Damage Evaluation of Unconsolidated Sandstone Particle Migration Reservoir Based on Well–Seismic Combination

Abstract

The primary factor constraining the performance of unconsolidated sandstone reservoirs is blockage from particle migration, which reduces the capacity of liquid production. By utilizing logging, seismic, core−testing, and oil−well production data, the reservoir damage induced by particle migration in the Bohai A oilfield was characterized and predicted through combined well–seismic methods. This research highlights the porosity, permeability, median grain diameter, and pore structure as the primary parameters influencing reservoir characteristics. Based on their permeability differences, reservoirs can be categorized into Type I (permeability ≥ 800 mD), Type II (400 mD < permeability < 800 mD), and Type III (permeability ≤ 400 mD). The results of the core displacement experiments revealed that, compared to their initial states, the permeability change rates for Type I and Type II reservoirs exceeded 50%, whereas the permeability change rate for Type III reservoirs surpassed 200%. Furthermore, by combining this quantitative relationship model with machine learning techniques and well–seismic methods, the distribution of permeability change rates caused by particle migration across the entire region was successfully predicted and validated against production data from three oil wells. In addition, to build a reliable deep learning model, a sensitivity analysis of the hyperparameters was conducted to determine the activation function, optimizer, learning rate, and neurons. This method enhances the prediction efficiency of reservoir permeability changes in offshore oilfields with limited coring data, providing important decision support for reservoir protection and field development.