Artificial Intelligence Prediction Method for Tight Carbonate Reservoir Fracture Distribution Based on Seismic Attributes
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摘要: 裂缝是致密碳酸盐岩储层的重要渗流通道，影响油藏开发效果.由于裂缝的地球物理响应弱且复杂，使得裂缝预测困难.在深度挖掘地震属性中裂缝特征信息的基础上，建立了基于人工智能的裂缝分布预测方法.该方法通过支持向量机算法优选裂缝敏感属性，利用梯度提升决策树（GBDT）算法深度挖掘单井裂缝发育情况与地震属性之间的非线性关系，梯度提升决策树算法对于异常值有较强的鲁棒性，可以较好地解决裂缝地震响应弱且复杂的问题.该方法在中东扎格罗斯盆地某油田古近系渐新统‒新近系中新统Asmari组主力产油层位的致密碳酸盐岩储层中进行了实例应用，优选出方差、曲率、倾角偏差、倾角、方位角5种裂缝敏感地震属性，利用梯度提升决策树集成不同地震属性中的裂缝特征，建立裂缝分布预测模型，对研究区碳酸盐岩储层裂缝分布进行了预测.与常用裂缝预测方法的对比实验表明，本方法的裂缝预测结果与单井裂缝解释更为符合.预测结果表明，研究区北部裂缝更为发育，构造高部位附近裂缝更为发育，与生产动态认识相符合.Abstract: Fractures are important seepage channels in tight carbonate reservoir which affect reservoir development. Fracture prediction is difficult due to the weak and complex geophysical response of fractures. Based on the deep mining of fracture characteristic information in seismic attributes, in this paper it establishes an artificial intelligence-based fracture distribution prediction method. This method uses the support vector machine algorithm to optimize the fracture sensitive attributes, and uses the gradient boosting decision tree (GBDT) algorithm to deeply explore the nonlinear relationship between the fracture development of a single well and the seismic attributes. Gradient boosting decision tree algorithm has strong robustness to outliers, and can better solve the problem of weak and complex fracture seismic response. This method has been applied to a tight carbonate reservoir in the Oligocene to Neogene Asmari Formation of an oilfield in Zagros Basin, Middle East. Five fracture sensitive seismic attributes including variance, curvature, dip deviation, dip angle and azimuth angle were optimized. The gradient boosting decision tree was used to integrate the fracture characteristics of different seismic attributes, and the fracture distribution prediction model was established to predict the fracture distribution of carbonate reservoir in the study area. Compared with conventional fracture prediction methods, the results of this method are more consistent with fracture interpretation of single well. The prediction results show that the fractures both in the northern part of the study area and near the structural heights are more developed, which is consistent with the understanding of production dynamics.
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图 1 研究区位置（据刘小兵等，2019修改）
Fig. 1. Location of the study area (modified from Liu et al., 2019)
图 5 梯度提升决策树的裂缝预测流程示意图（a）及梯度提升决策树原理示意图（b）
Fig. 5. Schematic diagram of fracture prediction flow of gradient boosting decision tree (a) and schematic diagram of gradient boosting decision tree (b)
图 7 梯度提升决策树模型参数取值与测试样本预测结果的相关系数之间的关系
Fig. 7. Relationship between the parameters of gradient boosting decision tree model and the correlation coefficient of prediction results of test data
图 8 不同人工智能方法裂缝预测结果对比
Fig. 8. Comparison of fracture prediction results of different artificial intelligence methods
图 9 裂缝预测结果与单井裂缝发育强度交会图
Fig. 9. Crossplot of fracture prediction results and fracture development intensity of single well
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