1. A system to enhance borehole image analysis, the system comprising: one or more processors; one or more input and output units in communication with the one or more processors and positioned to receive as input a plurality of different openhole log data sets, a plurality of different core data sets, and a plurality of different borehole image reading sets, each of the plurality of different openhole log data sets associated with one or more of a plurality of hydrocarbon wells, each of the plurality of different core data sets associated with one or more of the plurality of hydrocarbon wells, each of the plurality of different borehole image reading sets including a plurality of borehole image readings and associated with one or more of the plurality of hydrocarbon wells, one or more of the plurality of different borehole image reading sets associated with each of the plurality of hydrocarbon wells, core data including core grain size, the plurality of hydrocarbon wells associated with a hydrocarbon reservoir, and to output an approximated grain size and an approximated material type for each borehole image reading; one or more displays in communication with the one or more processors; and non-transitory memory medium in communication with the one or more processors, the memory medium including: a neural network mapping module including computer-readable instructions stored therein that when executed cause the system to perform the steps of: comparing the plurality of different openhole log data sets to the plurality of different core data sets, performing iteratively an unsupervised neural network map, responsive to the plurality of different openhole log data sets and the plurality of different core data sets, to thereby identify one or more material types associated with the plurality of hydrocarbon wells, and generating, responsive to the neural network map, a material-type scheme, the material-type scheme associating each of the identified one or more material types with a plurality of material characteristics, the material characteristics including one or more of the following: material resistivity value and material grain size, and an image-based petrophysical analysis module including computer-readable instructions stored therein that when executed cause the system to perform the steps of: distributing each of the plurality of borehole image readings of each of the plurality of different borehole image reading sets into one of a plurality of resistivity classes responsive to the material-type scheme to thereby associate each borehole image reading with an approximated material type, each of the plurality of resistivity classes having one or more predetermined associated material resistivity values, calibrating each borehole image reading responsive to the plurality of different core data sets and the material-type scheme to produce an approximated grain size associated with each borehole image reading, and depicting on the one or more displays, for each hydrocarbon well, the associated one or more of the plurality of different borehole image reading sets, the approximated material type for each borehole image reading of the associated one or more of the plurality of different borehole image reading sets, and the approximated grain size for each borehole image reading of the associated one or more of the plurality of different borehole image reading sets to thereby increase consistency in categorizing subsurface material associated with the plurality of hydrocarbon wells by material type and enhance interpretation of subsurface material texture, fabric, and features to predict subsurface material composition of the hydrocarbon reservoir.