An offshore oil platform reliability study (RAM study)


Offshore oil platforms are large structures located on the sea surface, whose purpose is the oil or gas extraction, processing and transferring to an end user; basically it is an industrial installation that conveys obvious limits due to the presence of equipment in a limited space.

An oil platform production is regulated by commercial treaties and subject to government authorizations, which defines the exact valid period for the administrative concession. Consequently, it is essential to optimize installation and production costs throughout the platform life in order to maximize the investment.

This necessity is highlighted in the “Take or Pay” contracts (which assumes a minimum fixed royalty quota to be paid to the country´s government in where the facility is located, regardless of whether or not extractions occurs), or where the local economy and energy autonomy is highly reliant on oil extraction.

These facilities have all the necessary infrastructure for this purpose, highlighting the process equipment and auxiliary services, emergency systems, heliport, office buildings and housing module, among others.

To quantitatively define how to maximize production continuously throughout the lifetime of an installation, a thorough evaluation of the reliability, maintainability and availability parameters is necessary. These three concepts define, precisely, a reliability study, usually called RAM study.

The RAM study is decisive to achieve this objective, especially when the installation design is validated and the maintenance strategy is planned.

Relevant concepts in a RAM study

Reliability: it is the probability that a system will remain in service or fulfill a certain function, under certain conditions and in a certain time.

Maintainability: it is defined as the time required to restore the system’s operating conditions after a failure or a programmed activity occurs, either for its repair or replacement.

Availability: it is the system’s usable time (during which it will develop the function for which it has been designed) according to the installation´s life. It is also known by the current system´s production versus the target production for the period considered.

MTTF (Mean Time to Failure): is the average time between a device´s failures.

MTTR (Mean Time to Repair): is the time needed to repair a device after a failure occurs.

RBD: reliability block diagrams.

A RAM study application to an offshore oil platform

A RAM analysis can be applied to any production process, in this particular case it has been carried out for the Oil & Gas sector to which the oil platform under study belongs.

The RAM study is developed in the following stages:

1. Main equipment and subsystems identification

Selection of the plant´s main systems and their respective subsystems, defining critical equipment, e.g., tanks, containers, valves, compressors, pumps, among others.

In the platform studied by TEMA, the following main subsystems are identified:

  • Production wells
  • Relief system
  • Water injection head
  • Diesel storage systems
  • Hydraulic system
  • Drainage systems

Figure 1 shows these subsystems and their critical equipment.

Figure 1. Example of main systems and equipment identification on the platform


2. Equipment database analysis

Failure and repair characteristics are assigned to each system in the information gathering phase.

This assembled data is used as input to the software necessary to develop a RAM model.

The most relevant information to consider is:

  • Equipment failure rate, based on its own maintenance statistics or recognized databases. The mean time to failure (MTTF) and the mean time between repairs (MTTR) are among the most relevant data to be considered.
  • Location, configuration, operation of the systems to be evaluated

Table 1 shows typical values of failure rates for some subsystems.

Subsystems Element Failure rate

(108 hours)

MTTF (years) MTTR (hours)
Water well injection head Underwater Safety Valve (SSSV) 5,66 20,17 23
Surface Safety Master Valve (SSV) 5,66 20,17 23
Throttle valve 14,37 7,94 14
Hydraulic Globe Valve 4,10 27,84 6
Torch Torch spacer 27,00 4,23 8
Open drain valve 0,21 543,60 6
Closed drain valve 0,20 570,78 7

Table 1 Failure rate example, MTTF and MTTR for the well and torch water injection system. Reference: Oreda 2015


3. Block diagram construction and equipment criticality definition

The RAM study entails the process modelling through specialized software, which is based on the construction of block diagrams (Reliability Block Diagram, RBD) that take into account the critical equipment and all information collected.

Two main parameters are defined for each critical team in the process simulation:

  • Normal operating status (in operation or in stand-by),
  • Criticality, which is defined as the percentage of production loss in case an equipment failures.

Figure 2 shows the hydraulic drive circuit configuration for the studied platform, taking into account the storage tank, filters and distribution pumps.

