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AAPG GEO 2010 Middle East
Geoscience Conference & Exhibition
Innovative Geoscience Solutions – Meeting Hydrocarbon Demand in Changing Times
March 7-10, 2010 – Manama, Bahrain

Integrating Rock Physics, Seismic Reservoir Characterization and Static Modeling of Carbonates; a Case Study from the UAE

Fathy El-Wazeer1; Antonio Vizamora1; Aysha Al Hamedi1; Habeeba Al-Housani1; Peter Abram2; Sito Busman2

(1) ADCO, Abu Dhabi, United Arab Emirates.

(2) Shell, Abu Daubi, United Arab Emirates.

An integrated workflow to generate seismically constrained reservoir models is described. Several key technologies were used including carbonates rock physics estimation, seismic forward modeling and comparison to surface 3D seismic, as well as probabilistic seismic inversion.

Using the SUN rock model as a framework, the team derived relationships between the reservoir properties (like modeled facies, porosity and fluid content) and the elastic properties (Vp, Vs and density). The theory of the Sun model was chosen as a key for relating reservoir properties to seismic: relations were identified between Suns frame flexibility factor (describing the elasticity of the rock frame determined by pore geometry), velocities, densities and porosities. Those relations were compared to the information on sedimentology, diagenesis, structural position and reservoir rock types. Detailed well to seismic match enabled estimating a fit-for-purpose average wavelet to be used over the entire field. The fluid substitution of the well logs, log blocking, and the fluid properties made it possible to model the fluid content impact on the seismic, and to better understand the impact of peg-leg multiples still present in the seismic data.

The generation of 3D synthetic seismic based on the static model included the use of the relations obtained from the rock physics model, the well log blocking and the derived seismic wavelet. The match between real and modeled synthetic seismic indicates how well the parameters in the static model describe the reservoir, and the relevance of the variables (rock & fluid properties, layering, and wavelet) included in the forward modeling. The seismic match was improved by iteratively fine tuning the different variables used to generate the synthetic seismic. The optimization process highlighted which variables control the seismic response. These were subsequently used to define the stochastic parameters and the uncertainties in the probabilistic seismic inversion. The inversion algorithm used utilizes the constrained static model as input and can invert to any of the variables present in it. Therefore it was possible to obtain probability distributions for porosity, fluid saturation and rock rigidity in any location of the reservoir that match the seismic.

The workflow was applied to the Bab field in Abu Dhabi, UAE. The resulting static model reflects more accurately the lateral variability of the rock properties while preserving vertical resolution.