Abstract: A method is disclosed for generating a machine learning model to predict a reservoir fluid property, such as gas-oil ratio or density, based on standard mud-gas and petrophysical data. It has been found that this model predicts these reservoir fluid properties with an accuracy that is close to that which can be achieved using advanced mud-gas data. This is advantageous, as than standard mud-gas data and petrophysical data is much more readily available than advanced mud-gas data.

Abstract: The present disclosure relates to techniques for prediction of reservoir fluid properties of a hydrocarbon reservoir fluid, such as the density, the saturation pressure, the formation volume factor and the gas-oil ratio of the reservoir fluid. To predict the reservoir fluid properties, a model is generated by selecting a subset of available reservoir samples based on a degree of biodegradation of the samples, generating an input data set comprising input data and target data, the input data comprising measured or predicted mud-gas data; and generating a model using the input data. The application of this technique allows a continuous log of the selected property to be generated using mud-gas data collected during the well drilling process.