Evaluation of methane emissions reduction methods in the oil and natural gas operations using a decision support system under quartic fuzzy DEAMEIM-MARCOS model

• Policy options for methane emissions reduction are optimized. • The criteria of intelligent DSS are sustainability and HSE aspects. • For uncertainty assessment, quartic fuzzy set is considered. • An integrated method of DEAMEIM and MARCOS approaches is introduced. Methane is an important greenhouse gas that has been linked to climate change impacts and the industry of oil and natural gas (O&G) energy is a major source of methane emissions. These emissions arise from leaks and regular venting that occurs throughout O&G operations. Mitigating these emissions from the operations of the studied industry has advantages for air quality and health. There are several policy options that are considered as solutions available to mitigate the emissions of methane from O&G operations. Choosing the appropriate policy option is a complex multi-criteria decision-making (MCDM) problem that needs to use an intelligent and robust decision support system (DSS) to employ a smart and resilient model to decrease uncertainty in the decision-making process. The proposed DSS of this work incorporates the Delphi method and Method based on the Removal Effects of Criteria (MEREC) integration method (DEAMEIM) and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) model under the quartic fuzzy set (QFS). Moreover, a hybrid criteria system, which involves 19 criteria has been used to evaluate policy options for methane emissions reduction. The criteria are selected according to the integration of 1) sustainability pillars and 2) health, safety, and environmental (HSE) aspects. The results of evaluations exhibit that the Regulation of methane leak detection and repair (LDAR) programs, is the most suitable scenario for methane emissions reduction from the operations. Computational analysis confirms the practicality and applicability of the DSS in determining the best possible scenario.