We all know that RAM analysis is a well established methodology used to predict asset performance. We also know that of the challenges of applying this methodology to oil and gas developments; where a large number of interrelated parameters and system inter dependency must be taken into account. In order to achieve a comprehensive picture of the production efficiency, the manner in which the system is operated must be taken into account in addition to the standard analysis of reliability and maintainability. We need to add the Operability term.
The operability term takes the analysis one step further and introduces an additional dimension to the method, providing actual volumetric production results. This is important in a case such as a system that is available only half of its life. If such a system were required to operate only half of the time, the production efficiency (or production availability) will present a different value in comparison to simple availability. In addition, by including the production rate, this approach to RAM analysis is able to account for subtle factors with wide implications such as degraded failures of equipment, production ramping time and production bottlenecks.
Let’s start by looking into some terminology:
Several meanings can be associated to the term availability. Its definition depends on the type of maintenance activities that have been incorporated into the availability calculation. To exemplify this, three different interpretations of availability are discussed below:
- Inherent availability (Ai): This measures the availability when only corrective maintenance is considered without including maintenance resource analysis i.e.only the repair time (MTTR). This assumes a perfect maintenance support environment. The measure reflects availability performance when failures occur and ignores planned shutdowns, which are considered to be an inherent component of the system availability.
- Achievable Availability (Aa): This includes both corrective and preventive maintenance (unplanned and planned shutdowns) without including maintenance resource analysis. Again, the assumption for this interpretation is a perfect maintenance support environment. This measure adds to the aforementioned interpretation the availability performance but where planned shutdowns are included as part of the analysis. Aa is therefore a useful approach to design a planned maintenance strategy. In this case the average availability can be calculated as:
Uc = Unavailability due to corrective maintenance
Up = Unavailability due to preventive (planned) maintenance
R = Reliability expressed as mean time to failure
A = Average availability
M = Maintainability expressed as the average time to return failed item to service
P = Number of planned shutdowns per year
S = Mean time required for planed shutdowns per shutdown
- Operational Availability (Ao): This includes both corrective and preventive maintenance (unplanned and planned shutdowns) including maintenance resource analysis i.e. time for troubleshooting, acquiring and delivering spare parts, active repair time and testing. This interpretation of the availability term requires complete consideration of the installation or site’s resource levels and organisational effectiveness.
Comparisons can be made upon the three different interpretations of availability:
- Operational availability is the lower limit as it accounts for a larger number of parameters in comparison to Achieved and Inherent Availability – unplanned and planned shutdowns and maintenance resources analysis.
- Inherent Availability is the highest limit – this it the maximum of uptime that can be taken from the analysis
- Comparing the Achievable availability (which accounts for planned shutdowns ) with Inherent availability results in the isolation of the effectiveness of planned maintenance activities
- Comparing the Operational availability (the only view taking into account maintenance resources) isolates the effectiveness of existing maintenance resources
All these key performance indicators (KPIs) are equally important. For instance, if the achievable availability is not known then the analyst may attempt to achieve performance beyond what is possible.
Therefore: Ai > Aa > Ao
Normally, as the plant gets older, the maintenance will increase which will drive the achievable availability (Aa) downwards. On the other hand, maintenance and operations management will look for actions to move the operational availability (Ao) upwards. In an ideal world the two naturally diverging availability terms will converge and maintain the system availability in an optimum position. Extending the life of ageing assets is a good example of this in action. In this case, additional availability can then only be achieved by change to the system design.
As discussed above, in a traditional RAM analysis, the availability of a system is measured by taking into account the failure pattern and the maintenance philosophy of the system. The end result of the analysis are variations of the system up-time – the output refers to events where the system is either working or not. This is a limited view of the system behaviour. For instance, partial loss is not taken into account and one cannot say the system is half-available.
In order to obtain a complete picture of the performance, not only the up-time of the system must be evaluated, but also its operations. This is particularly important for oil and gas developments where systems are complex, time dependant and interrelated.
Adding the additional aspect of operability to the methodology, introduces another dimension, – the production rate – to be explored. This opens the modelling capabilities to a new range of scenarios – we discussed this here.
Examples of operations added to the methodology are as follows:
- Flaring Operation
- Export requirements
- Recovery Mechanism
- Buffer and Tank management
- Logistics and transportation
- Ramping time
But how much value the operability term adds to the final calculation?
Let’s look into a case study where we add one operability factor at the time and see how does it impact the results.
Case study – Operability term
As discussed above traditional RAM analysis does not include operability factors. For our base case (this is a FPSO), the availability of the system is 95.332% with a standard deviation of 0.405%.
The following elements are therefore not considered in the calculation of system availability:
- Flow profile: One of the most relevant aspects of this industry is the variable production profile. All Oil and Gas wells decline in productivity over time as they produce. Most highly productive oil and gas wells decline noticeably during the first 3 – 6 months, as the initial pressure drops. The tendency is to visualise a straight line trend that continues to go to zero within a very few years. Such a straight line does not accurately represent the nature of the decline curve.
- Flaring operations: An oil field development produces both oil and associated gas, with the gas normally being compressed and exported via a pipeline system. In the event that an export gas compressor fails, at times it is permissible to continue oil production for a limited amount of time by flaring the associated gas. There may also be flare regulatory limits that needs to be considered.
- Ramp-up operations: When an oil and gas facility faces a complete shut-down, it takes time to return the unit to its normal production rate. Time is taken to restore process conditions such as temperature and pressure to safe and normal levels before beginning to export the final product.
- Degraded failures
- Logical events on operational constraints: Operational constraints can be added into the model as logical events. These are rule based events which can appear in the simulation timeline based on criteria being met.
Now adding each parameter, we get the following variation on efficiency:
Production efficiency for the same system design when including additional analysis elements
There is no linearity in regards to different compared cases – adding operability will not necessarily improve production efficiency. Operability will provide a better replication of real life scenarios, a more complete range of results to be evaluated and a better understanding of the system behaviour.
Variations of over 2-3% can be seen by adding more advanced operational aspects. Such variation is extremely significant for an oil and gas development which typically involves production volumes amounting to hundreds of millions of dollars.
Revenue loss due to downtime
RAM analysis plays a key role when designing systems and analysing optional maintenance strategies in the oil and gas industry. Informed decision can be made and uncertainty in regards to the production behaviour can be accurately predicted and therefore reduced.
However, traditional RAM analysis does not give a complete picture of the system performance. In order to understand properly the production loss due to failures and maintenance strategies, it is important to add operational aspects to the analysis.
Author: Victor Borges