论文标题
使用人工神经网络预测剪切波速度
Predicting of shear wave velocity using Artificial Neural Networks
论文作者
论文摘要
剪切波速度是确定地球力学研究中岩性，孔隙率和动态特性的重要参数。但是，由于时间和成本限制，在所有间隔和所有井中都不可用剪切波速度。在本文中，确定了与剪切波速度有很强相关性的井对数，并用于预测Tano North场的剪切波速度。在三种不同条件下，使用了四种不同的方法来估计剪切波速度。然后，基于剪切波速度的实际和预测值之间的确定系数和平均绝对百分比相对误差，比较了最终结果。这项工作的结果表明，基于多个变量的神经网络可以比其他使用的方法更好地估计剪切波速度。
Shear wave velocity is an important parameter for determining lithology, porosity and the dynamic properties in geo-mechanical studies. However, due to time and cost limitations, shear wave velocity is not available at all intervals and in all wells. In this paper, well logs with strong correlation to shear wave velocity were determined and used to predict the shear wave velocity for the Tano North Field. Four different methods were used to estimate the shear wave velocity under three different conditions. Then, based on obtained coefficient of determination and average absolute percent relative error between real and predicted values of shear wave velocity, the final results were compared. The results of this work demonstrated that the neural network based on multiple variables can estimate the shear wave velocity better than other methods used.
