Optically Triggered Nucleation Eliminates Hydration Hysteresis in Swelling Minerals
Not Available
Teng, Yuntian (ORCID:0000000193438826)↗
Engineering topics
Chen, Cheng
Publications and source records attributed to Chen, Cheng.
Experimental and numerical investigation of fracture conductivity between non-smooth rock surfaces with and without proppant
The enhancement of fracture conductivity is vital for the efficient recovery of subsurface resources, such as geothermal energy and petroleum hydrocarbons. Proppants, granular materials injected into hydraulic fractures to maintain their conductivity, have been studied primarily in the context of smooth fractures (i.e., fractures between smooth rock surfaces). However, non-smooth fractures (i.e., fractures between rough rock surfaces) are common in geoenergy reservoirs and thus require further investigations. In this study, we conducted laboratory measurements of fracture conductivity on shale slabs with non-smooth surfaces and carried out numerical simulation using the lattice Boltzmann (LB) method, which aimed to investigate the conductivity of non-smooth fractures with and without proppants placement. When ceramic proppant with an areal concentration of 2 lb/ft 2 was placed in the fracture, the conductivity was enhanced by roughly 3-8 times compared to fractures without proppant. In fractures with proppant, gas-measured conductivity was higher than that measured with water due to proppant embedment caused by water. The experiments demonstrate the advantages of using proppant in fractures, even if the rock surface roughness can provide certain fracture conductivity via the self-propping mechanism. For fractures without proppants, high rock surface roughness is not necessarily favorable for enhancing fracture conductivity because the self-propping mechanism requires shear slip along the fracture surface. If there is no shear slip, high rock surface roughness can cause a detrimental effect on the fracture conductivity due to the interlocking effect. Utilizing advanced experimental equipment and LB modeling, this research explores the interplays between proppant placement, fracture geometry, and stress conditions to develop a comprehensive understanding of the productivity in non-smooth fractures. Further, the outcomes of this investigation indicate the importance of creating fractures with surface roughness during hydraulic fracturing and will contribute to the development of more efficient stimulation techniques for subsurface energy extraction.
15 GEOTHERMAL ENERGY↗
Review: Pre-Darcy flows in low-permeability porous media
The widely used Darcy’s law specifies a linear relation between the Darcy velocity of fluid flow and the pressure gradient that drives the flow. However, studies have shown that Darcy velocity can exhibit a nonlinear dependence on the pressure gradient in low-permeability porous media such as clay and shale when the pressure gradient is adequately low. This phenomenon is referred to as low-velocity non-Darcian flow or pre-Darcy flow. This paper provides a comprehensive review of the theories, experimental data, and modeling methods for pre-Darcy flow in low-permeability porous media. The review begins by outlining the fundamental mechanisms underlying pre-Darcy flow that regulate the unique characteristics such as nonlinear dependence of the Darcy velocity on the pressure gradient and its relevance to fluid–rock interactions. The review then proceeds to present a thorough compilation of experimental investigations performed in various low-permeability geomaterials including tight sandstones, shales, and clays. Next, empirical and theoretical models and simulation methods that have been developed to fit and interpret experimental data are reviewed. Finally, the review underscores the challenges encountered in conducting and interpreting pre-Darcy flow experiments and suggests future research directions. By analyzing previous experimental investigations, this review aims to offer a valuable resource for researchers and practitioners seeking to enhance their understanding of fluid dynamics in low-permeability geomaterials. This provides insights into the application of pre-Darcy flow in numerous natural and engineered processes such as shale oil and gas recovery, contaminant transport in low-permeability aquifers, and geological disposal of nuclear waste.
