同济大学学报(自然科学版)
同濟大學學報(自然科學版)
동제대학학보(자연과학판)
JOURNAL OF TONGJI UNIVERSITY(NATURAL SCIENCE)
2009年
7期
888-892
,共5页
钻井液%流变方程%参数估计%线性回归%非线性回归
鑽井液%流變方程%參數估計%線性迴歸%非線性迴歸
찬정액%류변방정%삼수고계%선성회귀%비선성회귀
drilling fluid%rheological equation%parameter estimation%linear regression%non-linear regression
钻井液流变模式的参数估计问题大多使用线性回归方法求解,然而,线性回归方法改变了测量误差的统计特征,使得所得到的流变参数估计不具有无偏性和方差最小等特点.针对3种非线性流变方程(幂律模式、赫巴模式和卡森模式)的特点,分别提出了非线性最小二乘估计的新算法.该算法不需要人工给定迭代初始值,迭代过程稳定收敛到最小点,不会陷入极小点陷阱,存储需求小,收敛速度很快,所得到的流变参数估计具有拟合残差近似无偏性和方差几乎最小的优良统计特征.大量的实际钻井液算例表明,新方法具有比线性回归方法更小的拟合方差,拟合残差统计特性优于线性回归方法.
鑽井液流變模式的參數估計問題大多使用線性迴歸方法求解,然而,線性迴歸方法改變瞭測量誤差的統計特徵,使得所得到的流變參數估計不具有無偏性和方差最小等特點.針對3種非線性流變方程(冪律模式、赫巴模式和卡森模式)的特點,分彆提齣瞭非線性最小二乘估計的新算法.該算法不需要人工給定迭代初始值,迭代過程穩定收斂到最小點,不會陷入極小點陷阱,存儲需求小,收斂速度很快,所得到的流變參數估計具有擬閤殘差近似無偏性和方差幾乎最小的優良統計特徵.大量的實際鑽井液算例錶明,新方法具有比線性迴歸方法更小的擬閤方差,擬閤殘差統計特性優于線性迴歸方法.
찬정액류변모식적삼수고계문제대다사용선성회귀방법구해,연이,선성회귀방법개변료측량오차적통계특정,사득소득도적류변삼수고계불구유무편성화방차최소등특점.침대3충비선성류변방정(멱률모식、혁파모식화잡삼모식)적특점,분별제출료비선성최소이승고계적신산법.해산법불수요인공급정질대초시치,질대과정은정수렴도최소점,불회함입겁소점함정,존저수구소,수렴속도흔쾌,소득도적류변삼수고계구유의합잔차근사무편성화방차궤호최소적우량통계특정.대량적실제찬정액산례표명,신방법구유비선성회귀방법경소적의합방차,의합잔차통계특성우우선성회귀방법.
The estimation problem of rheological parameter for drilling fluid usually resorts to the linear regression method to get the solution.As a result, the statistic feature of measurement error becomes different,and the derived rheological parameter estimation tends to be no unbiasedness and with the least variance.A new method of non-linear least square estimation is proposed according to the characteristics of three non-linear rheological equations.There is no need to give an initial iteration value artificially.The iteration process can converge steadily to a unique minimum point without falling into the minimum point trap.Little storage space is in need but with a rapid convergence.A rheological parameter estimation derived on the basis of the algorithm presents superior statistic features of near unbiasedness of fitting residual error and with almost the least variance.Lots of practical drilling fluid cases validate that the new method is of smaller fitting variance with the similar mean,and a superior statistical feature of fitting residual error over that of linear regression.