Geological property modeling with neural network representations

Geological property modeling with neural network representations

A neural network trainer trains neural networks to estimate secondary data at locations throughout a geological formation where secondary data is unknown. The neural networks are trained to estimate secondary data using locations in the geological formation as input. Subsequently, the secondary data...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Ward, Steven Bryan, Shi, Genbao, Hassanpour, Mehran
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung:A neural network trainer trains neural networks to estimate secondary data at locations throughout a geological formation where secondary data is unknown. The neural networks are trained to estimate secondary data using locations in the geological formation as input. Subsequently, the secondary data is deleted from memory using the trained neural network as a proxy representation to reduce memory footprint and allow for estimation of secondary data at locations where it is unknown.