Quantitative prediction of petrophysical parameters, such as porosity, is crucial for the evaluation and development of coalbed methane (CBM) reservoirs. However, conventional methods based on linear assumptions and empirical formulas often fall short due to the strong heterogeneity of coal seams, complex lithologies and structures, and the highly non-linear relationship between seismic elastic parameters and reservoir properties under deep-buried conditions. While machine learning techniques have shown promise in petrophysical prediction, many existing approaches struggle to effectively capture long-range dependencies within sequential log data. This study proposes a deep learning-based method that integrates comprehensive input feature selection with a bidirectional long short-term memory (Bi-LSTM) network incorporating dropout regularization for enhanced petrophysical parameter prediction. The proposed method is designed to fully exploit the non-linear mapping between seismic elastic parameters (e.g., P-wave velocity, S-wave velocity, density, elastic impedance) and petrophysical parameter (porosity). By combining the bidirectional contextual learning capability of Bi-LSTM, the model effectively captures feature relationships within depth sequences. Comparative analysis against a fully connected neural network and a standard LSTM network demonstrates the superiority of the proposed method. The analysis also reveals the optimal feature combination and network parameter setting (sequential length, sampling interval, etc.). Results indicate that the Bi-LSTM model achieves a significant improvement in prediction accuracy, outperforming other models, and demonstrating better generalization capability in blind well tests. The method provides a reliable and effective tool for quantitative reservoir characterization, offering substantial potential for application in deep CBM exploration.