Criticality-based maintenance

Data science consulting for industry from DNV GL

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Maintenance logistics is one of the key opportunity areas for production optimisation in the offshore oil and gas industry. Having critical parts (or more broadly speaking, critical maintenance resources) in “hand” can significantly reduce the downtime associated to planned maintenance and failure events.

The optimisation process should be based on criticality ranking criteria, taking into account the potential bottlenecks and constraints related to a specific failure mode and its respective maintenance resource. This method provides means of identifying which systems/equipment/component poses the highest potential impact on the asset production. The item with the highest production loss rating should be targeted for the application of some maintenance improvement strategy.

RAM studies and its inherent dynamic simulation approach – taking into account the transient state of the asset – is the best platform to simulate the asset’s behaviour. During the initial stages of the project lifecycle, the top ten equipment items evaluated using the “criticality” criteria would then be subject to validation during the operational stage. Developing a deep understanding on how your system is likely to behave is essential to increase the probability of successful operations.

In Maros and Taro, this can be easily achieved by setting the capacity loss at repair to zero impact. The downtime calculation in Maros and Taro is broken down in two stages:

  • Resource availability analysis
  • Actual repair time

Please refer to the following blog post to understand this break down in more details:
A walkthrough of a typical failure event.

Setting up the “actual repair time” portion to zero enables analysts to isolate the losses coming from maintenance logistics. Analysts are able to predict and rank spares parts and other maintenance resources to reduce downtime and get the production up as quickly as possible.

When working with Maros, this is a sensitivity case which is pretty much an easy extension of the base case. It is important note that the base case should include the maintenance strategy defined via Maintenance Profiles. Using the Equipment grid, go to the Capacity Loss at Repair (“Cap Loss Repair) column and set everything to 0%, as shown below:

Plant - Criticality-based Maintenance - sensitivity-manager

Sensitivity Manager

The criticality results are then compared with the base case and the criticality-based maintenance analysed:

Plant - Criticality-based Maintenance - legend

Legend

Plant - Criticality-based Maintenance - cbm

Portion of production loss

The graph above will show the portion of the production loss allocated to the maintenance logistics operations, taking into account mobilisation time, crew availability, and spare available etc. For example, from this graph it is possible to conclude that the “Interstage heater – Entire unit” event is the most critical from a logistics perspective.

Drilling down into more details, the CBM will list all potential optimisation areas where logistics are playing a significant role in the calculated downtime.

Plant - Criticality-based Maintenance - logistics-criticality

Critically-based maintenance

Being able to forecast the ranking of equipment items is of fundamental importance to enable a fast turn-around time, thus reducing downtime. This technique enables an informed decision-making process on maintenance strategy by identifying which assets are critical to the production. Criticality-based maintenance also informs procurement strategy so that inventories, and the costs associated with keeping them, are reduced but not at the expense of increased downtime.

Author: Victor Borges

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