Cross Functional Improvements in Modelling Complex Carbonate Reservoirs

Abstract

Abstract Geological Modelling (GM) of heterogeneous carbonates can be challenging to history match with actual reservoir performance. Reservoir heterogeneities that present challenges to history match production include High Permeability Streaks (HPS), Stromatoporoid layers (STR), Fractured Dolomites (FD) and in this reservoir, extensive porosity-deteriorated bitumen-affected volumes. Local experience and knowledge help address model limitations and inconsistencies, and correlating diverse data types is critical to bridge gaps when uncertainties compound: this case study details multi-disciplinary improvements of such GM. 3D seismic was reprocessed to improve signal-to-noise ratio (SNR), fault interpretation and AVO/AVAz and seismic attributes, stochastic inversion, and rock physics analysis was used to improve facies and porosity modelling, facilitated by a strong, three-way relationship between Acoustic Impedance, porosity, and STR layers. In addition, newer horizontal wells with wellbore images helped improve the understanding and distribution of STR and bitumen presence and validated the updated seismic fault interpretation. 3D seismic inversion identified a top bitumen surface scenario that was supported in well observations. Refinement of the stratigraphic framework, zonation and layering captured heterogeneities including vertical and lateral lithofacies distributions and sequence boundaries, and an updated rock typing (RT) scheme characterized reservoir heterogeneities (HPS & Bitumen presence). Updated Saturation Height Function (SHF) and Free Water Level assumptions accommodated the new structure and RT scheme. Multiple models were tested to characterize saturation models from Pc curves and statistical analysis of saturation residuals and hydrocarbon pore thickness was used to assess and rank model performance. The selected SHF model was based on model efficacy on multiple scales from field, domain, wells, layers and by RT. Integrating multi-disciplinary data types to resolve reservoir challenges is a crucial aspect to deliver fit for purpose geological and dynamic models in mature fields. This case study illustrates the value of merging geophysics, stratigraphic, lithologic and petrophysical data and analysis to update model properties and performance and identify and address uncertainties.