Diffraction imaging for fracture detection: synthetic case study

Klokov, Alexander; Baina, R.; Landa, Evgeny; Thore, P.; Tarrass, I.

doi:10.1190/1.3513545

Cited by 25 publications

(6 citation statements)

References 5 publications

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“…The differences between reflected waves and diffracted waves in kinematics are substantial in the common-shot gathers, which allows us to extract the diffraction from the shot records (Landa et al, 1987). Dip filtering (Bansal and Imhof, 2005), focusing and defocusing (Khaidukov et al, 2004) hybrid Radon transform (Klokov et al, 2010), and PWD filtering (Fomel, 2002;Taner et al, 2006) have been developed for separating diffraction. These methods utilized the fact that the travel time curve of the diffracted wave is quasi-parabolic in the common-shot gathers while the reflected wave is quasi-linear.…”

Section: Introductionmentioning

confidence: 99%

Diffraction Extraction and Least-Squares Reverse Time Migration Imaging for the Fault-Karst Structure With Adaptive Sampling Strategy

et al. 2022

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The ultra-deep fault-karst structure discovered in the Tarim Basin in Western China is a fractured-vuggy carbonate reservoir with great potential for development. The diffraction generated by fractures, small-scale caves and vugs is often used for reservoir identification and seismic interpretation. Since the diffraction is much weaker than the reflection, it is difficult to separate the diffraction from the full wavefield. We use a plane-wave destruction (PWD) filter to extract the diffraction from the full data. In order to obtain a high-accuracy and amplitude-preserving imaging profile, we use the least-squares reverse time migration (LSRTM) method to image the separated diffraction. The large amount of calculation is the most challenging problem of the LSRTM algorithm. In view of this, we develop an adaptive sampling strategy to improve computing efficiency and reduce memory requirement. We use a fault model, a vugs-fractures model, and a fault-karst model to demonstrate the effectiveness and practicability of the proposed method. The numerical examples show that the proposed method can enhance the imaging resolution of the fault-karst structure and save computing cost without losing accuracy. In addition, a test on field data processing demonstrates the advantages of our algorithm.

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Section: Introductionmentioning

confidence: 99%

Diffraction Extraction and Least-Squares Reverse Time Migration Imaging for the Fault-Karst Structure With Adaptive Sampling Strategy

et al. 2022

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“…Moreover, because of rapid amplitude attenuation, diffracted wavefields recorded on the surface have much weaker energy in comparison with reflected wavefields. It is difficult to recognize weak diffracted wavefields in seismic records as they are easily masked by dominant strong reflected wavefields (Klem‐Musatov, 1994; Klokov et al ., 2010). Consequently, diffraction separation from specular reflected data are a great challenge and occupies a key position in diffraction implementation.…”

Section: Introductionmentioning

confidence: 99%

Diffraction separation by variational mode decomposition

Lin

Zhao

Cui

2021

Geophysical Prospecting

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Diffracted wavefields with superior illumination encode key geologic information about small‐scale geologic discontinuities or inhomogeneities in the subsurface and thus possess great potential for high‐resolution imaging. However, the weak diffracted wavefield is easily masked by the dominant reflected data. Diffraction separation from specular reflected data still plays an important role and plays a major role in diffraction imaging implementation. To solve this problem, a new diffraction‐separation method is proposed that uses variational mode decomposition to suppress reflected data and separate diffracted wavefields in the common‐offset or poststack domains. The variational mode decomposition algorithm targets reflected wavefield by decomposing seismic data into an ensemble of band‐limited intrinsic mode functions representing linear and strong reflected data. This method is based on the principle of energy sparsity and can utilize the kinematic and dynamic differences between reflected and diffracted wavefields to effectively predict linear reflected data. Synthetic and field data examples using complex body geometries demonstrate the effectiveness and performance of the proposed method in enhancing diffracted wavefield and attenuating reflected data as well as increasing the signal‐to‐noise ratio, which helps to clearly image small‐scale subwavelength geologic structures.

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“…Imaging and monitoring of these structures can be essential for the geological interpretation. On the other hand, the energy of diffraction is one or even two orders of magnitude weaker than the reflection (Klokov, Baina, Landa, Thore, & Tarrass, 2010), hence diffractions are significantly lost during the conventional seismic processing or masked in convention stacked sections. Many diffraction separation and imaging methods have been proposed to resolve this dilemma.…”

Section: Chapter 1: Introductionmentioning

confidence: 99%

Diffraction imaging using geometric-mean reverse time migration and common-reflection surface

Yin

Nakata

2019

GEOPHYSICS

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Diffracted waves contain a great deal of valuable information about small-scale subsurface structure such as faults, pinch-outs, karsts, and fractures, which are closely related to hydrocarbon accumulation and production. Therefore, diffraction separation and imaging with high spatial resolution play an increasingly critical role in seismic exploration. We have applied the geometric-mean reverse time migration (GmRTM) method to diffracted waves for imaging only subsurface diffractors based on the difference of the wave phenomena between diffracted and reflected waves. Numerical tests prove the advantages of this method on diffraction imaging with higher resolution as well as fewer artifacts compared to conventional RTM even when we only have a small number of receivers. Then, we developed a workflow to extract diffraction information using a fully data-driven method, called common-reflection surface (CRS), before we applied GmRTM. Application of this workflow indicates that GmRTM further improves the quality of the image by combining with the diffraction-separation technique CRS in the data domain.

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Diffraction imaging for fracture detection: synthetic case study

Cited by 25 publications

References 5 publications

Diffraction Extraction and Least-Squares Reverse Time Migration Imaging for the Fault-Karst Structure With Adaptive Sampling Strategy

Diffraction Extraction and Least-Squares Reverse Time Migration Imaging for the Fault-Karst Structure With Adaptive Sampling Strategy

Diffraction separation by variational mode decomposition

Diffraction imaging using geometric-mean reverse time migration and common-reflection surface

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