Predictive Reservoir Modelling Case Study: Ghasha - The World's Largest Offshore Sour Gas Development

Covering an area of over 2000 sq km, Abu Dhabi's offshore ultra-sour Ghasha Mega Project presented several unique subsurface and modelling challenges due to the limited spatial coverage in legacy well data and the sedimentological and stratigraphic complexity of the thin interbedded anhydrite-limestone-dolomite reservoirs of the Arab Formation. With an average well spacing of ~7 km, uncertainties related to reservoir quality distribution and continuity were critical concerns in constructing the reservoir models that underpinned the Ghasha field development plan. To mitigate these uncertainties, seismic QI results were integrated with well data and regional depositional trends, as the primary deterministic predictors, to develop a 3D geostatistical reservoir model, that explained approximately 64% of the observed variance in reservoir porosity. The predictive model integrated a stochastically downscaled seismic acoustic impedance (AI), constrained by stratigraphic cyclicity, a gross regional sedimentological model, local diagenetic trends, seismic-derived geo-bodies, and spatial probability volumes. The largely stationary residual porosity variance (~36%) was modelled via 3-dimensional nested semi-variogram structures, enabling realistic anisotropy and semi-stationary heterogeneity. Following the project's final investment decision (FID), newly drilled wells provided an independent dataset to evaluate model performance. Comparative analysis shows that predicted facies and porosity distributions correlate with new well data (R² ≈ 0.87), with mean absolute error ~3% in key reservoir zones. This paper presents the detailed model methodology and the comparative analysis of new well data against model predictions, demonstrating the impact of integrating multi-scale seismic, well and regional geological constraints in hierarchical geostatistical reservoir models.