Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes; Flow Measurement and Instrumentation; Vol. 76

Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes; Flow Measurement and Instrumentation; Vol. 76

Detaylı Bibliyografya
Parent link:Flow Measurement and Instrumentation
Vol. 76.— 2020.— [101849, 18 p.]
Müşterek Yazar: Национальный исследовательский Томский политехнический университет Инженерная школа природных ресурсов Отделение нефтегазового дела
Diğer Yazarlar: Ghorbani H. Hamzeh, Wood D. A. David, Mohamadian N. Nima, Rashidi S. Sina, Davoodi Sh. Shadfar, Soleimanian A. Alireza, Shahvand A. K. Amirafzal Kiani, Mehrad M. Mohammad
Özet:Title screen
A Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (QL) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble oil emulsion (BS&W). The test data points evaluated include a wide range of oil flow rate conditions and values for the four input variables recorded. The TSFIS algorithm applied involves five data processing steps: a) pre-processing, b) fuzzification, c) rules base and adaptive neuro-fuzzy inference engine, d) defuzzification, and e) post-processing of the fuzzy model. The developed TSFIS model for the Resalat oil field database predicted oil flow rate to a high degree of accuracy (root mean square error = 247 STB/D, correlation coefficient = 0.9987), which improves substantially on the commonly used empirical algorithms used for such predictions. TSFIS can potentially be applied in wellhead choke fuzzy controllers to stabilize flow in specific wells based on real-time input data records.
Режим доступа: по договору с организацией-держателем ресурса
Dil:İngilizce
Baskı/Yayın Bilgisi: 2020
Konular:
электронный ресурс
труды учёных ТПУ
multi-phase oil
gas
water flow rate
fuzzy machine learning
wellhead choke variables
empirical relationships
Takagi-Sugeno fuzzy inference system
fuzzy system control
машинное обучение
эмпирические особенности
Online Erişim:https://doi.org/10.1016/j.flowmeasinst.2020.101849
Materyal Türü: Elektronik Kitap Bölümü
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=663280

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