A computer-implemented method (200, 300) of training and applying a model for predicting hydrocarbon production and hydrocarbon production uncertainty in a subsurface volume of interest, the method being implemented in a computer system comprising one or more physical computer processors, non-transient electronic storage, and a graphical user interface, the method comprising: obtaining (202, 204, 206), via the graphical user interface, (i) training production data, corresponding to the subsurface volume of interest, (ii) training engineering parameters, corresponding to the subsurface volume of interest, and (iii) a training set of geological parameters and corresponding training geological parameter uncertainty values, corresponding to the subsurface volume of interest; obtaining an initial production model, being a machine learning model; training (210, 212, 214) the initial production model to generate a trained production model using as training data the training production data, the training engineering parameters, and the training set of geological parameters and corresponding training geological parameter uncertainty values; storing the trained production model; obtaining, via the graphical user interface, a target set of geological parameters and corresponding target geological parameter uncertainty values; obtaining, via the graphical user interface, target engineering parameters; applying (306, 308) the target set of geological parameters, corresponding target geological parameter uncertainty values, and target engineering parameters to the trained production model, via the graphical user interface, to generate a set of production values and corresponding production uncertainty values; generating a representation using visual effects to depict at least a portion of the set of production values and corresponding production uncertainty values as a function of position within the subsurface volume of interest; and displaying the representation, wherein the initial production model and the trained production model comprise a machine learning probabilistic random forest regression. obtaining (202, 204, 206), via the graphical user interface, (i) training production data, corresponding to the subsurface volume of interest, (ii) training engineering parameters, corresponding to the subsurface volume of interest, and (iii) a training set of geological parameters and corresponding training geological parameter uncertainty values, corresponding to the subsurface volume of interest; obtaining an initial production model, being a machine learning model; training (210, 212, 214) the initial production model to generate a trained production model using as training data the training production data, the training engineering parameters, and the training set of geological parameters and corresponding training geological parameter uncertainty values; storing the trained production model; obtaining, via the graphical user interface, a target set of geological parameters and corresponding target geological parameter uncertainty values; obtaining, via the graphical user interface, target engineering parameters; applying (306, 308) the target set of geological parameters, corresponding target geological parameter uncertainty values, and target engineering parameters to the trained production model, via the graphical user interface, to generate a set of production values and corresponding production uncertainty values; generating a representation using visual effects to depict at least a portion of the set of production values and corresponding production uncertainty values as a function of position within the subsurface volume of interest; and displaying the representation, wherein the initial production model and the trained production model comprise a machine learning probabilistic random forest regression.