A knowledge-based heterogeneity characterization framework for 3D steam-assisted gravity drainage reservoirs

Abstract In typical field-scale steam-assisted gravity drainage (SAGD) projects, shale barriers would often act as flow barriers that may adversely impact the ensuing steam chamber development. Efficient inference and proper representation of such heterogeneities from production data remain challenging. A novel hybrid knowledge-based workflow for 3D SAGD heterogeneity inference is presented. A convolutional neural network (CNN) proxy model is integrated with the genetic algorithm (GA) to infer shale parameters. A total number of 1000 heterogeneous 3D cases are constructed and subjected to numerical simulation and the corresponding production data is recorded. A dataset is assembled from the simulation results corresponding to these 1000 cases to train a set of CNN-based proxy models: discrete wavelet transform (DWT) is applied to parameterize a 3D reservoir model and the corresponding production time-series data. A GA-based workflow is introduced to infer the unknown shale barrier arrangement from a given (known) production profile by searching for a set of shale barrier parameters that would minimize the difference between the known profile and the predictions. The proposed workflow would yield an ensemble of 3D models of shale barrier distribution that are consistent with the actual production histories. The proposed methodology is tested with cases involving both idealized and irregularly-shaped shale barrier configurations. The proposed hybrid characterization workflow provides a robust and computationally-efficient alternative for inferring uncertain 3D heterogeneous features and can be easily extended to solve other similar inverse problems in various engineering fields.