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

被引：11
|
作者：
Zhang, Yanbin [1 ]
He, Jincong [1 ]
Yang, Changdong [1 ]
Xie, Jiang [1 ]
Fitzmorris, Robert [2 ]
Wen, Xian-Huan [3 ]
机构：
[1] Chevron Energy Technol Co, Richmond, CA 94802 USA
[2] Chevron Energy Technol Co, Reservoir Performance Serv Unit, Richmond, CA USA
[3] Chevron Energy Technol Co, Reservoir Performance Serv Unit, Reservoir Simulat & Optimizat Res Team, Richmond, CA USA
来源：
SPE JOURNAL | 2018年 / 23卷 / 04期
关键词：
GAS-RESERVOIRS; SIMULATION; WELL; FLOW;
D O I：
10.2118/191126-PA
中图分类号：
TE [石油、天然气工业];
学科分类号：
0820 ;
摘要：
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.
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页码：1105 / 1125
页数：21
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