58 GEOSCIENCES↗
Utah FORGE 5-2419: Temperature Testing of Proppants for EGS and Stimulation of Electromagnetic Fracture Mapping Using Electrically-Conducive Proppants - 2024 Annual Workshop Presentation
This is a presentation on the Temperature Testing of Proppants for EGS and Stimulation of Electromagnetic Fracture Mapping Using Electrically-Conducive Proppants by Stevens Institute of Technology, presented by Cheng Chen. This video slide presentation discusses well controlled laboratory experiments testing the high-temperature performance of electrically-conductive and non-electrically-conductive proppants in maintaining fractures hydraulic and electrical conductivities to understand and quantify the thermal-hydrologic-mechanical-chemical mechanisms that regulate the observed behaviors. This will be tested using proppant/fracture imaging via borehole electromagnetic measurements. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024.
15 GEOTHERMAL ENERGY↗
Comparative Assessment of U-Net-Based Deep Learning Models for Segmenting Microfractures and Pore Spaces in Digital Rocks
Segmentation of high-resolution X-ray microcomputed tomography (µCT) images is crucial in digital rock physics (DRP), affecting the characterization and analysis of microscale phenomena in the porous media. The complexity of geological structures and nonideal scanning conditions pose significant challenges to conventional image segmentation approaches. Motivated by the recent increasing popularity of deep learning (DL) techniques in image processing, this work undertakes a comparative study of DL models, specifically U-Net and its variants, for segmenting multiple targets with distinguished features in digital rocks, including discrete fracture networks (DFNs), pore spaces, and solid rock. Particularly, DFNs have a smaller volumetric fraction over others, bringing in a substantial challenge of imbalanced segmentation. The primary focus is to evaluate the architecture and feature enhancement strategies of various DL models, including U-Net, attention U-Net, residual U-Net, U-Net++, and residual U-Net++. The models were designed as 2.5D, utilizing a central 2D image and its two adjacent upper and lower 2D images as input to provide a pseudo-3D context. In addition, because the ground truth of segmentation was unknown for real-world digital rocks, we created a benchmark data set following the inverse operations of segmentation. The data synthesis started from the label images (i.e., solid rock, pore spaces, and DFNs), followed by simulating partial volume blurring, adding random background noise, and introducing ring artifacts to mimic real raw X-ray µCT images. The data set, which included various rock types (i.e., sandstone and artificial data), scanning resolution, and magnitudes of noise and artifacts, was divided into training and testing data sets with a 90% and 10% ratio, respectively. Moreover, in addition to the conventional pixel-wise evaluation metrics, the physics-based metric of the lattice-Boltzmann method (LBM) simulated permeability provided more comprehensive assessments. The results demonstrated that the residual connections, nested architectures, and redesigned skip connections contribute to the model performance and give the residual U-Net++ the highest accuracy. The improvements were mainly on the boundaries and small targets, especially the DFNs, which dominate the interconnectivity and therefore affect the permeability greatly. This study also rigorously evaluated the efficiency and generalization of each model, demonstrating that the sophisticated architectures achieved excellent practicability and maintained robust performance on completely unseen data, ensuring their suitability for diverse and challenging DRP applications.
58 GEOSCIENCES↗
Machine learning and deep learning for mineralogy interpretation and CO 2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin
Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO 2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. In conclusion, the study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.
58 GEOSCIENCES↗
Anomalous excitonic phase diagram in band-gap-tuned Ta2Ni(Se,S)5
Abstract During a band-gap-tuned semimetal-to-semiconductor transition, Coulomb attraction between electrons and holes can cause spontaneously formed excitons near the zero-band-gap point, or the Lifshitz transition point. This has become an important route to realize bulk excitonic insulators – an insulating ground state distinct from single-particle band insulators. How this route manifests from weak to strong coupling is not clear. In this work, using angle-resolved photoemission spectroscopy (ARPES) and high-resolution synchrotron x-ray diffraction (XRD), we investigate the broken symmetry state across the semimetal-to-semiconductor transition in a leading bulk excitonic insulator candidate system Ta 2 Ni(Se,S) 5 . A broken symmetry phase is found to be continuously suppressed from the semimetal side to the semiconductor side, contradicting the anticipated maximal excitonic instability around the Lifshitz transition. Bolstered by first-principles and model calculations, we find strong interband electron-phonon coupling to play a crucial role in the enhanced symmetry breaking on the semimetal side of the phase diagram. Our results not only provide insight into the longstanding debate of the nature of intertwined orders in Ta 2 NiSe 5 , but also establish a basis for exploring band-gap-tuned structural and electronic instabilities in strongly coupled systems.
