基于量子衍生布谷鸟的脊波过程神经网络及TOC预测
发布时间：2018-09-05 09:58
【摘要】：为提高总有机碳含量(TOC)的预测精度,针对测井曲线的时变、奇异性特征,选用脊波函数作为过程神经元的激励函数,提出一种连续脊波过程神经元网络.模型训练方面首先给出基于正交基展开的梯度下降法;其次为提高模型训练收敛能力,提出一种沿Bloch球面纬线实施莱维飞行的量子衍生布谷鸟算法,并用于模型参数优化;最后将训练好的脊波过程神经网络应用于泥页岩TOC预测,通过相关性选取对TOC响应敏感的测井曲线作为模型特征输入.实验对比结果表明,该方法的预测精度较高,较其他过程神经网络提高7个百分点.
[Abstract]:In order to improve the prediction accuracy of total organic carbon content (TOC), a continuous ridgelet process neural network is proposed in this paper, which is based on the time-varying and singular characteristics of log curves, and the ridgelet function is selected as the excitation function of the process neurons. In the aspect of model training, the gradient descent method based on orthogonal basis expansion is presented firstly, and then, in order to improve the convergence ability of model training, a quantum derived cuckoo algorithm is proposed to perform Levi flight along the Bloch spherical weft, and it is used to optimize the model parameters. Finally, the trained ridgelet process neural network is applied to shale TOC prediction, and logging curves sensitive to TOC response are selected as model feature input by correlation. The experimental results show that the prediction accuracy of this method is higher than that of other process neural networks by 7 percentage points.
【作者单位】： 东北石油大学计算机与信息技术学院;山东科技大学信息科学与工程学院;中国石油大学(华东)非常规油气与新能源研究院;
【基金】：国家自然科学基金项目(61170132,41330313) 黑龙江省自然科学基金项目(F2015021)
【分类号】：P631.81;TP183
，
本文编号：2223937
[Abstract]:In order to improve the prediction accuracy of total organic carbon content (TOC), a continuous ridgelet process neural network is proposed in this paper, which is based on the time-varying and singular characteristics of log curves, and the ridgelet function is selected as the excitation function of the process neurons. In the aspect of model training, the gradient descent method based on orthogonal basis expansion is presented firstly, and then, in order to improve the convergence ability of model training, a quantum derived cuckoo algorithm is proposed to perform Levi flight along the Bloch spherical weft, and it is used to optimize the model parameters. Finally, the trained ridgelet process neural network is applied to shale TOC prediction, and logging curves sensitive to TOC response are selected as model feature input by correlation. The experimental results show that the prediction accuracy of this method is higher than that of other process neural networks by 7 percentage points.
【作者单位】： 东北石油大学计算机与信息技术学院;山东科技大学信息科学与工程学院;中国石油大学(华东)非常规油气与新能源研究院;
【基金】：国家自然科学基金项目(61170132,41330313) 黑龙江省自然科学基金项目(F2015021)
【分类号】：P631.81;TP183
，
本文编号：2223937
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