基于井控多属性机器学习的缝洞型储层预测方法

Fracture-cavity reservoir prediction based on well-controlled multi-attribute machine learning

碳酸盐岩缝洞体具有强非均质性特征,单一地震属性预测和常规地震属性融合方法未考虑钻井过程中放空、漏失等信息,预测误差较大。基于实钻井井震标定,将放空漏失点属性特征作为约束条件,提出基于井控多属性机器学习的缝洞型储层预测方法,实现缝洞体精细预测。首先根据实钻井井震标定结果,提取漏失点位置的不同敏感地震属性值作为数据输入数组,根据漏失点特征定义的储层类型作为输出数组,形成训练集数据;然后基于支持向量机(support vector machines,SVM)方法,对训练集数据进行模型训练,得到符合先验信息的井震一致的预测模型;最后将该模型应用于塔里木盆地顺北地区奥陶系缝洞型储层预测。结果表明该方法能很好地反映真实储层类型特征,与钻井特征有很高的吻合度。

Carbonate fracture-cavity reservoirs are characterized by strong heterogeneity. Single seismic attribute prediction and conventional seismic fusion methods do not take additional information such as mud leakage into consideration,which could lead to large errors. According to well seismic calibration,a fracture-cavity reservoir prediction method based on well-controlled multi-attribute machine learning was proposed to finely predict fracture-cavity reservoirs,with the attributes of mud-leakage points taken as the constraints. Firstly,in accordance with the results of well seismic calibration,different sensitive seismic attribute values at the leakage points were extracted as input data,and the reservoir types defined by the features of leakage points were output to form training set data. Then,based on the support vector machine(SVM)method,model training was conducted on the data to obtain a prediction model highly consistent with prior seismic attributes. Finally,the model was applied to predict the Ordovician fracture-cavity reservoir in Shunbei Oilfield,Tarim Basin.The prediction results show that this method can reflect the real reservoir characteristics and fits well with drilling features.