Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling

Afshar, Mohammad Reza; Gholami, Amin; Asoodeh, Mojtaba

doi:10.1007/s11814-013-0248-8

Cited by 51 publications

(11 citation statements)

References 19 publications

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“…These models have achieved encouraging results in modeling phenomena and parameters such as asphaltene precipitation, minimum miscible pressure, viscosity, porosity, permeability, bubble point pressure (Asoodeh et al 2014a, b;Bagheripour et al 2015;Gholami et al 2014c;Rasuli Nokandeh et al 2012;Hemmati-Sarapardeh et al 2013;Afshar et al 2014;Gholami et al 2015;Naseri et al 2014;Hezarkhani 2014, 2015;Ashoori et al 2010). Moreover, intelligent models have been used for developing a quantitative correlation between the RI and SARA fraction data.…”

Section: Introductionmentioning

confidence: 99%

Prediction of crude oil refractive index through optimized support vector regression: a competition between optimization techniques

Gholami

Ansari

Hosseini

2016

J Petrol Explor Prod Technol

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Refractive index (RI) provides valuable information about various reservoir engineering calculations, making it a key parameter for characterizing crude oils. Determination of this index through experiment is capitalintensive, time consuming, and also toil. Hence, it is essential to search for an efficient and accurate estimation of crude oil RI. In this study, an intelligent approach, based on optimized support vector regression (SVR), is introduced to find a quantitative correlation between crude oil RI and SARA (saturate, aromatic, resin, and asphaltene) fraction data. Optimization of SVR is implemented through three searching approaches, viz. hybrid of grid and pattern search (HGP), genetic algorithm (GA), and imperialist competitive algorithm (ICA). Using these approaches, three models are constructed and tested on experimental data gathered from open source literature. To evaluate the performance of these models, their outputs are compared with corresponding experimental data in terms of statistical criteria. The comparative study clearly shows the advantage of ICA over its rivals (GA and HGP) in optimizing the SVR parameters. ICA optimized support vector regression results in an R 2 of 0.9971 and MSE of 1.48548e-05 demonstrating its efficacy in obtaining crude oil refractive index form SARA data.

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Section: Introductionmentioning

confidence: 99%

Prediction of crude oil refractive index through optimized support vector regression: a competition between optimization techniques

Gholami

Ansari

Hosseini

2016

J Petrol Explor Prod Technol

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“…Achieving this will require determine the optimal values of FL parameters which can be attained through including of optimization model in those formulation. Various efforts have been made to coupling the optimization algorithm and fuzzy model (Zargar et al 2015b;Asoodeh et al 2015;Afshar et al 2014). These studies demonstrate the high efficiency of optimization algorithm in enhancing the performance of fuzzy logic model.…”

Section: Optimized Fuzzy Logicmentioning

confidence: 99%

Fusing of optimized intelligence models by virtue of committee machine for estimation of the residual shear strength of clay

Gholami

Bodaghi

2016

Model. Earth Syst. Environ.

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Owing to high dependency of landslide stability to residual shear strength (RSS) of clay, provide a sophisticated strategy for modeling of this parameter is advantageous. This paper present strategy based upon fusing of optimized intelligence models for estimation of RSS of clay as a function of readily available data. The developed model is achieved through implementing two following steps. In the first step, two optimized models including optimized neural network, and optimized fuzzy logic are developed for estimation of RSS of clay. Optimizing method which implanted in predictive models for improving those performance is bat-inspired algorithm. In second step, committee machine (CM) is employed for combining outputs of aforementioned optimized models. Bat-inspired is incorporated in CM for determining optimal contribution of optimized elements in final prediction. The superior performance of CM rather than its elements is ascertain through those evaluation based on statistical criteria. Results of this study infer that proposed methodology provide an alternative way for making quantitative formulation between RSS of clay and its index properties.Keywords Residual shear strength (RSS) Á Optimized neural network (ONN) Á Optimized fuzzy logic (OFL) Á Committee machine (CM) Á Bat-inspired algorithm (BA)

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“…In this method the difference between output value and corresponding target value in the training dataset which was introduced to the network is calculated. Then the calculated error is propagated backward through the network and the weights and biases of the network are tuned by several iterations to reduce the network performance function (Afshar et al, 2014). The same algorithm can be applied in ANFIS architecture.…”

Section: Background Of Anfismentioning

confidence: 99%

Implementing ANFIS for prediction of reservoir oil solution gas-oil ratio

Zamani

Rafiee-Taghanaki

Karimi

et al. 2015

Journal of Natural Gas Science and Engineering

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Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling

Cited by 51 publications

References 19 publications

Prediction of crude oil refractive index through optimized support vector regression: a competition between optimization techniques

Prediction of crude oil refractive index through optimized support vector regression: a competition between optimization techniques

Fusing of optimized intelligence models by virtue of committee machine for estimation of the residual shear strength of clay

Implementing ANFIS for prediction of reservoir oil solution gas-oil ratio

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