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首页> 外文期刊>Applied Soft Computing >Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir
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Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir

机译：演化人工神经网络与帝国竞争算法预测储层含油量。

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摘要

Multiphase flow meters (MPFMs) are utilized to provide quick and accurate well test data in numerous numbers of oil production applications like those in remote or unmanned locations topside exploitations that minimize platform space and subsea applications. Flow rates of phases (oil, gas and water) are most important parameter which is detected by MPFMs. Conventional MPFM data collecting is done in long periods; because of radioactive sources usage as detector and unmanned location due to wells far distance. In this paper, based on a real case of MPFM, a new method for oil rate prediction of wells base on Fuzzy logic, Artificial Neural Networks (ANN) and Imperialist Competitive Algorithm is presented. Temperatures and pressures of lines have been set as input variable of network and oil flow rate as output. In this case a 1600 data set of 50 wells in one of the northern Persian Gulf oil fields of Iran were used to build a database. ICA-ANN can be used as a reliable alternative way without personal and environmental problems. The performance of the ICA-ANN model has also been compared with ANN model and Fuzzy model. The results prove the effectiveness, robustness and compatibility of the ICA-ANN model.
机译：多相流量计（MPFM）用于在众多采油应用中提供快速，准确的试井数据，例如在偏远地区或无人值守的顶侧开采中，从而最大限度地减少了平台空间和海底应用。相（油，气和水）的流速是MPFM检测到的最重要的参数。传统的MPFM数据收集需要很长时间。由于放射源被用作探测器，而且由于井的距离太远，因此无人值守。本文基于MPFM的实际情况，提出了一种基于模糊逻辑，人工神经网络和帝国主义竞争算法的油井预测新方法。管路的温度和压力已设置为网络的输入变量，油流量已设置为输出。在这种情况下，使用伊朗北部波斯湾北部油田之一的1600个数据集（含50口井）来建立数据库。 ICA-ANN可以用作可靠的替代方法，而不会造成个人和环境问题。 ICA-ANN模型的性能也已与ANN模型和Fuzzy模型进行了比较。结果证明了ICA-ANN模型的有效性，鲁棒性和兼容性。

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