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Prediction of Permeability Reduction by External Particle Invasion Using Artificial Neural Networks and Fuzzy Models

机译:利用人工神经网络和模糊模型预测外来颗粒侵入引起的渗透率降低。

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The transport of fine particles is one of the major causes of permeability reduction in porous media. A number of mathematical models have been suggested in the literature to simulate and quantify this reduction. The simplest models include analytical solutions of the equations that describe the phenomenon while more complex models are solved by numerical methods. In this study, an Artificial Neural Network (ANN) is developed to predict the permeability reduction by external particle invasion in non-consolidated porous medium. A comparison was also made with the results of a Fuzzy Model (FM) developed for the same purpose. For the training process the results of 42 laboratory experiments were employed. The input data covered an extensive range of porosity, permeability, injection rates and fines concentrations. The developed ANN and FM were tested with 8 sets of experiments that were not used in the training. The results show that the ANN can match and predict with high precision the permeability reduction as a function of pore volumes of fine suspensions injected. The FM predicts the permeability reduction with moderated precision.
机译:微粒的运输是多孔介质中渗透率降低的主要原因之一。在文献中已经提出了许多数学模型来模拟和量化这种减少。最简单的模型包括描述现象的方程的解析解,而更复杂的模型则通过数值方法求解。在这项研究中,开发了一个人工神经网络(ANN)来预测外部颗粒在非固结多孔介质中的渗透率降低。还针对相同目的开发的模糊模型(FM)的结果进行了比较。在培训过程中,采用了42个实验室实验的结果。输入数据涵盖了广泛的孔隙率,渗透率,注入速率和细粉浓度。已开发的ANN和FM通过8套未在训练中使用的实验进行了测试。结果表明,人工神经网络可以精确地匹配和预测渗透率的降低,该降低率是所注入的精细悬浮液的孔体积的函数。 FM以适中的精度预测渗透率的降低。

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