The seismic imaging of carbonate fields offshore Abu Dhabi is complicated by shallow overburden anomalies (e.g. channels, sink holes, karst features, etc.), strong anisotropy and complex multiple generation mechanisms. Noisy data and converted wave energy create further difficulties. All of these pose challenges for conventional time imaging (PreSTM), resulting in structural uncertainty and unreliable reservoir characterization. Successful imaging requires an accurate velocity model. This is important in the shallow overburden area where inaccurately modelled localized anomalies will amplify errors to deeper targets as waves pass through them, creating artificial pull-ups and push-downs. Seismic anisotropy has a key role in accurate subsurface imaging. In this region, anisotropy is complicated, with values ranging from negative delta to large positive epsilon. Inaccurately estimated anisotropy will result in over compensated velocities, and may cause cycle-skipping in diving-wave Full Waveform Inversion (FWI). Shallow water topography and strong impedance contrasts in the area lead to a substantial amount of free-surface and inter-bed multiples. The repetitiveness of the flat mega-scale geology makes it difficult to distinguish and attenuate multiples from primaries. In this paper, we demonstrate that extensive pre-stack depth migration (PreSDM) technologies including Dip Constraint Tomography (DCT), Structural Constraint Tomography (SCT), Vertical Seismic Profile (VSP) constraint anisotropy update and a well-designed de-multiple flow can successfully resolve the challenges mentioned above.

Seismic processing to PSDM is now a common approach in Abu Dhabi to improve the subsurface imaging. However due to the amount of well information in this region, it is challenging to provide additional geological understanding through seismic reservoir characterization. This paper demonstrates the impact of PSTM and PSDM processing results through various trials of deterministic seismic AI inversions to provide meaningful geological information and its pitfalls. The upper Jurassic Formation in this study area is currently categorized as un-developed reservoir and consists of high porosity and permeability grainstone dominant carbonate within a limited areal extension, the so-called Oolite belt in this region. Aimed to improve the seismic image for this reservoir and above super-giant reservoirs, PSTM and PSDM processing were implemented. Through PSDM processing, a robust velocity model was generated to solve complex overburden geologies such as high velocity channels, strongly anisotropic shale and lateral heterogeneity of these giant-reservoirs. Here the PSTM and PSDM results are evaluated during a deterministic AI inversion step. Due to the small number of wells penetrating this undeveloped reservoir, two low frequency models were evaluated: one log interpolation only and two using merge log interpolation with PSTM and PSDM velocities for the ultra-low frequency component, respectively. Each seismic inversion result for both PSTM/PSDM seismic with these low frequency models shows a good agreement with log calculated AI and porosity. However, the low frequency model strongly influences the result away from well locations. In view of the regional geological trend, inversion results from PSDM seismic with PSDM processing velocity shows a good match to the regional geological features such as the extension of the Oolite belt and the inner ramp to mid ramp boundary. Additionally this seismic inversion result shows local high porosity regions at the crestal area and porosity changes at the flank area. These variations could be interpreted as porosity preservation after oil migration and local fracturation in depositional environment, respectively. AI inversion results from PSTM lead to more pessimistic GRV estimates and may lead to wells placed in less productive areas during the appraisal and development phases. Additionally these results mislead regional geological interpretation and potential exploration targets. The benefit of PSDM processing is not limited to better imaging but also produces a robust velocity model leading to an improved understanding of reservoir distribution and characteristics. Integrating regional geological knowledge and seismic inversion using PSDM velocity models will help to identify exploration targets, derisk future appraisal wells and reservoir development plans.

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