Zelong Xie

Yangtze University

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Thomas J. Webster

Hebei University of Technology

Christopher F. Cogswell

Northeastern University

Tianyi Wang

Chinese Academy of Sciences

Yishan Lou

Yangtze University

Liang Zhu

Yangtze University

Sunho Choi

Jeonju University

Hui Ji

Xiamen University

Andrew T. Y. Wolek

Northwestern University

Jieda Chen

Nanjing University of Science and Technology

Xiangxin Yin

Shenyang University of Technology

Cooperative Institutions

Yangtze University

Northeastern University

Universidad del Noreste

State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation

Shenyang University of Technology

Key Laboratory of Drilling and Production Engineering for Oil and Gas, Hubei Province

China University of Petroleum, Beijing

Chinese Academy of Sciences

Wuhan University of Technology

Minzu University of China

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Impact of Deep Shale Gas Dense-Cutting Fracturing Parameters on EUR

Processes (2024)

Tianyi Wang Guofa Ji Jiansheng Liu Zelong Xie

Deep shale formations pose significant challenges in forming high-conductivity fractures, leading to low ultimate recoverable reserves (EUR) per well under conventional fracturing techniques. Dense-cutting fracturing is a commonly employed method to enhance the EUR of individual wells; however, the critical process parameters influencing EUR remain unclear. This study develops a novel EUR calculation model tailored for deep shale gas dense-cutting, integrating the Warren-Root model with the constant-volume gas reservoir material balance equation. The model comprehensively incorporates Knudsen diffusion and adsorption-desorption phenomena in deep shale gas, corrects apparent permeability, and employs the finite element method to simulate dynamic pressure depletion during production. The study examines the impact of fracture half-lengths, cluster spacing, fracture conductivity and horizontal section lengths on EUR under tight-cutting fracturing. Orthogonal experiments combined with multiple linear regression analysis reveal the hierarchy of influence among the four factors on EUR: horizontal section length > fracture half-length > cluster spacing > fracture conductivity. The study derives EUR correlation expressions that incorporate the effects of crack half-length, cluster spacing, fracture conductivity, and horizontal segment length. The orthogonal experimental results indicate that EUR exhibits positive correlations with crack half-length, fracture conductivity, and horizontal segment length, while showing a negative correlation with cluster spacing. The multiple regression equation achieves a coefficient of determination (R2) of 0.962 and an average relative error of 3.79%, outperforming traditional prediction methods in both accuracy and computational simplicity. The findings are of substantial significance for the rapid estimation of EUR in individual wells following deep shale gas fracturing and offer valuable theoretical insights for practical engineering applications.

Shale Gas

10.3390/pr13010066

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Pore structure–CO₂ adsorption property relations of supported amine materials with multi-pore networks

Journal of Materials Chemistry A (2017)

Christopher F. Cogswell Zelong Xie Andrew T. Y. Wolek Y. Wang Alyssa M. Stavola

Conventional supported amine adsorbents to date are known to suffer from the trade-off between increasing amine content and decreasing access to amine sites. To address this challenge size selection of loaded amines may be a useful tool.

10.1039/c7ta01616f

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Tuning of Metal–Organic Frameworks by Pre‐ and Post‐synthetic Functionalization for Catalysis and Separations

Christopher F. Cogswell Zelong Xie Sunho Choi

This chapter discusses the broad use of functionalized metal–organic frameworks for catalysis and separations. Functionalization here refers to the addition of chemical functionality by the inclusion of some nonstructural group to the framework. Functionalization methods and types are detailed, as well as the specific limitations and advantages of each functionalization for catalytic or separation applications. The sections are broken down into either pre-synthetic or post-synthetic functionalizations, with post-synthetic methods further broken down into various categories. These secondary categories include physical impregnation of functional groups, covalent attachment, in situ reactions, solvent-assisted linker exchange, and metal site replacement/addition. This section acts as a primer on these functionalization methods, which have generated a vast literature of their own in recent years.

