Research on Fracture Parameter Optimization Method Based on Generative Adversarial Network in Small Sample Size

Abstract This study addresses the optimization of fracturing parameters in the fractured gas reservoirs of the Tarim Basin, especially under the challenge of small sample sizes and high data acquisition costs. It proposes an innovative method based on Generative Adversarial Networks (GAN). Traditional fracturing technology parameter optimization often leads to model overfitting and poor generalization due to limited data. This research adopts GAN for data augmentation to generate additional training samples, thereby improving the training effect of machine learning models. The combination of genetic algorithms and model fusion techniques further optimizes production capacity prediction and fracturing parameter adjustment. Application of this method to actual data from the Tarim Basin shows that it effectively enhances the accuracy of parameter optimization and the generalization ability of the model, providing significant guidance for gas reservoir development under similar complex geological conditions.