Relative Importance Analysis of a Refined Multi-parameter Phosphorus Index Employed in a Strongly Agriculturally Influenced Watershed. Zhou, Bin & Vogt, Rolf & Lu, Xueqiang & Xu, Chong-Yu & Zhu, Liang & Shao, Xiaolong & Liu, Honglei & Xing, Meinan. Generalization and Information Storage in Networks of Adaline ‘‘Neurons’’. Conductive fracture identification using neural networks. An Automated Flowing Bottom-Hole Pressure Prediction for a Vertical Well Having Multiphase Flow Using Computational Intelligence Techniques. Gas Lift Optimization Using Artificial Neural Network and Integrated Production Modeling. Shokir, Eissa & Hamed, Mazen & Ibrahim, Azza & Mahgoub, Ismail. Journal of Petroleum Science and Engineering - J PET SCI ENGINEERING. Casing collapse risk assessment and depth prediction with a neural network system approach. Salehi, Saeed & Hareland, Geir & Dehkordi, Keivan & Ganji, Mehdi & Abdollahi, Mahmoud. Gas Lift Optimization using Artificial Neural Network. Artificial Neural Network for Prediction of Hydrate Formation Temperature.
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Petroleum Science and Technology - PET SCI TECHNOL. Prediction of liquid viscosity of pure organic compounds via artificial neural networks. Virtual-Intelligence Applications in Petroleum Engineering: Part 1-Artificial Neural Networks. Artificial Neural Networks Advantages and Disadvantages. Bulletin of Mathematical Biophysics 5, 115-133. A logical calculus of ideas imminent in nervous activity. A nodal approach for applying systems analysis to the following and artificial lift oil or gas well. Artificial Neural Network as a Tool for Reservoir Characterization and its Application in the Petroleum Engineering. Application of Artificial Intelligence to Estimate Oil Flow Rate in Gas-Lift Wells. Machine Learning Derived Correlation to Determine Water Saturation in Complex Lithologies. Iranian Chemical Engineering Journal (Special Issue) – Vol.8 – No. Prediction of Gas Lift Parameters Using Artificial Neural Networks. Khamehchi, E., Rashidi, F., & Rasouli, H. Proceedings of the National Academy of Sciences of the United States of America, 79(8), 2554–2558. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Application of Neural Networks for Improved Gravel-Pack Design. Optimal Hydrate Inhibition Policies with the Aid of Neural Networks. This technique can considerably help in the immediate optimal design of gas lift wells.Įlgibaly, Ahmed & Elkamel, A. It has been concluded that ANN has an excellent competing ability for gas lift optimization prediction compared to conventional methods and can be used interchangeably. Also, this paper presents a new theory about the relative importance of gas lift system input data in predicting optimum parameters of gas lift system. ANN models were trained to obtain the optimum structure and then tested against pipesim models.
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Artificial neural network (ANN) models were also used based on gas lift databases and gas lift monitoring systems.
#PIPESIM GAS LIFT CORRELATIONS SOFTWARE#
In this paper, conventional nodal analysis models using Pipesim software were used to predict the optimization parameters based on wells flowing survey, reservoir and well parameters and calculations of multiphase flow behavior. However, capital costs of compression stations are very high, so it is necessary to optimize gas lift wells by determining the optimum gas lift injection rate and optimum oil rate for each well. Lifting costs for a large number of wells are generally low. It is also used in deep and deviated wells and on offshore platforms. Gas lift has been widely used in the oil fields that suffer from sand production. Gas Lift is employed to maintain the production above the available limit by means of injecting gas into the tubing through the casing–tubing annulus and a gas lift orifice installed in the tubing. Gas lift is one of the most widespread methods of artificial lift technologies used when wells’ production rate drops below the economic limit.