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Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes
, Article Flow Measurement and Instrumentation ; Volume 76 , 2020 ; Wood, D. A ; Mohamadian, N ; Rashidi, S ; Davoodi, S ; Soleimanian, A ; Kiani Shahvand, A ; Mehrad, M ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
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...