36 MATERIALS SCIENCE↗
Role of electron-phonon coupling in excitonic insulator candidate Ta 2 NiSe 5
Not Available
Chen, Cheng↗
Spectral evidence for unidirectional charge density wave in detwinned BaNi 2 As 2
In the iron-based superconductors, unconventional superconductivity emerges in proximity to intertwined electronic orders consisting of an electronic nematic order and a spin density wave (SDW). Recently, BaNi 2 As 2 , like its well-known iron-based analog BaFe 2 As 2 , has been discovered to host a symmetry-breaking structural transition but coupled to a unidirectional charge density wave (CDW) instead of SDW, providing a novel platform to study intertwined orders. Here, in this work, through a systematic angle-resolved photoemission spectroscopy study combined with a detwinning B 1 g uniaxial strain, we identify distinct spectral evidence of band evolution due to the structural transition as well as CDW-induced band folding. In contrast to the nematicity and spin density wave in BaFe 2 As 2 , the structural and CDW order parameters in BaNi 2 As 2 are observed to be strongly coupled and do not separate in the presence of uniaxial strain. Furthermore, no nematic band splitting is resolved above the structural transition. Our measurements point to a likely lattice origin of the CDW order in BaNi 2 As 2 .
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Electronic origin of half-metal to semiconductor transition and colossal magnetoresistance in spinel HgCr 2 Se 4
Half metals are ferromagnets hosting spin-polarized conducting carriers and are crucial for spintronics applications. The chromium spinel HgCr 2 Se 4 represents a unique type of half-metal, which features a half-metal to semiconductor transition (HMST) and exhibits colossal magnetoresistance (CMR) across the ferromagnetic-paramagnetic (FM-PM) transition. Here, using angle-resolved photoemission spectroscopy, we find that the Fermi surface of n-type HgCr 2 Se 4 (n–HgCr 2 Se 4 ) consists of a single electron pocket which moves above the Fermi level (E F ) upon the FM-PM transition, leading to the HMST. Such a Lifshitz transition manifests a giant band splitting which originates from the exchange interaction unveiled with a specific chemical nonstoichiometry. The exchange band splitting and the chemical nonstoichiometry are two key ingredients to the HMST and CMR, consistent with our ab initio calculation. Our findings provide spectroscopic evidence of the electronic origin of the anomalous properties of HgCr 2 Se 4 , which address the unique phase transition in half-metals.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Topology Hierarchy of Transition Metal Dichalcogenides Built from Quantum Spin Hall Layers
The evolution of the physical properties of 2D material from monolayer limit to the bulk reveals unique consequences from dimension confinement and provides a distinct tuning knob for applications. Monolayer 1T'-phase transition metal dichalcogenides (1T'-TMDs) with ubiquitous quantum spin Hall (QSH) states are ideal 2D building blocks of various 3D topological phases. However, the stacking geometry has been previously limited to the bulk 1T'-WTe 2 type. Here, the novel 2M-TMDs consisting of translationally stacked 1T'-monolayers are introduced as promising material platforms with tunable inverted bandgaps and interlayer coupling. By performing advanced polarization-dependent angle-resolved photoemission spectroscopy as well as first-principles calculations on the electronic structure of 2M-TMDs, a topology hierarchy is revealed: 2M-WSe 2 , MoS 2 , and MoSe 2 are weak topological insulators (WTIs), whereas 2M-WS 2 is a strong topological insulator (STI). Further demonstration of topological phase transitions by tunning interlayer distance indicates that band inversion amplitude and interlayer coupling jointly determine different topological states in 2M-TMDs. Finally, it is proposed that 2M-TMDs are parent compounds of various exotic phases including topological superconductors and promise great application potentials in quantum electronics due to their flexibility in patterning with 2D materials.
