In the oil and gas industry the use of a reliability based design is becoming increasingly important because of the increasingly requirements for safety and economy. In contrast to the traditional working stress design (WSD), Quantitative Risk Assessment (QRA) provides the methodology for quantifying the risk of the design for a particular scenario. The full QRA methodology was discussed and a mathematical model, based on Generalised Pareto Distribution (GPD) and Asymptotic important sampling (AIS) techniques, was built to give more precise answer by analysing limited random data points rather than using the assumed pre-defined distributions. Particular attention is paid to the tails of the distribution to obtain a good fit. The methods developed are compared with the traditional methods such as First/Second Order Reliability Method (FORM/SORM), Monte Carlo Simulation (MCS) to assess the efficiency and accuracy. It is shown that, for the examples considered, the proposed methods provide accurate and efficient results for the probability of failure. Another important characteristic of this method is that it uses the random data and does not need the user to determine the distribution type of the variables. And the mathematical model built in the present research is a generalised method and can be use for other risk assessment.