Surface Modification

Linker

10.1002/9783527809097.ch10

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A Used Sailboat Pricing Problem Based on Graph Convolutional Neural Network

2022 International Conference on Computers, Information Processing and Advanced Education (CIPAE) (2023)

Zechao Xie Xiangxin Yin Jiangyan Zhang Zelong Xie Keming Zhuang

Used sailboats change with age and market conditions, and prediction models that extract feature correlations are needed to identify hidden features that affect the listing price of used sailboats and accurately predict the listing price of used sailboats. In this paper, we introduce a graph neural network model and an attention machine mechanism for the problem of predicting the listing price of used sailboats. We collected data on the brand, variety, regional GDP and cargo throughput of sailboats. To abstract information about the dependencies between the two data. On the data side, each sample is used as a node and feature vector (brand, variant, regional GDP, cargo throughput, etc.) to construct the graph data. Treat each sample as a node feature. The similarity between two graph nodes is calculated using Pearson similarity algorithm and used as initialized edge weights. On this model, a graph convolutional neural network (GCN) is constructed with an MSE loss function and the final loss is obtained after bringing the data into the model for training. The attention mechanism is then introduced and the attention weight matrix is output to obtain the weights of the sailboat feature vector, where the average cargo throughput (tons) has the greatest effect on the listed price of the sailboat used.

Feature vector

Feature (linguistics)

10.1109/cipae60493.2023.00128

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Magnesium oxide nanomaterial for carbon capture and potential biomedical applications

Zelong Xie

Metal oxides, specifically alkali metal oxides and alkaline earth metal oxides, represent a class of crystalline solid that contains metal cations and oxide anions. Among these metal oxides, magnesium oxide (MgO) has been extensively studied due to its abundantly existing precursors in nature and its applicable physical and chemical properties for water remediation, air emissions treatment, and some medical applications. In the first part of this work, a method of using metal organic frameworks (MOF) as a template material for the synthesis of porous oxide for carbon capture and storage (CCS) was explored to address the overwhelming global climate change issue. Particularly, the solution of how to prevent magnesium oxide particles from sintering during cyclic adsorption and desorption of carbon dioxide (CO2) was inspected. The results showed that a regenerable porous magnesium oxide with high CO2 capture capacity and material regenerability was achieved via this synthesis method. This work suggested that the carbon contents remaining on the surfaces of magnesium oxides may preserve its porous structure and prevent further sintering and structure collapse of the material, evidenced by the adsorbent's retained porosity and capture capacities over repeated carbonation and decarbonation cycles. This demonstration of the synthesis of regenerable metal oxides by thermal decomposition of MOF templates may enable broader applicability for other energy-related metallic oxides. The next part of this work aimed to investigate porous MgO coatings for implants to combat bacterial infections during surgical procedures. In particular, the correlation between surface area/pore volume of obtained MgO and its antibacterial activities was studied. The results of this study suggested that higher surface areas provided better inhibition on both Gram-positive bacteria, methicillin-resistant Staphylococcus aureus and Gram-negative bacteria, Pseudomonas aeruginosa. Those results offered a new perspective to better understand the bacteria-killing mechanism of magnesium oxides and even other metallic oxides.

Alkaline earth metal

Carbon fibers

Nanomaterials

10.17760/d20398255

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3D Bioprinting in Tissue Engineering for Medical Applications: The Classic and the Hybrid

Polymers (2020)

Zelong Xie Ming Gao Anderson Oliveira Lobo Thomas J. Webster

Three-dimensional (3D) printing, as one of the most popular recent additive manufacturing processes, has shown strong potential for the fabrication of biostructures in the field of tissue engineering, most notably for bones, orthopedic tissues, and associated organs. Desirable biological, structural, and mechanical properties can be achieved for 3D-printed constructs with a proper selection of biomaterials and compatible bioprinting methods, possibly even while combining additive and conventional manufacturing (AM and CM) procedures. However, challenges remain in the need for improved printing resolution (especially at the nanometer level), speed, and biomaterial compatibilities, and a broader range of suitable 3D-printed materials. This review provides an overview of recent advances in the development of 3D bioprinting techniques, particularly new hybrid 3D bioprinting technologies for combining the strengths of both AM and CM, along with a comprehensive set of material selection principles, promising medical applications, and limitations and future prospects.

3D bioprinting

Biomaterial

3d printed

Material selection

Biocompatible material

10.3390/polym12081717

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Citations (119)

An Advanced Long Short-Term Memory (LSTM) Neural Network Method for Predicting Rate of Penetration (ROP)

ACS Omega (2022)