Figure 2 Example of a block diagram for the oil platform hydraulic station. Reference: TEMA.

Table 2 shows the configuration and effect on production in case of failure of each element of such subsystem.

Description Configuration Effect on production loss
Production system 1×100% 100%
Suction tank pumps 1×100% 100%
Nitrogen injection valve 1×100% 100%
Filter 2×100% 25%
Hydraulic oil pump 2×100% 50%

Table 2. Oil platform hydraulic station platform configuration and criticality. Reference: TEMA


4. Maintenance philosophy

The information associated with the maintenance philosophy for each block system and each equipment must be incorporated or assumptions must be made if this information is not available.

The information takes into account:

  • Preventive maintenance frequency
  • Installation general maintenance
  • Replacement time for failure cases

One of the most important challenges in oil platforms is to define the maintenance time based not only on the availability of human resources but also on the necessary materials and spare parts.

It is important to consider the spare parts or equipment’s delivery times due to transfer’s limitations, which is particularly relevant on offshore installations, as well as the longer time required for maintenance work on platform, as is the case with underwater installations. Another typical limiting parameter for marine platforms is the maximum number of personnel permitted on board, as well as the difficult access to equipment in congested areas. All these considerations are fundamental for the simulation model and can define the success (or lack of) of a good planning.

Maintenance Frequency (h) Time required (h)
Pressure vessels 43800 (5 years) 240
Pumps and motors

(type 1, preventive)

8000 (1 years approx.) 8
Pumps and motors

(type 2, preventive))

24000 (2,7 years) 72

Table 3 Example of maintenance frequency and duration for equipment present on an oil platform. Reference: TEMA.


5. Simulation

Once the block diagram construction is finished, the information is gathered and the maintenance philosophy is defined, a simulation of the process carried out in the oil platform is run by a specialized software.

Based on the Monte Carlo numerical statistical method, the program will provide the percentage of facility availability and each subsystem criticality in the overall result.

This numerical simulation represents the plant’s components life and how each one affects the total availability of the platform in order to obtain the deterministic results that allow the bottlenecks identification.


6. Results obtained

Under normal operating conditions (inherent design) the configuration of the studied oil platform demonstrates an availability exceeding 99%; while, if the 25 years’ average is considered, it drops to 98%, taking into account the installation’s general maintenance (turnaround), which is carried out every 5 years. See figure 3.

Figure 3. Oil platform availability prediction. Reference: TEMA


The additional operational cost due to equipment unavailability, is defined by the differential between normal operation and average lifetime availability. Consequently, it is important to detail each subsystem individual contribution in the installation´s final availability (figure 4).

Once the bottlenecks have been identified, the guidelines for the operating costs’ optimization and the investments which will bring most benefits will be outlined.

In the studied oil platform, the systems that contribute most to decrease the availability are:

  • Main static equipment, whose failure, due to the lack of redundancy, will lead to the installation’s total shutdown.
  • Well production valves and water injection valves, which play an important role in the installations’ criticality as they are part of the main oil rig operation circuit.

The platform analysis results, according to the operational and maintainability singularities, predict a system availability greater than 98% for 25 years of operation. According to these results, this installation is effectively designed for the availability required by the customer, as long as the defined maintainability assumptions are met, especially in regard to the maintenance frequencies, static equipment inspection and spare parts availability.

Figure 4. Oil platform subsystems’ relative criticality. Reference: TEMA


7. Conclusions

Unplanned operational halts entail large economic and social losses, especially in the energy and critical infrastructure sector.

A RAM study, in particular when carried out at an early stage of design, is a fundamental tool to outline the necessary measures to ensure an installation’s availability and to ensure that the desired production or supply objective is achieved.

Using the bottlenecks identification, process improvements can be proposed, either from the system configuration’s perspective, equipment redundancy, maintenance philosophy or spare parts availability.

Furthermore, the application of a cost-benefit assessment to the RAM study’s results will allow a thoughtful prioritization and measure’s selection.