一种新的砂泥岩孔隙度估计模型及其应用

针对不同岩性的储层孔隙类型不同,孔隙度结构也存在较大差异,导致支持向量回归机(SVR)在孔隙度预测中效果不理想这一问题,提出在孔隙度预测模型中考虑岩性信息的方法。该方法将样本岩性转化为一种与岩性变化相关性好的属性值,以此构造出一种新的预测模型。对于模型参数优选,提出使用网格粗选和智能精选相结合的方法,网格粗选确定最优解的近似范围,智能精选(遗传算法、粒子群算法)可以在局部区间搜索到最优解。利用优选出的参数建立预测模型,并将预测结果与实测资料进行对比。对比结果表明:加入岩性信息提高了模型的预测精度;在参数精选中,使用智能方法的预测精度高于常规网格搜索法。

A new model for estimating porosity of sandstone and mudstone and its application

In view of the problem that support vector regression(SVR)cannot provide better porosity prediction because different lithologic reservoirs have different pore types and different porosity structures, a new model for estimating porosity, taking lithology information into account, is proposed. In the model, the lithology information of the sample is converted to attribute values that are closely associated with the lithology information. A method that combines the grid search algorithm for rough screening and intelligent search algorithms(genetic algorithms and particle swarm optimization)for fine filtering was used to optimize the model parameters. The grid search algorithm for rough screening was used to determine the approximate scope of the optimal solution, and intelligent search algorithms for fine filtering were used to determine the optimal solution in a local region. The optimized parameters were used to establish the forecasting model. The predicted results were compared with the measured data. The results show that the prediction accuracy of the model is greatly improved when the lithology information is taken into account, and the prediction accuracy of the intelligent search algorithms for fine filtering is higher than that of traditional methods.