Wei Yu – författare

Shale Gas and Tight Oil Reservoir Simulation delivers the latest research and applications used to better manage and interpret simulating production from shale gas and tight oil reservoirs. Starting with basic fundamentals, the book then includes real field data that will not only generate reliable reserve estimation, but also predict the effective range of reservoir and fracture properties through multiple history matching solutions. Also included are new insights into the numerical modelling of CO2 injection for enhanced oil recovery in tight oil reservoirs. This information is critical for a better understanding of the impacts of key reservoir properties and complex fractures.

Models the well performance of shale gas and tight oil reservoirs with complex fracture geometries Teaches how to perform sensitivity studies, history matching, production forecasts, and economic optimization for shale-gas and tight-oil reservoirs Helps readers investigate data mining techniques, including the introduction of nonparametric smoothing models

The development of naturally fractured reservoirs, especially shale gas and tight oil reservoirs, exploded in recent years due to advanced drilling and fracturing techniques. However, complex fracture geometries such as irregular fracture networks and non-planar fractures are often generated, especially in the presence of natural fractures. Accurate modelling of production from reservoirs with such geometries is challenging. Therefore, Embedded Discrete Fracture Modeling and Application in Reservoir Simulation demonstrates how production from reservoirs with complex fracture geometries can be modelled efficiently and effectively.

This volume presents a conventional numerical model to handle simple and complex fractures using local grid refinement (LGR) and unstructured gridding. Moreover, it introduces an Embedded Discrete Fracture Model (EDFM) to efficiently deal with complex fractures by dividing the fractures into segments using matrix cell boundaries and creating non-neighboring connections (NNCs). A basic EDFM approach using Cartesian grids and advanced EDFM approach using Corner point and unstructured grids will be covered.

Embedded Discrete Fracture Modeling and Application in Reservoir Simulation is an essential reference for anyone interested in performing reservoir simulation of conventional and unconventional fractured reservoirs.

Highlights the current state-of-the-art in reservoir simulation of unconventional reservoirs Offers understanding of the impacts of key reservoir properties and complex fractures on well performance Provides case studies to show how to use the EDFM method for different needs

As unconventional reservoir activity grows in demand, reservoir engineers relying on history matching are challenged with this time-consuming task in order to characterize hydraulic fracture and reservoir properties, which are expensive and difficult to obtain. Assisted History Matching for Unconventional Reservoirs delivers a critical tool for today's engineers proposing an Assisted History Matching (AHM) workflow. The AHM workflow has benefits of quantifying uncertainty without bias or being trapped in any local minima and this reference helps the engineer integrate an efficient and non-intrusive model for fractures that work with any commercial simulator. Additional benefits include various applications of field case studies such as the Marcellus shale play and visuals on the advantages and disadvantages of alternative models. Rounding out with additional references for deeper learning, Assisted History Matching for Unconventional Reservoirs gives reservoir engineers a holistic view on how to model today's fractures and unconventional reservoirs.

Provides understanding on simulations for hydraulic fractures, natural fractures, and shale reservoirs using embedded discrete fracture model (EDFM) Reviews automatic and assisted history matching algorithms including visuals on advantages and limitations of each model Captures data on uncertainties of fractures and reservoir properties for better probabilistic production forecasting and well placement

Edge Intelligence in Cyber-Physical Systems: Foundations and Applications provides a comprehensive overview of best practices for building edge intelligence into cyber-physical systems. This book covers the foundations and applications of synergizing machine learning at the edge of CPS, leveraging an edge computing infrastructure. Divided into four parts, the first section of the book reviews the foundations, principles, and representative application domains of CPS. The second part covers machine learning, edge computing, and their needs in CPS, defining edge intelligence and its principles, challenges, and research directions. The third part presents tutorials and foundational research works on realizing edge intelligence in representative CPS. The fourth part explores the problem space of threats and countermeasures in building edge intelligence into CPS. Researchers, graduate students and professionals in computer science, data science, and electrical engineering will find this to be a valuable resource on the principles and applications of edge intelligence in cyber-physical systems as well as the development of interdisciplinary techniques to advance the field.Provides comprehensive overview of CPS design challenges and representative application domainsIntroduces the principles of machine learning and edge computing and their applications in cyber-physical systemsExplores edge intelligence applications as well as complete case studies of problem formalization, method/algorithm design, and performance evaluation

Network-Constrained Data-Driven Control of High-Speed Rail Systems: Adaptive and Learning-Based Approaches addresses critical challenges in high-speed railway (HSR) operational control systems, focusing on enhancing safety, efficiency, and automation in an era of rapid network expansion. The book introduces a transformative framework for data-driven adaptive control and multi-train cooperative control under dynamic network constraints. It integrates next-generation 5G-R communication to enable real-time train-to-train (T2T) coordination, reducing dependency on fixed infrastructure and addressing vulnerabilities like faded channels and interference. By combining rigorous theoretical analysis with simulations, the book proposes solutions to improve operational precision, resilience against disruptions, and transportation capacity.This resource is helpful for researchers, engineers, and graduate students in high speed railway control systems, offering innovative strategies to advance autonomous operations and meet the demands of high-density, high-speed rail networksPresents a data-driven adaptive and learning control framework for high-speed trains under network constraintsDiscusses the theory and method of multi-train cooperative control in detail: particularly, how to realize real-time information interaction and dynamic adjustment between trains with the support of train-to-train communicationDiscusses the influence of network constraints (such as fading measurement, malicious attacks, etc.) on train cooperative control, and proposes a series of compensation strategiesFocuses on current, high-speed rail control technology, but also contains a forward-looking discussion of future high-speed rail communication and control technology, such as the application of 5G-R communication system and autonomous driving technology

This unique text will enable readers to understand the fundamental theory, current techniques, and potential applications of Cloud Radio Access Networks (C-RANs). Leading experts from academia and industry provide a guide to all of the key elements of C-RANs, including system architecture, performance analysis, technologies in both physical and medium access control layers, self-organizing and green networking, standards development, and standardization perspectives. Recent developments in the field are covered, as well as open research challenges and possible future directions. The first book to focus exclusively on Cloud Radio Access Networks, this is essential reading for engineers in academia and industry working on future wireless networks.

This book constitutes the proceedings of the 11th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2016, held in Bozeman, MT, USA, in August 2016.The 50 full papers and 9 invited papers presented werde carefully reviewed and selected from 148 submissions.

Reaching deep into the wealth of Chinese philosophical wisdom, this book offers rich insights into a way of educating that has found staunch advocates among educators through the ages. The ‘free-spirit education’, which calls on educators to respect and nurture the natural goodness of each child, affords an educational principle that is embedded in one of the most important Confucian classics: The Doctrine of the Mean. This book analyzes the meaning, history, principles, and educational application of ‘free-spirit education’ and also explores its contemporary development in the context of a school improvement initiative. It introduces the intellectual origins of ‘free-spirit education’, the application in ’process-based inductive teaching’ and cases from the field. It presents the collection of pedagogical cases that are rooted in the traditions of Chinese philosophic inquiry and viewed through the lens of contemporary pedagogy for human development. This book is a useful reference for university faculty, educational researchers, school teachers and leaders, graduate and undergraduate students in curriculum studies and in philosophy, social science, and education, curriculum developers, and all those educators who are interested in understanding ‘free-spirit education’—a key component of the humanistic traditions of Chinese education.