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An automata networks based preprocessing technique for artificial neural network modelling of primary production levels in reservoirs

机译：基于自动机网络的预处理技术，用于油藏初级生产水平的人工神经网络建模

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摘要

Primary production in lakes and reservoirs develops as a result of complex reactions and interactions. Artificial neural networks (ANN) emerges as an approach in quantification of primary productivity in reservoirs. Almost all of the past ANN applications employed input data matrices whose vectors represent either water quality parameters or environmental characteristics. Most of the time, the components of input matrices are determined using expert opinion that implies possible factors that affect output vector. Major disadvantage of this approach is the possibility of ending-up with an input matrix that may have high correlations between some of its components. In this paper, an automata networks (AN) based preprocessing technique was developed to select suitable and appropriate constituents of input matrix to eliminate redundancy and to enhance calculation efficiency. The proposed technique specifically provides an apriori rough behavioral modeling through identification of minimal AN interaction topology. Predictive ANN models of primary production levels were developed for a reservoir following AN based pre-modeling step. The achieved levels of model precisions and performances were acceptable: the calculated root mean square error values (RMSE) were low; a correlation coefficient (R) as high as 0.83 was achieved with an ANN model of a specific structure. (c) 2006 Elsevier B.V. All rights reserved.
机译：由于复杂的反应和相互作用，湖泊和水库的初级生产得以发展。人工神经网络（ANN）作为量化储层初级生产力的一种方法而出现。几乎所有过去的ANN应用程序都使用输入数据矩阵，其矢量代表水质参数或环境特征。在大多数情况下，输入矩阵的成分是使用专家意见确定的，专家意见暗示了可能影响输出向量的因素。这种方法的主要缺点是可能导致输入矩阵的某些部分之间可能具有高度相关性。在本文中，开发了一种基于自动机网络（AN）的预处理技术，以选择合适和适当的输入矩阵组成，以消除冗余并提高计算效率。所提出的技术通过识别最小AN交互拓扑，专门提供了先验的粗略行为建模。在基于AN的预建模步骤之后，为储层开发了主要生产水平的预测ANN模型。达到的模型精度和性能水平是可以接受的：计算出的均方根误差值（RMSE）低;使用特定结构的ANN模型，相关系数（R）高达0.83。 （c）2006 Elsevier B.V.保留所有权利。

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