Artificial neural network based hybrid modeling approach for flood inundation modeling

2021 ◽  
Vol 592 ◽  
pp. 125605
Author(s):  
Shuai Xie ◽  
Wenyan Wu ◽  
Sebastian Mooser ◽  
Q.J. Wang ◽  
Rory Nathan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hybrid Modeling ◽  
Flood Inundation ◽  
Modeling Approach ◽  
Artificial Neural ◽  
Flood Inundation Modeling ◽  
Inundation Modeling

scholarly journals Data-driven modeling to optimize the injection well placement for waterflooding in heterogeneous reservoirs applying artificial neural networks and reducing observation cost

2020 ◽  
Vol 38 (6) ◽  
pp. 2413-2435 ◽  
Author(s):  
Xinwei Xiong ◽  
Kyung Jae Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Injection Well ◽  
Data Driven ◽  
Well Placement ◽  
Heterogeneous Reservoirs ◽  
Modeling Approach ◽  
Artificial Neural ◽  
Well Data ◽  
Data Driven Modeling

Secondary recovery methods such as waterflooding are often applied to depleted reservoirs for enhancing oil and gas production. Given that a large number of discretized elements are required in the numerical simulations of heterogeneous reservoirs, it is not feasible to run multiple full-physics simulations. In this regard, we propose a data-driven modeling approach to efficiently predict the hydrocarbon production and greatly reduce the computational and observation cost in such problems. We predict the fluid productions as a function of heterogeneity and injection well placement by applying artificial neural network with small number of training dataset, which are obtained with full-physics simulation models. To improve the accuracy of predictions, we utilize well data at producer and injector to achieve economic and efficient prediction without requiring any geological information on reservoir. The suggested artificial neural network modeling approach only utilizing well data enables the efficient decision making with reduced computational and observation cost.

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