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In this paper, Artificial Neural Networks (ANN) are used to predict the bottom-hole flowing pressure in vertical multiphase flow. Two-phase flow of gas and liquids is commonly encountered in the production and transportation of oil and gas.Knowing the bottom-hole pressure (BHP) of a well and the productivity index (PI or J) can help predict the well potential during its life-cycle. In other words, well production monitoring can be performed, which is a key objective for oil production maximization and operational cost reduction. Different correlations considering different operating conditions and flow models were studied in order to find the most effective input parameters. ANN accuracy is highly dependent on the validity of the input and output data. After gathering the input and output data from selected southern Iranian oil fields, all the data were filtered with the help of existing models to eliminate the unreliable data. Then, 167 data sets were normalized and carefully imported into the ANN models. Different ANN models with different numbers of hidden layers and transfer functions were developed and tested, and the best one with the least error was chosen. The accuracy of the pressure predicted by the developed ANN model was improved by approximately five times as compared with existing correlations. To show the accuracy of the method, the results are compared with those obtained from the existing correlations.

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CO 2 capture and sequestration is inevitable. The concentration of the CO 2 in the atmosphere is increasing continuously which will cause global warming among other consequences. Among storage options, the underground storage in depleted oil and gas reservoirs and unminable coals are considered the most economical storage options. On the other hand, natural gas consumption, which is considered to be a clean fuel, has increased significantly during the past years. Therefore seeking for new unconventional energy resources, especially gas seems to be inevitable. This goal is followed not only because of economical benefits but also because of environmental issues we are encountering these days. The purpose of this study is to develop an Artificial Neural network (ANN) tool to predict the important performance indicators such as methane recovered and CO 2 injected, which are critical in CO2 storage projects in coal seams. We have combined the simulation method with artificial intelligence tools to predict the complex behavior of coal bed methane (CBM) reservoirs.In the first step a simulation is done using CMG software. A dual porosity model, which accounts for the optimum conditions during CO 2 sequestration and consequently the optimum methane recovery from coal bed reservoirs was developed. Then the data extracted from the simulated CBM reservoir was employed to train the ANN model. Different parameters related to the coal seam such as porosity, permeability, initial pressure, thickness, temperature and initial water saturation are considered as the input for the network. The outputs are the CO 2 injected and the recovered methane, which show the performance of the CO 2 injection project. The Back-Propagation learning algorithm was used and different transfer functions and numbers of hidden layers were tried to find the best model with the least error. The tested neural network predictions were plotted versus the real data available and also different error analyses were carried out to prove the accuracy of the model. The R-Squared for the predicted values for the CO 2 injected and the recovered methane were 0.92 and 0.94; the average percent arithmetic deviations were 4.8% and 4.5% respectively.

There are vast resources of heavy oil and bitumen reservoirs in the Western Canadian Basin. For many of them up to 95 % of reserves still remain in place, and by considering the increase in future energy demand these abundant resources can be considered as potential sources for future years. Recently, solvent‐based heavy oil recovery methods such as vapour extraction (VAPEX) have gained attention due to the potential environmental and economic assets over thermal processes. Due to the complexity of the mechanisms associated with the solvent injection process (i.e. diffusion and gravity drainage processes), such models are incapable of accurately predicting the production rate during the VAPEX process. In this study, the artificial neural networks (ANN) technique is utilized to tackle the limitations that analytical methods encounter while predicting the complex relationships, where there is uncertainty, imprecision, and partial truth. Hence, in the first phase of the research a comprehensive experimental study in two large‐scale, visual rectangular VAPEX models was carried out by utilizing various injection solvents. Based on an extensive literature review and experimental results, the drainage height, solvent type, permeability, porosity, and heavy oil viscosity were considered as the inputs of the model to predict the heavy oil production rate as the output of the model. After trying different training scenarios, it was found that the back‐propagation learning algorithm can be successfully used to predict the ultimate recovery factor after implementing the VAPEX method in the heavy oil system of interest.