Abstract: A method is disclosed which includes obtaining a reference image of a first rock core from a wellbore, and obtaining a disoriented image of a second rock core from the wellbore. The method further includes determining, using a computer processor, a reorientation angle between the disoriented image and the reference image. The method further includes determining an oriented image of the second rock core based, at least in part, on rotating the disoriented image through the reorientation angle.

Abstract: System and methods are disclosed. The method includes obtaining an observed stratigraphic thickness map, initial bathymetry map, and initial subsidence sequence for a model of the geological region of interest, where the model comprises a plurality of cells each representing a portion of the geological region. The methods further includes simulating, using a forward stratigraphic modeler, a predicted stratigraphic thickness map for each cell based on the initial subsidence sequence, then iteratively, forming an objective function for each cell based, at least in part, on the observed stratigraphic thickness map and the predicted stratigraphic thickness map, determining if the objective function for each cell satisfies a stopping criterion, and updating the subsidence sequence for cells not satisfying the criterion.

Abstract: Methods and systems for calibrating a subsidence map are disclosed. The method includes selecting a stratigraphic model that represents a geological formation and defining the subsidence map with a first set of variable values. The method further includes obtaining target outputs measured from the geological formation. The method still further includes determining first model outputs from the stratigraphic model by inputting the subsidence map with the first set of variable values into the stratigraphic model and determining a first residual between the target outputs and the first model outputs using an objective function.

Abstract: Systems and methods for method for optimizing settings of a camera and microscope for capturing thin section images of a reservoir rock in a hydrocarbon reservoir. The method includes performing a frequency analysis of each pixel of the thin section image, associating each pixel in the thin section image with a class identifier selected from a plurality of class identifiers, each class identifier associated with range of frequencies, determining a percentage of pixels in each class identifier, determining a percentage of pixels comprising noise, generating a machine learning model using a structured data set, and determining, using the machine learning model, an optimized setting for the camera and an optimized setting for the microscope based on the thin section image with the least noise.