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粒度分析方法在石油地质研究中,特别是储层沉积相研究中,有很广泛的用途。其中,粒度子体分离问题需要求解一个约束最小值问题,但是拟合目标函数具有函数值和导数矩阵不易计算、函数值多峰的特点,常规数值优化算法不易奏效。使用序贯数论网格优化算法(RSNTO)研究了粒度混合正态分布子体参数求解问题,考察了不同范数定义的拟合函数形式对子体参数求解的影响程度。以某油田铁5井深度储层S1+2样品粒度实验数据为算例,进行了数值试验。数值试验结果表明,RSNTO算法可以很好地解决子体参数拟合问题;并且,使用一致范数代替欧氏范数定义拟合目标函数,数值试验结果显示,前者的拟合效果在粒度中间值范围内更好一些,计算出来的累计百分含量曲线与实测点更贴近。
The particle size analysis method has a very wide range of uses in the research of petroleum geology, especially in the study of reservoir sedimentary facies. Among them, the particle size separation problem needs to solve a constrained minimum problem, but the fitting objective function has the characteristics that the function value and derivative matrix are not easy to calculate and the function value is multi-peak. The conventional numerical optimization algorithm is not easy to work. The sequential number theory grid optimization algorithm (RSNTO) was used to study the parameter solving of mixed particle with mixed normal distribution. The influence of the fitting function form with different norm definition on the solution of parametric parameters was investigated. A numerical experiment was carried out using the experimental data of the S1 + 2 sample size in the depth reservoir of Tie 5 well in a certain oilfield as an example. The numerical results show that the RSNTO algorithm can well solve the fitting problem of the sub-body parameters. And the fitting objective function is defined by using the uniform norm instead of the Euclidean norm. Numerical results show that the fitting effect of the former is in the middle of granularity The better the range, the calculated cumulative percentage curve closer to the measured point.