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Porosity and permeability are the key factors in assessing the hydrocarbon productivity of unconventional (shale) reservoirs, which are complex in nature due to their heterogeneous mineralogy and poorly connected nano- and micro-pore systems. Experimental efforts to measure these petrophysical properties posse many limitations, because they often take weeks to complete and are difficult to reproduce. Alternatively, numerical simulations can be conducted in digital rock 3D models reconstructed from image datasets acquired via e.g., nanoscale-resolution focused ion beam–scanning electron microscopy (FIB-SEM) nano-tomography. In this study, impact of reservoir confinement (stress) on porosity and permeability of shales was investigated using two digital rock 3D models, which represented nanoporous organic/mineral microstructure of the Marcellus Shale. Five stress scenarios were simulated for different depths (2,000–6,000 feet) within the production interval of a typical oil/gas reservoir within the Marcellus Shale play. Porosity and permeability of the pre- and post-compression digital rock 3D models were calculated and compared. A minimal effect of stress on porosity and permeability was observed in both 3D models. These results have direct implications in determining the oil-/gas-in-place and assessing the production potential of a shale reservoir under various stress conditions.

Three sets of synthetic images were created from two original datasets. A suite exhibiting greyscale contrast was produced from an 8.96-μm voxel size 3D X-ray microscopy image of a sandstone rock and a two suites (one showing greyscale contrast and one showing both greyscale and textural contrast) were produced from a 5 × 5 × 5 nm voxel size FIB-SEM image of a shale rock. The performance of three image segmentation algorithms (global multi-Otsu thresholding, seeded watershed region growing, and machine learning-based multivariant classification) was then assessed by their ability to recover their respective original segmented 3D images. While all algorithms performed well at low noise levels, machine learning-based classification proved significantly more noise tolerant than either of the traditional algorithms. It was also able to segment the non-greyscale (textural based) contrast, something the traditional completely failed to do, with voxel misclassification rates for the traditional techniques above 50% at a 0 noise level within the textural contrast regions. Machine learning-based classification, in contrast, achieved misclassification rates of less than 5% in the same regions.

The present study investigated the effect of stimulus-response compatibility on the representation of atypical biological kinematics during observational practice. A compatible group observed an atypical model that moved rightward, whereas an incompatible group observed an atypical model that moved leftward. Both groups were instructed to observe the model with the intention to later reproduce the movement trajectory. This was examined in a posttest where participants were asked to move rightward with a kinematic profile that matched the atypical kinematics. Compared to a control group that did not engage in practice, and irrespective of whether the stimulus was observed in a spatially compatible or incompatible orientation, participants from both experimental groups reproduced velocity profiles that were comparable and similar to the atypical biological kinematics. Bayesian analysis indicated equality between the 2 experimental groups, thus suggesting comparable sensorimotor processing. Therefore, by rotating the incompatible stimulus by 180 degrees during observational practice, the current study has isolated the processing and representation of atypical biological kinematics to the underlying sensorimotor processes, rather than spatial encoding of peak velocity via processes associated with stimulus-response compatibility. (PsycINFO Database Record

© 2018 The Authors Non-invasive laboratory-based X-ray microtomography has been widely applied in many industrial and research disciplines. However, the main barrier to the use of laboratory systems compared to a synchrotron beamline is its much longer image acquisition time (hours per scan compared to seconds to minutes at a synchrotron), which results in limited application for dynamic in situ processes. Therefore, the majorit y of existing laboratory X-ray microtomography is limited to static imaging; relatively fast imaging (tens of minutes per scan) can only be achieved by sacrificing imaging quality, e.g. reducing exposure time or number of projections. To alleviate this barrier, we introduce an optimized implementation of a well-known iterative reconstruction algorithm that allows users to reconstruct tomographic images with reasonable image quality, but requires lower X-ray signal counts and fewer projections than conventional methods. Quantitative analysis and comparison between the iterative and the conventional filtered back-projection reconstruction algorithm was performed using a sandstone rock sample with and without liquid phases in the pore space. Overall, by implementing the iterative reconstruction algorithm, the required image acquisition time for samples such as this, with sparse object structure, can be reduced by a factor of up to 4 without measurable loss of sharpness or signal to noise ratio.

