多种深度学习算法联合提高地震相分析及油气储层预测效果

联合使用多种深度学习算法,更好地挖掘地震数据中的隐蔽和有用信息,实现相互补充和优化,对于减少地震相分析结果的不确定性,具有重要意义。为此,提出了一种从标签训练到数据挖掘再到优化的全过程深度学习地震相分析的方法和流程。首先,通过自组织映射网络图(SOM)进行波形分类,为监督学习提供具有代表性的训练数据;然后,利用卷积神经网路(CNN)和循环神经网路(RNN)进行地震相分析,将预测得到的地震相分析结果输入到生成对抗神经网络(GAN)进行算法优化及运算结果的不确定性分析;最后,结合实际资料分析给出最优结果。本文提出和实现了SOM+CNN/RNN+GAN的监督和非监督联合的深度学习地震相分析的方法及实用流程,通过在研究区河道砂体储层油气预测的实际应用,证明该方法提高了地震相分析及油气储层预测结果的可靠性及效果。

A Joint Various Deep Learning Methods to Improve the Reliability of Seismic Facies Analysis and Reservoir Characterization Results

It is of great significance to reduce the uncertainty of seismic phase analysis results by combining multiple deep learning algorithms to mine hidden and useful information in seismic data, and to achieve mutual complementarity and optimization. Therefore, a method and process of deep learning seismic phase analysis from label training to data mining to optimization were proposed. Firstly, waveform classification is performed by the SOM of the self-organizing mapping network diagram, which provides representative training data for supervised learning. Then, the convolutional neural network CNN and the circulating neural network RNN are used for seismic phase analysis, and the predicted seismic phase analysis results are input to the generative adversarial neural network GAN for optimization between algorithms and uncertainty analysis of operation results, and finally the optimal results are given based on actual data analysis. The method and practical process of SOM+CNN/RNN+GAN combined supervised and unsupervised deep learning seismic facies analysis are proposed and realized, and it is proved that the method improves the reliability and effect of seismic facies analysis and oil and gas reservoir prediction results through the practical application of oil and gas prediction in river channel sand reservoir reservoirs in the study area.