Density stability estimation method from pre-stack seismic data

2021 ◽  
pp. 109373
Author(s):  
Zhaoyun Zong ◽  
Qianhao Sun
Keyword(s):  
Seismic Data ◽  
Estimation Method ◽  
Stability Estimation

Local SNR estimation method for seismic data

2016 ◽  
Author(s):  
Bingxiu Li ◽  
Dian Wang ◽  
Yang Liu ◽  
Cai Liu
Keyword(s):  
Seismic Data ◽  
Estimation Method ◽  
Snr Estimation

scholarly journals STABILITY ESTIMATION METHOD FOR ARMOR UNITS FOR BREAKWATERS WITH PARAPETS AGAINST TSUNAMI OVERFLOW

2018 ◽  
pp. 26
Author(s):  
Jun Mitsui ◽  
Shin-ichi Kubota ◽  
Akira Matsumoto
Keyword(s):  
Slope Angle ◽  
Estimation Method ◽  
Hydraulic Model ◽  
Water Jet ◽  
Rear Side ◽  
Stability Estimation ◽  
Model Experiments ◽  
Wide Range ◽  
Rubble Mound ◽  
Caisson Breakwater

This paper presents a stability estimation method for armor units to cover a rubble mound on the rear side of a caisson breakwater against tsunami overflow while taking into account the influence of the shape of the superstructures of the caisson. In this method, the required mass of the armor units is obtained from the impinging velocity onto the rear side mound. This is calculated by using the overflow depth. Accordingly, the influence of the shape of the superstructure is taken into account directly. This method also takes the influence of the slope angle into account properly by using the formula by Isbash. In addition, the influence of the impingement position of the overflow nappe and the influence of the thickness of the water jet are considered. The validity of this method is confirmed by comparing with the results of hydraulic model experiments conducted in a wide range of conditions.

A Stability Estimation Method

1997 ◽  
Vol 25 (2) ◽  
pp. 163-168
Author(s):  
T. Lidansky ◽  
N. Naidenova ◽  
I. Vassileva ◽  
R. Vassilevska-Ivanova
Keyword(s):  
Estimation Method ◽  
Stability Estimation

Estimation of the depth-domain seismic wavelet based on velocity substitution and a generalized seismic wavelet model

Geophysics ◽  
2021 ◽  
pp. 1-50
Author(s):  
Jie Zhang ◽  
Xuehua Chen ◽  
Wei Jiang ◽  
Yunfei Liu ◽  
He Xu
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Regularization Parameter ◽  
Estimation Method ◽  
Model Parameters ◽  
Wavelet Estimation ◽  
Seismic Wavelet ◽  
Hydrocarbon Reservoir ◽  
Convolution Model

Depth-domain seismic wavelet estimation is the essential foundation for depth-imaged data inversion, which is increasingly used for hydrocarbon reservoir characterization in geophysical prospecting. The seismic wavelet in the depth domain stretches with the medium velocity increase and compresses with the medium velocity decrease. The commonly used convolution model cannot be directly used to estimate depth-domain seismic wavelets due to velocity-dependent wavelet variations. We develop a separate parameter estimation method for estimating depth-domain seismic wavelets from poststack depth-domain seismic and well log data. This method is based on the velocity substitution and depth-domain generalized seismic wavelet model defined by the fractional derivative and reference wavenumber. Velocity substitution allows wavelet estimation with the convolution model in the constant-velocity depth domain. The depth-domain generalized seismic wavelet model allows for a simple workflow that estimates the depth-domain wavelet by estimating two wavelet model parameters. Additionally, this simple workflow does not need to perform searches for the optimal regularization parameter and wavelet length, which are time-consuming in least-squares-based methods. The limited numerical search ranges of the two wavelet model parameters can easily be calculated using the constant phase and peak wavenumber of the depth-domain seismic data. Our method is verified using synthetic and real seismic data and further compared with least-squares-based methods. The results indicate that the proposed method is effective and stable even for data with a low S/N.