We introduce a solvable Lagrangian model for droplet bouncing. The model predicts that, for an axisymmetric drop, the contact time decreases to a constant value with increasing Weber number, in qualitative agreement with experiments, because the system is well approximated as a simple harmonic oscillator. We introduce asymmetries in the velocity, initial droplet shape, and contact line drag acting on the droplet and show that asymmetry can often lead to a reduced contact time and lift-off in an elongated shape. The model allows us to explain the mechanisms behind non-axisymmetric bouncing in terms of surface tension forces. Once the drop has an elliptical footprint the surface tension force acting on the longer sides is greater. Therefore the shorter axis retracts faster and, due to the incompressibility constraints, pumps fluid along the more extended droplet axis. This leads to a positive feedback, allowing the drop to jump in an elongated configuration, and more quickly.

Mudstone (shale) reservoir evaluation and efficient development poses a significant challenge due to the heterogeneous nature of these complex formations. Organic-rich shales are characterized by intricate mineralogy, ultra-low nanoporosity, and nano-darcy permeability making these tight source/reservoir rocks challenging in obtaining economically viable hydrocarbons. Consequently, characterizing and quantifying minerology and intra-/interparticle (non)organic-hosted porosity at the pore-scale, and up-scaling it to the core-scale remains a significant focus of evaluating reservoir quality in shale plays. Current advances in correlative multi-scale and multi-modal 2D/3D imaging of nanoporous geomaterials, such as shales, provide a tremendous opportunity to characterize and represent these rocks over multiple length scales – from core-to pore-scale. Subsequently, these image datasets can be then used for advanced image analysis and digital rock modeling to reconstruct 2D/3D models used to analyze their petrophysical properties. In this study, the Mancos Shale from the Uinta Basin – one of the most promising shale plays in the United States (Hawkins et al. 2016) – was characterized from centimeter-to nanometer-scale via digital rock analysis using correlative micro 3D X-ray computed tomography (micro-CT), micro 3D X-ray microscopy (micro-XRM), light microscopy (LM) mosaic map, scanning electron microscopy (SEM) mosaic map, and focused ion beam (FIB) – SEM (FIB-SEM) nano-tomography image datasets. These multi-resolution and multi-dimensional image datasets, were then registered with each other, and used to reconstruct digital rock 2D/3D models from which shale petrophysical properties such as lithology, mineralogy, and porosity were extracted. Additionally, the imaged mineralogy and porosity results were compared with traditional laboratory experimental data, such as XRD and helium porosity measurements.

Table S3. Table describing study participant adherence in all three intervention arms. (DOCX 15 kb)

Table S1. Table listing education topics covered in all three intervention arms. (DOCX 15 kb)

Mesoscopic simulations of hydrocarbon flow in source shales are challenging, in part due to the heterogeneous shale pores with sizes ranging from a few nanometers to a few micrometers. Additionally, the sub-continuum fluid–fluid and fluid–solid interactions in nano- to micro-scale shale pores, which are physically and chemically sophisticated, must be captured. To address those challenges, we present a GPU-accelerated package for simulation of flow in nano- to micro-pore networks with a many-body dissipative particle dynamics (mDPD) mesoscale model. Based on a fully distributed parallel paradigm, the code offloads all intensive workloads on GPUs. Other advancements, such as smart particle packing and no-slip boundary condition in complex pore geometries, are also implemented for the construction and the simulation of the realistic shale pores from 3D nanometer-resolution stack images. Our code is validated for accuracy and compared against the CPU counterpart for speedup. In our benchmark tests, the code delivers nearly perfect strong scaling and weak scaling (with up to 512 million particles) on up to 512 K20X GPUs on Oak Ridge National Laboratory’s (ORNL) Titan supercomputer. Moreover, a single-GPU benchmark on ORNL’s SummitDev and IBM’s AC922 suggests that the host-to-device NVLink can boost performance over PCIe by a remarkable 40%. Lastly, we demonstrate, through a flow simulation in realistic shale pores, that the CPU counterpart requires 840 Power9 cores to rival the performance delivered by our package with four V100 GPUs on ORNL’s Summit architecture. This simulation package enables quick-turnaround and high-throughput mesoscopic numerical simulations for investigating complex flow phenomena in nano- to micro-porous rocks with realistic pore geometries. Program summary Program title: USERMESO 2.5 Program files doi: http://dx.doi.org/10.17632/zzpv74bz9m.1 Licensing provisions: GNU General Public License 3 Programming language: CUDA C/C++ with MPI and OpenMP Nature of problem: Particle-based simulation of multiphase flow and fluid–solid interaction in nano- to micro-scale pore networks of arbitrary pore geometries. Solution method: Fluid particles and solid wall particles are modeled with a many-body dissipative particle dynamics (mDPD) model – a mesoscopic model for coarse-grained fluid and solid molecules. The pore surface wall boundary for arbitrary surface geometries is modeled with a no-slip boundary condition for fluid particles that prevents fluid particles from indefinitely penetrating in the walls. The time evolution of the system is integrated using the Velocity-Verlet algorithm. Restrictions: The code is compatible with NVIDIA GPUs with compute capability 3.0 and above. Unusual features: The code is implemented on GPGPUs with significantly improved speed.

