| Summary: | Gas plants consist of several pieces of both critical static and rotating equipment, which operate continuously under severe operating conditions. These pieces of equipment are permanently subjected to be inspected and maintained during total shutdown of plant facilities to execute Turnaround Maintenance (TAM) event. The TAM is the largest maintenance activities used in most oil and gas companies in terms of both cost and time. Oil and gas companies have suffered losses in the production and enormity in the TAM cost due to duration and interval of TAM which have randomly estimated without taking the size and age of plants into account. Sirte Oil Company (SOC) was a good example and used as a reference point for other gas plants to achieve the aim of this thesis associated with optimising TAM scheduling for gas plants (decreasing duration and increasing interval of TAM) by implementing the TAM model.
The contribution of this research is in developing the TAM model, consisting of four stages, which is broken down into four main stages: First stage; removing Non-critical pieces of Equipment (NEs) from the Scope of Work (SoW) of TAM to proactive maintenance strategies. Second stage; selecting Critical Static pieces of Equipment (CSEs) that constitute the highest risk based on Risk-Based Inspection (RBI). Third stage; selecting Critical Rotating pieces of Equipment (CREs) that constitute the highest risk based on Risk-Based Failure (RBF). Fourth stage; defining the optimum duration and interval of TAM based on Failure Distributions (FDs).
Consequently, the TAM model developed in this study provides a novelty in the TAM event and decision making process. This is basically about optimisation of TAM scheduling in the medium and long-term, characterized by decreasing duration and increasing interval of TAM based on both CSEs and CREs to achieve the TAM model results. The result is the reduction in TAM cost and production losses, and the improvement in reliability and availability requirements of gas plants according to the residual life of critical equipment and operating conditions.
To ensure reliability and consistency of the TAM model, it was validated with three Libya-plants SOC and data from three published case studies. The results from the validation of the TAM model are consistent with the real duration and interval of TAM in most plants SOC. The research concludes that the developed TAM model is a reliable and applicable tool to assist decision-makers in the estimation of TAM scheduling for any a processing plant.
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