Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector

Roshani, Gholam Hossein; Feghhi, S.A.H.; Mahmoudi-Aznaveh, Ahmad; Adineh-Vand, A.

doi:10.1016/j.measurement.2014.01.030

Cited by 129 publications

(46 citation statements)

References 15 publications

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“…Many researchers have used the different types of ANNs in gamma densitometry [3][4][5][6][7][8][9][10][11][12][13] in order to classification, clustering and prediction. Cong et al [14] reviewed applications of ANNs in flow and heat transfer problems in nuclear engineering.…”

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confidence: 99%

Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation

2015

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confidence: 99%

Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation

2015

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“…This experiment compared the proposed model with a model trained using the BP neural network. The BP neural network [2,[24][25][26], support vector machine (SVM) [27][28][29], artificial neural network (ANN) [4,[30][31][32], radial basis function neural network (RBFNN) [3,33], K-nearest neighbor (KNN) [34], and decision tree [34] methods are widely used in the field of oil and gas prediction. Logging data and fracturing data are averaged separately for each feature of each well.…”

Section: Influence Of Data Normalization On Accuracymentioning

confidence: 99%

A Novel Multi-Input AlexNet Prediction Model for Oil and Gas Production

Wang

Yin

Guo

et al. 2018

Mathematical Problems in Engineering

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In the process of oilfield development, it is important to predict the oil and gas production. The predicted value of oil production is the amount of oil that may be obtained within a certain area over a certain period. Because of the current demand for oil and gas production prediction, a prediction model using a multi-input convolutional neural network based on AlexNet is proposed in this paper. The model predicts real oilfield data and achieves good results: increasing prediction accuracy by 17.5%, 20.8%, 11.6%, 8.9%, 6.9%, and 14.9% with respect to the backpropagation neural network, support vector machine, artificial neural network, radial basis function neural network, K-nearest neighbor, and decision tree methods, respectively. It addresses the uncertainty of oil and gas production caused by the change in parameter values during the process of petroleum exploitation and has far-reaching application significance.

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“…Nazemi et al also used two detectors and one radioactive source to predict the void fraction independent of flow regime with this difference that both of detectors registered transmitted photons (Nazemi et al 2016a). More studies about radiation-based nuclear gauges and also some applications of ANN in nuclear engineering can be found in references El Abd (2014), Jing et al (2006), Salgado et al (2014), Nazemi et al (2014), Hanus et al (2014a, b), Mosorov et al (2016), Zych et al (2014), Jung et al (2009), Nazemi et al (2015Nazemi et al ( , 2016b Karami et al (2018), Roshani and Nazemi (2018) and Roshani et al (2013Roshani et al ( , 2014Roshani et al ( , 2016Roshani et al ( , 2017aRoshani et al ( , 2018a.…”

Section: Introductionmentioning

confidence: 99%

An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network to model the stratified three-phase flow of gas–oil–water

2019

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The problem of how to accurately measure the volume fractions of oil-gas-water mixtures in a pipeline remains as one of the key challenges in the petroleum industry. The current research highlights the capability of a hybrid system of the Jaya optimization algorithm and the adaptive neuro-fuzzy inference system (ANFIS), to model the stratified three-phase flow of gas-oil-water. As a matter of fact, the present study devotes to forecast the volume fractions in the stratified three-phase flow, on the basis of a dual-energy metering system, including the 152 Eu and 137 Cs and one NaI detector, using the aforementioned hybrid model. Since the summation of volume fractions are constant (equal to 100%), a constraint modelling problem exists, meaning that the hybrid model must forecast only two volume fractions. In this paper, three main hybrid models are employed. The first network is applied to forecast the gas and water volume fractions, the next one to forecast the water and oil volume fractions, and the last one to forecast the oil and gas volume fractions. For the next step, the hybrid models are trained based on numerically obtained data from the MCNP-X code.

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Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector

Cited by 129 publications

References 15 publications

Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation

Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation

A Novel Multi-Input AlexNet Prediction Model for Oil and Gas Production

An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network to model the stratified three-phase flow of gas–oil–water

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