EFFECT OF OPTIMIZING PREDICTIVE MAINTENANCE SCHEDULING ON THE PREVENTION OF PIPELINE CORROSION IN OIL AND GAS INDUSTRY IN NIGERIA

Abstract

Maintenance scheduling is a crucial aspect of asset management in the oil and gas industry. This study is a descriptive survey design and it was carried out to investigate the effect of predictive maintenance scheduling on the prevention of pipeline corrosion in the oil and gas industry in Nigeria. The researcher developed three research objectives, three research questions and one hypothesis that guided this study. The researcher constructed a fifteen-item questionnaire titled Predictive Maintenance Scheduling for Preventing Pipeline Corrosion (PMSPPC) which was adequately validated and administered to 205 respondents which were purposefully selected from different oil and gas companies. The tool for data analysis adopted in this study was the mean, standard deviation, chi-square and percentage scores. The major finding in this study was that preventive maintenance scheduling did not exert any significant effect on the prevention of pipeline corrosion in the oil and gas industry. Based on the study findings, the researcher concluded that effective Predictive Maintenance Scheduling can significantly reduce the likelihood of pipeline corrosion and failures thereby optimize maintenance resources and reduce costs associated with environmental spills and leaks caused by corrosion-related failures. Some of the recommendations in the study are: further research is needed to develop more advanced machine learning algorithms and data analytics techniques for predictive maintenance scheduling, the development of more effective data integration and management systems is necessary to support predictive maintenance scheduling, personnel training and development on predictive maintenance scheduling techniques and tools is very important as there will continuous monitor and evaluate the effectiveness of predictive maintenance scheduling measures and there may be need to integrate predictive maintenance scheduling with existing maintenance management systems and processes.