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首页> 外文期刊>Energy sources >The Prediction of Photoelectric Factor, Formation True Resistivity, and Formation Water Saturation from Petrophysical Well Log Data: A Committee Neural Network Approach
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The Prediction of Photoelectric Factor, Formation True Resistivity, and Formation Water Saturation from Petrophysical Well Log Data: A Committee Neural Network Approach

机译：用岩石物理测井资料预测光电因子，地层真实电阻率和地层水饱和度：委员会神经网络方法

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摘要

Photoelectric factor, formation true resistivity, and formation water saturation are three functional parameters of a hydrocarbon reservoir that could provide invaluable data for reservoir characterization and formation evaluation. The present study proposes an improved strategy for making a quantitative formulation between conventional well log data and the mentionewd parameters. At the first stage of this study, three architectures of artificial neural networks, including generalized regression neural network, radial basis neural network, and Bayesian regulation backpropagation neural network, were employed to predict the aforementioned parameters from conventional well log data. Consequently, a committee neural network was constructed by virtue of hybrid genetic algorithm-pattern search technique. The propounded committee neural network combines the results of generalized regression neural network, radial basis neural network, and Bayesian regulation backpropagation neural network to improve the accuracy of final prediction. It assigns a weight factor to each of the individual artificial neural networks indicating its contribution in overall prediction. A set of data points was used for model construction and another set was employed to assess the model performance. The results showed that integration of artificial neural networks using the concept of committee machine could improve the precision of target prediction, although each of the artificial neural networks has performed adequately for prediction of photoelectric factor and formation true resistivity. The values obtained for formation water saturation are not as accurate as results obtained for photoelectric factor and formation true resistivity, although the correlation coefficient between measured and predicted values for formation water saturation is higher.
机译：光电系数，地层真实电阻率和地层水饱和度是烃油藏的三个功能参数，可以为油藏表征和地层评估提供宝贵的数据。本研究提出了一种改进的策略，可以在常规测井数据和提到的参数之间进行定量表述。在本研究的第一阶段，采用了三种人工神经网络架构，包括广义回归神经网络，径向基神经网络和贝叶斯规则反向传播神经网络，可以从常规测井数据中预测上述参数。因此，利用混合遗传算法-模式搜索技术构建了委员会神经网络。提出的委员会神经网络将广义回归神经网络，径向基神经网络和贝叶斯规则反向传播神经网络的结果相结合，以提高最终预测的准确性。它为每个人工神经网络分配一个权重因子，以指示其在总体预测中的作用。一组数据点用于模型构建，另一组数据用于评估模型性能。结果表明，尽管每个人工神经网络在预测光电因子和地层真实电阻率方面都表现出色，但使用委员会机器概念的人工神经网络集成可以提高目标预测的精度。尽管地层水饱和度的测量值和预测值之间的相关系数较高，但地层水饱和度的值不如光电系数和地层真实电阻率的结果准确。

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