Water and Gas Flooding Oil Monitored by a Realtime Unet-Neural-Network-Based Method

To achieve real-time and accurate detections of residual oil distribution during water or CO₂ flooding, this study utilizes the high-frequency Ground Penetrating Radar (GPR) for monitoring of the flooding process in real time. The U-Net neural networks are trained to invert for the subsurface dielectric constants and conductivity distributions. The study first utilizes the gprMax forward tool to simulate the dynamic response changes of rock electrical parameters during flooding and constructs a high-resolution training dataset of 100,000 samples. Each sample contains the relationships between a subsurface electrical parameter model and its corresponding multi-transmitter, multi-receiver GPR responses. A deep learning inversion network based on the U-Net architecture is trained to extract multi-scale features through an encoder-decoder structure, achieving an end-to-end mapping from GPR echo signals to subsurface electrical parameters. Numerical and physical core experimental results show that the method accurately inverts the electrical parameter distributions of the oil, water, and gas in the sandstone model, successfully capturing the position and morphology changes of the displacement front. The average relative error of dielectric constant inversion is controlled within 5%, with the error mainly concentrated in high-conductivity water regions for conductivity inversion results. Compared to traditional full waveform inversion methods, the proposed approach offers a fast inversion solution and is less affected by the initial model and noise. The results reveal the feasibility and superiority of the neural network based deep learning method in GPR electromagnetic inversion, providing a new method for real-time flooding monitoring and intelligent reservoir development during oil and gas flooding.