A review of multi-attributes decision-making models for offshore oil and gas facilities decommissioning

Abstract

With the development of the offshore fossil energy industry, the designing life of many offshore oil and gas facilities will end. The decommissioning of these facilities has become an urgent task due to unpredictable costs, high risks, and environmental protection issues of public concern. Decision-making, as the core in the pre-decommissioning stage, plays a decisive role in the cost, risk, and impact of the entire decommissioning. Therefore, the multi-attribute decision-making model has attracted much attention from industry and academia. An efficient, accurate, and simply using multi-attribute decision-making model can enable governments, energy companies, other marine users, and environmental protection organizations to reasonably fulfill their concerns. It is of great significance to all parties. This review mainly studies the multi-attribute decision-making models that have been used in the decommissioning of offshore oil and gas facilities, and conducts a more detailed interpretation of them, including the relevant regulations, frameworks, methodology, preferences and advantages and disadvantages of different models. In addition, a more comprehensive review of the cost assessment model, an important part of the decision-making model, is carried out, including the general framework and methodology of the cost assessment model, and the accuracy of the models is explored. And then the current evaluation method of accuracy of the cost assessment model raises the author's personal doubts. At the end of the article, this paper names two core problems of the current decision-making model, that is, the lack of basic data and the incomplete MCDA (Multi-criteria Decision Analysis) method. This review can provide a comprehensive reference and feasible research directions for future scholars who aims to study the decommissioning of offshore oil and gas facilities especially in the North Sea in the UK and point out the direction for the industry to improve its current multi-attribute decision-making models.