A computer-implemented method 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 , 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 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 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 , 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 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 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. <EOS>