Application Research of Artificial Neural Networks in Well Logging: Seismic Data Joint Inversion

Author: Liu Zhengping
Publisher:
Publish Date: 2003-04-01
Features: This book can serve as a reference book or textbook for senior undergraduate and graduate students in applied geophysics and petroleum and natural gas geological exploration in higher education institutions. It can also be used as a reference for scientific research and technical personnel in petroleum and natural gas exploration and development, artificial intelligence, information processing, and applied research. The book introduces the significance and importance of multi-source data joint inversion in geophysical exploration, as well as the artificial neural network concepts and basic knowledge required to read this book. It elaborates on the theory, methods, and examples of artificial neural networks in the application of well and seismic data processing. It presents two well-seismic data joint inversion methods based on neural network algorithms: 1) The theoretical foundation, implementation strategy, and multiple application examples of an artificial neural network algorithm for multi-well multi-parameter nonlinear inversion where seismic data is the input and well logging parameters are the output; 2) Iterative inversion theory and algorithms that align with traditional iterative inversion strategies but use neural networks to simulate forward operators, along with application examples. It describes the neural network algorithm principles and application examples for estimating seismic wavelets and general linear system functions under well data constraints based on classical linear system response theory.