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The common-reflection-surface (CRS) technique provides an effective workflow for seismic data preparation and imaging in large areas of regional studies. In a case study from the North Sea, the multi-parameter stacking technique is used to combine and homogenize vintage seismic data in time domain, and to accelerate the model building cycle in depth imaging. CRS time processing may directly start from input data acquired at diverse natural binning grids, and provide the regularisation and interpolation to a uniform output grid in one step. The regular CRS gathers show an almost complete CMP-offset coverage and a high signal to-noise ratio, and thus enhance the resolution of prestack migration in time and depth. Depth model building departs from the CRS attributes which provide the initial model via CRS tomography, and benefits in the model update by residual moveout analysis from the enhanced signal-to-noise ratio in the prestack data.

The Common-Reflection-Surface (CRS) method may improve seismic processing beyond imaging, e.g. in an enhanced Amplitude Versus Offset (AVO) analysis. Various applications have shown that the more realistic subsurface assumptions, and the increased fold of the CRS imaging allow to extend AVO analysis into noise zones and to deep targets with low signal quality. Extreme fluctuations of AVO parameters are removed, and AVO anomalies are enhanced. The CRS method assumes subsurface reflector elements with dip and curvature, which implies large-fold stacking surfaces extending both across offset, and across neighboring CMP locations. The extension across neighboring CMPs defines a CRS gather at the central CMP location, comprising data from a multitude of traces. The CRS moveout correction compensates for the local dip across these neighboring CMPs, thus contrasting to conventional AVO super-gathers based on NMO correction that collect dipping events horizontally at varying phase. The presented case studies show that CRS-AVO attribute stacks are produced with a much higher signal-to-noise ratio from CRS gathers than from CMP gathers in conventional AVO. The CRS-AVO attribute sections clearly distinguish anomalies at known or expected gas-bearing reservoirs.

Noise suppression and signal enhancement prior to prestack depth migration (PreSDM) may significantly increase the resolution of the depth image, and the effectiveness of the PreSDM workflow. The Common-Reflection-Surface (CRS) technique was previously used for this enhancement of seismic prestack data, providing so-called CRS gathers with regularized CMP and offset coverage, and with a strong noise suppression. These CRS gathers considerably improved the depth image in Kirchhoff PreSDM but were not suited for shot-based PreSDM algorithms. This case study now presents a straightforward way to produce geometry-preserving CRS gathers that similarly increase the signal-to-noise ratio. In a first implementation, CRS prestack data interpolation is performed at the existing trace locations providing a straightforward and automatic preservation of the original shot geometry. Application to 3D seismic land data demonstrates the improved signal-to-noise ratio and resolution both in the geometry-preserving CRS shot gathers, and in the corresponding QC stack. As in the Kirchhoff migration of regularized CRS gathers, such enhancements are expected for Reverse Time Migration of CRS shot gathers as well.

The results of this study were obtained from an area in the North German Basin (NGB) located east of Bremen, Germany, where gas is produced from a deep Rotliegend sandstone reservoir. Faults and fractures play a major role in gas productivity. The deformations could have a negative influence on the productivity of the gas field as fractures are cemented and tight and may act as permeability barriers.

A number of investigations of the North German Basin have shown the impact of 3D seismic for a reliable reservoir characterisation. This study tries geostatistical methods to combine reservoir parameters (porosity, porosity thickness) and seismic attributes (e.g. amplitude, impedance etc.) in the following manner: 1 . Analysis of seismic attributes (amplitude, frequency, impedance etc) and reservoir parameters (porosity, porosity thickness) and their crosscorrelation 2. Processing of input data (declustering, normal score transformation, resampling, smoothing) and check for trueness of parameter distributions 3. Geostatistical analysis and variogram modelling of seismic attributes and reservoir parameters 4. Kriging of the reservoir parameters (wells only). Cokriging of seismic and well information 5. Stochastic simulation to investigate alternatives and extreme realisations