Investigation of Environmental Pollution of Offshore Gas Platforms in South Pars Field with Principal Component Analysis and Bayesian Network Model from 2019 to 2020: A Descriptive Study

Abstract

Background and Objectives: Globally, rising energy demand has driven humans to extract oil and gas, even in the depth of seas. This is also important in the Persian Gulf and has caused a lot of environmental pollution. The purpose of this study was to determine the environmental pollution of offshore gas platforms by Principal Component Analysis methods and Bayesian network model. Materials and Methods:This descriptive study was conducted from 2019 to 2020 on offshore gas platforms in South Pars field. The required data were collected through interviews with experts. Then, using the analysis of the main components, the factors with less effect were eliminated and other factors in Bayesian networks were investigated.Results: Based on the results of Bayesian network, for the overall pollution of the platform, the probability of the average level was 0.95%, the probability of the high level was 8.11%, the probability of the very high level was 18.5%, and the probability of the critical level was 72.5%. The results of the Principal Component Analysis section showed that the highest variance is related to the first Principal Component and its value is approximately 40%, and also the highest eigenvalue is related to this component with the value of 1.909. Conclusion:The effect of gas platform activity in the two parts of water pollution and biodiversity damage has the greatest effect. Among the two factors with very high levels of pollution, the effect of gas platform on biodiversity and living species is more pronounced than the others. Also, the combination of two methods of Principal Component Analysis and Bayesian networks for data management in the field of platform environmental management is fruitful.