Tushar Sharma – författare

Nanotechnology for CO2 Utilization in Oilfield Applications delivers a critical reference for petroleum and reservoir engineers to learn the latest advancements of combining the use of CO2 and nanofluids to lower carbon footprint. Starting with the existing chemical and physical methods employed for synthesizing nanofluids, the reference moves into the scalability and fabrication techniques given for all the various nanofluids currently used in oilfield applications. This is followed by various, relevant characterization techniques. Advancing on, the reference covers nanofluids used in drilling, cementing, and EOR fluids, including their challenges and implementation problems associated with the use of nanofluids.

Finally, the authors discuss the combined application of CO2 and nanofluids, listing challenges and benefits of CO2, such as carbonation capacity of nanofluids via rheological analysis for better CO2 utilization. Supported by visual world maps on CCS sites and case studies across the industry, this book gives today's engineers a much-needed tool to lower emissions.

This comprehensive book presents the latest advances in chemical EOR, considered to be an efficient technique to recover bypassed oil and residual oil trapped in reservoirs. The volume first provides an introduction to chemical EOR and discusses its viability. From there, it delves in the various EOR methods, including low-salinity water flooding, polymer and surfactant flooding, foam flooding, nanofluid flooding, hybrid methods, ionic liquid applications, and others. The book covers chemical synthesis of EOR agents and numerical simulation of compositional models in porous media, including a description of possible application of nanotechnology acting as a booster of traditional chemical EOR processes.

This comprehensive book presents the latest advances in chemical EOR, considered to be an efficient technique to recover bypassed oil and residual oil trapped in reservoirs. The volume first provides an introduction to chemical EOR and discusses its viability. From there, it delves in the various EOR methods, including low-salinity water flooding, polymer and surfactant flooding, foam flooding, nanofluid flooding, hybrid methods, ionic liquid applications, and others. The book covers chemical synthesis of EOR agents and numerical simulation of compositional models in porous media, including a description of possible application of nanotechnology acting as a booster of traditional chemical EOR processes.

This book assumes that the reader is already familiar with Java fundamentals which is in line with the prerequisite of having a OCAJP certification.

The book sports considerable supportive material to help the reader in effective exam preparation in the form of appendices:

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.

Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries andframeworks are also covered.

Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem.

Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today!