Non-parametric regression and neural-network infill drilling recovery models for carbonate reservoirs

Computers and Geosciences

Volumn 26, Issue 8, 2000, Pages 975-987

  Wu, C H ^a   Soto, R D ^a   Valko, P P ^a   Bubela, A M ^a  

^a TEXAS A AND M UNIVERSITY   (United States)

Author keywords

Dominant variables; Forecast models; Multivariate principal component; Oil recovery; Reservoir engineering; West Texas

Indexed keywords

HYDROCARBON RESERVOIR; NEURAL NETWORK; WELL TECHNOLOGY;

ARTIFICIAL NEURAL NETWORK; CARBONATE; HYDROCARBON RESERVOIR; REGRESSION ANALYSIS; RESERVOIR CHARACTERIZATION;

EID: 0033849319     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0098-3004(00)00033-9     Document Type: Article

References (10)

1. Fast learning process of multilayer neural networks using recursive least squares method
2. Estimating optimal transformations for multiple regression and correlation
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4. Identification and control of industrial scale processes via neural networks
5. Integrated recovery efficiency forecast models for San Andres reservoirs of Central Basin Platform and Northern Shelf, West Texas. SPE 27697 (poster)
6. An application of idealized carbonate depositional sequences for reservoir characterization. SPE 28458 (poster)
7. Infill drilling enhances waterflood recovery
8. Statistical and fuzzy infill drilling models for carbonate reservoirs. SPE 37728
9. A new approach to seismic data integration during reservoir characterization using optimal non-parametric transformations. SPE 36500