Table S2. Summary of study measures at specified time points. (DOCX 15 kb)

Table S4. EQ-5D and Health Care Costs Diary Feasibility Data. (DOCX 14 kb)

Table S1. Participant demographics and clinical characteristics between those that were provided with accelerometers and those that were not. (DOCX 18Â kb)

This paper will discuss research findings that examine second language writers⿿ (L2) attitudes towards producing outlines to prepare for an essay task. A mixed-method study was conducted with 49 undergraduate students in three different classes taking an English Composition 2 course at a university in the UAE. To establish a baseline measurement, participants completed surveys and participated in focus group interviews asking them their opinions about doing outlines at the beginning of the course. After the initial survey and interviews, Group 1 was asked to complete a traditional print-based outline to prepare for an essay. Group 2 were asked to produce a multimodal (text, image, and video) outline on an app to help them prepare for the same essay. After completing their respective outlines, both groups responded to questionnaires and took part in focus-group interviews asking them their opinions about doing outlines. Although there wasn⿿t a significant difference in attitudes between the traditional outline group and the multimodal group, both groups responded that outlining helped them better develop and organize their essays. These findings suggest L2 writers may believe there is a correlation between outlining and successful essay writing. Participants who completed multimodal outlines also responded favourably to learning new technology⿿implications that suggest technology can increase engagement in the learning process.

We investigate the impact of initial pore structure and velocity field heterogeneity on the dynamics of fluid/solid reaction at high Peclet numbers (fast flow) and low Damkohler number (relatively slow reaction rates). The Diamond Lightsource Pink Beam was used to image dissolution of Estaillades and Portland limestones in the presence of CO 2 -saturated brine at reservoir conditions (10 MPa and 50 °C representing ~ 1 km aquifer depth) at two flow rates for a period of 2 h. Each sample was scanned between 51 and 94 times at 4.76-μm resolution and the dynamic changes in porosity, permeability, and reaction rate were examined using image analysis and flow modelling. We find that the porosity can either increase uniformly through time along the length of the samples, or may exhibit a spatially and temporally varying increase that is attributed to channel formation, a process that is distinct from wormholing, depending on initial pore structure and flow conditions. The dissolution regime was structure-dependent: Estaillades with a higher porosity showed more uniform dissolution, while the lower porosity Portland experienced channel formation. The effective reaction rates were up to two orders of magnitude lower than those measured on a flat substrate with no transport limitations, indicating that the transport of reactant and product is severely hampered away from fast flow channels.

