The objective of this paper is to demonstrate the success of an alternative numerical modeling approach to build a static model by incorporating Forward Stratigraphic Modelling (FSM) as geological input. This new methodology was performed on a field in the Malay Basin where early production wells indicated the high uncertainty in oil-originally-in-place, facies distribution and reservoir connectivity. For this reason, a new approach was developed for a static model in the area that provides new insights of subsurface reservoirs, de-risking future field assets and mitigates the subsurface uncertainty.
Process-based simulations as presented with FSM present realistic scenarios of lithology distribution and vertical barriers that enable advanced subsurface characterization. FSM process built a quantitative method that simulate sediment distribution from regional to reservoir architecture for A field D and E sands. The main parameters for simulation run include regional understanding of sediment sources, in-situ organic sediment production, global sea-level curve enhanced by Milankovitch cycles and main long-term processes that control the subsidence of the area.
FSM prediction combined with regional seismic, cores and well log data have provided a robust scenario of reservoir characteristics for static model. The results of the study detailed high-resolution sequence stratigraphy, significant changes in the depositional system and sand accumulation through time. The results of FSM were quality-checked with the A field well dataset for consistency. After performance of sensitivity analysis, the best-matched model was chosen for subsequent static model building process. In generating static depo- and rock type models, the FSM result were compared with the Geostatistical Stochastic Inversion (GSI) for property distribution away from the well control. The result of FSM guided model building showed A field D reservoirs as relatively having better sand quality with good lateral connectivity. A field E sand however is a more complex reservoir with limited areal and vertical connectivity. Overall, the total STOIIP for D reservoirs improved significantly while E reservoirs are comparable with existing model. The dynamic modelling was calibrated to field and wells performance (production history, MDT, DST, etc.) taking into account main remaining uncertainties and risks and evaluation of multiple field development options.
With thorough integrated analysis of A field and its surroundings, integrated FSM and GSI derived static model reflects accurate facies distribution of the area compared with conventional workflows. It was used as an aid for Field A development optimization and increased the probability to find good reservoir facies as proven from findings of recently drilled development wells.
