Using neural networks to predict porosity thickness from 3D seismic

First Break

Volumn 18, Issue 9, 2000, Pages 377-384

  Trappe, H ^a   Hellmich, C ^a  

^a TEEC   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

HYDROCARBON RESERVOIR; NEURAL NETWORK; POROSITY;

ARTIFICIAL NEURAL NETWORK; HYDROCARBON RESERVOIR; POROSITY; SEISMIC DATA;

EID: 0033795184     PISSN: 02635046     EISSN: None     Source Type: Journal    
DOI: 10.1046/j.1365-2397.2000.00091.x     Document Type: Article

References (11)

1. Seismic reservoir interpretation of deep Permian carbonates and sandstones (NW German gas province)
2. Evaluation of remaining oil potential with 3D seismic using neural networks
3. Evaluating gas in-place from 3D seismic data - Rotliegend formation, North Germany
4. Part 2: Using artificial neural networks for nonlinear attribute calibration
5. Complex seismic trace analysis
6. Areal prediction of porosity thickness from 3D seismic data by means of neural networks
7. Seismic characterization of Rotliegend reservoirs - from bright spot to stochastic simulation
8. Neuronale Netzwerke zur Permeabilitatsbestimmung in Rotliegendsandsteinen
9. Seismische Reservoircharakterisierung des Stassfurtkarbonates im Westemsland