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Petroleum engineers are always in a race to maximize the recovery factor out of naturally trapped hydrocarbon resources. Unconventional resources such as organic-rich shales have unlocked significant reserves attributed to the novel production technologies of lateral drilling assisted by hydraulic fracturing. Even though such techniques have enabled the exploitation of shales, the ultimate recovery remained fractional, a challenge to be answered through further improvement. Carbon dioxide injection in unconventional resources, which was initially implemented for coalbed methane, has been recently an active area of investigation for organic-rich shales. In this paper, we present a molecular modeling study of carbon dioxide injection in the organic matter of the shale matrix. We built the molecular model, consistent with the repeated organic matter characterization in the literature. Molecular dynamics (MD) protocol was developed to form a three-dimensional (3-D) configuration of kerogen, followed by Gibbs Monte Carlo simulation for the adsorption/desorption calculations, and self-diffusivity calculations through MD. The aim was to delineate the impact of carbon dioxide injection on the adsorption/desorption behavior coupled with its influence on the transport. Injection of carbon dioxide was found to shift the adsorption isotherm favoring the depletion of methane. The ultimate recovery raised from 54% (no injection of CO 2 ) up to 92% depending on the carbon dioxide concentration and its temperature. Moreover, the injection of carbon dioxide was found to have a minimal impact on the self-diffusivity of methane in kerogen bodies and their associated microcracks.

Deep neural networks have received considerable attention in clinical imaging, particularly with respect to the reduction of radiation risk. Lowering the radiation dose by reducing the photon flux inevitably results in the degradation of the scanned image quality. Thus, researchers have sought to exploit deep convolutional neural networks (DCNNs) to map low-quality, low-dose images to higher-dose, higher-quality images, thereby minimizing the associated radiation hazard. Conversely, computed tomography (CT) measurements of geomaterials are not limited by the radiation dose. In contrast to the human body, however, geomaterials may be comprised of high-density constituents causing increased attenuation of the X-rays. Consequently, higher-dose images are required to obtain an acceptable scan quality. The problem of prolonged acquisition times is particularly severe for micro-CT based scanning technologies. Depending on the sample size and exposure time settings, a single scan may require several hours to complete. This is of particular concern if phenomena with an exponential temperature dependency are to be elucidated. A process may happen too fast to be adequately captured by CT scanning. To address the aforementioned issues, we apply DCNNs to improve the quality of rock CT images and reduce exposure times by more than 60%, simultaneously. We highlight current results based on micro-CT derived datasets and apply transfer learning to improve DCNN results without increasing training time. The approach is applicable to any computed tomography technology. Furthermore, we contrast the performance of the DCNN trained by minimizing different loss functions such as mean squared error and structural similarity index.

Computed tomography (CT) is an important tool to characterize rock samples allowing quantification of physical properties in 3D and 4D. The accuracy of a property delineated from CT data is strongly correlated with the CT image quality. In general, high-quality, lower noise CT Images mandate greater exposure times. With increasing exposure time, however, more wear is put on the X-Ray tube and longer cooldown periods are required, inevitably limiting the temporal resolution of the particular phenomena under investigation. In this work, we propose a deep convolutional neural network (DCNN) based approach to improve the quality of images collected during reduced exposure time scans. First, we convolve long exposure time images from medical CT scanner with a blur kernel to mimic the degradation caused because of reduced exposure time scanning. Subsequently, utilizing the high- and low-quality scan stacks, we train a DCNN. The trained network enables us to restore any low-quality scan for which high-quality reference is not available. Furthermore, we investigate several factors affecting the DCNN performance such as the number of training images, transfer learning strategies, and loss functions. The results indicate that the number of training images is an important factor since the predictive capability of the DCNN improves as the number of training images increases. We illustrate, however, that the requirement for a large training dataset can be reduced by exploiting transfer learning. In addition, training the DCNN on mean squared error (MSE) as a loss function outperforms both mean absolute error (MAE) and Peak signal-to-noise ratio (PSNR) loss functions with respect to image quality metrics. The presented approach enables the prediction of high-quality images from low exposure CT images. Consequently, this allows for continued scanning without the need for X-Ray tube to cool down, thereby maximizing the temporal resolution. This is of particular value for any core flood experiment seeking to capture the underlying dynamics.

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