A method to compensate for migration stretch to improve the resolution of amplitude variation with offset, S-impedance (ZS), and density (ρ)

Because of their improved leverage against ground roll and multiples, as well as the ability to estimate azimuthal anisotropy, wide-azimuth 3D seismic surveys routinely now are acquired over most resource plays. For a relatively shallow target, most of these surveys can be considered to be long offset as well, containing incident angles up to 45°. Unfortunately, effective use of the far-offset data often is compromised by noise and normal moveout (NMO) (or, more accurately, prestack migration) stretch. The conventional NMO correction is well-known to decrease the frequency content and distort the seismic wavelet at far offsets, sometimes giving rise to tuning effects. Most quantitative interpreters work with prestack migrated gathers rather than unmigrated NMO-corrected gathers. However, prestack migration of flat reflectors suffers from the same limitation called migration stretch. Migration stretch leads to lower S-impedance ([Formula: see text]) and density ([Formula: see text]) resolution estimated from inversion, misclassification of amplitude variation with offset (AVO) types, and infidelity in amplitude variation with azimuth (AVAZ) inversion results. We have developed a matching pursuit algorithm commonly used in spectral decomposition to correct the migration stretch by scaling the stretched wavelets using a wavelet compensation factor. The method is based on hyperbolic moveout approximation. The corrected gathers show increased resolution and higher fidelity amplitudes at the far offsets leading to improvement in AVO classification. Correction for migration stretch rather than conventional “stretch-mute” corrections provides three advantages: (1) preservation of far angles required for accurate [Formula: see text] inversion, (2) improvement in the vertical resolution of [Formula: see text] and [Formula: see text] volumes, and (3) preservation of far angles that provide greater leverage against multiples. We apply our workflow to data acquired in the Fort Worth Basin and retain incident angles up to 42° at the Barnett Shale target. Comparing [Formula: see text], [Formula: see text], and [Formula: see text] of the original gather and migration stretch-compensated data, we find an insignificant improvement in [Formula: see text], but a moderate to significant improvement in resolution of [Formula: see text] and [Formula: see text]. The method is valid for reservoirs that exhibit a dip of no more than 2°. Consistent improvement is observed in resolving thick beds, but the method might introduce amplitude anomalies at far offsets for tuning beds.

A comparative study of common-reflection-surface prestack time migration and data regularization: Application in crooked-line data

To improve the time-domain imaging of poor-quality seismic data, the common-reflection-surface (CRS) stack method was introduced to simulate zero-offset (ZO) stacked sections from a multicoverage data set based on automatic coherence analysis of seismic signals. This method produces improved ZO stacked sections with a high signal-to-noise ratio (S/N) and good continuity of reflection events. However, the stacking results may have some undesirable artifacts that can degrade the poststack migrated image. To overcome these drawbacks, I have developed a prestack data regularization method, based on CRS partial stacks, which produces prestack data with high S/N and enhanced reflection events. The regularized data are usually applied for velocity analysis and conventional prestack migration in the time and depth domains. Recently, the CRS stacking operator has also been applied for developing a new type of prestack beam migration. This new migration combines the classic Kirchhoff migration with the CRS stack method, in which the beam-forming process stacks locally coherent events that are performed using the CRS operator during migration. This work reviews this CRS-based prestack migration method in the time domain and presents a comparative study with the main standard applications of the CRS stack method, such as CRS stacking plus poststack time migration and CRS-based regularization plus prestack time migration (PSTM). To evaluate its effectiveness and reliability, CRS-based PSTM and CRS-based prestack data regularization were applied in a crooked line. The time-migrated image resulting from the regularized data has strong migration artifacts due to the crookedness of the seismic line; in contrast, the CRS-based time migration provides a good-quality image without migration artifacts.

Data-driven wavefield focusing and imaging with multidimensional deconvolution: Numerical examples for reflection data with internal multiples

Standard imaging techniques rely on the single scattering assumption. This requires that the recorded data do not include internal multiples, i.e., waves that have bounced multiple times between reflectors before reaching the receivers at the acquisition surface. When multiple reflections are present in the data, standard imaging algorithms incorrectly image them as ghost reflectors. These artifacts can mislead interpreters in locating potential hydrocarbon reservoirs. Recently, we introduced a new approach for retrieving the Green’s function recorded at the acquisition surface due to a virtual source located at depth. We refer to this approach as data-driven wavefield focusing. Additionally, after applying source-receiver reciprocity, this approach allowed us to decompose the Green’s function at a virtual receiver at depth in its downgoing and upgoing components. These wavefields were then used to create a ghost-free image of the medium with either crosscorrelation or multidimensional deconvolution, presenting an advantage over standard prestack migration. We tested the robustness of our approach when an erroneous background velocity model is used to estimate the first-arriving waves, which are a required input for the data-driven wavefield focusing process. We tested the new method with a numerical example based on a modification of the Amoco model.