Construction of dynamic simulation and prediction algorithm of formation fluid for optimization of static pressure measurement

In this paper, an algorithm for dynamic simulation and prediction of formation fluid for optimization of static pressure measurement is proposed, aiming at balancing accuracy and efficiency to meet the needs of large-scale reservoir dynamic analysis under complex geological conditions. The algorithm is based on the hybrid architecture of physical constraints and data-driven, which combines the modified Black-Oil physical model and the LSTM-Transformer dual-channel data-driven model, and introduces adaptive coupling module and error compensation mechanism. The weights of physical model and data-driven model are adjusted by dynamic weight factor, and the simulation results are corrected in real time by combining error propagation model to reduce the accumulation of measurement errors. The experiment was validated using both synthetic data and actual data from the Bohai Bay BZ25-1 oilfield. The results show that the algorithm outperforms traditional finite volume method (FVM), pure data-driven models, and hybrid models without error compensation in metrics such as mean absolute error (MAE), pressure gradient correlation coefficient (PCC), and computation time. It successfully predicted abnormal high-pressure zones, demonstrating strong dynamic adaptability and computational efficiency, providing robust support for real-time monitoring and development optimization in complex fault-block oilfields.