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Application of neural networks for analysis, imitation modelling and optimal control of oil fields

机译:神经网络在油田分析,模拟建模和最优控制中的应用

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摘要

It is necessary to solve monitoring problems of oil recovery, analysis problems of oil reservoir conditions, problems of fluid filtration processes modelling and to form forecasts for effective oil field development. Results of these researches are the information and intellectual base for decision making in optimal control of oil field development systems. However, traditional methods of analysis and mathematical modelling of oil reservoirs and their software implementation are not always suitable for the use by oil producers and engineers and modelling itself is more "state of art" than professional. These traditional analysis methods are based on solving of differential equations and boundary problems of mathematical physics. The development of new technologies of imitation modelling is necessary for solving existing mathematical modelling problems. In this report the authors discuss possibilities of neural network technology application for solving some live problems of analysis and modelling problems of systems with hydrodynamic interacting wells and oil reservoirs.
机译:有必要解决石油开采的监测问题,油藏条件分析问题,流体过滤过程建模问题,并为有效的油田开发形成预测。这些研究的结果为油田开发系统的最佳控制决策提供了信息和知识基础。但是,传统的油藏分析和数学建模方法及其软件实现并不总是适合石油生产商和工程师使用,建模本身比专业人士更“先进”。这些传统的分析方法基于微分方程和数学物理学的边界问题的求解。模仿建模的新技术的发展对于解决现有的数学建模问题是必要的。在本报告中,作者讨论了神经网络技术在解决具有流体动力相互作用的油井和油藏的系统的一些实时分析和建模问题中的应用可能性。

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