Geological and Geophysical Integration to Predict Key Carbonate Reservoir Properties and Heterogeneities in a Large Offshore Middle Eastern Carbonate Field

Abstract

Abstract The Cretaceous carbonate reservoir of the study area, a large offshore Middle Eastern field, is diverse and of spatially variable quality. This is due to the original properties of the sedimentary facies, the effect of several subsequent phases of diagenesis and fracturing. The reservoir heterogeneity results in complex dynamic field behavior which can be difficult to predict, such as early water breakthrough, strong water-cut and uneven pressure support. In order to better predict and optimize future production, a robust geological model is needed. All available data (geological, geophysical and dynamic) were integrated taking into account the limitations of each dataset to ensure the reliability of the model. This paper focuses on two key steps in geomodel construction: facies and porosity modeling. The facies and porosity modeling was constrained by numerous data from cored exploration wells and horizontal wells. Thirteen classes of facies, differentiated by mud-/grain-composition, porosity and permeability range, were identified on cores. These were then regrouped into eight electrofacies, with classification based on well log response within a sedimentary sequence framework, in order to enable the inclusion of data from non-cored wells. Lastly, a 3D pseudo-porosity cube was generated to serve as an additional large-scale constraint. It was obtained from applying the linear correlation between acoustic impedance and porosity from well data to an acoustic impedance dataset resulting from a post-stack inversion. Facies and porosity changes occur vertically within 3-8 meter thick layers as described from the well data. The vertical resolution of the pseudo-porosity cube is roughly 15 meters but it extends over most of the field. Facies were populated in the reservoir model using regional geological knowledge and a sedimentological model calibrated at wells, with qualitative checks for consistency with seismic-scale average pseudo-porosity or impedance maps. Porosity was propagated according to the distribution associated with each facies and the pseudo-porosity cube as a soft trend. Once the porosity was modeled, it was converted to pseudo-impedance to generate a synthetic seismic dataset for comparison with original seismic data, allowing significant seismic-scale inconsistencies to be easily detected. In addition the pseudo-impedance was compared to the acoustic impedance cube resampled at reservoir model scale to better understand the source of the large-scale inconsistencies. Once the areas and origin of mismatch between the seismic data and the geological model were identified, the reservoir model porosity, and consequently the facies, had to be locally modified to ensure the consistency with the geophysical information. The modified reservoir model porosity was then again used to regenerate to synthetic seismic data and compared to the original seismic data in order to check if the modifications were sufficient to obtain optimum coherency between all data. This kind of feedback loop is an efficient iterative process, a time-consuming but necessary step for relevant geomodel quality control. The consistency between all available geological and geophysical information yields high confidence in the result. It ensures the robustness of the geological model and consequently ensures a sound basis for dynamic modeling.