A Physics-Based Data-Driven Model for History Matching, Prediction, and Characterization of Unconventional Reservoirs

Zhang, Yanbin (Chevron Energy Technology Company) | He, Jincong (Chevron Energy Technology Company) | Yang, Changdong (Chevron Energy Technology Company) | Xie, Jiang (Chevron Energy Technology Company) | Fitzmorris, Robert (Chevron Energy Technology Company) | Wen, Xian-Huan (Chevron Energy Technology Company)

OnePetro 

Summary

We developed a physics-based data-driven model for history matching, prediction, and characterization of unconventional reservoirs. It uses 1D numerical simulation to approximate 3D problems. The 1D simulation is formulated in a dimensionless space by introducing a new diffusive diagnostic function (DDF). For radial and linear flow, the DDF is shown analytically to be a straight line with a positive or zero slope. Without any assumption of flow regime, the DDF can be obtained in a data-driven manner by means of history matching using the ensemble smoother with multiple data assimilation (ES-MDA). The history-matched ensemble of DDFs offers diagnostic characteristics and probabilistic predictions for unconventional reservoirs.