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The AAPG/Datapages Combined Publications Database
AAPG Bulletin
Abstract
AAPG Bulletin, V.
Imaging 3-D 
fracture
networks around boreholes
fracture networks around boreholesHaiqing Wu,1 David D. Pollard2
1Chevron Petroleum Technology Co., San Ramon, California, 94583; email: [email protected]
2Department of Geological and Environmental Sciences, Stanford University, Stanford, California, 94305; email: [email protected]
AUTHORS
Haiqing Wu received his B.S. and M.S. degrees from Peking University, China, and a Ph.D. from Stanford University. He is a senior research scientist at Chevron Petroleum Technology Company. His research interests focus on quantitative structural analysis including subseismic fault prediction and modeling, subsurface fracture characterization, automated image interpretation and visualization, reservoir in-situ stress analysis, and experimental simulation of jointing.
David D. Pollard received a B.A. degree from Pomona College, a Ph.D. from Stanford University, and a D.I.C. from Imperial College, all in geology. He is a professor in the Department of Geological and Environmental Sciences at Stanford and is codirector of the Rock Fracture Project, an industrial affiliates program. His research interests focus on understanding rock fracturing and faulting with applications to fluid flow in heterogeneous reservoirs using outcrop and subsurface data, laboratory experiments, and numerical modeling.
ACKNOWLEDGMENTS
This work was supported by the Stanford Rock Fracture Project, Stanford University. We would like to thank Scott Johnson and Dale Julander of Chevron, Laird Thompson of Mobil, Bill Belfield of ARCO, and Paul Hsieh of the U.S. Geological Survey for providing image log data, and Michael J. Heymans, two AAPG anonymous reviewers, Emanuel J. Willemse, Wayne Narr, and Douglas Goff for reviewing the whole or parts of this article.
ABSTRACT
A new method has been developed for building and visualizing three-dimensional (3-D) subsurface natural fracture networks in rocks surrounding boreholes using image logs, such as CAST, EMI, FMI, FMS, ROSI, and others. The method correlates fracture patterns with different stages of fracture network development for individual sets of fractures and extrapolates fracture density and connectivity from one dimension (1-D) in boreholes to 3-D in the surrounding rocks. The application of this work is 3-D visualizations of fracture distributions in volumes close to the boreholes for well planning, reservoir-scale fracture model building, reservoir flow simulation, and hydraulic fracture
control.
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