Abstract: Systems and methods are disclosed. The method includes obtaining geochemical data and geological data for a number of oil samples and training a machine learning network using the geochemical data and the geological data. Each oil sample includes hydrocarbon molecules and the geochemical data includes abundances of the hydrocarbon molecules. The method further includes obtaining a new oil sample from a subterranean region of interest and determining new geochemical data for the new oil sample using gas chromatography. The method still further includes predicting new geological data for the new oil sample by inputting the new geochemical data into the trained machine learning network.

Abstract: A method for generating a core description is disclosed. The method includes coring and collecting rock cores from geographical locations in the subterranean formation, detecting, using an augmented reality (AR) device worn by a user, content of an identifying tag of a rock core within a device view of the AR device to identify a well where the rock core is obtained, retrieving, by the AR device from a data repository, historical data of the well, activating, by the AR device, a sensor to acquire additional data from the rock core to supplement the historical data, and presenting, by the AR device, an AR image including a first image of the historical data and the additional data superimposed over a second image of the rock core, where the user generates the core description based on viewing the AR image.

Abstract: A photomicrograph of a rock sample is analyzed. One or more classification attributes of the photomicrograph are recorded. One or more maceral attributes of the photomicrograph are determined using automated face detection. Based on the one or more classification attributes and the one or more maceral attributes, one or more technical attributes of the photomicrograph are determined using automated face recognition. The one or more maceral attributes and the one or more technical attributes are provided.

Abstract: A photomicrograph of a rock sample is analyzed. One or more classification attributes of the photomicrograph are recorded. One or more maceral attributes of the photomicrograph are determined using automated face detection. Based on the one or more classification attributes and the one or more maceral attributes, one or more technical attributes of the photomicrograph are determined using automated face recognition. The one or more maceral attributes and the one or more technical attributes are provided.