| 000 | 02768nlm1a2200385 4500 | ||
|---|---|---|---|
| 001 | 663661 | ||
| 005 | 20231030041902.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\34831 | ||
| 035 | _aRU\TPU\network\34449 | ||
| 090 | _a663661 | ||
| 100 | _a20210224a2021 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aNL | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aPrediction of oil flow rate through orifice flow meters: Optimized machine-learning techniques _fM. Farsi, B. H. Shojaei, D. Wood [et al.] |
|
| 203 |
_aText _celectronic |
||
| 300 | _aTitle screen | ||
| 330 | _aFlow measurement is an essential requirement for monitoring and controlling oil movements through pipelines and facilities. However, delivering reliably accurate measurements through certain meters requires cumbersome calculations that can be simplified by using supervised machine learning techniques exploiting optimizers. In this study, a dataset of 6292 data records with seven input variables relating to oil flow through 40 pipelines plus processing facilities in southwestern Iran is evaluated with hybrid machine-learning-optimizer models to predict a wide range of oil flow rates (Qo) through orifice plate meters. Distance-weighted K-nearest-neighbor (DWKNN) and multi-layer perceptron (MLP) algorithms are coupled with artificial-bee colony (ABC) and firefly (FF) swarm-type optimizers. The two-stage ABC-DWKNN Plus MLP-FF model achieved the highest prediction accuracy (root mean square errors = 8.70 stock-tank barrels of oil per day) for oil flow rate through the orifice plates, thereby removing dependence on unreliable empirical formulas in such flow calculations. | ||
| 333 | _aРежим доступа: по договору с организацией-держателем ресурса | ||
| 461 | _tMeasurement | ||
| 463 |
_tVol. 174 _v[108943, 17 p.] _d2021 |
||
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 701 | 1 |
_aFarsi _bM. _gMohammad |
|
| 701 | 1 |
_aShojaei _bB. H. _gBarjouei Hossein |
|
| 701 | 1 |
_aWood _bD. _gDavid |
|
| 701 | 1 |
_aGhorbani _bH. _gHamzeh |
|
| 701 | 1 |
_aMohamadian _bN. _gNima |
|
| 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 |
|
| 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 _c20210224 _gRCR |
|
| 856 | 4 | _uhttps://doi.org/10.1016/j.measurement.2020.108943 | |
| 942 | _cCF | ||