Incorporating seismic characterization results into Bul Hanine geological model

Abstract Bul Hanine field is located offshore Qatar with primary oil production from the Reservoir-X carbonates. In 2005 and 2006, Qatar Petroleum recognized that future development of this mature field would require a modern, state of the art, reservoir model, and initiated several projects to achieve that goal: reprocessing and elastic inversion of the 1995 vintage 3D seismic, petrophysical data collection and analysis, and comprehensive reservoir characterization. This paper illustrates how Qatar Petroleum, with contractual assistance from PGS, Total and Beicip-Franlab, has applied advanced reservoir characterization techniques to constrain petrophysical property distribution using elastic inversion products and therein reducing uncertainty in a reservoir model. Following detailed rock typing core and log analysis from approximately 5400 feet of core and from 26 wells, and logs from 90 well penetrations, the team observed that there was considerable heterogeneity in this "hard" well data, and that distribution of the petrophysical properties between wells would suffer in the absence of additional control. To address the lack of inter-well control, an attempt was made to extract reservoir property information from the seismic data. Using optimally reprocessed existing 3D seismic data (to eliminate noise and preserve relative amplitudes) and pre-stack elastic inversion, advanced reservoir characterization techniques yielded volume data including lithology, lithology probability, and porosity that could be used as geo-statistical constraints. Initially, a detailed petro-elastic analysis was performed on select wells to calibrate well-derived elastic properties with seismic data in order to design the most appropriated seismic characterization workflow. The results demonstrated that acoustic and elastic impedances could be used to discriminate Calcites, Dolomites, and Anhydrites. Well analysis also indicated a robust relationship between impedance and porosity and each dominant lithology. Subsequently, a pre-stack inversion was conducted prior to 3D discriminate analysis to produce dominant lithology and associated probability volumes. Following this, seismic reservoir characterization resulted in generation of a lithology based porosity volume. During geo-statistical modeling, dominant lithology probability volumes were used as a co-simulation parameter for generating a lithology model, and the seismic porosity volume was used as co-simulation parameter for porosity distribution, resulting in a high-resolution static model of the Reservoir-X reservoir. This work demonstrates the added value of pre-stack seismic reservoir characterization for modeling of the Reservoir-X carbonate reservoir. In addition to porosity, this technique gives light to lithology changes throughout the reservoir, providing otherwise unobtainable information of rock-type distribution. Introduction Reservoir characterization is typically ground-truthed by well data; however wells are often a great distance from each other and thus provide only limited understanding of the actual lateral heterogeneity of a reservoir. Despite poor vertical resolution, seismic data is among the very few data types that provide lateral resolution in excess of that offer by wells alone. This paper presents a case study detailing the methodology for defining the relationship between seismic data and well petrophysical properties (lithology and porosity) and the subsequent generation of seismic derived constraints for high resolution geological modeling. The target of the reservoir characterization work is the nominally 200 foot thick Reservoir-X formation, a predominately carbonate interval that also contains dolomite and anhydrite layers. The project objectives were to generate seismic volumes that could provide quantitative value in reservoir characterization. Knowledge of the Reservoir-X lithology plays a critical part in identifying reservoir dynamic properties; often differing lithologies have similar porosity but dramatically different permeability. Defining dominant lithology from seismic data would offer significant improvement over a purely well log derived lithology trend. In intervals with homogeneous lithology the ability to define porosity trends enhances well log derived porosity volumes.