Understanding Trinidad Gas Reservoir Performance: Mango, Mahogany, and Immortelle Field Water Drive Reservoir Anomalies

2012 ◽  
Author(s):  
Iannie Vashti Roopa ◽  
Zareef Khan ◽  
Kirk Baksh
Keyword(s):  
Gas Reservoir ◽  
Reservoir Performance

Fractured Gas-Reservoir Performance

2007 ◽  
Vol 46 (12) ◽  
Author(s):  
F.B. Thomas ◽  
D.B. Bennion
Keyword(s):  
Gas Reservoir ◽  
Reservoir Performance

Gas Reservoir Performance in Abnormally High Pressure Carbonates

1996 ◽  
Author(s):  
Wei-Chun Chu ◽  
H. Kazemi ◽  
R.E. Buettner ◽  
T.L. Stouffer
Keyword(s):  
High Pressure ◽  
Gas Reservoir ◽  
Reservoir Performance

Deep net simulator (DNS): a new insight into reservoir simulation

2020 ◽  
Vol 60 (1) ◽  
pp. 124
Author(s):  
Shahdad Ghassemzadeh ◽  
Maria Gonzalez Perdomo ◽  
Manouchehr Haghighi ◽  
Ehsan Abbasnejad
Keyword(s):  
Deep Learning ◽  
Reservoir Simulation ◽  
Vital Role ◽  
Data Driven ◽  
Percentage Error ◽  
Gas Reservoirs ◽  
Gas Reservoir ◽  
Flow Equations ◽  
Reservoir Performance ◽  
Reservoir Simulations

Reservoir simulation plays a vital role as a diagnostics tool to better understand and predict a reservoir’s behaviour. The primary purpose of running a reservoir simulation is to replicate reservoir performance under different production conditions; therefore, the development of a reliable and fast dynamic reservoir model is a priority for the industry. In each simulation, the reservoir is divided into millions of cells, with fluid and rock attributes assigned to each cell. Based on these attributes, flow equations are solved through numerical methods, resulting in an excessively long processing time. Given the recent progress in machine learning methods, this study aimed to further investigate the possibility of using deep learning in reservoir simulations. Throughout this paper, we used deep learning to build a data-driven simulator for both 1D oil and 2D gas reservoirs. In this approach, instead of solving fluid flow equations directly, a data-driven model instantly predicts the reservoir pressure using the same input data of a numerical simulator. Datasets were generated using a physics-based simulator. It was found that for the training and validation sets, the mean absolute percentage error (MAPE) was less than 15.1% and the correlation coefficient, R2, was more than 0.84 for the 1D oil reservoirs, while for the 2D gas reservoir MAPE < 0.84% and R2 ≈1. Furthermore, the sensitivity analysis results confirmed that the proposed approach has promising potential (MAPE < 5%, R2 > 0.9). The results agreed that the deep learning based, data-driven model is reasonably accurate and trustworthy when compared with physics-derived models.

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