A novel method for permeability prediction is presented using multivariant structural regression. A machine learning based model is trained using a large number (2,190, extrapolated to 219,000) of synthetic datasets constructed using a variety of object-based techniques. Permeability, calculated on each of these networks using traditional digital rock approaches, was used as a target function for a multivariant description of the pore network structure, created from the statistics of a discrete description of grains, pores and throats, generated through image analysis. A regression model was created using an Extra-Trees method with an error of www.digitalrocksporta.org) with a Root Mean Square Fractional Error of

Multiphase flow in permeable media is a complex pore-scale phenomenon, which is important in many natural and industrial processes. To understand the pore-scale dynamics of multiphase flow, we acquired time-series synchrotron X-ray micro-tomographic data at a voxel-resolution of 3.28 μm and time-resolution of 38 s during drainage and imbibition in a carbonate rock, under a capillary-dominated flow regime at elevated pressure. The time-series data library contains 496 tomographic images (gray-scale and segmented) for the complete drainage process, and 416 tomographic images (gray-scale and segmented) for the complete imbibition process. These datasets have been uploaded on the publicly accessible British Geological Survey repository, with the objective that the time-series information can be used by other groups to validate pore-scale displacement models such as direct simulations, pore-network and neural network models, as well as to investigate flow mechanisms related to the displacement and trapping of the non-wetting phase in the pore space. These datasets can also be used for improving segmentation algorithms for tomographic data with limited projections. Machine-accessible metadata file describing the reported data (ISA-Tab format)

The Time-Of-Propagation detector is a Cherenkov particle identification detector based on quartz radiator bars for the Belle II experiment at the SuperKEKB e + e − − collider. The purpose of the detector is to identify the type of charged hadrons produced in e + e − − collisions, and requires a single photon timing resolution below 100 picoseconds. A novel front-end electronic system was designed, built, and integrated to acquire data from the 8192 microchannel plate photomultiplier tube channels in the detector. Waveform sampling of these analog signals is done by switched-capacitor array application-specific integrated circuits. The processes of triggering, digitization of windows of interest, readout, and data transfer to the Belle II data acquisition system are managed by Xilinx Zynq-7000 programmable system on a chip devices.

We have experimentally investigated the impact of heterogeneity on the dissolution of two limestones, characterised by distinct degrees of flow heterogeneity at both the pore and core scales. The two rocks were reacted with reservoir-condition CO2-saturated brine at both scales and scanned dynamically during dissolution. First, 1 cm long 4 mm diameter cores were scanned during reactive flow with a 4 μm voxel size between 10 and 71 times using 4D X-ray micro-tomography (μ-CT) over the course of 90 min. Second, 3.8 cm diameter, 8 cm long cores were reacted at the same conditions inside a reservoir-condition flow apparatus and imaged using a medical-grade X-ray computed tomography scanner (XCT). Each sample was imaged ~13 times over the course of 90 min at a 250 × 250 × 500 μm resolution. These larger cores were then scanned inside a μ-CT at a 27 μm voxel size to assess the alteration pore-space heterogeneity after reaction. Both rock types exhibited channel widening at the mm scale and progressive high porosity pathway dissolution at the cm scale. In the more heterogeneous rock, dissolution was more focussed and progressed along the direction of flow. Additionally, the dissolution pathways contained a distinct microstructure captured with the μ-CT that was not visible at the resolution of the XCT, where the reactive fluid had not completely dissolved the internal pore-structure. This microstructure was further analyzed by performing a direct simulation of the flow field and streamline tracing on the image voxels. We found that at the larger scales the interplay between flow and reaction significantly affects flow in the unreacted regions of the core. When flow is focussed in large reacted channels, this focusing is carried through to the unreacted parts of the rock where flow continues to be confined to preferential pathways after passing the reaction front. This focussing effect is greater with increasing pore space heterogeneity indicating that the representative elementary volume (REV) for dissolution is far greater than the dissolution front itself. This study of scale dependence using in situ 4D tomography provides insight into the mechanisms that control local reaction rates at the mm and cm scales. Furthermore, this work suggests that under these conditions at larger scales it is likely to be structural heterogeneity that dominates the pattern of dissolution and therefore the evolution of high permeability pathways.

Table S2. Elements of Health Coaching (adapted from Spring et al.). Table describing elements of health coaching. (DOCX 20 kb)

CIHR ADT Ex RCT Protocol Paper Supplemental Methods. Sample size for 2-arm non-inferiority study with dual primary outcome. Methods used to determine trial sample size. (PDF 137 kb)