Data-driven methods in laboratory-scale study of enhanced oil recovery

Data-driven methods have become ubiquitous across the engineering and applied sciences. In the petroleum sciences, a significant body of work has arisen applying such techniques to modeling and analysis of reservoir-scale data, but laboratory-scale data and applications have received relatively less attention. In this work, we develop data-driven methods for assimilating experimental data from two domains: image-based characterization of shale source rocks and in-situ combustion kinetics modeling. In the first half of this work, we develop methods for modality translation and synthesis of shale images, specifically for reconstructing or synthesizing 3D volumetric data when only 2D training data is available. We propose an experimental and computational workflow applying image translation and super-resolution models to predict destructive shale microscopy images from non-destructive input data. We then propose an approach to regularizing image-to-image models to improve volume reconstruction using only 2D training data. The results show that our models improve the volume prediction in terms of morphological image features and create image volumes suitable for flow simulations. We also propose a fundamentally new approach to synthesizing porous media images. Our approach is based on generative flow models and is the first approach that can generate grayscale and multimodal image data from only 2D training images. We apply this method to synthesizing baseline sandstone and limestone samples, as well as scanning electron microscopy and dual-mode focused ion beam milled scanning electron microscopy and nano-computed tomography images. The synthetic images are similar to the ground truth data in both appearance and morphological descriptors as gauged by Minkowski functionals distributions. In the second half, we develop methods for modeling and upscaling in-situ combustion chemical kinetics. We develop a data-driven model for predicting oxygen consumption during heavy oil combustion directly from laboratory data, and apply this method to simulating combustion kinetics and analyzing heating schedules for ramped temperature oxidation experiments. Our results show that a data-driven model accurately predicts heavy oil oxidation from a relatively small sample of experimental data. We then present a fully-automated parameter estimation and uncertainty quantification approach for in-situ combustion chemical reaction models. Our approach is generalized to any in-situ combustion reaction model and requires no manual history matching or parameter initialization. We apply this parameter calibration workflow to multiple reaction models for two different heavy oil samples. Our results show that reaction schemes can differ significantly in both stoichiometry and kinetics parameters when calibrated to different heavy oil samples. Very different reaction models were shown to provide fits of similar accuracy to the same heavy oil sample. Our results also suggest that it is the number of stages in pseudocomponent cracking, not the number of pseudocomponents or reactions, that most impacts the ability of a reaction model to fit oxidation data for a heavy oil sample. Overall, this work demonstrates the capabilities of using data-driven modeling to expand our characterization capabilities and improve our understanding of enhanced oil recovery processes at the laboratory-scale. It is our hope that the work presented here will enable new directions in nanoscale characterization of shales and upscaling of in-situ combustion chemical reaction models

Keywords: Electrical Engineering.
Advisor: Kovscek Anthony R. (Anthony Robert)
Boyd Stephen P. 
Pauly John (John M.)