Hui Ji Yishan Lou Shuting Cheng Zelong Xie Liang Zhu

Rate of penetration (ROP) is an essential factor in drilling optimization and reducing the drilling cycle. Most of the traditional ROP prediction methods are based on building physical model and single intelligent algorithms, and the efficiency and accuracy of these prediction methods are very low. With the development of artificial intelligence, high-performance algorithms make reliable prediction possible from the data perspective. To improve ROP prediction efficiency and accuracy, this paper presents a method based on particle swarm algorithm for optimization of long short-term memory (LSTM) neural networks. In this paper, we consider the Tuha Shengbei block oilfield as an example. First, the Pearson correlation coefficient is used to measure the correlation between the characteristics and eight parameters are screened out, namely, the depth of the well, gamma, formation density, pore pressure, well diameter, drilling time, displacement, and drilling fluid density. Second, the PSO algorithm is employed to optimize the super-parameters in the construction of the LSTM model to the predict ROP. Third, we assessed model performance using the determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). The evaluation results show that the optimized LSTM model achieves an R2 of 0.978 and RMSE and MAPE are 0.287 and 12.862, respectively, hence overperforming the existing methods. The average accuracy of the optimized LSTM model is also improved by 44.2%, indicating that the prediction accuracy of the optimized model is higher. This proposed method can help to drill engineers and decision makers to better plan the drilling operation scheme and reduce the drilling cycle.

Rate of penetration

10.1021/acsomega.2c06308

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A hierarchical integration pyramid to increase translation of biomaterials based on recent successes in multiscale synthetic biomaterials research

Current Opinion in Biomedical Engineering (2019)

A-Andrew D. Jones Zelong Xie Thomas J. Webster

Regenerative Medicine

Life span

Pyramid (geometry)

10.1016/j.cobme.2019.04.005

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Study on Radial Leakage Model and Law of Fractured Formation Drilling Fluid

Processes (2023)

Zelong Xie Liang Zhu Shubo Bi Hui Ji Tianyi Wang

Wellbore leakage mostly occurs in structurally developed fractured formations. Analyzing the real-time leakage rate during the drilling process plays an important role in identifying the leakage mechanism and its rules on-site. Based on the principles of fluid mechanics and using Herschel-Bulkley (H-B) drilling fluid, by reasonably simplifying the drilling fluid performance parameters, fracture roughness characteristic parameters, pressure difference between the wellbore and formation, and the radial extension length of drilling fluid, the radial leakage model is improved to improve the calculation accuracy. Using the Euler format in numerical analysis to solve the model and with the help of numerical analysis software, the radial leakage law of this flow pattern in the fractures is obtained. The results show that the deformation coefficient of the fracture index, fracture aperture, pressure difference, leakage rate, and cumulative leakage rate are positively correlated. The larger the curvature of the fracture, the rougher the fracture, and the smaller the leakage rate and cumulative leakage rate. The larger the consistency coefficient of the drilling fluid, the greater the additional resistance between the fractures, and the smaller the leakage rate and cumulative leakage rate. As the extending length of the fracture increases, the invasion of drilling fluid decreases, the leakage rate slows down, and eventually reaches zero, with the maximum cumulative leakage rate.

Leakage (economics)

10.3390/pr11051388

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Study on the Law of Rock Drillability under High-Temperature and High-Pressure Conditions in the Western Margin of the Ordos Basin

Processes (2024)

Liang Zhu Zelong Xie Xiaoming Li Yipeng Zhao Tianyi Wang

The deep strata in the Ordos Basin exhibit characteristics of high temperature and high stress. Conventional methods for assessing drillability (normal temperature and pressure) fail to accurately understand the drilling resistance characteristics of deep rocks in this region, leading to improper guidance for selecting formation drilling tools and prolonging drilling cycles. This study employs physical experiments and numerical simulations to conduct drillability tests on core samples taken from the region under high-temperature and high-pressure conditions, simultaneously simulating the rock breaking process under different temperature and pressure conditions. The study investigates the variation patterns of rock drillability grade values and von Mises stress values during rock breaking under single-factor and multi-factor analyses of temperature and pressure conditions. Combining these variation patterns, an optimization analysis of the back rake angle of PDC drill bits used in drillability experiments is conducted to guide the selection of drill bits on site. The results indicate that the variation patterns of von Mises values from finite element simulations are consistent with the drillability grade values under high-temperature and high-pressure conditions. Under single-factor conditions, von Mises stress and drillability grade values generally increase with rising temperature before decreasing, while they increase with increasing confining pressure. Under multi-factor conditions, confining pressure is the primary influencing factor within the range of 0 to 50 MPa, while the influence of temperature becomes prominent between 180 °C to 200 °C, with a weakening effect of confining pressure. Model application: Selecting a back rake angle of 30° for PDC drill bits yields optimal rock-breaking results. The research findings hold significant implications for understanding the low rock-breaking efficiency of deep strata, optimizing drill bit parameters, and enhancing drilling efficiency.

Margin (machine learning)

10.3390/pr12061261

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