RAM Analysis

RAM stands for reliability and maintainability analysis

Peter Carr Consultants Ltd has merged with EPCONSULT. Please refer to the EPCONSULT site for up-to-date details of our RAM Analysis Services

What is RAM Analysis?

RAM analysis is a numerical analysis technique that quantifies the reliability, availability and maintainability of a complex system, for example, an oil or gas production or process facility. Some definitions may be helpful:-

Definitions

Reliability can be defined as the probability that an item will satisfactorily perform its intended function for a specified time.

Maintainability can be defined as the probability that an item will be retained in or restored to a specified condition within a given period of time when maintenance is performed in accordance with prescribed procedures and resources.

Availability expresses both reliability and maintainability in a single measure. Most studies consider steady state availability. This can be defined as the proportion of deployed time that an item is available for use, when the deployed time considered is very large.

Benefits of RAM Analysis

Typically a complex facility is divided into a number of subsystems. Those facilities identified as having the greatest influence on availability are then investigated in more detail and design changes made to optimise performance.

In the early stages of a project, RAM analysis can help in concept selection. Later, it can help with making detail design decisions and in determining what pre-investment should be made in spare parts and repair facilities.

Software for RAM Analysis

In-house software is available for performing RAM analyses using either Monte Carlo simulation or analytical methods.

Analytical methods for RAM analysis make the simplifying assumption that failure and repair times are exponentially distributed. Monte Carlo methods allow any probability distributions to be used to describe the failure and repair times. Monte Carlo approaches also allow more complex interactions between the components of the system to be considered. The Monte Carlo approach involves repeatedly sampling times to failure and repair from the selected failure and repair probability distributions. The performance of the system over many lifetime cycles is simulated to obtain a statistical estimate of system parameters such as availability.

We are able to link RAM analysis with process simulation, so that when part of a system is down, a reduced throughput can be calculated and taken into account in the availability computation. The varying production profile over the field life can also be taken into account.

We can model all components from the reservoir through to the delivery point.