Data-Driven Modeling Approach to Predict the Recovery Performance of Low-Salinity Waterfloods

2021 ◽  
Author(s):  
Shams Kalam ◽  
Rizwan Ahmed Khan ◽  
Shahnawaz Khan ◽  
Muhammad Faizan ◽  
Muhammad Amin ◽  
...  
Keyword(s):  
Data Driven ◽  
Low Salinity ◽  
Modeling Approach ◽  
Recovery Performance ◽  
Data Driven Modeling

Data-Driven Modeling Approach for Recovery Performance Prediction in SAGD Operations

2013 ◽  
Author(s):  
Peter John Dzurman ◽  
Juliana Yuk Wing Leung ◽  
Stefan David Joseph Zanon ◽  
Ehsan Amirian
Keyword(s):  
Performance Prediction ◽  
Data Driven ◽  
Modeling Approach ◽  
Recovery Performance ◽  
Data Driven Modeling

scholarly journals From SIR to SEAIRD: A novel data-driven modeling approach based on the Grey-box System Theory to predict the dynamics of COVID-19

2021 ◽  
Author(s):  
K. Midzodzi Pekpe ◽  
Djamel Zitouni ◽  
Gilles Gasso ◽  
Wajdi Dhifli ◽  
Benjamin C. Guinhouya
Keyword(s):  
System Theory ◽  
Data Driven ◽  
Modeling Approach ◽  
Data Driven Modeling

scholarly journals A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation

2017 ◽  
Vol 13 (7) ◽  
pp. e1005627 ◽  
Author(s):  
Warren D. Anderson ◽  
Danielle DeCicco ◽  
James S. Schwaber ◽  
Rajanikanth Vadigepalli
Keyword(s):  
Data Driven ◽  
Modeling Approach ◽  
Physiological Dysregulation ◽  
Data Driven Modeling ◽  
Disease Specific

Data-Driven Modeling Approach for Mistuned Cyclic Structures

AIAA Journal ◽  
2021 ◽  
pp. 1-13
Author(s):  
Sean T. Kelly ◽  
Andrea Lupini ◽  
Bogdan I. Epureanu
Keyword(s):  
Data Driven ◽  
Modeling Approach ◽  
Data Driven 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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