الفهرس 
CHAPTER 1: INTRODUCTION AND JET PUMPOnly 14 pages are availabe for public view
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Abstract
Production rate from oil fields is reduced due to various parameters with time due to the decrease in reservoir pressure. So, it is necessary to use some methods to compensate the reduction of production rate. Artificial lift refers to use of artificial means to increase the flow of liquids, such as crude oil or water, from a production well and is the most suitable way to increase production rate. It is achieved by the reduction of down-hole pressure. Artificial lift includes many methods and it is very important to select the best method, considering the field conditions. In this master thesis jet pumps have been reviewed. Jet pumps are considered as a high volume artificial lift method and operate by passing a high pressure fluid jet through a venturi-system. A one-dimensional mathematical model for the jet pump is presented and discussed. Because many oil-wells produce a mixture of gas and liquid, the jet pump model is extended to two-phase flow model. In addition, the pressure at jet pump exit is necessary for the jet pump model. Therefore, a two-phase pressure DROP model is needed to be combined with the jet pump model to facilitate the application of the model in oil field application. Several two-phase pressure DROP models ranged in simplicity from homogenous to the more elaborated mechanistic models are applied in the present study along with the artificial neural network model (ANN). The presented ANN is learned by a large data base collected from four different sources of 7581 data sets including 1165 data sets from Magapetco EEMM field. The data sets cover the most of flow regimes and all inclination angle ranges from -90° to 90°. Three statistical parameters were introduced to select the best-performing model. The test data were also adopted to explore the effects of pipe diameter, gas-liquid ratio, liquid types and pipe inclination on liquid holdup. The analytical model was verified experimentally in the present work. The comparison reveals that the neural network model has a good accuracy results among the tested models. In addition, the traverse procedure is examined by comparing the ANN model predictions with measured pressure distribution along the tubing of four flowing wells from EEMM field and the predictions of the commercial code, PROSPER. It can be concluded that the ANN model can predict the pressure distribution with an acceptable accuracy. Also different tests applied to predict the down-hole pressure of 11 flowing wells obtained from literature and four cases from EEMM indicated that the ANN model predicted the down-hole pressure with an acceptable error.
The jet pump model is validated by comparing the model predictions with single phase and two-phase experimental and numerical results from the literature. The comparison shows that the proposed model can predict the single phase jet pump performance with good accuracy while it fail to predict the two-phase jet pump performance at gas-liquid ratio higher than that corresponding to the maximum efficiency. The jet pump model and the ANN model are used together to predict the performance of three oil well jet pumps from which two cases are from EEMM field. The results show that the combined model can predict the oil well jet pump performance with a fair accuracy.