Opportunity maintenance is a form of preventive maintenance based upon “convenient” replacement of equipment items or components by taking the advantage of the unplanned or planned shutdown of a system where we have suitable maintenance resources already on location.
The objective of opportunistic maintenance is to improve system availability and reduce production loss; however, the key to success is being able to determine when a component should be replaced during its useful working life to gain a cost-effective improvement.
Opportunistic maintenance is considered effective for an oil and gas asset due to the high-level of dependency presented by the different systems. For example, considering an offshore platform, a failure event in the separation system is likely to shut down other parts of platform such as the oil export system. Thus, when a downtime opportunity is created by the failed component, the maintenance team may take the opportunity while at the facility to perform preventive maintenance for other components satisfying a pre-specified decision rule. As a result, substantial cost can be saved when compared to awaiting the regular maintenance schedule of the opportune maintained item(s).
In the field of maintenance engineering, Monte Carlo simulation provides us with a very powerful decision support tool as it allows the analyst to easily compare the various maintenance policies. The decision on the maintenance strategy could be based on the cost comparison of performing or not performing replacements, and the conditional probability could be used to calculate the expected cost.
Let’s look at an example…
A normally unmanned installation (more on NUI from another blog I posted) requires a number of resources to perform its corrective and preventive maintenance tasks. For this specific case study, we are utilising a scheduled helicopter to perform small maintenance tasks as well as unscheduled helicopter to perform critical repairs. Obviously, the planned helicopter comes at smaller daily rates when compared to daily rates of the unscheduled helicopter.
In the RAM analysis software tools Maros and Taro, we define an opportunistic maintenance task to occur as a fraction of Mean Time To Failure (MTTF). Thus if we set up an opportunistic maintenance that is to replace a component when the timeline reaches 90% of the MTTF, the software will replace that item. It is also possible to define a reduced fraction of the actual repair time. This is true for most systems as the repair task conducted in a controlled environment tend to take less time to be performed.
So we will investigate 3 variations of the base case:
- Case 1: Opportunistic Maintenance with 75% of the MTTF
- Case 2: Opportunistic Maintenance with 90% of the MTTF
- Case 3: Opportunistic Maintenance with 105% of the MTTF
For all the cases, we are assuming a reduced Mean Time To Repair (MTTR) of 20%.
Implementing opportunistic maintenance strategy in this type of platform helps to optimise the utilisation of the maintenance resources; but what to expect from Maros and Taro results upon completion of the simulation process?
- If the opportunistic maintenance task can replace items before they fail, in a controlled environment, we should expect a reduced criticality and a reduced repair time.
- If the opportunistic maintenance task can repair critical items before they fail, in a controlled environment, we should expect the unscheduled helicopter to be mobilised fewer times. This will result in a reduced OpEx and also increase production efficiency.
- As the maintenance resource will be already at the location to perform the maintenance task, there wouldn’t be a large mobilisation time (i.e. the maintenance crew doesn’t need to wait for the maintenance resources to be available nor for themselves to be transported). This will also increase the production efficiency.
The easiest way to compare results when using Maros and Taro models is via the Comparison View in Sensitivity Manager. The Comparison View empowers the analyst to compare different cases to the Base Case. The results for the different cases are clearly shown using legends; so, looking at our results:
- Main KPIs in Figure 1 show: For the case of 75% and 90% of the MTTF, we can see a similar increase in production efficiency but for the case where we wait until 105% of the MTTF before permitting opportunistic maintenance, the efficient improvement is marginal
Figure 1:This table summarises some important parameters obtained from the simulation. It is good practice to look at certain parameters (described below) to make sure that the model has been built correctly and that the simulation has not encountered problems.
- Maintenance Results as shown in Figure 2: We see a reduced number of mobilisations for the unscheduled helicopter for all sensitivities. Take a look at the number of mobilisations column in Figure 2 (Nr.Mobs Delta), it shows that mobilisation occurs up to 334 times less when compared to the base case.
Figure 2: These Indicators are only displayed when Maintenance is explicitly modelled.
- Economics: Looking at financial results in Figure 3, we should see a different Revenue stream (due to the higher efficiency) and also a reduced Operational expenditure (due to the reduced number of mobilisations)
Figure 3: A standard calculation considers the revenue produced to calculate the NPV value, while a Negative (-ve’) calculation uses the amount of lost production. This graph focuses on the Negative NPV
Author: Victor Borges