A Fast Inversion of Induction Logging Data in Anisotropic Formation Based on Deep Learning

2020 ◽  
Vol 17 (12) ◽  
pp. 2050-2054 ◽  
Author(s):  
Gaoyang Zhu ◽  
Muzhi Gao ◽  
Fanmin Kong ◽  
Kang Li
Keyword(s):  
Deep Learning ◽  
Induction Logging ◽  
Anisotropic Formation ◽  
Fast Inversion

A weak-anisotropy approximation to multicomponent induction responses in cross-bedded formations

Geophysics ◽  
2006 ◽  
Vol 71 (4) ◽  
pp. F61-F66 ◽  
Author(s):  
Tsili Wang
Keyword(s):  
Apparent Resistivity ◽  
Transversely Isotropic ◽  
Anisotropy Ratio ◽  
Weak Anisotropy ◽  
Bedding Planes ◽  
Geometric Average ◽  
Induction Logging ◽  
Anisotropic Formation ◽  
The Cross

The multicomponent induction logging response to a cross-bedded formation has been modeled under a weak-anisotropy approximation. With the approximation, a cross-bedded formation can be modeled as a transversely isotropic (TI) medium. The validity of the approximation has been tested for the main (coplanar and coaxial) components of the induction response. The conditions for the weak-anisotropy approximation to be valid depend on the component of the response. For the coplanar components, the approximation is valid for an anisotropy ratio up to 2 if the relative dipping angle between the cross-bedded formation and the borehole axis is below [Formula: see text]. For the coaxial component, the approximation reduces to a previously established result that the apparent resistivity for such a component is the geometric average of the resistivities, parallel and perpendicular to the bedding planes of an anisotropic formation, respectively, if the borehole is ignored. Hence, the approximation holds for the coaxial component regardless of the anisotropy ratio.

scholarly journals Application of OpenMP to Wireline Triaxial Induction Logging in 1D Layered Anisotropic Medium

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Zhijuan Zhang ◽  
Ning Yuan ◽  
Richard Liu
Keyword(s):  
Real Time ◽  
High Performance ◽  
Data Interpretation ◽  
Forward Modeling ◽  
Computer Hardware ◽  
Time Inversion ◽  
Data Inversion ◽  
Induction Logging ◽  
Anisotropic Formation ◽  
Logging Tool

Efficient and accurate forward modeling of logging tool responses is essential for data inversion in the log data interpretation in both real time and postprocessing. With the aggressive advancement of various high-performance computing techniques and computer hardware technology, it is possible to significantly improve the efficiency of the forward modeling. In this paper, we apply OpenMP to parallelize the simulation of triaxial induction logging tools in 1D multilayered anisotropic formation. The parallel process is explained in detail and numerical examples are presented to demonstrate the effect of the parallel programming. Comparison of the original code and the parallel code shows that the latter is much faster without loss of accuracy, which is very promising for future real-time inversion.

Modeling and understanding the triaxial induction logging in borehole environment with dip anisotropic formation

2008 ◽  
Author(s):  
Hanming Wang ◽  
Peter Wu ◽  
Richard Rosthal ◽  
Gerry Minerbo ◽  
Tom Barber
Keyword(s):  
Induction Logging ◽  
Anisotropic Formation

scholarly journals 1-D INVERSION OF TRIAXIAL INDUCTION LOGGING IN LAYERED ANISOTROPIC FORMATION

2012 ◽  
Vol 44 ◽  
pp. 383-403 ◽  
Author(s):  
Zhijuan Zhang ◽  
Ning Yuan ◽  
Ce Richard Liu
Keyword(s):  
Induction Logging ◽  
Anisotropic Formation

Sensitivity study and inversion of the fully‐triaxial induction logging in cross‐bedded anisotropic formation

2008 ◽  
Author(s):  
Hanming Wang ◽  
Sofia Davydycheva ◽  
Jinjuan Zhou ◽  
Mark Frey ◽  
Tom Barber ◽  
...  
Keyword(s):  
Sensitivity Study ◽  
Induction Logging ◽  
Anisotropic Formation ◽  
And Inversion
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