Systematic and robust referencing of reservoir analogues. Ranking and evaluation of reservoirs based on a set of key geologic parameters. Estimation of expected recovery. Rapid decisions on investment allocation.
Fast and efficient way to generate high fidelity surrogate responses to the various realizations of a simulation model. The model is built using a convolutional network encoder decoder architecture that takes the reservoir permeability as an input and outputs the pressure and saturation fields at various time steps.
Select a functional form or define your own, choose which parameters to calibrate to get instantly the resulting decline fitted curve. You can also choose different automation levels and run in batch mode for large datasets.