36 MATERIALS SCIENCE↗
Field Laboratory for Emerging Stacked Unconventional Plays in Central Appalachia
The goal of the Field Laboratory for Emerging Stacked Unconventional Plays (ESUP) in Central Appalachia project was to investigate and characterize the resource potential for multi-play production of emerging unconventional reservoirs in Central Appalachia. Project activities included drilling, logging and coring of a vertical characterization well drilled to basement to approximately 15,000 feet in depth. The data from drilling and well logs as well recovered core samples were used by the research team to support characterization of the geology and potential pay zones within the Nora Gas Field of southwestern Virginia and the greater Central Appalachian Basin. This project was led by Dr. Nino Ripepi of the Virginia Center for Coal and Energy Research (VCCER) in close collaboration with EnerVest Operating LLC (EnerVest). VCCER is housed in the Department of Mining and Minerals Engineering at Virginia Tech and was created by an Act of the Virginia General Assembly on March 30, 1977, as an interdisciplinary study, research, information, and resource facility for the Commonwealth of Virginia. EnerVest is a top tier, low-cost oil and natural gas company with a long history of traditional operating relationships with institutional investors as well as public and private companies. This report summarizes two major research activities: 1) Log and Core Analysis and 2) Numerical Modeling. Core analysis comprises the characterization efforts of the ESUP Field Laboratory and includes a discussion of the well logs run in the deep characterization wells, an initial analysis of those, the number, size and location of cores recovered, core testing and analysis done to date as well as plans for future analysis. Numerical modeling is comprised of seven major modeling efforts, including (1) Multiphysics shale transport modeling work, (2) the developed in-house compositional simulator studies, (3) reservoir modeling studies using GEM software, (4) fracture modeling work using EFRAC3D software and (5) numerical modeling efforts with ABAQUS software, (6) Numerical modeling efforts using FLAC3D and PFC3D, and (7) Automatic Machine Learning (AutoML) Studies.
02 PETROLEUM↗
Continuous symmetry breaking in a two-dimensional Rydberg array
Spontaneous symmetry breaking underlies much of our classification of phases of matter and their associated transitions. The nature of the underlying symmetry being broken determines many of the qualitative properties of the phase; this is illustrated by the case of discrete versus continuous symmetry breaking. Indeed, in contrast to the discrete case, the breaking of a continuous symmetry leads to the emergence of gapless Goldstone modes controlling, for instance, the thermodynamic stability of the ordered phase. Here, we realize a two-dimensional dipolar XY model that shows a continuous spin-rotational symmetry using a programmable Rydberg quantum simulator. We demonstrate the adiabatic preparation of correlated low-temperature states of both the XY ferromagnet and the XY antiferromagnet. In the ferromagnetic case, we characterize the presence of a long-range XY order, a feature prohibited in the absence of long-range dipolar interaction. In conclusion, our exploration of the many-body physics of XY interactions complements recent works using the Rydberg-blockade mechanism to realize Ising-type interactions showing discrete spin rotation symmetry.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Application of unsupervised deep learning to image segmentation and in-situ contact angle measurements in a CO 2 -water-rock system
Rock surface wettability is a critical property that regulates multiphase flows in porous media, which can be quantified using the surface contact angle (CA). X-ray micro-computed tomography (μCT) provides an effective approach to in-situ measurements of surface CAs. However, the CA measurement accuracy depends significantly on the quality of CT image segmentation, which is the clustering of CT pixels into separate phases. Inspired by this, we developed a deep learning (DL)-based CA measurement workflow. Motivated by the recent tremendous progress in unsupervised learning techniques and aiming to avoid expensive manual data annotations, an unsupervised DL pipeline for CT image segmentation was proposed and implemented, which includes unsupervised model training and post-processing. The unsupervised model training