Numerical methods to simulate spontaneous imbibition in microscopic pore structures: A review

Spontaneous imbibition, as a fundamental flow phenomenon, is widely utilized in fossil energy production, carbon dioxide and underground hydrogen storage. With the development of computing, the exploration of flow laws of spontaneous imbibition has evolved from macroscopic theoretical models to pore-scale numerical analysis. Currently, the solutions for multiphase flow in pore media mainly consider the volume of fluid and the phase field, and have been classed into level set methods based on macroscopic Navier-Stokes equations and the Shan-Chen, free energy, color gradient, and phase-field methods based on mesoscopic lattice Boltzmann equations. However, no comprehensive review article has summarized the strengths and limitations of these methods. Therefore, this work focuses on critically reviewing and commenting on the fundamentals and limitations of porescale models applied to spontaneous imbibition. In addition, recent works applying these methods are systematically reviewed. Our study aims to provide the scientific community with an expert opinion to understand the basic methods for solving the existing problems of spontaneous imbibition in porous media. Future research directions are suggested, namely, focusing on developing the reconstruction pore medium algorithms, establishing modeling methods for non-stationary states, exploring the flow laws in mixed wetting conditions, linking macroscopic and microscopic flow laws, and developing models for coupled multiphase flow numerical computation with machine learning. Overall, this review provides a comprehensive understanding of spontaneous imbibition simulation methods, promotes a thorough knowledge of spontaneous imbibition in porous media, provides guidance on exploring flow laws, and inspires researchers to give more credit to spontaneous imbibition studies.Document Type: Invited reviewCited as: Zhou, Y., Guan, W., Zhao, C., Zou, X., He, Z., Zhao, H. Numerical methods to simulate spontaneous imbibition in microscopic pore structures: A review. Capillarity, 2024, 11(1): 1-21. https://doi.org/10.46690/capi.2024.04.0

Spontaneous imbibition behavior in porous media with various hydraulic fracture propagations: A pore-scale perspective

Hydraulic fracturing technology can improve the geologic structure of unconventional oil and gas reservoirs, yielding a complex fracture network resulting from the synergistic action of hydraulic and natural fractures. However, the impact of spontaneous imbibition associated with hydraulic fracture propagation on the reservoir matrix remains poorly understood. In this study, combining the Cahn-Hilliard phase field method with the Navier-Stokes equations, pore-scale modeling was employed to capture the evolution of the oil-water interface during dynamic spontaneous imbibition for hydraulic fracture propagation in a two-end open mode. This pore-scale modeling approach can effectively circumvent the challenges of conducting spontaneous imbibition experiments on specimens partitioned by hydraulic fractures. A direct correlation was established between the pressure difference curve and the morphology of discharged oil phase in the primary hydraulic fracture, providing valuable insights into the distribution of oil phase in spontaneous imbibition. Furthermore, it was shown that secondary hydraulic fracture propagation expands the longitudinal swept area and enhances the utilization of natural fractures in the transverse swept area during spontaneous imbibition. When secondary hydraulic fracture propagation results in the interconnection of upper and lower primary hydraulic fractures, competitive imbibition occurs in the matrix, leading to reduced oil recovery compared to the unconnected models. Our results shed light upon the spontaneous imbibition mechanism in porous media with hydraulic fracture propagation, contributing to the refinement and application of hydraulic fracturing techniques.Document Type: Original articleCited as: Zhou, Y., Guan, W., Zhao, C., Zou, X., He, Z., Zhao, H. Spontaneous imbibition behavior in porous media with various hydraulic fracture propagations: A pore-scale perspective. Advances in Geo-Energy Research, 2023, 9(3): 185-197. https://doi.org/10.46690/ager.2023.09.0

LLaVA-Grounding: Grounded Visual Chat with Large Multimodal Models

With the recent significant advancements in large multi-modal models (LMMs), the importance of their grounding capability in visual chat is increasingly recognized. Despite recent efforts to enable LMMs to support grounding, their capabilities for grounding and chat are usually separate, and their chat performance drops dramatically when asked to ground. The problem is the lack of a dataset for grounded visual chat (GVC). Existing grounding datasets only contain short captions. To address this issue, we have created GVC data that allows for the combination of grounding and chat capabilities. To better evaluate the GVC capabilities, we have introduced a benchmark called Grounding-Bench. Additionally, we have proposed a model design that can support GVC and various types of visual prompts by connecting segmentation models with language models. Experimental results demonstrate that our model outperforms other LMMs on Grounding-Bench. Furthermore, our model achieves competitive performance on classic grounding benchmarks like RefCOCO/+/g and Flickr30K Entities. Our code will be released at https://github.com/UX-Decoder/LLaVA-Grounding

