Deep neural network coupled with distance-based model selection for efficient history matching

This paper develops a novel approach of deep neural network (DNN)-based inverse modeling with selecting more reliable supervised-learning datasets with distance-based maps allocated at individual well levels. To mitigate divergence and overshooting in multi-scaled data assimilation, the DNN-based inverse model introduces a stacked autoencoder (SAE) that reduces the dimension of the training data. The proposed workflow also implements k-medoids clustering by selecting geo-models that have dynamic performances close to the true responses of producers to obtain plausible supervised-learning datasets. History-matching accuracy and forecasting performance are investigated in comparison of typical ensemble Kalman filter (EnKF)-based data assimilation for a waterflooding problem of heterogeneous fluvial channel reservoirs. The proposed approach is capable of matching the oil production rates of all producers in the range of 1.1-11.3% mean absolute percentage error (MAPE) and can forecast the future performances within 15.5% errors, while the errors of the EnKF method are up to six times higher. The proposed workflow can estimate the water-breakthrough time and the water productions accurately by generating more reliable geo-models with geological realism.