Hydraulic Fracture Geometry Characterization Based on Distributed Fiber Optic Strain Measurements - Wu, Kan (Assistant Professor, Department of Petroleum Engineering an; Liu, Yongzan (Texas A&M University, USA); Jin, Ge (Assistant Professor of Geophysics and co-PI of Reservoir Ch
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Distributed fiber optic strain measurements, revolutionizing subsurface monitoring and hydraulic fracture characterization, offers a resource for geophysicists, reservoir engineers, and completion engineers. This cutting-edge technology leads the way in innovation with its ability to detect rock deformation and changes in strain along optical fibers, providing exceptional spatial resolution and measurement sensitivity. Its applications are broad and impactful, ranging from monitoring subsurface carbon storage and enhancing geothermal systems to advancing unconventional reservoir development.…mehr

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Produktbeschreibung
Distributed fiber optic strain measurements, revolutionizing subsurface monitoring and hydraulic fracture characterization, offers a resource for geophysicists, reservoir engineers, and completion engineers. This cutting-edge technology leads the way in innovation with its ability to detect rock deformation and changes in strain along optical fibers, providing exceptional spatial resolution and measurement sensitivity. Its applications are broad and impactful, ranging from monitoring subsurface carbon storage and enhancing geothermal systems to advancing unconventional reservoir development. Despite the technology's advancements, accurately interpreting data of strain measurement from the hydraulic fracturing process poses a significant challenge due to the complex conditions in the subsurface. This book presents a comprehensive approach for analyzing strain responses from both horizontal and vertical monitoring wells to quantify hydraulic fracture propagation and the evolution of fracture geometry. The development of a forward geomechanics model significantly enhance the understanding of the field data. The introduction of a groundbreaking inversion model allows for in-depth data analysis and maximizes the dataset's value. Moreover, this book applies its findings through two field studies in unconventional reservoirs, illustrating the practical application of the technology. These case studies highlight effective field data interpretation and the critical insights that can be obtained. This book aims to elucidate data interpretation and analysis of complex subsurface measurements related to hydraulic fracture propagation, providing engineers with a novel perspective on subsurface exploration.
Autorenporträt
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.