Deep RNN Reservoir Parameter Optimization via DOE

The patent replaces traditional mechanical/data analysis methods with machine learning algorithms (random forest, neural networks) to identify significant reservoir parameters. This substitution enables the system to handle non-linear relationships and noise in data more effectively, improving parameter identification accuracy while maintaining computational efficiency through automated feature selection processes.

The patent transforms the approach by changing from traditional statistical analysis parameters to machine learning-based parameter identification. This involves using training datasets with known significant parameters to train models, then applying these trained models to identify significant parameters in new datasets, thereby improving accuracy through learned patterns rather than simple correlation analysis.

The patent extracts only the significant parameters from the comprehensive set of reservoir, fracture, and treatment parameters using machine learning feature selection. By identifying and isolating the subset of parameters that have the most impact on production enhancement, the system achieves thorough optimization without being overwhelmed by the complexity of analyzing all parameters simultaneously.

The patent segments the comprehensive parameter set into significant and non-significant groups using trained machine learning models. This segmentation allows the optimization process to focus computational resources on the critical parameters that drive production enhancement, thereby achieving thorough optimization while reducing overall analysis complexity.

The patent performs preliminary action by training machine learning models in advance using comprehensive training datasets with known significant parameters. Once trained, these models can quickly identify significant parameters in new datasets without requiring re-training, thereby reducing the time and computational resources needed for ongoing parameter identification while maintaining high accuracy.

The patent creates a copied version of the knowledge gained during model training by applying the trained model to identify significant parameters in new datasets. This copying approach allows the system to leverage the computational investment made during training to rapidly analyze new data without repeating the full training process, thus reducing time loss while maintaining prediction accuracy.