01 January 1970 1 880 Report

Machine Learning (ML)

When data are brought together for a given model, whether, Machine Learning (ML) will be able to figure out the areas, where field measurements are required?

Whether ML could accommodate the application of physical laws to field data, which would possibly reveal additional information about 'unmeasured' or 'difficult to measure' field properties?

Whether ML could offer insight to the system being modelled?

At least, ML could act as a Parsimonious model for any given physical system, which are based on the simplest conceptual mechanisms and employ fewest parameters, while also providing an acceptable representation of a given physical system by providing the basic insights to the system functioning and critical processes?

Whether the forecasts from ML could test hypotheses about system responses and allow quantitative comparisons of alternative proposed scenarios?

How exactly ML is expected to improve the model performance, given the fact that the complexity of petroleum reservoir systems and the uneven spread, poor quality or even absence of observed data present considerable difficulties for oil/gas drainage modelling?

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