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PVT properties are necessary for reservoir/well performance forecast and optimization. In absence of PVT laboratory measurements, finding the right correlation to estimate accurate PVT properties could be challenging. PVT Property Correlations: Selection and Estimation discusses techniques to properly calculate PVT properties from limited information. This book covers how to prepare PVT properties for dry gases, wet gases, gas condensates, volatile oils, black oils, and low gas-oil ration oils. It also explains the use of artificial neural network models in generating PVT properties. It presents numerous examples to explain step-by-step procedures in using techniques designed to deliver the most accurate PVT properties from correlations. Complimentary to this book is PVT correlation calculator software. Many of the techniques discussed in this book are available with the software. This book shows the importance of PVT data, provides practical tools to calculate PVT properties, and helps engineers select PVT correlations so they can model, optimize, and forecast their assets. - Understand how to prepare PVT data in absence of laboratory reports for all fluid types - Become equipped with a comprehensive list of PVT correlations and their applicability ranges - Learn about ANN models and their applications in providing PVT data - Become proficient in selecting best correlations and improving correlations results
Large sets of petroleum fluid data exist for the various reservoir conditions and properties that occur in practice. Petroleum Reservoir Fluid Property Correlations, written by three internationally well-known and respected petroleum engineers, is the result of several years of exhaustive research that gathered data sets from databases all over the world. The data were compared against the results of many published correlations of fluid properties in order to find the "best in class" required in the petroleum industry. Those findings are offered here as recommended use in reservoir engineering calculations. The data sets cover natural gases, reservoir oils, and reservoir waters (brines). The result of this research project is the best correlation for each fluid property. Key Features: * Best-in-class correlations contained in one volume * The most accurate data for reservoir engineering calculations * Correlations that cover all reservoir hydrocarbons and brines Petroleum Reservoir Fluid Property Correlations will prove to be a valuable resource for reservoir engineers, production engineers who need to determine which set of correlation equations are most useful for their work, and graduate-level reservoir engineering courses.
The last three chapters of this book deal with application of methods presented in previous chapters to estimate various thermodynamic, physical, and transport properties of petroleum fractions. In this chapter, various methods for prediction of physical and thermodynamic properties of pure hydrocarbons and their mixtures, petroleum fractions, crude oils, natural gases, and reservoir fluids are presented. As it was discussed in Chapters 5 and 6, properties of gases may be estimated more accurately than properties of liquids. Theoretical methods of Chapters 5 and 6 for estimation of thermophysical properties generally can be applied to both liquids and gases; however, more accurate properties can be predicted through empirical correlations particularly developed for liquids. When these correlations are developed with some theoretical basis, they are more accurate and have wider range of applications. In this chapter some of these semitheoretical correlations are presented. Methods presented in Chapters 5 and 6 can be used to estimate properties such as density, enthalpy, heat capacity, heat of vaporization, and vapor pressure. Characterization methods of Chapters 2-4 are used to determine the input parameters needed for various predictive methods. One important part of this chapter is prediction of vapor pressure that is needed for vapor-liquid equilibrium calculations of Chapter 9.
Understanding the properties of a reservoir's fluids and creating a successful model based on lab data and calculation are required for every reservoir engineer in oil and gas today, and with reservoirs becoming more complex, engineers and managers are back to reinforcing the fundamentals. PVT (pressure-volume-temperature) reports are one way to achieve better parameters, and Equations of State and PVT Analysis, Second Edition, helps engineers to fine tune their reservoir problem-solving skills and achieve better modeling and maximum asset development. Designed for training sessions for new and existing engineers, Equations of State and PVT Analysis, Second Edition, will prepare reservoir engineers for complex hydrocarbon and natural gas systems with more sophisticated EOS models, correlations and examples from the hottest locations around the world such as the Gulf of Mexico, North Sea and China, and Q&A at the end of each chapter. Resources are maximized with this must-have reference. - Improve with new material on practical applications, lab analysis, and real-world sampling from wells to gain better understanding of PVT properties for crude and natural gas - Sharpen your reservoir models with added content on how to tune EOS parameters accurately - Solve more unconventional problems with field examples on phase behavior characteristics of shale and heavy oil
This edition expands its scope as a conveniently arranged petroleum fluids reference book for the practicing petroleum engineer and an authoritative college text.
This book wxplains the fundamentals of reservoir engineering and their practical application in conducting a comprehensive field study.Two new chapters have been included in this second edition: chapter 14 and 15.
Phase Behavior provides the reader with the tools needed to solve problems requiring a description of phase behavior and specific pressure/volume/temperature (PVT) properties.
This book presents a one-stop reference to the empirical correlations used extensively in geotechnical engineering. Empirical correlations play a key role in geotechnical engineering designs and analysis. Laboratory and in situ testing of soils can add significant cost to a civil engineering project. By using appropriate empirical correlations, it is possible to derive many design parameters, thus limiting our reliance on these soil tests. The authors have decades of experience in geotechnical engineering, as professional engineers or researchers. The objective of this book is to present a critical evaluation of a wide range of empirical correlations reported in the literature, along with typical values of soil parameters, in the light of their experience and knowledge. This book will be a one-stop-shop for the practising professionals, geotechnical researchers and academics looking for specific correlations for estimating certain geotechnical parameters. The empirical correlations in the forms of equations and charts and typical values are collated from extensive literature review, and from the authors' database.
Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve specific engineering challenges. The reference begins with fundamentals, covering preprocessing of data, types of intelligent models, and training and optimization algorithms. The book moves on to methodically address artificial intelligence technology and applications by the upstream sector, covering exploration, drilling, reservoir and production engineering. Final sections cover current gaps and future challenges. - Teaches how to apply machine learning algorithms that work best in exploration, drilling, reservoir or production engineering - Helps readers increase their existing knowledge on intelligent data modeling, machine learning and artificial intelligence, with foundational chapters covering the preprocessing of data and training on algorithms - Provides tactics on how to cover complex projects such as shale gas, tight oils, and other types of unconventional reservoirs with more advanced model input