Integration of genetic algorithm and a coactive neuro-fuzzy inference system for permeability prediction from well logs data

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.

Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation: case study of Nasser Lake in Egypt

“…The procedure also includes the normalizing axon, which aims to normalize the output variables into a range (0, 1). The fuzzy axons are very useful for MF properties, which can be changed though the model of back-propagation (Saemi & Ahmadi, 2008). In the current research, the original CANFIS algorithm is improved.…”

Using fuzzy logic for estimating monthly pan evaporation from meteorological data in Emara/ South of Iraq

“…Fuzzy inference is the process of formulating the mapping from a given input to an output equation using fuzzy logic, and then the mapping provides a basis from which decisions can be made or discerned. Basically, fuzzy logic system has four components [10] as follows (Fig. 3); Fuzzification is the process of decomposing a system input and/or output into one or more fuzzy sets, Fuzzy Rules is IF-THEN rule statements which are used to formulate the conditional statements that comprise fuzzy logic, Fuzzy Inference Engine is a process that elaborates and combines rule outputs, and Defuzzification is a process that transforms the fuzzy output into a crisp domain.…”

Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data

“…In the case of multilayer algorithms, no fully comprehensive quest of entire aspirant models nevertheless the time of computation is abridged and the independent amount of variables in the line of being processed turn out to be larger. Regarding the nature of making it active and effective function, the algorithms of the GMDH network can be illustrious into harmonic, multiplicative-additive, fuzzy and polynomials [40,41].…”