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.

Abstract: The geochemical parameters of reservoir fluid do not directly and universally correlate with the fluid type of the reservoir fluid, e.g. reservoir oil and reservoir gas. However, within an individual hydrocarbon basin, the local reservoir oils and the local reservoir gases are often geochemically distinct. Therefore, by examining various geochemical parameters for reservoir fluid samples taken from a particular region of interest, it is possible to identify region-specific thresholds for those geochemical parameters, and also to identify particular region-specific thresholds having a high degree of confidence for distinguishing between different reservoir fluid types. Advantageously, many geochemical parameters can be determined using mud-gas data, and in some cases using only standard mud-gas data.

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.