(SpringerBriefs in Petroleum Geoscience & Engineering) 1st ed. 2021 Edition
by Qiuyang Shen (Author), Jiefu Chen (Contributor), Xuqing Wu (Contributor), Yueqin Huang (Contributor), Zhu Han (Contributor)
This book presents a comprehensive introduction to well logging and
the inverse problem. It explores challenges such as conventional data
processing methods’ inability to handle local minima issues, and
presents the explanations in an easy-to-follow way.
The book
describes statistical data interpretation by introducing the
fundamentals behind the approach, as well as a range of sampling
methods. In each chapter, a specific method is comprehensively
introduced, together with representative examples.
The book begins with basic information on well logging and logging while drilling, as well as a definition of the inverse problem. It then moves on to discuss the fundamentals of statistical inverse methods, Bayesian inference, and a new sampling method that can be used to supplement it, the hybrid Monte Carlo method. The book then addresses a specific problem in the inversion of downhole logging data, and the interpretation of earth model complexity, before concluding with a meta-technique called the tempering method, which serves as a supplement to statistical sampling methods.
Given its scope, the
book offers a valuable reference guide for drilling engineers, well
logging tool physicists, and geoscientists, as well as students in the
areas of petroleum engineering and electrical engineering.
