Environmental decision-making in offshore oil and gas (OOG) operations can be extremely complex due to conflicting objectives or criteria, availability of vague and uncertain information, and interdependency among multiple decision-makers. Most existing studies ignore conflicting preferences and strategic interactions among decision-makers. This paper presents a game theoretical approach to solve multi-criteria conflict resolution problem under constrained and uncertain environments. Uncertainties in the quantification of imprecise data are expressed using rough numbers. A multi-criteria game is developed to model a decision problem in which three groups of decision-makers (i.e.; operators, regulators and service engineers) are involved. This game is solved using the generalized maximin solution concept. With the solution (i.e.; optimal weights of the criteria), the rough numbers can be aggregated to an expected payoff for each alternative. Finally, the weights of upper and lower limits of a rough number are employed to transform the expected payoff into a crisp score, based on which all alternatives are ranked to identify the best one. A numerical example is outlined to demonstrate the application of the proposed method to the selection of management scenarios of drilling wastes.@en

To implement an environmental management system (EMS) in offshore oil and gas (OOG) operations, decision makers always encounter a problem of how to prioritize the environmental issues for establishing an environmental policy. Analytic hierarchy process (AHP) is a popular method to perform multi-attribute decision-making to solve this problem. In order to deal with vague information, various fuzzy AHP methods have been proposed. However, these methods suffer four serious limitations: (1) there is a tremendous computational requirement; (2) sometimes only triangular fuzzy numbers can be used; (3) adding or deleting criteria/attributes is not easy to operate in the algorithm; (4) inconsistent judgments is more likely to be expected with fuzzy numbers. This paper proposes a hybrid approach using fuzzy inference system (FIS) and fuzzy AHP which not only eliminates the above limitations but also serves as a robust tool for the prioritization of environmental issues in OOG operations. In this approach, a five-level hierarchy is developed. The highest level of the hierarchy corresponds to the goal - prioritization of significance of environmental issues, and the lowest level corresponds to environmental issues, whereas intermediate levels correspond to major concerns (environmental risks) and sub-parameters of risk. The FIS is applied at the lower levels of the hierarchy to infer the major risk parameters. After this, the scores representing the extent of risk are calculated. Fuzzy AHP is used at the higher levels to synthesize the Significance Scores that will help to prioritize environmental issues. An application of the proposed approach is demonstrated through a numerical example.

@en

Activities in offshore oil and gas (OOG) that cause environmental impacts can be systematically managed through an environmental management system (EMS). Environmental performance evaluation (EPE) is an essential part of an EMS. However, previous studies on EPE indicate that existing lists of indicators little insight into how indicators are modified to more accurately assess environmental performance. In this paper, a way is proposed to identify and define specific environmental performance indicators on a case-by-case basis, which consists of five steps: (1) describing environmental requirements; (2) determining favourable outcomes corresponding to the requirements; (3) identifying required activities or issues to achieve the outcomes; (4) searching for proper measures of the activities or issues; and (5) generating a list of key indicators. Based on these steps, a quality function deployment (QFD) approach is developed to determine key indicators and evaluate environmental performance. To handle uncertainties in QFD, the decision makers' evaluations are quantified through rough numbers using the concept of rough sets. The outputs of the proposed approach are different environmental performance indices. Using these indices, decision makers can easily determine whether an improved performance has been achieved through an EMS. The proposed approach is transparent and promising for use as a unified tool for EPE. An application of the proposed approach is demonstrated through a numerical example.@en