Kan Wu, Yongzan Liu, Ge Jin, Aishwarya Srinivasan

eBook, ePUB

Hydraulic fracture geometry characterization based on distributed fiber optic strain measurements (eBook, ePUB)

Modeling and Field Data for Unconventional and Geothermal Wells

Beschreibung

Fiber optic-based measurements are innovative tools for the oil and gas industry to utilize in monitoring wells in a variety of applications including geothermal activity. Monitoring unconventional reservoirs is still challenging due to complex subsurface conditions and current research focuses on qualitative interpretation of field data. Hydraulic Fracture Geometry Characterization from Fiber Optic-Based Strain Measurements delivers a critical reference for reservoir and completion engineers to better quantify the propagation process and evolution of fracture geometry with a forward model and novel inversion model. The reference reviews different fiber optic-based temperature, acoustic, and strain measurements for monitoring fracture behaviors and includes advantages and limitations of each measurement, giving engineers a better understanding of measurements applied in all types of subsurface formations. Stress/strain rate responses on rock deformation are given a holistic approach, including guidelines and an automatic algorithm for identification of fracture hits. Last, a novel inversion model is introduced to show how fracture geometry can be used for optimization on well placement decisions. Supported by case studies, Hydraulic Fracture Geometry Characterization from Fiber Optic-Based Strain Measurements gives today's engineers better understanding of all complex subsurface measurements through fiber optic technology. - Examine the basics of distributed fiber optic strain measurements - Conduct a detailed analysis of strain responses observed in both horizontal and vertical monitoring wells - Present a systematic approach for interpreting strain data measured in the field - Highlight the significant insights and values that can be derived from the field measured strain dataset - Support monitoring and modeling for subsurface energy extraction and safe storage

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Autor

Dr Kan Wu is an associate Professor and Class of '75 DVG Career Development Professor in Harold Vance department of petroleum engineering at Texas A&M University. Her research interests include data interpretation and forward modeling of Distributed Fiber Optic Strain Sensing, hydraulic fracture modeling, monitoring, and optimization, subsurface monitoring of Carbon storage and Enhanced Geothermal Systems, Hybrid physics and data-driven modeling, multi-scale and multi-physics modeling. Wu has authored or co-authored more than 100 technical papers, which have been cited more than 5000 times (Source: Google Scholar). Wu is a founder and director of Advanced Geomechanics Fracture & Reservoir Application Consortium (AGFRAC). This consortium is at the forefront of advancing subsurface monitoring techniques using distributed fiber optic strain sensing, aiming to optimize injection and production processes in oil and gas reservoirs, CO2 storage, and geothermal development and address critical energy and environmental challenges. Wu was honored with the Karen E. Olson '87 and Louis H. Turner Faculty Award for Excellence in Research in 2023. Additionally, in 2022, she received the Award for Best Application Paper sponsored by the International Geomechanics Symposium. She is serving as a Distinguished Lecturer for the Society of Petroleum Engineers (SPE) for the 2023-2024 term. Wu holds a Ph.D. degree in petroleum engineering from The University of Texas at Austin.Yongzan Liu is currently a research scientist at Schlumberger-Doll Research, located in Cambridge, Massachusetts, US. Before joining SLB, he was a postdoctoral research fellow in the Energy Geosciences Division at Lawrence Berkeley National Laboratory. He has been working on various research topics related to energy geomechanics, including coupled thermal-hydraulic-mechanical (THM) modeling of subsurface fractured reservoirs, hydraulic fracture monitoring and characterization, enhanced geothermal systems (EGS), system behaviors of gas hydrate accumulations, geological carbon storage, etc. He is currently interested in the integration of advanced numerical modeling and monitoring techniques to better understand the complex processes in subsurface reservoirs. He has authored/co-authored more than 30 technical papers. He holds a Ph.D. degree from Texas A&M University, an MS degree from the University of Alberta, and a BS degree from China University of Petroleum (East China), all in Petroleum Engineering.Dr. Ge Jin is an accomplished geophysicist and educator with a distinguished record of scholarship. He currently serves as an Assistant Professor of Geophysics and Co-director of the Reservoir Characterization Project at the Colorado School of Mines (Mines). With a focus on geomechanical applications, Dr. Jin also closely collaborates with the Advanced Geomechanics Fracture & Reservoir Application Consortium (AG-FRAC) at Texas A&M University in collaboration with Dr. Kan Wu. Dr. Jin has published more than 50 peer-reviewed papers in leading journals and has been granted 11 patents for his innovative research. His research interests lie in several areas, including Distributed Fiber-Optic Sensing (DFOS), machine learning, and seismic imaging and interpretation. Before his current academic job, he spent five years as a research geophysicist in the oil and gas industry, during which time he developed an in-depth understanding of the practical issues faced by the industry and filed numerous patent applications. His research in DFOS has led to several groundbreaking applications, which include reservoir distributed strain monitoring, DAS-based microseismic imaging, DFOS-based production logging, DFOS-based well integrity monitoring, borehole tube-wave analysis, and reservoir guided wave analysis. Dr. Jin obtained his Ph.D. in Geophysics from Columbia University in the City of New York, after completing his dual B.S. in Geophysics and Computer Science from Peking University.

Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 300
• Erscheinungstermin: 5. Juni 2024
• Englisch
• ISBN-13: 9780323953610
• Artikelnr.: 70914344