Using seismic inversion and net pay to calibrate Eagle Ford Shale producible resources

2015 ◽  
Vol 3 (3) ◽  
pp. SV69-SV78
Author(s):  
Bo Chen ◽  
Dhananjay Kumar ◽  
Anthony Uerling ◽  
Sheryl Land ◽  
Omar Aguirre ◽  
...  
Keyword(s):  
Seismic Data ◽  
Strong Correlation ◽  
Estimation Method ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Production Volume ◽  
Well Log ◽  
Engineering Analysis ◽  
Eagle Ford Shale ◽  
Eagle Ford

We found a strong correlation between the estimated production volume and hydrocarbon resources in thicker and more porous intervals in the Eagle Ford Shale through integrated petrophysical and engineering analysis. The wells analyzed were selected with similar operational designs so that the rock properties were the main variables impacting the production volume. Seismic data were used to characterize such desired rock properties, including thickness and porosity, to evaluate the producing potentials across the field. Seismic interpretation provided the top and base of the Eagle Ford reservoir, and hence, its thickness. Seismic inversion calibrated the acoustic impedance. Also, the seismic net pay estimation method predicted the thickness of the more porous intervals. The calculated seismic net pay agreed with the well log data. As petrophysical analysis suggested, the seismic net pay also formed a strong correlation with the production volume and has been used to predict the producible resources for new wells, identify refract candidates, and evaluate completion trial methods in the Eagle Ford Shale.

Effective frequency estimation method for sinusoidal interference in seismic data

Geophysics ◽  
2021 ◽  
pp. 1-80
Author(s):  
Yijun Yuan ◽  
Shichang Zhou ◽  
Yun Wang ◽  
Jianjun Gao
Keyword(s):  
Seismic Data ◽  
Cross Correlation ◽  
Frequency Estimation ◽  
Amplitude Spectrum ◽  
Estimation Method ◽  
Real Data ◽  
Estimation Algorithm ◽  
Search Technique ◽  
Spectrum Generation ◽  
Frequency Search

The removal of sinusoidal interference is an important step in seismic data processing, especially for data with low signal-to-noise ratios. The intermittent character of sinusoidal interference makes it challenging to identify and attenuate. To address this issue, we propose a method to accurately identify sinusoidal interference and rapidly estimate its frequencies. A spectrum-generation strategy is presented to generate an amplitude spectrum with noticeable sinusoidal interference. An initial estimate of the affected frequencies is found using a frequency-search technique based on the amplitude spectrum. The estimate is then refined by an iterative frequency estimation algorithm, which includes fast frequency estimation and normalized cross-correlation calculation. After modeling the noise using the precise frequency estimation, the sinusoidal interference in seismic data can then be suppressed by adaptively subtracting the estimated noise from the raw seismic data. The effectiveness of the proposed method in identifying sinusoidal interference is verified by testing it on synthetic and field data and by comparing the results with those from existing methods. Synthetic and real data examples indicate that the method is most applicable to land seismic data.

Nonstatinary deconvolutive Radon transform

Geophysics ◽  
2021 ◽  
pp. 1-35
Author(s):  
Hojjat Haghshenas Lari ◽  
Ali Gholami
Keyword(s):  
Radon Transform ◽  
Seismic Data ◽  
Estimation Method ◽  
Original Data ◽  
Seismic Events ◽  
Seismic Data Processing ◽  
Convolution Operation ◽  
Inverse Transform ◽  
Time Space ◽  
Overlap Method

Different versions of the Radon transform (RT) are widely used in seismic data processing tofocus the recorded seismic events. Multiple separation, data interpolation, and noise attenuationare some of RT applications in seismic processing work-flows. Unfortunately, the conventional RTmethods cannot focus the events perfectly in the RT domain. This problem arises due to theblurring effects of the source wavelet and the nonstationary nature of the seismic data. Sometimes,the distortion results in a big difference between the original data and its inverse transform. Wepropose a nonstationary deconvolutive RT to handle these two issues. Our proposed algorithm takesadvantage of a nonstationary convolution technique. that builds on the concept of block convolutionand the overlap method, where the convolution operation is defined separately for overlapping blocks.Therefore, it allows the Radon basis function to take arbitrary shapes in time and space directions. Inaddition, we introduce a nonstationary wavelet estimation method to determine time-space-varyingwavelets. The wavelets and the Radon panel are estimated simultaneously and in an alternative way.Numerical examples demonstrate that our nonstationary deconvolutive RT method can significantlyimprove the sparsity of Radon panels. Hence, the inverse RT does not suffer from the distortioncaused by the unfocused seismic events.

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