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. <EOS>