The latest Deep QI development provides a more realistic, data driven overview of the subsurface and increases performance and flexibility when performing ML based workflows.
Training dataset sizes for machine learning applications determine which algorithms should be used to obtain the most accurate results.
Aligning with QI project dataset sizes (10s of wells), we've added the Random Forest regression algorithm to optimize the ML workflow.
Train with well data, deploy at the well and in 3D. Automated parameter optimization, feedback and metrics for confidence and understanding of the prediction are provided for the user.