Modeling Borehole Radar by Finite-difference Time-domain in Conductive Sandstone

2015 ◽  
Vol 20 (1) ◽  
pp. 19-29 ◽  
Author(s):  
C. Ma ◽  
Q. Zhao ◽  
L. Wang ◽  
S. Liu
Keyword(s):  
Finite Difference ◽  
Time Domain ◽  
Finite Difference Time Domain ◽  
Borehole Radar ◽  
Difference Time

A modeling study of borehole radar for oil‐field applications

Geophysics ◽  
2002 ◽  
Vol 67 (5) ◽  
pp. 1486-1494 ◽  
Author(s):  
Yong‐Hua Chen ◽  
Michael L. Oristaglio
Keyword(s):  
Signal Processing ◽  
Finite Difference ◽  
Penetration Depth ◽  
Time Domain ◽  
Finite Difference Time Domain ◽  
Oil Field ◽  
Acoustic Methods ◽  
Borehole Radar ◽  
Radar Waveforms ◽  
Difference Time

This paper examines the suitability of borehole radar for near‐wellbore imaging. The maximum imaging range is primarily determined by the conductivity of the formation in which the borehole lies and the reflectivity of the targets. Under similar medium contrast, formation interfaces result in much stronger reflections than fractures. Complex horizontal borehole geometries are modeled with a 3‐D finite‐difference time‐domain (FDTD) code. Borehole effects, which are often almost insurmountable for acoustic methods, are very small for radar. As a result, the reflections in general are visually identifiable on the synthetic radar waveforms even before any signal processing. Therefore, borehole radar is a promising approach to map structures in the immediate vicinity of the borehole for a penetration depth of at least a few meters in relatively less‐conductive reservoirs (e.g., <0.03 S/m). As such, it complements borehole acoustic methods and has potential for geosteering applications.

Rigorous time-domain analysis of statistically oriented graphene sheet fluctuations

2017 ◽  
Vol 36 (5) ◽  
pp. 1351-1363 ◽  
Author(s):  
Athanasios N. Papadimopoulos ◽  
Stamatios A. Amanatiadis ◽  
Nikolaos V. Kantartzis ◽  
Theodoros T. Zygiridis ◽  
Theodoros D. Tsiboukis
Keyword(s):  
Monte Carlo ◽  
Standard Deviation ◽  
Finite Difference ◽  
Surface Waves ◽  
Time Domain ◽  
Finite Difference Time Domain ◽  
Mean Value ◽  
Content Type ◽  
The Mean ◽  
Difference Time

Purpose Important statistical variations are likely to appear in the propagation of surface plasmon polariton waves atop the surface of graphene sheets, degrading the expected performance of real-life THz applications. This paper aims to introduce an efficient numerical algorithm that is able to accurately and rapidly predict the influence of material-based uncertainties for diverse graphene configurations. Design/methodology/approach Initially, the surface conductivity of graphene is described at the far infrared spectrum and the uncertainties of its main parameters, namely, the chemical potential and the relaxation time, on the propagation properties of the surface waves are investigated, unveiling a considerable impact. Furthermore, the demanding two-dimensional material is numerically modeled as a surface boundary through a frequency-dependent finite-difference time-domain scheme, while a robust stochastic realization is accordingly developed. Findings The mean value and standard deviation of the propagating surface waves are extracted through a single-pass simulation in contrast to the laborious Monte Carlo technique, proving the accomplished high efficiency. Moreover, numerical results, including graphene’s surface current density and electric field distribution, indicate the notable precision, stability and convergence of the new graphene-based stochastic time-domain method in terms of the mean value and the order of magnitude of the standard deviation. Originality/value The combined uncertainties of the main parameters in graphene layers are modeled through a high-performance stochastic numerical algorithm, based on the finite-difference time-domain method. The significant accuracy of the numerical results, compared to the cumbersome Monte Carlo analysis, renders the featured technique a flexible computational tool that is able to enhance the design of graphene THz devices due to the uncertainty prediction.

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