| dc.description.abstract | Accurate measurement of flow rate of the multiphase flow of oil, gas and water from the oil wells, is an important part of the oil and gas industry. This enables the safe operation and proper optimization of the production. Therefore much research has been dedicated to improve the accuracy of measurements. Various methods like Virtual flow metering and Multi phase flow meters are used. With the increasing availability of process data, machine learning algorithms have been applied to create models that are beneficial to the oil and gas industry. They can be used for various parameter estimations, predictive maintenance and so on. The application of these algorithms for flow rate estimation provides a more accurate representation of the oil and gas production process.The goal of this thesis is to use the simulator data, to create machine learning models. These models are used to predict the flow rates of oil, gas and water from the wells. Two oil wells are evaluated here. Ten machine learning algorithms are evaluated. LSTM provides the best results with MAPE of 1.96\% for Well 1 and 1.56\% for Well 2. In addition, the effects of noise on the models are explored. Median filter with window size of three provides good noise reduction. Finally the uncertainty of the prediction are quantified using 95\% confidence intervals in XGBoost models. | |