Visual In-Context Prompting

In-context prompting in large language models (LLMs) has become a prevalent approach to improve zero-shot capabilities, but this idea is less explored in the vision domain. Existing visual prompting methods focus on referring segmentation to segment the most relevant object, falling short of addressing many generic vision tasks like open-set segmentation and detection. In this paper, we introduce a universal visual in-context prompting framework for both tasks. In particular, we build on top of an encoder-decoder architecture, and develop a versatile prompt encoder to support a variety of prompts like strokes, boxes, and points. We further enhance it to take an arbitrary number of reference image segments as the context. Our extensive explorations show that the proposed visual in-context prompting elicits extraordinary referring and generic segmentation capabilities to refer and detect, yielding competitive performance to close-set in-domain datasets and showing promising results on many open-set segmentation datasets. By joint training on COCO and SA-1B, our model achieves $57.7$ PQ on COCO and $23.2$ PQ on ADE20K. Code will be available at https://github.com/UX-Decoder/DINOv.Comment: technical repor

In vitro production and immunogenicity of a Clostridium difficile spore-specific BclA3 glycopeptide conjugate vaccine

The BclA3 glycoprotein is a major component of the exosporangial layer of Clostridium difficile spores and in this study we demonstrate that this glycoprotein is a major spore surface associated antigen. Here, we confirm the role of SgtA glycosyltransferase (SgtA GT) in BclA3 glycosylation and recapitulate this process by expressing and purifying SgtA GT fused to MalE, the maltose binding protein from Escherichia coli. In vitro assays using the recombinant enzyme and BclA3 synthetic peptides demonstrated that SgtA GT was responsible for the addition of β-O-linked GlcNAc to threonine residues of each synthetic peptide. These peptide sequences were selected from the central, collagen repeat region of the BclA3 protein. Following optimization of SgtA GT activity, we generated sufficient glycopeptide (10 mg) to allow conjugation to KLH (keyhole limpet hemocyanin) protein. Glycosylated and unglycosylated versions of these conjugates were then used as antigens to immunize rabbits and mice. Immune responses to each of the conjugates were examined by Enzyme Linked Immunosorbent Assay ELISA. Additionally, the BclA3 conjugated peptide and glycopeptide were used as antigens in an ELISA assay with serum raised against formalin-killed spores. Only the glycopeptide was recognized by anti-spore polyclonal immune serum demonstrating that the glycan moiety is a predominant spore-associated surface antigen. To determine whether antibodies to these peptides could modify persistence of spores within the gut, animals immunized intranasally with either the KLH-glycopeptide or KLH-peptide conjugate in the presence of cholera toxin, were challenged with R20291 spores. Although specific antibodies were raised to both antigens, immunization did not provide any protection against acute or recurrent disease

Market dynamics, innovation, and transition in China's solar photovoltaic (PV) industry: a critical review

China's photovoltaic (PV) industry has undergone dramatic development in recent years and is now the global market leader in terms of newly added capacity. However, market diffusion and adoption in China is not ideal. This paper examines the blocking and inducement mechanisms of China's PV industry development from the perspective of technological innovation. By incorporating a Technological Innovation System (TIS) approach, the analysis performed here complements the previous literature, which has not grounded itself in a theoretical framework. In addition, to determine the current market dynamics, we closely examine market concentration trends as well as the vertical and horizontal integration of upstream and downstream actors (74.8% and 36.3%). The results of applying the TIS framework reveal that poor connectivity in networks, unaligned competitive entities and a lack of market supervision obstruct the development of China's PV industry. Therefore, we maintain that inducement mechanisms are required to instigate learning-by-doing capacities, which may help overcome blocking mechanisms and offset functional innovation deficiencies. In addition, policy implications are proposed for promoting the development of the PV industry in China

Impacts of conversion of cropland to grassland on the C-N-P stoichiometric dynamics of soil, microorganisms, and enzymes across China: A synthesis

