Integration of genetic algorithm and a coactive neuro-fuzzy inference system for permeability prediction from well logs data |
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Authors: | Mohsen Saemi Morteza Ahmadi |
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Institution: | (1) Mining Engineering Department, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran |
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Abstract: | Permeability is one of the reservoir fundamental properties, which relate to the amount of fluid contained in a reservoir
and its ability to flow. These properties have a significant impact on petroleum fields operations and reservoir management.
The most reliable data of local permeability are taken from laboratory analysis of cores. Extensive coring is very expensive
and this expense becomes reasonable in very limited cases. Thus, the proper determination of the permeability is of paramount
importance because it affects the economy of the whole venture of development and operation of a field. In this study, we
introduce a new hybrid network based on Coactive Neuro-Fuzzy Inference System (CANFIS). CANFIS is a dependable and robust
network that developed to identify a non-linear relationship and mapping between petrophysical data and core samples. Then
to improve the system performance, genetic algorithm (GA) was integrated in order to search of optimal network parameters
and decrease of noisy data in training samples. An Iranian offshore gas field is located in the Persian Gulf, has been selected
as the study area in this paper. Well log data are available on substantial number of wells. Core samples are also available
from a few wells. It was shown that the new proposed strategy is an effective method in predicting permeability from well
logs. |
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Keywords: | Permeability Coactive neuro-fuzzy inference systems Genetic algorithm Petrophysical data Artificial intelligence |
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