Coherence methods in mapping AVO anomalies and predicting P-wave and S-wave impedances

Filters for migrated offset substacks are designed by partial coherence analysis to predict ‘normal’ amplitude variation with offset (AVO) in an anomaly free area. The same prediction filters generate localized prediction errors when applied in an AVO-anomalous interval. These prediction errors are quantitatively related to the AVO gradient anomalies in a background that is related to the minimum AVO anomaly detectable from the data. The prediction-error section is thus used to define a reliability threshold for the identification of AVO anomalies. Coherence analysis also enables quality control of AVO analysis and inversion. For example, predictions that are non-localized and/or do not show structural conformity may indicate spatial variations in amplitude–offset scaling, seismic wavelet or signal-to-noise (S/N) ratio content. Scaling and waveform variations can be identified from inspection of the prediction filters and their frequency responses. S/N ratios can be estimated via multiple coherence analysis. AVO inversion of seismic data is unstable if not constrained. However, the use of a constraint on the estimated parameters has the undesirable effect of introducing biases into the inverted results: an additional bias-correction step is then needed to retrieve unbiased results. An alternative form of AVO inversion that avoids additional corrections is proposed. This inversion is also fast as it inverts only AVO anomalies. A spectral coherence matching technique is employed to transform a zero-offset extrapolation or near-offset substack into P-wave impedance. The same technique is applied to the prediction-error section obtained by means of partial coherence, in order to estimate S-wave velocity to P-wave velocity (VS/VP) ratios. Both techniques assume that accurate well ties, reliable density measurements and P-wave and S-wave velocity logs are available, and that impedance contrasts are not too strong. A full Zoeppritz inversion is required when impedance contrasts that are too high are encountered. An added assumption is made for the inversion to the VS/VP ratio, i.e. the Gassmann fluid-substitution theory is valid within the reservoir area. One synthetic example and one real North Sea in-line survey illustrate the application of the two coherence methods

Advance Wave Modeling and Diffractions for High-Resolution Subsurface Seismic Imaging

Seismic modeling and Imaging for the small-scale feature in a complex subsurface geology such as salt deposit, fracture reservoir, and Carbonate is not casual because of propagated wave affected by many objects once it hits the geologic structure in the subsurface. The principal goal of newly developed seismic modeling & imaging is to get a subsurface image of structural features with greatest sharpness or resolution. Using model dataset the Sigsbee and Marmousi, we illustrate the accuracy of conventional and advance wave modeling techniques. However, in conventional a Finite difference (FD) algorithm is used to generate the data and in advanced wave modeling, the low-rank (LR) approximation is used to acquire zero-offset configuration data. A field dataset from Malaysian basin is re-processed and imaged using diffraction imaging which shows an enhancement in structural interpretation. Furthermore, the results gained from the proposed modeling and imaging approach significantly enhance the bandwidth of the imaged data. Finally, a frequency spectrum shows a recovery of low-frequency from 0 to 60 Hz which is an optimal resolution of seismic imaging

Simple Processing Sequence to VSP-Seismic data matching in Sindbad oil field, south of Iraq

The purpose of this research is to get the batter matching between VSP and seismic data after preforming a simple processing sequence on Zero-offset VSP survey. Sindbad oil field is chosen to study goals and it\u27s containing only one well with VSP survey (Snd2) that covering depth from Zubair to Sulaiy Formations and 2D seismic lines of Basrah Survey (2Br & 5Br). In order to get the mentioned information from VSP the main steps of processing sequence (Velocity calculation, Amplitude recovery, wave separation, deconvolution and stacking) has been used to measure (RMS and AVG) velocity and make the corridor stack image of VSP p-waves in Promax landmark software. The principle of VSP and seismic data matching is depended on phase and time shift. The matching filter in Omega software depends on frequency content, phase, locations of the two data and amplitude difference which gives us batter correlation for matching. The final test of these filters shows good matching between Snd-2 VSP and 2Br2 Seismic line

Design &implementation of complex-valued FIR digital filters with application to migration of seismic data

One-dimensional (I-D) and two-dimensional (2-D) frequency-space seismic migration FIR digital filter coefficients are of complex values when such filters require special space domain as well as wavenumber domain characteristics. In this thesis, such FIR digital filters are designed using Vector Space Projection Methods (VSPMs), which can satisfy the desired predefined filters' properties, for 2-D and three-dimensional (3-D) seismic data sets, respectively. More precisely, the pure and the relaxed projection algorithms, which are part of the VSPM theory, are derived. Simulation results show that the relaxed version of the pure algorithm can introduce significant savings in terms of the number of iterations required. Also, due to some undesirable background artifacts on migrated sections, a modified version of the pure algorithm was used to eliminate such effects. This modification has also led to a significant reduction in the number of computations when compared to both the pure and relaxed algorithms. We further propose a generalization of the l-D (real/complex-valued) pure algorithm to multi-dimensional (m-D) complex-valued FIR digital filters, where the resulting frequency responses possess an approximate equiripple nature. Superior designs are obtained when compared with other previously reported methods. In addition, we also propose a new scheme for implementing the predesigned 2-D migration FIR filters. This realization is based on Singular Value Decomposition (SVD). Unlike the existing realization methods which are used for this geophysical application, this cheap realization via SVD, compared with the true 2-D convolution, results in satisfactory wavenumber responses. Finally, an application to seismic migration of 2-D and 3-D synthetic sections is shown to confirm our theoretical conclusions. The proposed resulting migration FIR filters are applied also to the challenging SEGIEAGE Salt model data. The migrated section (image) outperformed images obtained using other FIR filters and with other standard migration techniques where difficult structures contained in such a challenging model are imaged clearly

A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal

Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured "noises". As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused by wave-field bouncing between layers. Since only approximate models of these phenomena are available, we propose a flexible framework for time-varying adaptive filtering of seismic signals, using sparse representations, based on inaccurate templates. We recast the joint estimation of adaptive filters and primaries in a new convex variational formulation. This approach allows us to incorporate plausible knowledge about noise statistics, data sparsity and slow filter variation in parsimony-promoting wavelet frames. The designed primal-dual algorithm solves a constrained minimization problem that alleviates standard regularization issues in finding hyperparameters. The approach demonstrates significantly good performance in low signal-to-noise ratio conditions, both for simulated and real field seismic data