A Physics-Based Data-Driven Model for History Matching, Prediction, and Characterization of Unconventional Reservoirs
- Yanbin Zhang (Chevron Energy Technology Company) | Jincong He (Chevron Energy Technology Company) | Changdong Yang (Chevron Energy Technology Company) | Jiang Xie (Chevron Energy Technology Company) | Robert Fitzmorris (Chevron Energy Technology Company) | Xian-Huan Wen (Chevron Energy Technology Company)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- August 2018
- Document Type
- Journal Paper
- 1,105 - 1,125
- 2018.Society of Petroleum Engineers
- History Matching, Unconventional Reservoir, Data Driven, Simulation
- 29 in the last 30 days
- 387 since 2007
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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.
|File Size||2 MB||Number of Pages||21|
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