基于多粒度时序结构表示的异常检测算法在储层含油性检测中应用

传统储层含油性勘测方法利用地震波穿过地层时产生的相关地震属性和地质钻井资料结合传统地球物理方法进行综合研判，但该类勘测方法往往存在研判成本高且对专家先验知识依赖性强的问题。针对该问题，以江苏油田苏北盆地的地震资料为基础，并结合含油样本的稀疏性和随机性，提出了一种基于多粒度时序结构表示的异常检测算法，直接利用叠后地震道数据进行预测。该算法首先对于单个地震道数据提取多粒度时序结构并形成独立特征表示；其次，在提取多个粒度时序结构表示的基础上进行特征融合，以形成对地震道数据的融合表示；最后，通过对融合后的特征采用代价敏感方法进行联合训练和判别，从而得到对于该地震数据的含油性勘测结果。所提算法在江苏油田实际原始地震资料上进行了实验仿真，实验结果表明：所提算法相比长短期记忆（LSTM）和门控循环单元（GRU）算法在曲线下方的面积（AUC）指标上均提升了10%。

Multi-granularity temporal structure representation based outlier detection method for prediction of oil reservoir

The traditional methods for prediction of oil reservoir utilize the seismic attributes generated when seismic waves passing through the stratum and geologic drilling data to make a comprehensive judgment in combination with the traditional geophysical methods. However, this type of prediction methods has high cost of research and judgement and its accuracy strongly depends on the prior knowledge of the experts. To address the above issues, based on the seismic data of the Subei Basin of Jiangsu Oilfield, and considering the sparseness and randomness of oil-labeling samples, a multi-granularity temporal structure representation based outlier detection algorithm was proposed to perform the prediction by using the post-stack seismic trace data. Firstly, the multi-granularity temporal structures for the single seismic trace data was extracted, and the independent feature representations were formed. Secondly, based on extracting multiple granularity temporal structure representations, feature fusion was carried out to form the fusion representation of seismic trace data. Finally, a cost-sensitive method was utilized for the joint training and judgement to the fused features, so as to obtain the results of oil reservoir prediction for these seismic data. Experiments and simulations of the proposed algorithm were performed on an actual seismic data of Jiangsu Oilfield. Experimental results show that the proposed algorithm is improved by 10% on Area Under Curve (AUC) compared to both of the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms.