Seismic inversion with adaptive edge-preserving smoothing preconditioning on impedance model

Dai, Ronghuo; Chao, Yin; Zaman, Nueraili; Zhang, Fanchang

doi:10.1190/geo2016-0672.1

Cited by 24 publications

(7 citation statements)

References 29 publications

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“…Although DHDS gives more stable results than MHDS, it does not completely solve the problem of lateral discontinuity. Multi-trace inversion [37] and edge-preserving smoothing [38] may be good options for further improving the transversal continuity of the inversion while maintaining the resolution of the inversion results.…”

Section: Disscusionmentioning

confidence: 99%

Hybrid-Driven High-Resolution Prestack Seismic Inversion

Zhang,

Zhao,

Sun

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Pre-stack seismic inversion is considered among the most frequently utilized techniques for reservoir characterization. However, the resolution of the inverted parameters, such as P-, S-wave velocity and density, is low due to the limited band-width and side-lobe interference of the seismic wavelet. To address this issue, a hybrid two-step strategy that combines data-driven and model-driven methods is proposed to enable higher resolution and accuracy of the inverted results. We first construct a three-layer fully connected network to implement the mapping of seismic data to reflection coefficients. The method does not require exact seismic wavelet to be known and intensive human-computer interaction. It estimates reflectivity based on the extracted features from training data, which gives more accurate results compared to traditional sparse inversion methods. Then, the model-driven method (i.e., amplitude variation with offset/angle inversion method) is adopted to reconstruct P-wave velocity, S-wave velocity and density from the estimated reflection coefficients. The performance of the hybrid-driven strategy (HDS) is checked using synthetic model and real data. The results indicate that the proposed method provides more accurate and higher resolution inversion results for seismic reservoir characterization.

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Section: Disscusionmentioning

confidence: 99%

Hybrid-Driven High-Resolution Prestack Seismic Inversion

Zhang,

Zhao,

Sun

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Post-stack seismic attribute analysis is mature in oil and gas reservoir prediction. Pre-stack AVO inversion retains pre-stack information and extracts more sensitive information related to CBM, such as acoustic impedance reflection coefficients, gradients, intercept, and hydrocarbon detection factors (Dai et al, 2019). However, the prediction based on a single pre-stack AVO attribute may be influenced by complex pre-stack seismic data, leading to reduced accuracy in prediction results.…”

Section: Introductionmentioning

confidence: 99%

Coalbed Methane Content Prediction with a Seismic Multi-attribute Support Vector Regression Model

Yu,

Zhang,

Huang

2023

Preprint

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Accurate prediction of coalbed methane (CBM) content plays an essential role in CBM exploration and development. In this study, we selected eight seismic attributes with good responses to the CBM content as the input data. The support vector regression (SVR) model was employed to predict the CBM content and compared with the results of the traditional BP neural network method. The results reveal that the SVR model has higher accuracy compared to the BP neural network model and can better identify areas with high CBM content in the case of small samples. Last, we applied the seismic multi-attribute SVR model to predict the CBM content in an exploration area of the Qinshui Basin in China. The predicted high-gas-bearing areas are consistent with the field data, further verifying the effectiveness and practicality of our method for predicting CBM content.

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“…However, the traditional edge preservation (EPS) method has a fixed window size. If the window is too large, the smaller strata cannot be displayed, but if the window is too small, it is too sensitive to noise (Dai, 2019). Therefore, this study used adaptive edge preservation (Ad-EPS) filtering to represent discontinuous geological bodies.…”

Section: Introductionmentioning

confidence: 99%

Prestack AVO Inversion of Exact Zoeppritz Equation Using Adaptive Edge Preserving Filter

Cui

et al. 2023

Pure Appl. Geophys.

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Since traditional pre-stack inversion via amplitude variation with offset (AVO) uses an approximate formula, it is insufficiently accurate at large angles. To overcome this shortcoming, we used the exact Zoeppritz equation for nonlinear AVO inversion. A secondorder difference matrix is added to suppress the disturbance of the inversion in the case of noise.We used an adaptive edge preservation (Ad-EPS) filter to process the iterative model. An Ad-EPS filter can find the optimal window size for each filtered sample so that the formation boundary is sharper and more accurate in the inversion. For nonlinear optimization problems, we used the Levenberg-Marquardt method. The LM method adds a damping factor to the iterative equation to reduce the ill-posedness of the inversion and can add a secondorder-difference L2-norm-regularization term directly to the algorithm to make the inversion data closer to the model data. The LM method combines the exact Zoeppritz equation, L2 regularization constraints, and Ad-EPS filter into a nonlinear AVO inversion method called EZL2AEPS. The EZL2AEPS method is more accurate than the generally accurate Zoeppritz pre-stack inversion (EZPI) with respect to details such as formation boundaries and overall disturbances. The EZL2AEPS method can also suppress noise to a certain extent, reduce the effect of noise disturbance on the inversion, and produce blocky inversion results. Actual data show that the EZL2AEPS method achieves better inversion results and is a very advantageous AVO inversion method.

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Seismic inversion with adaptive edge-preserving smoothing preconditioning on impedance model

Cited by 24 publications

References 29 publications

Hybrid-Driven High-Resolution Prestack Seismic Inversion

Hybrid-Driven High-Resolution Prestack Seismic Inversion

Coalbed Methane Content Prediction with a Seismic Multi-attribute Support Vector Regression Model

Prestack AVO Inversion of Exact Zoeppritz Equation Using Adaptive Edge Preserving Filter

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