O0022 PM 22/ 03/ E2013 NULL R-O0022 CM 02/ 10/ VALVES PASSING E2013 R-O0022 R-O0022 PM 28/ 04/ E2013 R-O0022 R-O0022 PM 24/ 05/ E2013 R-O0022 R-O0022 Defect 27/ 06/ E2013 R-O0022 Excluded Excluded 15/ 11/ E2013 R-O0022 R-O0022 PM 22/ 08/

51 Methodology (3) Optimisation Fixed Interval Current Interval Optimised Interval An Optimised Interval is generated for each PM (or for each class of PM), based on Failure and Inspection costs 12 m 24 m 12 m Current Interval Optimised Interval RTF depending on: C tot = C f + C i 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.) 25

52 Methodology (4) Optimisation Probabilistic Approach A distribution of the Optimised Interval is generated for each PM (or for each class of PM), based on Failure and Inspection costs depending on: C tot = C f + C i 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.) 26

53 Methodology (4) Optimisation Probabilistic Approach 12 m Distributed Parameters: Frequency of previous inspections Frequency of failures (list of CM activities) Effectiveness of inspections (list of defects detected) Oil/gas price Material costs for inspections and repairs RTF The Optimised Interval is based on defined Criteria (i.e. risk acceptability): Mean value P m P 40 P 60 P 30 P 70 27

54 Value to Customer TYPICAL RESULTS 20-40% reduction in PM manhours for productioncritical equipment Very large reduction in PM manhours for non-productioncritical equipment where a run to failure philosophy is appropriate Maintenance OPEX (and Backlog) reduction, avoiding traditional over-conservative approaches 28

55 Value to Customer TYPICAL RESULTS 20-40% reduction in PM manhours for productioncritical equipment Very large reduction in PM manhours for non-productioncritical equipment where a run to failure philosophy is appropriate Maintenance OPEX (and Backlog) reduction, avoiding traditional over-conservative approaches Maintenance effort concentrated on areas that most affect production, and reduced in areas where it provides less benefit 28

56 Value to Customer TYPICAL RESULTS 20-40% reduction in PM manhours for productioncritical equipment Very large reduction in PM manhours for non-productioncritical equipment where a run to failure philosophy is appropriate Maintenance OPEX (and Backlog) reduction, avoiding traditional over-conservative approaches Maintenance effort concentrated on areas that most affect production, and reduced in areas where it provides less benefit Generate an Optimum Inspection Interval for each PM with a quantitative technique which combines technical, operational, and commercial considerations 28

57 Value to Customer TYPICAL RESULTS 20-40% reduction in PM manhours for productioncritical equipment Very large reduction in PM manhours for non-productioncritical equipment where a run to failure philosophy is appropriate Maintenance OPEX (and Backlog) reduction, avoiding traditional over-conservative approaches Maintenance effort concentrated on areas that most affect production, and reduced in areas where it provides less benefit Generate an Optimum Inspection Interval for each PM with a quantitative technique which combines technical, operational, and commercial considerations Comprehensive action plan for each optimised PM, with associated revised Job Plans, including rationalised maintenance instructions 28

58 Value to Customer TYPICAL RESULTS 20-40% reduction in PM manhours for productioncritical equipment Very large reduction in PM manhours for non-productioncritical equipment where a run to failure philosophy is appropriate Maintenance OPEX (and Backlog) reduction, avoiding traditional over-conservative approaches Maintenance effort concentrated on areas that most affect production, and reduced in areas where it provides less benefit Generate an Optimum Inspection Interval for each PM with a quantitative technique which combines technical, operational, and commercial considerations Comprehensive action plan for each optimised PM, with associated revised Job Plans, including rationalised maintenance instructions Dynamic Analysis 28

59 Value to Customer TYPICAL RESULTS 20-40% reduction in PM manhours for productioncritical equipment Very large reduction in PM manhours for non-productioncritical equipment where a run to failure philosophy is appropriate Maintenance OPEX (and Backlog) reduction, avoiding traditional over-conservative approaches Maintenance effort concentrated on areas that most affect production, and reduced in areas where it provides less benefit Generate an Optimum Inspection Interval for each PM with a quantitative technique which combines technical, operational, and commercial considerations Comprehensive action plan for each optimised PM, with associated revised Job Plans, including rationalised maintenance instructions Dynamic Analysis Sensitivity analysis: test effect of changes in gas price, labour rate, maintenance/repair costs, etc., including evaluation of uncertainties 28

60 Value to Customer TYPICAL RESULTS 20-40% reduction in PM manhours for productioncritical equipment Very large reduction in PM manhours for non-productioncritical equipment where a run to failure philosophy is appropriate The DNV GL approach is compliant with the requirements set by the OIL&GASUK guideline Maintenance Optimisation Reviews: Sharing Experience and Learning, 2016 Maintenance OPEX (and Backlog) reduction, avoiding traditional over-conservative approaches Maintenance effort concentrated on areas that most affect production, and reduced in areas where it provides less benefit Generate an Optimum Inspection Interval for each PM with a quantitative technique which combines technical, operational, and commercial considerations Comprehensive action plan for each optimised PM, with associated revised Job Plans, including rationalised maintenance instructions Dynamic Analysis Sensitivity analysis: test effect of changes in gas price, labour rate, maintenance/repair costs, etc., including evaluation of uncertainties 28

61 Have you got any question? 29

62 Thank you! DNV GL Oil and Gas, Performance Forecasting and Maintenance Optimisation Guy COZON, Team Leader Andrea MONFERINI, Principal Consultant DNV GL Software, Asset Performance Management Products Victor BORGES, Senior Product Manager SAFER, SMARTER, GREENER 30