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|---|---|---|---|
| 001 | 665379 | ||
| 005 | 20231030042001.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\36578 | ||
| 035 | _aRU\TPU\network\33974 | ||
| 090 | _a665379 | ||
| 100 | _a20210921a2021 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aNL | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aHybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs _fBehesht Abad Abouzar Rajabi, M. Mousavi Seyedmohammadvahid, N. Mohamadian [et al.] |
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| 203 |
_aText _celectronic |
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| 300 | _aTitle screen | ||
| 330 | _aGas condensate reservoirs display unique phase behavior and are highly sensitive to reservoir pressure changes. This makes it difficult to determine their PVT characteristics, including their condensate viscosity, which is a key variable in determining their flow behavior. In this study, a novel machine learning approach is developed to predict condensate viscosity in the near wellbore regions ([mu]c) from five input variables: pressure (P), temperature (T), initial gas to condensate ratio (RS), gas specific gravity ([gamma]g), and condensate gravity (API). Due to the absence of accurate recombination methods for determining [mu]c machine learning methods offer a useful alternative approach. Nine machine learning and hybrid machine learning algorithms are evaluated including novel mul-tiple extreme learning machine (MELM), least squares support vector machine (LSSVM) and multi-layer per-ceptron (MLP) and each hybridized with a particle swarm optimizer (PSO) and genetic algorithm (GA). The new MELM algorithm has some advantages including 1) rapid execution, 2) high accuracy, 3) simple training, 4) avoidance of overfitting, 5) non-linear conversion during training, 6) no trapping at local optima, 6) fewer optimization steps than SVM and LSSVM. Combining MELM with PSO, to find the best control parameters, further improves its performance. Analysis indicates that the MELM-PSO model provides the highest μc predic-tion accuracy achieving a root mean squared error (RMSE) of 0.0035 cP and a coefficient of determination (R2) of 0.9931 for a dataset of 2269 data records compiled from gas-condensate fields around the world. The MELM-PSO algorithm generates no outlying data predictions. Spearman correlation coefficient analysis identifies that P, [gamma]g and Rs are the most influential variables in terms of condensate viscosity based on the large dataset studied. | ||
| 333 | _aРежим доступа: по договору с организацией-держателем ресурса | ||
| 461 | _tJournal of Natural Gas Science and Engineering | ||
| 463 |
_tVol. 95 _v[104210, 26 p.] _d2021 |
||
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _ahybrid machine learning algorithms | |
| 610 | 1 | _agas condensate viscosity | |
| 610 | 1 | _amulti-layer extreme learning machine | |
| 610 | 1 | _amultilayer perceptron | |
| 610 | 1 | _aleast squares support vector machine | |
| 610 | 1 | _aалгоритмы | |
| 610 | 1 | _aмашинное обучение | |
| 610 | 1 | _aвязкость | |
| 610 | 1 | _aгазовые конденсаты | |
| 701 | 0 | _aBehesht Abad Abouzar Rajabi | |
| 701 | 1 |
_aMousavi Seyedmohammadvahid _bM. |
|
| 701 | 1 |
_aMohamadian _bN. _gNima |
|
| 701 | 1 |
_aWood _bD. А. _gDavid |
|
| 701 | 1 |
_aGhorbani _gHamzeh |
|
| 701 | 1 |
_aDavoodi _bSh. _cspecialist in the field of petroleum engineering _cResearch Engineer of Tomsk Polytechnic University _f1990- _gShadfar _2stltpush _3(RuTPU)RU\TPU\pers\46542 |
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| 701 | 1 |
_aAhmadi _bA. M. _gAlvar Mehdi |
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| 701 | 1 |
_aShahbazi _bKh. _gKhalil |
|
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bИнженерная школа природных ресурсов _bОтделение нефтегазового дела _h8084 _2stltpush _3(RuTPU)RU\TPU\col\23546 |
| 801 | 2 |
_aRU _b63413507 _c20210921 _gRCR |
|
| 856 | 4 | _uhttps://doi.org/10.1016/j.jngse.2021.104210 | |
| 942 | _cCF | ||