It is very important to determine or predict the bubble point pressure (BPP) with high accuracy in petroleum industry. Laboratory measurement of the BPP requires collecting actual samples from the bottom of the wellbore and simulates the reservoir conditions at the lab. This operation takes long time and high cost. To overcome this issue, many empirical correlations were developed to predict the BPP with wide range of average percent error. In this research, we will use artificial intelligent (AI) techniques to predict the bubble point pressure using published data (760 data sets). Two different AI techniques will be used, artificial neural network (ANN) (back propagation network (BPN) and radial basis functions networks (RBF)), and fuzzy logic tool (FL) to develop the model. The obtained results will be compared with the available correlations in the literature. The results obtained showed that all AI models were able to predict the bubble point pressure with a high accuracy. The new fuzzy logic (FL) model outperforms all the artificial neural network models and the most common published empirical correlations. BPN, RBF and FL models provide predictions of bubble point pressure with correlation coefficient of 0.9926, 0.9969, and 0.9995, respectively.

Well Inflow Performance Relationship (IPR) has a wide range of applications in both applied and theoretical sciences, especially in the petroleum production engineering. An accurate prediction of well IPR is very important to determine the optimum production scheme, design production equipment, and artificial lift systems. For these reasons, there is a need for a quick and reliable method for predicting oil well IPR in solution gas drive reservoirs. In this paper, back propagation network (BPN) and fuzzy logic (FL) techniques are used to predict oil well IPR in solution gas drive reservoirs. The models were developed using 207 data points collected from unpublished sources. Statistical analysis was performed to define the more reliable and accurate techniques to predict the IPR. According to the results, the new fuzzy logic well IPR model outperformed the artificial neural networks (ANN) model and the most common empirical correlations. The average absolute error, least standard deviation and highest correlation coefficient were used to evaluate the models results. The proposed fuzzy logic well inflow performance relationship model achieved an average absolute error of 1.8 %, standard deviation of 2.9% and the correlation coefficient of 0.997. The developed technique will help the production and reservoir engineers to better manage the production operation without the need for any additional equipment. It will also reduce the overall operating cost and increase the revenue.

Crude oil viscosity is a significant parameter for the fluid flow in both porous media and pipe lines. Therefore, it has to be determined using highly accurate methods. Oil viscosity is usually predicted with the correlations obtained from the laboratory measured data. However, some of the presented correlations have very complicated assumptions which make them very difficult to apply in most of the case studies reported. On the other hand, simplified correlations companies the accuracy. The present work in this paper studies predictive capabilities of Artificial Intelligence (AI) to estimate the oil viscosity. Artificial Neural Network (ANN) models are proposed to predict the undersaturated, saturated and dead oil viscosity in Yemeni fields. A data set consisting 545 of laboratory measurements on oil samples was gathered from different oil fields in Yemen. 70% of the data points were used to train the proposed ANN models while the remaining data set was tested the model performance. The performance of the ANN methods was compared with some of the conventional correlations such as (Beal's correlation, Khan's correlation, Kartoatmodjo and Schmidt correlation, Vasquez-Begg's correlation, Chew and Connaly correlation, Beggs and Robinson correlation, Elsharqawy correlation and Glaso's correlation). The result of this study shows the superiority of the Artificial Neural Network (ANN) models over the current models for predicting oil viscosity from PVT data. The comparative results displayed that the proposed ANN models performed better with higher accuracy than those obtained with published correlations.

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