Low fold poorly sampled vintage seismic data often suffers from poor fault imaging. This can have a critical impact on reserve estimation and well planning. Acquiring high density seismic data over producing fields requires overcoming logistic challenges along with additional costs and increased acquisition time. However, advances in seismic processing technology could improve the fault resolution of vintage seismic data in a cost effective manner. This has been proven in a case study from offshore Abu Dhabi.

The presence of strong surface wave energy, resulting from the shallow water environment and near surface heterogeneity, masked events in the deeper part of the section. Poor and irregular spatial sampling caused aliasing of the surface wave. In the vintage processing, strong de-noising was applied to tackle the aliasing issue, which smeared the fault definitions. During the re-processing, a joint low-rank and sparse inversion was applied to regularize and densify the input data to obtain a de-aliased surface wave noise model. Subsequent adaptive subtraction of the noise from the input removed strong surface waves without damaging the body waves.

The stack quality was improved by application of cascaded surface wave attenuation algorithms. Additional five dimensional Fourier reconstructions of the data improved the signal quality. A carefully designed fault-preserving residual noise attenuation workflow further reduced the residual noise content. Automatic picking of key stratigraphic horizons was carried out in order to evaluate the spatial resolution of the re-processing outcome. Sharper discontinuities along fault planes observed compared to the interpretation of the vintage seismic data. Increased confidence in fault interpretation is of value for structural restoration study and further reservoir understanding. In addition, several new, previously not-visible, small fault features were highlighted as evident from volumetric curvature and semblance analysis. They have been effectively utilized in a forthcoming drilling campaign to de-risk well operation.

Multi-dimensional data densification to de-alias surface waves and five dimensional re-construction of the signal proved to be beneficial to enhance the fault features on the poorly sampled seismic data.

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