Seismic well tie by aligning impedance log with inverted impedance from seismic data

2020 ◽  
Vol 8 (4) ◽  
pp. T917-T925
Author(s):  
Bo Zhang ◽  
Yahua Yang ◽  
Yong Pan ◽  
Hao Wu ◽  
Danping Cao
Keyword(s):  
Seismic Data ◽  
Dynamic Time Warping ◽  
Real Data ◽  
Seismic Inversion ◽  
Synthetic Seismogram ◽  
Well Logs ◽  
Time Warping ◽  
Seismic Wavelet ◽  
Dynamic Time ◽  
Depth Relationship

The accuracy of seismic inversion is affected by the seismic wavelet and time-depth relationship generated by the process of the seismic well tie. The seismic well tie is implemented by comparing the synthetic seismogram computed from well logs and the poststack seismogram at or nearby the borehole location. However, precise waveform matching between the synthetic seismogram and the seismic trace does not guarantee an accurate tie between the elastic properties contained represented by the seismic data and well logs. We have performed the seismic well tie using the impedance log and the impedance inverted from poststack seismic data. We use an improved dynamic time warping to align the impedance log and impedance inverted from seismic data. Our workflow is similar to the current procedure of the seismic well tie except that the matching is implemented between the impedance log and the inverted impedance. The current seismic well-tie converges if there is no visible changes for the wavelets and time-depth relationship in the previous and current tying loops. Similarly, our seismic well tie converges if there are no visible changes for the wavelets, inverted impedance, and time-depth relationship in the previous and current tying loops. The real data example illustrates that more accurate inverted impedance is obtained by using the new wavelet and time-depth relationship.

A semiautomatic method to tie well logs to seismic data

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. V47-V54 ◽  
Author(s):  
Roberto H. Herrera ◽  
Mirko van der Baan
Keyword(s):  
Seismic Data ◽  
Recognition Task ◽  
Original Data ◽  
Well Logs ◽  
Time Shift ◽  
Time Warping ◽  
Analysis Window ◽  
Dynamic Time ◽  
Improve Correlation ◽  
Pattern Recognition Task

We evaluated a semiautomatic method for well-to-seismic tying to improve correlation results and reproducibility of the procedure. In the manual procedure, the interpreter first creates a synthetic trace from edited well logs, determines the most appropriate bulk time shift and polarity, and then applies a minimum amount of stretching and squeezing to best match the observed data. The last step resembles a visual pattern recognition task, which often requires some experience. We replaced the last step with a constrained dynamic time-warping technique, to help guide the interpreter. The method automatically determined the appropriate amount of local stretching and squeezing to produce the highest correlation between the original data and the created synthetic trace. The constraint ensured that stretching and squeezing were kept within reasonable bounds, as determined by the interpreter. Results compared well with the manual method, leading to ties along the entire trace length in contrast to the shorter analysis window in the conventional method. Yet, we advise against unsupervised applications because the method is intended as a guide instead of a fully automated blind approach.

Nonstretching normal-moveout correction using a dynamic time warping algorithm

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. V27-V37 ◽  
Author(s):  
Shuangquan Chen ◽  
Song Jin ◽  
Xiang-Yang Li ◽  
Wuyang Yang
Keyword(s):  
Seismic Data ◽  
Dynamic Time Warping ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Optimization Method ◽  
Thin Layers ◽  
Time Warping ◽  
Dynamic Matching ◽  
Seismic Waveforms ◽  
Dynamic Time

Normal-moveout (NMO) correction is one of the most important routines in seismic processing. NMO is usually implemented by a sample-by-sample procedure; unfortunately, such implementation not only decreases the frequency content but also distorts the amplitude of seismic waveforms resulting from the well-known stretch. The degree of stretch increases with increasing offset. To minimize severe stretch associated with far offset, we use a dynamic time warping (DTW) algorithm to achieve an automatic dynamic matching NMO nonstretch correction, which does not handle crossing events and convoluted events such as thin layers. Our algorithm minimizes the stretch through an automatic static temporal correction of seismic wavelets. The local static time shifts are obtained using a DTW algorithm, which is a nonlinear optimization method. To mitigate the influence of noise, we evaluated a multitrace window strategy to improve the signal-to-noise ratio of seismic data by obtaining a more precise moveout correction at far-offset traces. To illustrate the effectiveness of our algorithm, we first applied our method to synthetic data and then to field seismic data. Both tests illustrate that our algorithm minimizes the stretch associated with far offsets, and the method preserves the amplitude fidelity.

Application of Dynamic Time Warping in Weighted Stacking of Seismic Data

2021 ◽  
pp. 1-5
Author(s):  
Chengyun Song ◽  
Lingxuan Li ◽  
Yaojun Wang ◽  
Kunhong Li ◽  
Jiying Tuo
Keyword(s):  
Seismic Data ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Dynamic Time

A depth variant seismic wavelet extraction method for inversion of poststack depth-domain seismic data

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. R569-R579 ◽  
Author(s):  
Rui Zhang ◽  
Zhiwen Deng
Keyword(s):  
Seismic Data ◽  
Extraction Method ◽  
Reservoir Characterization ◽  
Real Data ◽  
Velocity Model ◽  
Seismic Inversion ◽  
Data Depth ◽  
Spectral Variation ◽  
Seismic Wavelet ◽  
The Time Domain

Prestack depth seismic imaging is increasingly being used in industry, which has also led to an increasing need for its inversion results, such as acoustic impedance (AI), for reservoir characterization. Conventional seismic inversion methods for reservoir characterization are usually implemented in the time domain. A depth-time conversion would be required before inversion of depth-domain seismic data, which would depend on an accurate velocity model and a fine time-depth conversion algorithm. Thus, it could be beneficial that we can directly invert the depth migrated seismic data. Depth-domain seismic data could indicate a strong nonstationarity, such as spectral variation, which makes it difficult to use a constant wavelet for direct inversion in depth. To address this issue, we have developed a new wavelet extraction method by using a depth-wavenumber decomposition technique, which can generate depth variant wavelets to accommodate the nonstationarity of the depth-domain seismic data. The synthetic and real data applications have been used to test the effectiveness of our method. The directly inverted depth-domain AI indicates a good correlation with well-log data and a strong potential for reservoir characterization.

Dynamic warping of seismic images

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. S105-S115 ◽  
Author(s):  
Dave Hale
Keyword(s):  
Seismic Data ◽  
Dynamic Time Warping ◽  
Image Warping ◽  
Seismic Data Processing ◽  
Time Warping ◽  
Relative Time ◽  
Dynamic Time ◽  
Alignment Errors ◽  
Seismic Reflectors ◽  
Seismic Images

The problem of estimating relative time (or depth) shifts between two seismic images is ubiquitous in seismic data processing. This problem is especially difficult where shifts are large and vary rapidly with time and space, and where images are contaminated with noise or for other reasons are not shifted versions of one another. A new solution to this problem requires only simple extensions of a classic dynamic time warping algorithm for speech recognition. A key component of that classic algorithm is a nonlinear accumulation of alignment errors. By applying the same nonlinear accumulator repeatedly in all directions along all sampled axes of a multidimensional image, I obtain a new and effective method for dynamic image warping (DIW). In tests where known shifts vary rapidly, this new method is more accurate than methods based on crosscorrelations of windowed images. DIW also aligns seismic reflectors well in examples where shifts are unknown, for images with differences not limited to time shifts.

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