The orange curve is the actual field-wide production, and the gray line is the expected baseline if cyclic candidates were chosen the same way they were before the application of Data Physics technology.
Tachyus‘ Data Physics technology combines machine learning and reservoir physics to rapidly integrate relevant data sources in real time.
The models use a novel approach to integrate machine-learning techniques with the underlying reservoir-physics equations and limit the solutions to those that are consistent with the underlying reservoir physics.
Data Physics models leverage the inherent continuity of reservoir behavior.
The core modeling and optimization work flow begins with an extract-transform-load process of surface and subsurface data.
Actual field data are divided into two sets: training data, which are used to identify model parameters, and validation data, which are intentionally withheld from the training process and used to validate predictive power and statistical accuracy.
Once training data are fit and an ensemble is produced, the predictive accuracy of each member of the ensemble can be measured against the validation data set.