- 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
2. Reservoir-Fluid Classification
3. Dry Gases
4. Wet Gases
5. Gas Condensates
6. Volatile Oil
7. Black Oils
8. Low Gas-Oil Ratio Oils
9. Selection of PVT Correlations
10. Artificial Neural Network Models for PVT Properties
Appendix A: Oil Correlations Formula
Appendix B: Gas Correlations Formula
Appendix C: Oil Correlations Range of Applicability
Appendix D: Gas Correlations Range of Applicability
Appendix E: Artificial Neural Network (ANN) Models Rnage of Applicability
Appendix F: Worksheets for Oil PVT Correlations Selection
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.