智能预测和常规地震属性融合的产能“甜点”预测方法

Productivity “sweet spot” prediction method combining intelligentestimation and conventional seismic attributes

致密砂岩的产能预测是国内油气勘探研究的重点问题之一。鄂尔多斯盆地的伊陕斜坡西南部构造复杂、直井数量少,单一的传统预测方法对砂岩的产能“甜点”预测难度大、预测结果多解性强。为此,提出了智能预测和常规地震属性融合的产能“甜点”预测方法。首先结合地质、测井和地震资料,使用卷积神经网络(CNN)刻画研究区砂质碎屑流沉积微相,得出优势相带的分布范围,然后考虑研究区微幅构造对产能的控制,融合曲率属性与砂质碎屑流微相,最终获得产能“甜点”的三维空间展布。利用该方法提取并优选地震数据中产能敏感的属性,同时,利用深度学习预测方法对地震数据进行储层物性参数和沉积相预测,最后融合产能敏感属性和预测得到的沉积微相,得出产能“甜点”分布范围。该方法在研究区的应用结果表明,融合得到的产能“甜点”分布与试油数据拟合效果明显提高,且“甜点”区域在已钻水平井上与细砂岩和粉砂岩分布吻合,预测结果为后期的高产井位部署和剩余油的有效挖潜提供了参考。

The productivity prediction of tight sand is critical for oil and gas exploration in China.Owing to the complex structure and limited number of vertical wells in the study area of the Ordos Basin,it is difficult to characterize“sweet spots”of productivity using single traditional prediction methods.We proposed a method that combines seismic attributes and deep learning results to calculate“sweet spot”productivity using seismic data.In this study,a convolutional neural network was used to characterize the sedimentary microfacies of sandy debris flows in the study area by combining geological,logging,and seismic data to obtain the dominant facies distribution.Considering the impact of the local structure on the study area,the three-dimensional distribution of productivity“sweet spots”was obtained by integrating the curvature properties and sandy debris flow microfacies.This method was used to extract and optimize productivity-sensitive attributes from the seismic data.Simultaneously,deep learning was used to predict the reservoir properties and sedimentary facies from seismic data.Finally,the productivity“sweet spot”distribution was obtained by integrating the sensitive attributes and projected sedimentary microfacies.The application results showed that the productivity“sweet spot”distribution obtained by the above fusion was significantly improved and correlated well with the production data.The“sweet spot”area was consistent with the drilled horizontal well results.These results provide a reference for the future deployment of high-yield well locations and more effective exploitation of tight sand reservoirs.