Regardless of the economic activity, decommissioning decisions are often highly complex. This is due to the diversity of operational and local parameters, as well as the multitude of stakeholders involved, who generally have conflicting interests. This sets up a challenging multi-criteria decision problem on the activities to be carried out during the decommissioning process. This paper aims to present an overview of decision-support tools applied to decommissioning and covers many economic sectors, with a focus on the oil and gas sector and on multi-criteria decision analysis (MCDA) methods. The paper delves deep into the aspects to be considered before reaching a decision, examining the experiences and methods found both in industrial reports and in academic papers.

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.

This paper proposes a novel approach that makes use of continuous-time Markov chains
and regret functions to find an appropriate compromise in the context of multicriteria decision
analysis (MCDA). This method was an innovation in the relationship between uncertainty and
decision parameters, and it allows for a much more robust sensitivity analysis. The proposed
approach avoids the drawbacks of arbitrary user-defined and method-specific parameters by defining transition rates that depend only upon the performances of the alternatives. This results in a flexible and easy-to-use tool that is completely transparent, reproducible, and easy to interpret. Furthermore, because it is based on Markov chains, the model allows for a seamless and innovative treatment of uncertainty. We apply the approach to an oil and gas decommissioning problem, which seeks a responsible manner in which to dismantle and deactivate production facilities. The experiments, which makeuseofpublished data on the decommissioning of the field of Brent, account for 12 criteria and illustrate the application of the proposed approach.

The designed life of many offshore O&G facilities is reaching its end. Decommissioning these facilities has become a critical task due to unpredictable costs, high operational risks, potential social impacts, and finally, environmental protection issues. Decision-making plays a decisive role in finding a trade-off between the concerns and interests of stakeholders such as O&G companies, government bodies, environmental protection organizations, sea and coastal environment users, and local communities. The decision problem is often complex as it usually involves several criteria. Most of the time, there is no one perfect option available to suit all the criteria: an ‘ideal’ option does not usually exist. Therefore, a compromise must be found. To address this problem, the decision-maker may use advanced approaches such as Multi-Criteria Decision Analysis (MCDA). These models have recently attracted the attention of the industry. Here, we describe a new tool that improves the decision-making process by ranking the different decommissioning options of O&G subsea assets. This model considers 37 attributes gathered into 6 criteria: Safety, Environment, Waste management, Technical, Social, and Economic. Several decommissioning alternatives are compared. The new tool has been tested on the Brazilian decommissioning of Espadarte Field, showing remarkable performance in aiding the decision-making process.

Decommissioning in Brazil faces challenges regarding the safe and efficient deactivation of industrial facilities, considering risks such as safety and health. Associating decommissioning with operational risk management methodologies can bring benefits in resource savings, worker safety, assessment of future scenarios, and transparency and accountability among stakeholders and society at large. This study aims to perform a comparative analysis of Worker Health and Safety data criteria using a multicriteria decision analysis methodology developed by COPPE/UFRJ, to define the best decommissioning alternatives for subsea installations, using data presented by the SAFETEC (Safety and Environmental Technology) Report and the CNAE (National Classification of Economic Activities) in the context of operational risk analysis. In the Brazilian scenario, worker safety involves challenging and industry-specific issues, and understanding the activities that are effectively carried out in decommissioning could prompt reflection on future work and contribute to mitigating the operational risks to which workers in this sector are exposed.

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.