Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes. (December 2020)

• Record Type: Journal Article
• Authors: Ghorbani, Hamzeh; Wood, David A.; Mohamadian, Nima; Rashidi, Sina; Davoodi, Shadfar; Soleimanian, Alireza; Shahvand, Amirafzal Kiani; Mehrad, Mohammad
• Is Part Of: Flow measurement and instrumentation. Volume 76(2020)
• Publication Date: 2020-12
• Subjects: Multi-phase oil -- Gas -- Water flow rate -- Fuzzy machine learning -- Wellhead choke variables -- Empirical relationships -- Takagi-Sugeno fuzzy inference system -- Fuzzy system control; Fluid dynamic measurements -- Periodicals; Flow meters -- Periodicals; Fluides, Dynamique des -- Mesure -- Périodiques; Débitmètres -- Périodiques; 681.2805
• DOI: 10.1016/j.flowmeasinst.2020.101849
• Languages: English
• ISSNs: 0955-5986

Abstract

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 ( Q L ) 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. Highlights: Takagi-Sugeno fuzzy inference system predicts choke fluid flow rates. Adaptive neuro-fuzzy algorithm assists in optimizing fuzzy logic rules. Flow rates through wellhead choke predicted with high accuracy with 4 input variables. Upstream pressure, choke size, gas/liquid, base solids & water are the key inputs. Fuzzy inferences system is readily adapted for use with fuzzy choke controllers.