A Method for Predicting Three Formation Pressures in Ultra-Deep Wells in the Southern Margin Based on Well–Seismic Data Fusion and Machine Learning

Aiming at the challenges faced by ultra-deep wells in the southern margin of the Junggar Basin, such as strong uncertainty in formation information, difficulties in accurate prediction of three formation pressures, narrow safe density windows, and prominent downhole risks, this paper establishes a method for predicting three formation pressures with confidence levels and quantitatively evaluating drilling risks based on well–seismic fused data. Firstly, rock mechanics and in situ stress calculation models are optimized to characterize the strong uncertainty of deep formations. Then, high-precision well–seismic fused interval velocity is generated through multi-source data preprocessing, well–seismic data fusion, and construction of the MLP-TFT-MCMC hybrid model. On this basis, three formation pressures are calculated, and a pressure profile with confidence levels is constructed using an improved t-distribution. Verified by a field well case, the predicted results are consistent with actual data, providing technical support for the safe and efficient drilling of subsequent ultra-deep wells.