优化遗传算法在孔隙度预测中的应用

孔隙度是油藏描述和储量计算最重要的参数之一。因此 ,高精度的孔隙度预测是建立储层地质模型的关键。文中应用优化的遗传算法———演化程序 ,利用岩芯样品的孔隙度和测井数据建立合适的预测模型 ,应用声波、密度、自然伽马和自然电位测井数据进行储层孔隙度预测。预测结果表明 :与线性回归和灰色GM (0 ,5 )等传统的预测方法相比 ,应用演化程序进行孔隙度预测 ,提高了预测结果的精度 ,减轻了预测过程中对先验信息的依赖程度。

THE APPLICATION OF EVOLUTION PROGRAM TO POROSITY PREDICTION

Porosity is one of the most important factors characterizing a reservoir and calculating reserves. Therefore, high precision porosity prediction is the key to constructing geological model of reservoirs. Constructing the prediction model from core porosity and using acoustic, density, gr and sp log data, an application of optimized genetic algorithm\|evolution program to porosity prediction is presented in this paper. The prediction shows that in contrast to traditional methods such as linear congress and Gray GM(0,5), the application of evolution program to porosity prediction improves the precision of prediction results and reduces the dependency on prior information.