基于随机神经网络的储集层孔隙度预测技术在Y3区块的应用

Application of Reservoir Property Prediction Based on Probabilistic Neural Network(PNN) in Y3 Block

随机神经网络是通过对样本数据进行训练,寻求预测值和实测值之间误差最小的一种数学内插方法。通过曲线重构反演技术对原始地震数据体进行波阻抗反演,以提高地震资料对储集层的识别精度;在滤波参数选取中,通过共轭梯度算法计算最小检验误差,选取最佳的褶积算子和属性个数,使地震属性和目标参数最佳限度地吻合,避免了神经网络过度训练的缺陷。随机神经网络技术在Y3区块实际资料应用中,取得了良好的预测效果,相比线性回归法和传统神经网络方法提高了预测精度。

PNN is a mathematical interpolation method for seeking the smallest error between true value and predicted value through training sampled data.At first,the original seismic data volume is treated using log reconstruction-impedance inversion technology for improving the identification accuracy of the reservoirs.The filtering parameters is then selected using conjugate gradient algorithm to calculate the smallest test error and select the best convolution operator and number of attributes,allowing the seismic attributes and target parameters to keep the best match and avoid the overtraining of neural network.The case study by applying this technology to Y3 block has shown a good predicted effect and higher prediction precision compared with linear regression and traditional neural network methods.