Full text loading...
After successful derisking of the technique, time-lapse (4D) seismic is now being deployed across the Santos Basin pre-salt as a tool to enhance oil recovery and assist with reservoir management. Two main shortcomings of conventional 4D interpretation approaches for pre-salt reservoirs are the limited vertical resolution (especially given the heterogeneity of the reservoir) and the inadequate uncertainty handling (anchoring to a single solution in a low-signal-to-noise ratio environment).
To address those challenges, we developed a multi-scenario deep learning (DL) workflow for high resolution 4D inversion. Several training datasets were constructed from multiple scenarios to cover reasonable uncertainty ranges. Each scenario was trained separately with a Convolutional Neuron Network (CNN). The trained models can then be applied to the real 4D seismic data to generate a set of predicted reservoir property change volumes in almost real time. The efficiency and flexibility of this approach enables early engagement with the multi-disciplinary subsurface team and improves the quality of the reservoir description.
In this paper, we apply this workflow to a pre-salt 4D dataset. The results show improved flood front delineation, with multi-scenario predictions that can be assimilated in reservoir models in collaboration with the integrated team.