Title :
Dividing oil fields into regions with similar characteristic behavior using neural network and fuzzy logic approaches
Author :
Nikravesh, Masoud ; Kovscek, A.R. ; Patzek, T.W.
Author_Institution :
Earth Sci. Div., Lawrence Berkeley Lab., CA, USA
Abstract :
Presents the next generation of “intelligent” oil field surveillance and prediction software based on neural networks and fuzzy logic. We treat the entire oil field as a coupled, highly nonlinear system of water injectors and oil/water/gas producers. The oil field is divided into regions with similar characteristic behavior using neural network and fuzzy logic. Wells in each region are then modeled with specialized neural networks trained to recognize their particular behavior. The model helps to improve waterflood management, avoid reservoir damage, and increase oil recovery per unit volume of injected water. Finally, the model visualizes the global trajectory of an entire field project and allow engineers to recognize patterns of incipient reservoir damage and poor performance
Keywords :
engineering computing; fuzzy logic; neural nets; nonlinear systems; oil technology; pattern recognition; surveillance; coupled nonlinear system; fuzzy logic; global trajectory visualization; incipient reservoir damage; intelligent prediction software; intelligent surveillance software; neural network; oil field regions; oil recovery; oil well modelling; oil/water/gas producers; pattern recognition; performance; reservoir damage; similar characteristic behavior; water injectors; waterflood management; Couplings; Fuzzy logic; Hydrocarbon reservoirs; Neural networks; Next generation networking; Nonlinear systems; Petroleum; Surveillance; Visualization; Water resources;
Conference_Titel :
Fuzzy Information Processing Society, 1996. NAFIPS., 1996 Biennial Conference of the North American
Conference_Location :
Berkeley, CA
Print_ISBN :
0-7803-3225-3
DOI :
10.1109/NAFIPS.1996.534723