This paper is motivated by decommissioning studies in the field of oil and gas, which comprise a very large number of installations and are of interest to a large number of stakeholders. Generally, the problem gives rise to complicated multi-criteria decision aid tools that rely upon the costly evaluation of multiple criteria for every piece of equipment. We propose the use of machine learning techniques to reduce the number of criteria by feature selection, thereby reducing the number of required evaluations and producing a simplified decision aid tool with no sacrifice in performance. In addition, we also propose the use of machine learning to explore the patterns of the multi-criteria decision aid tool in a training set. Hence, we predict the outcome of the analysis for the remaining pieces of equipment, effectively replacing the multi-criteria analysis by the computational intelligence acquired from running it in the training set. Computational experiments illustrate the effectiveness of the proposed approach.
Oil and natural gas (O&G) industries are significant players in the global economy. The lifecycle of O&G installations has reached an age at which many must be decommissioned. Biological invasion is the process by which a species is introduced into a new geographic region caused by the interference of human activities. Scientists and policymakers have identified invasive species as a significant threat to marine ecosystems affecting biodiversity. Today, O&G habitats and invasive species represent only 9% of biodiversity studies relative this field. We provide an overview of invasive species linked to decommissioning operations worldwide and emphasize the Brazilian context to support the sustainable management of decommissioning operations. O&G facilities have contributed to the spread of invasive species, such as the bryozoan Watersipora subatra in Santa Barbara Channel (California) and the sun coral species Tubastraea coccínea and T. tagusensis in the Gulf of Mexico and the Brazilian coast. The Brazilian case highlighted in this study shows several platforms to be decommissioning, and the presence of sun coral along the coast which poses biodiversity in risk. Measures must be taken to control the sun coral dissemination and some recommendations were made in this study to support futures studies.