TY - JOUR
T1 - Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes
AU - Ghorbani, Hamzeh
AU - Wood, David A.
AU - Mohamadian, Nima
AU - Rashidi, Sina
AU - Davoodi, Shadfar
AU - Soleimanian, Alireza
AU - Shahvand, Amirafzal Kiani
AU - Mehrad, Mohammad
N1 - Funding Information:
Thanks to National Iranian South Oil Company (NISOC) employees specially Mr. Kooti, Mr. Mansoori, Ms. Kalaei who helped us to compile the dataset.
Publisher Copyright:
© 2020 Elsevier Ltd
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - Empirical relationships
KW - Fuzzy machine learning
KW - Fuzzy system control
KW - Gas
KW - Multi-phase oil
KW - Takagi-Sugeno fuzzy inference system
KW - Water flow rate
KW - Wellhead choke variables
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U2 - 10.1016/j.flowmeasinst.2020.101849
DO - 10.1016/j.flowmeasinst.2020.101849
M3 - Article
AN - SCOPUS:85096495600
VL - 76
JO - Flow Measurement and Instrumentation
JF - Flow Measurement and Instrumentation
SN - 0955-5986
M1 - 101849
ER -