Improving Safety Performance in Nigeria’s Oil and Gas Industry: A Comparative Risk Model Analysis
Mgbowaji Zacchaeus *
World Bank Africa Centre of Excellence in Oilfield Chemicals Research, University of Port Harcourt, Choba, Rivers State, Nigeria.
John Nwenearizi Ugbebor
Centre for Occupational Health, Safety and Environment (COHSE), University of Port Harcourt, Choba, Rivers State, Nigeria.
Ikechukwu Ozoemenam Agbagwa
World Bank Africa Centre of Excellence in Oilfield Chemicals Research, University of Port Harcourt, Choba, Rivers State, Nigeria.
Unyeawaji Brownson Ntesat
Department of Agricultural and Environmental Engineering, Rivers State University, Nkpolu-Oroworukwo, PMB 5080, Port Harcourt, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
This study examined a risk-based model for the enhancement of safety performance in Nigeria’s oil and gas sector. A non-probabilistic convenience sampling method was adopted to consider the selected five oil and gas companies in operations within this study area. A quantitative approach was employed using a comprehensive hazard checklist developed based on industry standards and expert insights, utilizing a structured questionnaire to collect data from industry experts, workers, and stakeholders on hazard likelihood and severity. The 392-sample size used was selected purposively to represent the total population of oil and gas workers in these companies. 392 copies of the questionnaire were administered, and 357 copies were used for the study, with a response rate of 91.07%. The analysis revealed key risk factors for oil and gas offshore operations, such as structural failure due to poor design, valve and seal failures, security threats, fire/ explosions, blowouts, and oil spills. Modelling the risk score against the likelihood showed a good model fit (R2 = 0.831), indicating that the model explains 83.1% of the variance, and the likelihood coefficient was positive and significant. Modelling the risk score against the severity also showed a reasonably good fit (R2 = 0.676), with the severity coefficient being positive and significant. However, modelling the risk score against both likelihood and severity showed excellent fit (R2 = 0.994), explaining 99.4% of the variance. The likelihood and severity coefficients were positive and highly significant; this indicated that an increase in either factor would lead to an increase in the risk score. These models provide a quantitative way to assess risk in the oil and gas industry based on the key factors of likelihood and severity of potential hazards. This study bridged theoretical risk management concepts with practical applications and provided actionable recommendations for policymakers, significantly enhancing safety performance in a crucial economic sector.
Keywords: Questionnaire, assessment, hazardous, likelihood, severity, modelling risk management