A computer-implemented method comprising: a. receiving, at one or more computer processors, a borehole image, wherein the borehole image traverses a subsurface formation including sand and non-sand layers; b. dividing, via the one or more computer processors, the borehole image into multiple divided borehole images, wherein each divided borehole image traverses a depth interval of the subsurface formation; c. selecting, via the one or more computer processors, a subset of the multiple divided borehole images for use as training data; characterized in that it further comprises the steps of: d. labeling, via the one or more computer processors, the subset of the multiple divided borehole images with sand fraction values based on core analysis such that each depth interval has a corresponding sand fraction value, each sand fraction value being a fraction of the corresponding depth interval that is sand; e. training, via the one or more computer processors, a neural network to predict a sand fraction value using the subset of the multiple divided borehole images labelled with sand fraction values, the training generating a trained neural network; and f. predicting, via the one or more computer processors, a sand fraction value of a further borehole image of a subsurface volume using the trained neural network. a. receiving, at one or more computer processors, a borehole image, wherein the borehole image traverses a subsurface formation including sand and non-sand layers; b. dividing, via the one or more computer processors, the borehole image into multiple divided borehole images, wherein each divided borehole image traverses a depth interval of the subsurface formation; c. selecting, via the one or more computer processors, a subset of the multiple divided borehole images for use as training data; characterized in that it further comprises the steps of: d. labeling, via the one or more computer processors, the subset of the multiple divided borehole images with sand fraction values based on core analysis such that each depth interval has a corresponding sand fraction value, each sand fraction value being a fraction of the corresponding depth interval that is sand; e. training, via the one or more computer processors, a neural network to predict a sand fraction value using the subset of the multiple divided borehole images labelled with sand fraction values, the training generating a trained neural network; and f. predicting, via the one or more computer processors, a sand fraction value of a further borehole image of a subsurface volume using the trained neural network. d. labeling, via the one or more computer processors, the subset of the multiple divided borehole images with sand fraction values based on core analysis such that each depth interval has a corresponding sand fraction value, each sand fraction value being a fraction of the corresponding depth interval that is sand; e. training, via the one or more computer processors, a neural network to predict a sand fraction value using the subset of the multiple divided borehole images labelled with sand fraction values, the training generating a trained neural network; and f. predicting, via the one or more computer processors, a sand fraction value of a further borehole image of a subsurface volume using the trained neural network.