Study on characteristics identification and development adjustment optimization of reservoir water-driven state based on deep learning

Reservoir water drive development is the main way of oilfield exploitation, but its process is complex and changeable, and traditional methods have many limitations. Deep learning technology provides a new idea for reservoir analysis with its powerful feature extraction and modeling capabilities. In the research, through data preprocessing and feature extraction, the static geological data and dynamic production data are integrated, and the key features are extracted by sliding window and principal component analysis (PCA). The dynamic feature recognition model combines convolutional neural network (CNN) and Long Short-Term Memory (LSTM), which can extract spatial and temporal features from three-dimensional tensor data. The loss function is realized by weighted mean square error (MSE) and mean absolute error (MAE). The water injection optimization adjustment model is based on reinforcement learning framework, and the objective function balances the oil production income and water injection cost, taking into account the constraints of reservoir pressure and water cut to ensure the safety and economy of development. The model application and effect evaluation show that the MAE of pressure field prediction is 0.12 MPa, and the correlation coefficient of water saturation prediction is 0.91. The optimized water injection strategy has remarkable effects in improving oil production, reducing water cut and saving energy.