Accurate interpretation of well logging data is crucial for the evaluation of reservoir fluid properties in oil and gas exploration. Conventional well logging methods rely on petrophysical models that correlate parameters such as porosity, permeability, and oil and gas saturation with reservoir fluid properties to achieve reservoir classification. However, complex geological conditions often lead to issues such as anomalies, multi-factor coupling, and ambiguous fluid boundaries in well logging data. These challenges limit the adaptability of conventional methods and bring uncertainties in interpretation results. To improve the accuracy of reservoir fluid evaluation, this study incorporated cloud model theory into conventional well logging evaluation and proposed an evaluation method for reservoir fluid based on a 2D cloud model. The method selected porosity and gas saturation as key logging parameters and utilized cloud models to process the fuzziness and randomness in well logging data, thereby establishing a mathematical model for reservoir fluid classification. First, a 2D cloud model for well logging evaluation was derived based on cloud model theory, with clarified geophysical significance assigned to its mathematical parameters (expectation, entropy, and hyper-entropy). 2D cloud diagrams of the reservoir were generated using a cloud generator. Subsequently, similarity analysis was applied to quantitatively classify reservoir types, enhancing interpretation accuracy. To validate the effectiveness of this method, well logging data from the Kuqa Depression in the Tarim Basin were used for application analysis, with results compared with those obtained from conventional methods, cloud model evaluation, and well testing. The results showed that the proposed method accurately characterized reservoir fluid properties in complex reservoirs. Compared with conventional methods, the 2D cloud model not only provided qualitative classification of reservoir types but also quantified uncertainties in fluid properties, thus improving the stability and reliability of evaluation results. The findings indicate that the reservoir fluid evaluation method based on 2D cloud model effectively reflects reservoir fluid characteristics and exhibits strong adaptability in complex reservoir environments. The final evaluation results demonstrate strong consistency with well testing results, verifying the method’s feasibility and effectiveness. As a valuable supplement to conventional well logging interpretation, this method provides a new approach for improving the accuracy of well logging data interpretation and optimizing fluid property identification in complex reservoirs.