Analysis of Residual Oil Distribution Prediction Method by Deep Learning and Optimization Algorithm

Aiming at the problems of prediction accuracy, efficiency and adaptability of traditional methods for predicting residual oil distribution in reservoirs, this paper applies advanced algorithms to improve them. Traditional methods for predicting residual oil distribution in reservoirs mostly rely on physical models and classical statistical analysis, and have problems such as insufficient processing of data, difficulty in modeling nonlinear relationships, and high consumption of computing resources. To this end, this study uses cutting-edge technologies including convolutional neural networks, long short-term memory networks, and particle swarm optimization to enhance the model’s prediction accuracy and computational efficiency. By combining multiple data sources and dynamic learning mechanisms, the residual oil distribution prediction method is optimized. According to the experimental outcomes, the method used in this paper improves the prediction accuracy MSE to 0.08 and is superior to traditional methods in terms of computing time and enhancing model adaptability.