Fuzzy ruling between core porosity and petrophysical logs: Subtractive clustering vs. genetic algorithm–pattern search

Porosity, the void portion of reservoir rocks, determines the volume of hydrocarbon accumulation and has a great control on assessment and development of hydrocarbon reservoirs. Accurate determination of porosity from core analysis is highly cost, time, and labor intensive. Therefore, the mission of...

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Vydané v:	Journal of applied geophysics Ročník 99; s. 35 - 41
Hlavní autori:	Bagheripour, Parisa, Asoodeh, Mojtaba
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.12.2013
Predmet:	Algorithms Carbonate reservoir rocks Core porosity Fuzzy Fuzzy logic Fuzzy set theory Genetic algorithm–pattern search Logs Optimization Porosity Reservoirs Searching Subtractive clustering Fuzzy logic Core porosity Genetic algorithm–pattern search Carbonate reservoir rocks Subtractive clustering Optimization
ISSN:	0926-9851, 1879-1859
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Abstract	Porosity, the void portion of reservoir rocks, determines the volume of hydrocarbon accumulation and has a great control on assessment and development of hydrocarbon reservoirs. Accurate determination of porosity from core analysis is highly cost, time, and labor intensive. Therefore, the mission of finding an accurate, fast and cheap way of determining porosity is unavoidable. On the other hand, conventional well log data, available in almost all wells contain invaluable implicit information about the porosity. Therefore, an intelligent system can explicate this information. Fuzzy logic is a powerful tool for handling geosciences problem which is associated with uncertainty. However, determination of the best fuzzy formulation is still an issue. This study purposes an improved strategy, called hybrid genetic algorithm–pattern search (GA–PS) technique, against the widely held subtractive clustering (SC) method for setting up fuzzy rules between core porosity and petrophysical logs. Hybrid GA–PS technique is capable of extracting optimal parameters for fuzzy clusters (membership functions) which consequently results in the best fuzzy formulation. Results indicate that GA–PS technique manipulates both mean and variance of Gaussian membership functions contrary to SC that only has a control on mean of Gaussian membership functions. A comparison between hybrid GA–PS technique and SC method confirmed the superiority of GA–PS technique in setting up fuzzy rules. The proposed strategy was successfully applied to one of the Iranian carbonate reservoir rocks. •Fuzzy logic (FL) method was used for formulating conventional logs to porosity.•Fuzzy rules traditionally were extracted by subtractive clustering (SC) method.•FL model was optimized by hybrid genetic algorithm-pattern search (GA–PS) technique.•Comparison between GA–PS and SC methods showed superiority of GA–PS.
AbstractList	Porosity, the void portion of reservoir rocks, determines the volume of hydrocarbon accumulation and has a great control on assessment and development of hydrocarbon reservoirs. Accurate determination of porosity from core analysis is highly cost, time, and labor intensive. Therefore, the mission of finding an accurate, fast and cheap way of determining porosity is unavoidable. On the other hand, conventional well log data, available in almost all wells contain invaluable implicit information about the porosity. Therefore, an intelligent system can explicate this information. Fuzzy logic is a powerful tool for handling geosciences problem which is associated with uncertainty. However, determination of the best fuzzy formulation is still an issue. This study purposes an improved strategy, called hybrid genetic algorithm-pattern search (GA-PS) technique, against the widely held subtractive clustering (SC) method for setting up fuzzy rules between core porosity and petrophysical logs. Hybrid GA-PS technique is capable of extracting optimal parameters for fuzzy clusters (membership functions) which consequently results in the best fuzzy formulation. Results indicate that GA-PS technique manipulates both mean and variance of Gaussian membership functions contrary to SC that only has a control on mean of Gaussian membership functions. A comparison between hybrid GA-PS technique and SC method confirmed the superiority of GA-PS technique in setting up fuzzy rules. The proposed strategy was successfully applied to one of the Iranian carbonate reservoir rocks. Porosity, the void portion of reservoir rocks, determines the volume of hydrocarbon accumulation and has a great control on assessment and development of hydrocarbon reservoirs. Accurate determination of porosity from core analysis is highly cost, time, and labor intensive. Therefore, the mission of finding an accurate, fast and cheap way of determining porosity is unavoidable. On the other hand, conventional well log data, available in almost all wells contain invaluable implicit information about the porosity. Therefore, an intelligent system can explicate this information. Fuzzy logic is a powerful tool for handling geosciences problem which is associated with uncertainty. However, determination of the best fuzzy formulation is still an issue. This study purposes an improved strategy, called hybrid genetic algorithm–pattern search (GA–PS) technique, against the widely held subtractive clustering (SC) method for setting up fuzzy rules between core porosity and petrophysical logs. Hybrid GA–PS technique is capable of extracting optimal parameters for fuzzy clusters (membership functions) which consequently results in the best fuzzy formulation. Results indicate that GA–PS technique manipulates both mean and variance of Gaussian membership functions contrary to SC that only has a control on mean of Gaussian membership functions. A comparison between hybrid GA–PS technique and SC method confirmed the superiority of GA–PS technique in setting up fuzzy rules. The proposed strategy was successfully applied to one of the Iranian carbonate reservoir rocks. •Fuzzy logic (FL) method was used for formulating conventional logs to porosity.•Fuzzy rules traditionally were extracted by subtractive clustering (SC) method.•FL model was optimized by hybrid genetic algorithm-pattern search (GA–PS) technique.•Comparison between GA–PS and SC methods showed superiority of GA–PS.
Author	Asoodeh, Mojtaba Bagheripour, Parisa
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Cites_doi	10.1109/TSMC.1985.6313399 10.2118/62415-JPT 10.1016/S0020-7373(75)80002-2 10.1088/1742-2132/3/4/007 10.1090/S0025-5718-97-00777-1 10.1190/1.1487080 10.1109/91.928739 10.2478/s13533-012-0129-4 10.1007/s00603-011-0181-2 10.1016/S0920-4105(00)00037-1 10.1016/j.petrol.2012.04.021 10.1046/j.1365-2478.2001.00271.x 10.1016/S0004-3702(01)00141-2 10.1016/j.jappgeo.2013.06.006 10.2118/93269-MS 10.1016/S0019-9958(65)90241-X
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Keywords	Fuzzy logic Core porosity Genetic algorithm–pattern search Carbonate reservoir rocks Subtractive clustering Optimization
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Snippet	Porosity, the void portion of reservoir rocks, determines the volume of hydrocarbon accumulation and has a great control on assessment and development of...
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SubjectTerms	Algorithms Carbonate reservoir rocks Core porosity Fuzzy Fuzzy logic Fuzzy set theory Genetic algorithm–pattern search Logs Optimization Porosity Reservoirs Searching Subtractive clustering
Title	Fuzzy ruling between core porosity and petrophysical logs: Subtractive clustering vs. genetic algorithm–pattern search
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