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 presents legal contributions to the technical analysis of the decommissioning stages
of the oil industry. We seek to contribute to decision making based on multicriteria analysis about
the options for uninstalling specific equipment in the production chain: subsea equipment. The
objective is to demonstrate that there is legal predictability supported by the technical aspect for the
options that are possible in each case. The methodology used was the literary revision combined
with a national and international legislative analysis that allowed the presentation of the final conclusion.
The comparison between the international norms, as well as the Brazilian legislation, in particular,
the national solid waste policy demonstrated the legality of the application of a multicriteria
analysis to base the decisions by the companies, as well as the inspection agencies.
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.