A new approach to improve reservoir modeling via machine learning

In highly heterogeneous basins with complex subsurface geology, such as the Nile Delta Basin, accurate prediction of reservoir modeling has been a challenge. Reservoir modeling is a continuous process that begins with field discovery and ends with the last phases of production and abandonment. Currently, the stochastic reservoir modeling method is widely used instead of the traditional deterministic modeling method to consider spatial statistics and uncertainties. However, the modeling workflow is demanding and slow, typically requiring months from the initial model concept to flow simulation. In addition, errors from early model stages become cumulative and are difficult to change retroactively. To overcome these limitations, a new workflow is proposed that implements probabilistic neural network inversion to predict reservoir properties. First, well-log data were conditioned properly to match the seismic data scale. Then, the networks were trained and validated, using the conditioned well-log data and seismic internal/external attributes, to predict water saturation and effective porosity 3D volumes. The resulting volumes were sampled in simulation 3D grids and tested using a blind well test. Subsequently, the permeability was calculated from a porosity-permeability relationship inside the reservoir. Finally, a dynamic simulation project of the field was performed in which the historical field production and pressures were compared to the predicted values. One of the Pliocene deepwater turbidite reservoirs in the offshore Nile Delta was used to demonstrate the proposed approach. The results proved the accuracy of the model in predicting the reservoir properties and honoring the heterogeneity of the reservoir. The new approach represents a shortcut for the seismic-to-simulation process, providing a reliable and fast way of constructing a reservoir model and making the seismic-to-simulation process easier.