Pvt Property Correlations: Selection and Estimation

ISBN-13: 9780128125724 / Angielski / Miękka / 2018 / 432 str.

Ahmed Alzahabi; Ahmed El-Banbi; Ahmad Al-Maraghi

Opis

PVT Property Correlation: Selection and Estimation helps engineers target their correlations based on an expert system. This reference presents several techniques that will allow engineers to select the best PVT correlation, especially when encountering limited reservoir fluid data and conditions. Tested against a large database, these techniques cover different types of fluids, including recent techniques using neural networks. Complimentary software that checks the input reservoir parameters for selecting the most appropriate correlation and a PVT calculator to predict oil properties over a wide range of input data is included. In reservoir engineering, accurate determination of PVT properties is one of the most critical decisions in reservoir performance and forecasting. However, finding the right correlation is very time consuming and expensive, and today's engineers lack the proper tools for correlation assessment. This book demonstrates the importance of PVT data and correlations so that today's reservoir engineers can produce and model stronger and more reliant assets.

• Covers the importance of selecting the most appropriate values for PVT data and correlations without PVT experimental results
• Provides a a comprehensive list of all applicable ranges of PVT correlations, including gas condensate and volatile oil
• Teaches users how to apply practicality with an entire chapter devoted to a variety of reservoir problems and calculation examples

Spis treści

1. Introduction2. Reservoir-Fluid Classification3. Dry Gases4. Wet Gases5. Gas Condensates6. Volatile Oil7. Black Oils8. Low Gas-Oil Ratio Oils9. Selection of PVT Correlations10. Artificial Neural Network Models for PVT Properties

AppendixA: Oil Correlations FormulaB: Gas Correlations FormulaC: Oil Correlations Range of ApplicabilityD: Gas Correlations Range of ApplicabilityE: Artificial Neural Network (ANN) Models Rnage of ApplicabilityF: Worksheets for Oil PVT Correlations Selection

O autorze

Dr. Ahmed El-Banbi is currently a professor of Petroleum Engineering and chair of the department at the American University in Cairo (AUC). He has 25 years of diversified international experience in reservoir and petroleum engineering. He worked as an engineer, trainer, and a technology developer. Ahmed spent 12 years with Schlumberger where he held a variety of technical and managerial positions in 5 countries. He has considerable experience in managing multi-disciplinary teams and performing integrated reservoir studies. Previously, he had shorter assignments with a major oil company and a consulting company in addition to academic research and teaching experience. He authored and co-authored more than eighty technical papers, two book chapters, and holds one US patent. He has been on numerous SPE committees, program chair for the North Africa Technical Conference and Exhibition, and technical reviewer for the SPE Reservoir Engineering and Evaluation Journal and other journals. Ahmed holds BS and MS degrees from Cairo University, and an MS and PhD degrees from Texas A&M University; all in petroleum engineering. Dr. Ahmed Alzahabi is currently an Assistant Professor at the University of Texas of the Permian Basin. He earned a PhD and a MS, both in petroleum engineering from Texas Tech University and an MS from Cairo University. He previously served as a researcher at the Energy Industry Partnerships, working in the field of energy to solve complex problems for the industry. He is experienced in introducing new technologies in well-placement and fracturing in conventional and unconventional oil and gas reservoirs. His research involves developing techniques for Permian Wolfcamp exploitation. He has participated in six US patent applications, edited and reviewed for multiple journals, and is active in SPWLA, SPE, NAGPS, SEG, and AAPG. He has contributed a book chapter and is writing a book on Fracturing Horizontal wells. Mr. Ahmed El-Maraghi is a senior petroleum engineer with Qarun Petroleum Company. He has ten years experience in reservoir and production engineering. He has extensive experience in well test analysis and has performed several reservoir studies. Ahmed also worked as a trainer and software developer. He is an avid user and developer of artificial intelligence tools in petroleum engineering. Ahmed holds a BS from Suez Canal University, MS from Cairo University and he is currently a PhD candidate in Cairo University researching in neural networks applications in log interpretation. Ahmed authored and coauthored six papers.