Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared
| Autor: | Mohammad Madani, Abouzar Choubineh, Afshin Tatar, Nima Mohamadian, Pejman Ghazaeipour Abarghoyi, David A. Wood, Hamzeh Ghorbani |
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| Rok vydání: | 2020 |
| Předmět: |
Orifice flow meters
Multiple machine-learning algorithm comparisons Flow-rate prediction error analysis 020209 energy Energy Engineering and Power Technology 02 engineering and technology 020401 chemical engineering Square root lcsh:Engineering geology. Rock mechanics. Soil mechanics. Underground construction Geochemistry and Petrology Least squares support vector machine 0202 electrical engineering electronic engineering information engineering Range (statistics) 0204 chemical engineering lcsh:Petroleum refining. Petroleum products Mathematics Adaptive neuro fuzzy inference system Orifice plate Geology Geotechnical Engineering and Engineering Geology Flow-rate-predicting virtual meters Fuel Technology lcsh:TP690-692.5 Multilayer perceptron lcsh:TA703-712 Metrics influencing oil flow Gene expression programming Algorithm Body orifice |
| Zdroj: | Petroleum, Vol 6, Iss 4, Pp 404-414 (2020) |
| ISSN: | 2405-6561 |
| DOI: | 10.1016/j.petlm.2018.09.003 |
| Popis: | Fluid-flow measurements of petroleum can be performed using a variety of equipment such as orifice meters and wellhead chokes. It is useful to understand the relationship between flow rate through orifice meters (Qv) and the five fluid-flow influencing input variables: pressure (P), temperature (T), viscosity (μ), square root of differential pressure (ΔP^0.5), and oil specific gravity (SG). Here we evaluate these relationships using a range of machine-learning algorithms applied to orifice meter data from a pipeline flowing from the Cheshmeh Khosh Iranian oil field. Correlation coefficients indicate that (Qv) has weak to moderate positive correlations with T, P, and μ, a strong positive correlation with the ΔP^0.5, and a weak negative correlation with oil specific gravity. In order to predict the flow rate with reliable accuracy, five machine-learning algorithms are applied to a dataset of 1037 data records (830 used for algorithm training; 207 used for testing) with the full input variable values for the data set provided. The algorithms evaluated are: Adaptive Neuro Fuzzy Inference System (ANFIS), Least Squares Support Vector Machine (LSSVM), Radial Basis Function (RBF), Multilayer Perceptron (MLP), and Gene expression programming (GEP). The prediction performance analysis reveals that all of the applied methods provide predictions at acceptable levels of accuracy. The MLP algorithm achieves the most accurate predictions of orifice meter flow rates for the dataset studied. GEP and RBF also achieve high levels of accuracy. ANFIS and LSSVM perform less well, particularly in the lower flow rate range (i.e. |
| Databáze: | OpenAIRE |
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