Integrated Reservoir Characterization: Fast-Tracking Facies Modelling in a Deepwater Turbidite Reservoir, S Field, Suriname

This paper presents an integrated and accelerated workflow for facies modelling in the deepwater turbidite reservoirs of the S Field, Suriname, discovered by PETRONAS. Faced with challenges such as stratigraphic complexity, limited well control, and variable data quality, the study developed a robust methodology combining borehole image logs, core data, seismic attributes, and neural network classification to define depositional environments (EODs).

A multidisciplinary rock typing workflow was used to classify 15 lithofacies into three reservoir quality classes (Good, Moderate, Poor), which were then calibrated and applied in a 3D static model. Geological analogues from other basins helped guide lateral facies distribution. Synthetic wells were constructed to validate the model, showing strong alignment with DST results and confirming reservoir connectivity and deliverability.

The approach significantly reduced modelling time and improved geological consistency, offering a practical solution for fast-tracking reservoir characterization in complex deepwater settings.