was driven by a novel loss function constrained with feature similarity and spatial continuity and implemented by iterative forward and backward paths; the former clustered the pixel-wise feature vectors extracted by convolution neural networks, whereas the latter updated the parameters using gradient descent. An over-segmentation strategy was adopted for model training. The post-processing steps based on agglomerative hierarchical clustering (AHC) were implemented to further merge the over-segmented model output to the desired cluster number, which is intended to improve the efficiency of image segmentation. The developed unsupervised DL pipeline was compared with other commonly-used image segmentation methods using pixel-wise and physics-based evaluation metrics on a synthetic raw-image dataset, which had a known ground truth. The unsupervised DL pipeline showed the best performance. Next, the segmented images were input to an automatic CA measurement tool, and the results were validated by comparisons with manual measurements. The CA values from the manual and automatic measurements showed similar distributions and statistical properties. The automatic measurement demonstrated a wider spectrum because of the much larger number of measurement data points. The primary novelty of the unsupervised DL pipeline developed in this study lies in the novel loss function and the over-segmentation strategy associated with AHC post-processing. Finally, the workflow has been proven an efficient tool for pore-scale wettability characterization, which has a wide range of applications in fundamental studies of multiphase flows in natural porous media, which have critical implications to geological carbon sequestration, hydrocarbon energy recovery, and contaminant transport in groundwater.
42 ENGINEERING↗
A Metal-Organic Framework with Nonpolar Pore Surfaces for the One-step Acquisition of C[subscript 2]
Explore the source record for details and available documents.
Di, Zhengyi↗
A Metal‐Organic Framework with Nonpolar Pore Surfaces for the One‐Step Acquisition of C 2 H 4 from a C 2 H 4 and C 2 H 6 Mixture
Abstract Because C 2 H 4 plays an essential role in the chemical industry, economical and energy‐efficient separation of ethylene (C 2 H 4 ) from ethane (C 2 H 6 ) is extremely important. With the exception of energy‐intensive cryogenic distillation, there are few one‐step methods to obtain polymer‐grade (≥99.95 % pure) C 2 H 4 from C 2 H 4 /C 2 H 6 mixtures. Here we report a highly stable metal‐organic‐framework (MOF) FJI‐H11‐Me(des) (FJI‐H=Hong's group in Fujian Institute of Research on the Structure of Matter) which features one‐dimensional hexagonal nonpolar pore surfaces constructed by aromatic rings and alkyl groups. This FJI‐H11‐Me(des) adsorbs C 2 H 6 rather than C 2 H 4 between 273 and 303 K. Practical breakthrough experiments with C 2 H 4 containing 1 % C 2 H 6 have shown that FJI‐H11‐Me(des) can realize the acquisition in one‐step of polymer‐grade, 99.95 % pure C 2 H 4 under various conditions including different gas flow rates, temperatures and relative humidity.
Di, Zhengyi↗
Persistent exchange splitting in the chiral helimagnet $\mathrm{Cr}_{1/3}\mathrm{NbS}_2$
Here, using high-resolution angle-resolved photoemission spectroscopy and ab initio calculation, we systematically investigate the electronic structure of the chiral helimagnet Cr 1/3 NbS 2 and its temperature evolution. The comparison with NbS 2 suggests that the electronic structure of Cr 1/3 NbS 2 is strongly modified by the intercalation of Cr atoms. Our ab initio calculation, consistent with experimental results, suggests strong hybridization between Nb- and Cr-derived states near the Fermi level. In the chiral helimagnetic state (below the Curie temperature, T c ), we observe exchange splitting of the energy bands crossing the Fermi level, which follows the temperature evolution of the magnetic moment, suggesting a strong interaction between the conduction electrons and Cr spin moments. Interestingly, the exchange splitting persists far above T c with weak temperature dependence, in drastic contrast to the itinerant ferromagnetism described by the Stoner model, indicating the existence of short-range magnetic order. Our results provide important insights into the interplay between the electronic structure and magnetism in Cr 1/3 NbS 2 , which is helpful for understanding the microscopic mechanism of chiral helimagnetic ordering.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