Understanding pore-scale flow and transport processes is important for understanding flow and transport within rocks on a larger scale. Flow experiments on small-scale micromodels can be used to experimentally investigate pore-scale flow. Current manufacturing methods of micromodels are costly and time consuming. 3D printing is an alternative method for the production of micromodels. We have been able to visualise small-scale, single-phase flow and transport processes within a 3D printed micromodel using a custom-built visualisation cell. Results have been compared with the same experiments run on a micromodel with the same geometry made from polymethyl methacrylate (PMMA, also known as Perspex). Numerical simulations of the experiments indicate that differences in experimental results between the 3D printed micromodel and the Perspex micromodel may be due to variability in print geometry and surface properties between the samples. 3D printing technology looks promising as a micromodel manufacturing method; however, further work is needed to improve the accuracy and quality of 3D printed models in terms of geometry and surface roughness.

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Microporosity in carbonate reservoirs is globally pervasive and commonly used to explain high-porosity, low-permeability reservoirs, higher than expected water saturations, low resistivity pay zones and poor sweep efficiency. The potential for micropores to store and produce hydrocarbons has long been recognized, yet limitations on tools to evaluate microporosity has prevented rigorous evaluation. Here we demonstrate a workflow for evaluating microporosity through a combination of laser scanning confocal microscopy (LSCM) and pore network modelling. Specific values for microporosity and permeability calculated in our study should not be applied explicitly, as these are simulated values, but they demonstrate the viability of micropore networks to store and flow hydrocarbons. Carbonate reservoir assessment is critical not only in the petroleum industry, but also for applications in hydrothermal and mineral resources, carbon capture and storage, and groundwater supply. This approach can be applied to understand the potential for any reservoir to hold and transmit fluids.

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Three-dimensional (3-D) printing provides a fast, cost-effective way to produce and replicate complicated designs with minimal flaws and little material waste. Early use of 3-D printing for engineering applications in the petroleum industry has stimulated further adoption by geoscience researchers and educators. Recent progress in geoscience is signified by capabilities that translate digital rock models into 3-D printed rock proxies. With a variety of material and geometric scaling options, 3-D printing of nearidentical rock proxies provides a method to conduct repeatable laboratory experiments without destroying natural rock samples. Rock proxy experiments can potentially validate numerical simulations and complement existing laboratory measurements on changes of rock properties over geologic time scales. A review of published research from academic, government, and industry contributions indicates a growing community of rock proxy experimentalists. Three-dimensional printing techniques are being applied to fundamental research in the areas of multiphase fluid flow and reactive transport, geomechanics, physical properties, geomorphology, and paleontology. Further opportunities for geoscience research are discussed. Applications in education include teaching models of terrains, fossils, and crystals. The integration of digital data sets with 3-D printed geomorphologies supports communication for both societal and technical objectives. Broad benefits that could be realized from centralized 3-D printing facilities are also discussed.

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Petrographic description is one of the primary methods by which geoscientists develop an understanding of geologic systems and constrain source, reservoir, and seal characteristics. Geologic facies or rock types are routinely interpreted from petrographic features in thin sections, but these methods are time consuming and tedious. The interpretations can be heavily biased. Variations in experience base, microscope quality, and scale of observation can lead to inconsistent interpretations, absorbing costly time to achieve alignment. With advances in computing, millions of images can be analyzed in seconds through signal-processing technologies. With this capability, machine-learning methods can support geologic pattern identification to classify geologic features. Combined, automated image analysis and classification though machine learning can significantly reduce analysis time, interpreter bias and inconsistencies. These methods make it possible to share "expert knowledge" with nonexpert users. Image processing, segmentation and classification are key workflows for translating geologic features into discrete representations that can be used for computational modeling. Here, we present two examples where advanced image analysis and machine learning are used to predict geologic and petrophysical properties from optical microscopy thin-section images. The first example uses multiple machine-learning algorithms and pore geometries as a means for predicting and classifying rock properties, such as lithofacies, reservoir zone, porosity and permeability. The second example focuses on training and using convolutional neural networks to classify and predict Dunham textures (mudstone, wackestone, packstone and grainstone) from carbonate thin sections. Data for both machine-learning applications comes from a Jurassic bimodal carbonate reservoir. Key depositional lithofacies include micritic mudstones, bivalve-coated grain pack/grainstones, Cladocoropsis pack/grainstones, stromatoporoid coralgal pack/grainstones, and skeletal-oolitic grainstones. Key geological features, pore fabric analysis and depositional textures were documented via optical light microscopy. In addition, standard petrophysical measurements (porosity, permeability and grain density) were available from the corresponding core plugs. Although several key uncertainties exist in using machine-learning models for prediction of geological and petrophysical features, these two examples highlight the potential for combining advanced image analysis and data-driven analytical techniques. These approaches can be extended to other multiscale, multidimensional imaging applications in geoscience, such as core and borehole imaging, CT scanning, LIDAR and hyperspectral imaging.@en