In response to escalating land degradation, the conversion of cropland to grassland has emerged as a crucial mitigation strategy. This conversion has a significant influence on the stoichiometry of soil, microorganisms, and enzymes, specifically in relation to carbon (C), nitrogen (N), and phosphorus (P). A meta-analysis was conducted with 371 observations from 122 articles investigating the impacts of cropland to grassland conversion on the C-N-P stoichiometric dynamics of soils, microorganisms, and enzymes across China. The findings revealed that conversion significantly increased soil C:P (9.0%), soil N:P (5.6%), microbial C:N (15.5%), and notably, microbial C:P by 57.9%. This substantial increase in microbial C:P indicates that microbial communities are highly responsive to land use conversion. Contrastingly, the enzyme C:P ratio decreased by 19.8%, suggesting microbial adaptation to changing nutrient availability. The duration of conversion was positively correlated with soil C:P and N:P ratios, implying that relative P availability may decrease as conversion progresses. However, duration was negatively correlated with microbial C:P. Environmental factors such as clay content, mean annual temperature, and mean annual precipitation were positively correlated with microbial C:N and negatively correlated with microbial N:P, while soil pH was inversely correlated with microbial C:N. These results suggest the substantial influence of cropland to grassland conversion on soil, microbial, and enzyme stoichiometry, with particularly pronounced effects on microbial communities. The observed shifts in stoichiometric ratios suggest changes in nutrient cycling and availability following conversion. While these changes are primarily attributed to the land use conversion, we acknowledge that alterations in management practices, such as reduced fertilization, likely contribute to the observed stoichiometric shifts. Our findings emphasize the importance of considering both environmental factors and management practices when implementing grassland conversion initiatives

NMR Studies of Metal Hydrides in Carbon Scaffolds

Lithium borohydride (LiBH4) and alane (AlH3) are two of the top candidates for solid-state hydrogen storage. They both have a large mass-fraction of potentially reversibly stored H, but the enthalpy change during hydrogen desorption is too high for LiBH4 and too low for AlH3. To meet the Department of Energy requirement for onboard hydrogen storage applications, porous scaffolds have been studied to modify the thermodynamics and kinetics. Confining metal hydrides in these porous scaffolds leads to a large surface interface between the hydride and the scaffold, allowing some control of the interfacial energies between the scaffold and the hydride. In this thesis, I will show the 1H static NMR study of the ionic motions for LiBH4 in porous scaffolds and 15N MAS NMR study of the interaction between the scaffold and infiltrated LiBH4 or AlH3. LiBH4 in porous scaffolds displays a motionally-narrowed fraction of its 1H static NMR spectrum. Here, we report selective inversion experiments to measure the rate of exchange between the mobile and immobile BH_4^- groups. We find the exchange time constant to be nearly temperature independent at ~5 ms. In 15N MAS NMR, there are two species of nitrogen on the porous scaffolds, and the pyridinic nitrogen peak shifts after infiltrated with LiBH4 or AlH3. Pyridinic nitrogen and the infiltrated hydrides may be acting as a Lewis-acid/base complex

The synergistic impact of incentive and regulatory environmental policies on firms’ environmental performance

The imperative to enhance corporate environmental performance is not only pivotal for a company’s growth but also crucial for fulfilling societal responsibilities and protecting global environmental interests. Recognizing the inadequacies of standalone environmental policies, our study delves into the synergistic effects of incentive-based and regulatory approaches on the environmental performance of listed firms in China. We meticulously examine the interplay between environmental punishment and subsidies over the period of 2015–2019. Our analysis reveals that a strategic combination of punishment and subsidies can substantially improve firms' environmental performance. This effect intensifies with the increasing amounts of fines and subsidies. Additionally, we explore the dynamic effects of policy implementation. Our results indicate that subsidies implemented either a year before or after the imposition of punishment might diminish the effectiveness of standalone environmental penalty policies. Furthermore, our findings suggest that diverse regulatory policies enhance firm environmental performance by promoting investments in environmental protection and fostering green innovation. This discovery highlights the need for a nuanced understanding of policy mixes and their implications for corporate environmental strategies

An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)

The precondition of well testing interpretation is to determine the appropriate well testing model. In numerous attempts in the past, automatic classification and identification of well testing plots have been limited to fully connected neural networks (FCNN). Compared with FCNN, the convolutional neural network (CNN) has a better performance in the domain of image recognition. Utilizing the newly proposed CNN, we develop a new automatic identification approach to evaluate the type of well testing curves. The field data in tight reservoirs such as the Ordos Basin exhibit various well test models. With those models, the corresponding well test curves are chosen as training samples. One-hot encoding, Xavier normal initialization, regularization technique, and Adam algorithm are combined to optimize the established model. The evaluation results show that the CNN has a better result when the ReLU function is used. For the learning rate and dropout rate, the optimized values respectively are 0.005 and 0.4. Meanwhile, when the number of training samples was greater than 2000, the performance of the established CNN tended to be stable. Compared with the FCNN of similar structure, the CNN is more suitable for classification of well testing plots. What is more, the practical application shows that the CNN can successfully classify 21 of the 25 cases