相变储层地震精细描述与厚度预测:以苏北盆地为例

Seismic Fine Description and Thickness Prediction for Facies Change Reservoir:Take the Subei Basin as an Example

为了完成对相变储层地震精细描述与厚度预测研究,以苏北盆地为例展开研究。苏北盆地三河北滩坝砂储层,随着埋深的增加,地震同相轴由外坡的“单轴”变为中坡“双强轴”再变为内坡的“上强下弱”反射。研究区已钻井20口,储层预测属于小样本案例。如何搞清分相带的机理,并开展分相带的储层描述与预测,既具有理论研究意义,又有现实的必要性。对于相带划分,首先通过井震关系精细分析,搞清相变的本质:一是盖层、储层沉积厚度有结构性的变化;二是灰质成分分布的变化,两者综合造成同相轴相变;接着,提取研究区过零点个数、能量半时、最大振幅、主频、频带宽度、最小振幅6种典型地震属性,明确了相变分布范围,属性切片支持3个相带的划分;考虑到目的层与上覆盖层很近、常规零相位数据砂体特征不明显,开展压缩感知提频与90°相移目标处理,3个相变带的特征得到进一步明确。针对相变储层的厚度预测,首先采用属性与厚度拟合法进行了不分相带的厚度计算,但结果不理想;改用支持向量机(support vector machine,SVM)机器学习方法进行不分相带的厚度预测,预测结果表明SVM方法相对于前面的拟合法效果有所改进;再分别用以上两种方法进行分相带的厚度计算与预测,精度得到进一步提高。可为少井区、复杂相变储层的勘探开发,提供借鉴与参考。

In order to complete the study of seismic fine description and thickness prediction of facies change reservoir,Subei Basin was taken as an example.In the study of the beach bar sand reservoir in Sanhebei area of Subei Basin,with increasing depth,the seismic events changes from the outer slope s“single eveut”to the middle slope s“double strong eveuts”and then to the inner slope s“upper strong and lower weak”reflection.In this study area,there have been 20 wells drilled,and the prediction of the reservoir belongs to a small-sample case.It is essential to understand the mechanism of the facies separation and carry out the description and prediction of the storage layer in each facies separation,which has both theoretical research significance and practical necessity.For the facies zone division,first of all,it is necessary to clarify the essence of the facies change through fine analysis of the relationship between wells and seismic data.First,there are structural changes in the thickness of the overlying layer and the storage layer.Second,there are changes in the composition distribution of gray matter.These two factors combined cause the facies change of the event.Subsequently,by extracting the number of zero crossing points,energy-half-time,maximum amplitude,dominant frequency,bandwidth,and minimum amplitude from the seismic data,the distribution range of the facies change was clarified,and the attribute slices support the division of the study area into three facies separation.Considering that the objective layer is close to the upper covering layer and the sand body features are not distinct in the conventional zero-phase data,compressed sensing frequency enhancement and 90°phase shift target processing were conducted to further clarify the characteristics of the three facies change zones.Regarding the thickness prediction of the facies change reservoirs,first,the thickness is calculated using the attribute and thickness fitting method,but the results were not ideal.Then,SVM(support vector machine)machine learning method was used to predict the thickness without facies separation,and the results indicated that the SVM method was somewhat improved compared to the previous fitting method.Finally,the two methods were used to calculate and predict the thickness of the facies change reservoirs separately,and the accuracy was further improved.The results have some reference value for the exploration and development with few wells and complex facies change reservoir.