DEVELOPMENT OF INFILL DRILLING RECOVERY MODELS FOR CARBONATE RESERVOIRS USING NEURAL NETWORKS AND MULTIVARIATE STATISTICAL AS A NOVEL METHOD

DEVELOPMENT OF INFILL DRILLING RECOVERY MODELS FOR CARBONATE RESERVOIRS USING NEURAL NETWORKS AND MULTIVARIATE STATISTICAL AS A NOVEL METHOD

This work introduces a novel methodology to improve reservoir characterization models. In this methodology we integrated multivariate statistical analyses, and neural network models for forecasting the infill drilling ultimate oil recovery from reservoirs in San Andres and Clearfork carbonate format...

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Bibliographic Details
Main Authors: R SOTO, CH. H WU, A. M BUBELA
Format: Article
Language:English
Published: Instituto Colombiano del Petróleo (ICP) - ECOPETROL S.A. 1999-01-01
Series:CT&F Ciencia, Tecnología & Futuro
Subjects:
redes neuronales
yacimientos de carbonatas
modelos de recuperación
perforación de relleno
neural networks
carbonate reservoirs
recovery models
infill drilling
Online Access:http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0122-53831999000100001

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http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0122-53831999000100001

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