Well log data imputation is crucial for subsurface geology interpretation, which helps identify the most productive areas for drilling, minimize exploration risks, and maximize hydrocarbon recovery. Obtaining high-quality well data can be challenging due to various issues such as drilling problems, improper logging processes, or operational issues with logging tools. Accurate and reliable imputation methods are essential to address missing or incomplete information in well log data, allowing better decision-making in the oil and gas industry. This study presents a novel well log data imputation method for the West Natuna Basin in Indonesia, using time-series deep learning models. Focusing on the “Kappa” well dataset with missing Vp log data, we trained long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) models using a window-based time step sequence of 10-timesteps intervals as input. The LSTM was observed as the best model, achieving a mean absolute percentage error (MAPE) of about 2.2% and an R-squared (R2) of about 94%. This result suggests that deep sequence model can be effectively used for missing data imputation in well log. Lastly, a petrophysical analysis further underlines the importance of these findings, offering deeper insights into reservoir properties, and value of our research in real-world exploration scenarios. The findings can aid in improving the accuracy and reliability of imputation in well log data, enabling better decision-making in the oil and gas industry.