基于成像测井灰度值的GA-SVR在白云岩储层孔隙度预测中的应用——以川西地区L组为例

Application of GA-SVR Based on Gray Value of Imaging Logging in Porosity Prediction of Dolomite Reservoir—A Case Study of L Formation in Western Sichuan

孔隙度的准确性是优质储层识别和建模的重要基础。白云岩储层孔隙结构复杂,发育类型多,其孔隙度难以精确预测。通过采集川西地区L组白云岩储层182个取心样本、常规测井和成像测井资料,将6种机器学习模型应用于不同测井参数的训练与测试,并评价不同模型的预测性能。结果表明,引入不敏感损失函数度量的遗传算法支持向量回归机模型的预测效果最好,决定系数高达0.87。采用常规测井与成像测井相结合的机器学习综合算法,能有效降低白云岩储层非均质性的影响,提高孔隙度的解释精度,为优质白云岩储层的识别奠定了良好的基础。

The accuracy of porosity is an important basis for the identification and modeling of high-quality reservoir. The pore structure of heterogeneous dolomite reservoir is complex and there are many types of development, so it is difficult to accurately predict. By collecting 182 core samples, conventional logging data and imaging logging data of dolostone reservoir in L Formation in Western Sichuan. Six machine learning models are applied to the training and testing of different logging parameters, and the prediction performance of different models is evaluated. The results show that the genetic algorithm support vector regression model with epsilon has the best prediction effect, and the coefficient of determination is 0.87. The integrated method of combining conventional logging and imaging logging with machine learning can effectively reduce the influence of dolomite reservoir heterogeneity, improve the interpretation accuracy of porosity, lay a good foundation for the identification of high-quality dolomite reservoir, and explore a new way of porosity interpretation.