Latest revision as of 11:40, 20 April 2026

Abstract

The polar region possesses enormous potential for oil and gas resource development, making it a focus of worldwide attention. However, the harsh climatic and geological conditions, along with the fragile ecosystems in the Arctic, impose stringent technical requirements for oil and gas extraction. Simultaneously, drilling operations in polar regions generate substantial amounts of liquid and solid waste, which can pollute and damage the vulnerable local environment. Therefore, there is a need to establish a set of environmental risk identification techniques or risk assessment methodologies suitable for polar drilling. Current evaluation methods— including the analytic hierarchy process, Bayesian networks, neural networks, and grey correlation analysis—have limitations such as computational complexity and strong subjective influence, which may compromise the accuracy and reliability of the assessment results. Moreover, the outcomes of these evaluations heavily depend on sample sources. Given the complexity of the polar environment, data reliability and the need for rapid, efficient assessment methods are crucial. Accordingly, this paper proposes a cloud model-based environmental risk identification method for polar drilling, which enables multi-source acquisition of polar environmental data. The cloud model replaces the membership function used in conventional fuzzy evaluation methods, thereby accounting for both the fuzziness and randomness of the raw data and improving the accuracy of evaluation results. This comprehensive cloud model-based approach can reveal the randomness and fuzziness of the evaluation subject, facilitate the conversion between data and conceptual understanding, and produce evaluation results that balance subjective and objective considerations. Rooted in fuzzy mathematics and probability theory, the cloud model yields objective, reasonable, reliable, and persuasive assessment outcomes. Compared to traditional methods, the proposed approach demonstrates stronger robustness in handling uncertainty and data scarcity, offering a reliable tool for environmental risk identification and control in polar drilling.

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