Thickness Prediction of Glutenite Favorable Reservoir Using PCA-RF Method
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摘要: 东营凹陷沙三、沙四沉积时期，发育了大量不同时期的砂砾岩体，它们是非常规油气勘探中重要的储层类型。由于砂砾岩体具有纵向厚度变化大、横向展布不均匀、岩相变化快等特点，在地震属性分析与厚度预测时，用单一属性对储层厚度描述具有很大的不确定性。为此，提取了多种地震属性，采用主成分分析法（PCA）进行优化、去除冗余信息。考虑到随机森林（RF）具有预测精度高、对异常值容忍性强、训练速度快且不易过拟合等特点，引入该方法对砂砾岩储层厚度进行预测。针对属性自相似问题，PCA采用了两种方法：①直接对全部属性做降维处理，提取主成分进行预测（PCA-RF1）；②先对相似属性做降维处理，再组合其他属性进行预测（PCA-RF2）。原始RF、PCA-RF1、PCA-RF2方法还与人工神经网络方法（ANN）进行了效果对比，结果表明，基于相似属性降维处理的PCA-RF2方法，具有最佳应用效果。Abstract: During the deposition period of ES3 and ES4 in Dongying sag, a large number of glutenite bodies developed in different periods, which are important reservoir types in unconventional oil and gas exploration. Due to the characteristics of large variation in vertical thickness of glutenite body, uneven distribution in the lateral direction, and rapid change of lithofacies, the use of a single seismic attribute in the attribute analysis has great uncertainty in the description of reservoir thickness. To this end, a variety of seismic attributes are extracted, and the principal component analysis method is used to optimize and remove redundant information. Considering that the random forest (RF) has the characteristics of high prediction accuracy, strong tolerance to outliers, fast training speed and not easy to over fit, etc., this algorithm is introduced in this paper to predict the thickness of glutenite reservoirs. For the self-similarity of attributes, PCA adopts two methods:One is to directly reduce the dimension of all attributes and extract the principal components for prediction (PCA-PF1); the other is to first reduce the dimension of similar attributes and then combine other attributes to make prediction(PCA-RF2). The original RF, PCA-RF1, PCA-RF2 are also compared with the artificial neural network (ANN). The results show that the PCA-RF2 method with similar attribute dimensionality reduction has the best application effect.
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