Rock Physics Modelling and Stochastic Seismic Inversion to Predict Reservoir Properties and Quantify Uncertainties of a Complex Upper Jurassic Carbonate Reservoir From Offshore Abu Dhabi

The need to understand field-scale reservoir heterogeneity using seismic data requires implementing advanced solutions such as stochastic seismic inversion to go beyond the resolution of seismic data. Conventional seismic inversion techniques provide relatively low-resolution reservoir properties but do not provide quantitative estimates of the subsurface uncertainties. The objective of this study was to carry out a facies dependent geostatistical seismic inversion to generate multi-realization reservoir properties to improve the geological understanding of the two adjacent offshore fields in Abu Dhabi. An integrated approach of rock physics modelling and geostatistical inversion followed by porosity co-simulation was undertaken to characterize the spatially varying lithofacies and porosity of the complex carbonate reservoirs. Necessary checks to ensure highest quality data input included: 1) Rock physics modelling and shear sonic prediction 2) Invasion correction and production effect correction of elastic logs 3) Seismic feasibility analysis to define seismic facies and 4) Six angle stacks optimally defined to preserve AVO/AVA signature followed by AVO/AVA compliant post-stack processing. Subsequently, the joint facies driven geostatistical inversion was conducted to invert for multiple realizations high-resolution lithofacies and elastic rock properties. Finally, porosity was co-simulated and later ranked to map important geological variations. Based on the rock physics analysis, a 4 facies classification scheme (Porous Calcite, Porous Dolomite, Tight Calcite-Dolomite and Anhydrite) was adopted and used as input in the joint facies-elastic inversion. Before the geostatistical inversion, a deterministic inversion was performed that helped in refining the horizon interpretation of the surfaces used as a framework for the inversion. In geostatistical inversion, results are guided by variograms, facies, prior probability density functions, wells, inversion grid and seismic data quality. At start of the joint inversion, the parameters for inversion are defined in an unconstrained fashion aiming to obtain unbiased parameters which are blind to well control. Finally, using elastic properties constrained at the well locations, the joint geostatistical inversion was run to obtain multiple realizations of P-impedance, S-impedance, density and lithofacies. The cross-correlation between seismic and inverted synthetics was high across the whole area for all the partial angle stacks, with the lowest cross-correlation observed in the far angle stack. Lithofacies and elastic properties were used to co-simulate for porosity. The porosity results were then ranked to provide the P10, P50 and P90 models to be used for reservoir property model building. This study is an example of stochastically generating geologically consistent reservoir properties through high-resolution seismically constrained inversion results at 1ms vertical sampling. Lithofacies and elastic properties were jointly inverted, and co-simulated porosity results provided insights into high-resolution reservoir heterogeneity analysis through the ranking of equiprobable multiple realizations.