Abstract: A method of calculating extraction efficiency coefficients for mud-gas analysis includes: generating a simulated output drilling fluid based on a mixture of about 1 wt. % of a reference reservoir fluid and a balance of an input drilling fluid; simulating release of gas from the simulated output drilling fluid under predetermined conditions using an equations-of-state model; and determining an extraction efficiency coefficient for each gas component based on a ratio between the composition of the reference reservoir fluid and the composition of the simulated released gas.

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 of generating a model for predicting at least one property of a fluid at a sample location within a hydrocarbon reservoir includes simulating behaviour of one or more hydrocarbon reservoirs during production; generating a plurality of simulated fluid samples from the one or more simulated hydrocarbon reservoirs, the plurality of simulated fluid samples corresponding to a plurality of different spatial locations and/or different time locations within the one or more simulated hydrocarbon reservoirs; generating a training data set including input data and target data based on the simulated fluid samples, the input data including simulated mud-gas data for each sample location indicative of mobile and immobile hydrocarbons at the sample location, and the target data including the at least one property of only the mobile hydrocarbons at each sample locations; and constructing a model using the training data set such that the model can be used to predict the at least one property of the fluid at a sample

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: A microfluidic device includes a microfluidic substrate having a porous media channel, an oil inlet port in fluid communication with the porous media channel, a fluid inlet port in fluid communication with the porous media channel, and an outlet port in fluid communication with the porous media channel. The porous media channel has a plurality of dividers that provide the porous media channel with a network of fluid pathways. A method for assessing miscibility of an oil composition and a fluid includes flowing an aliquot of a fluid through a porous media channel to displace at least an oil composition from the porous media channel, and conducting an optical investigation of the porous media channel to assess the miscibility of the oil composition and the fluid at the test pressure and test temperature.