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.

引用

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页码：1105 / 1125

页数：21

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