在遥感影像配准过程中,通常假设控制点是“完美的”。然而,在实际情况中,由于控制点本身不可避免的带有一定的误差导致这种假设在一定情况下并不成立,并且将会影响遥感影像几何校正的精度。普通最小二乘方法OLS(O rd inary Least Square...在遥感影像配准过程中,通常假设控制点是“完美的”。然而,在实际情况中,由于控制点本身不可避免的带有一定的误差导致这种假设在一定情况下并不成立,并且将会影响遥感影像几何校正的精度。普通最小二乘方法OLS(O rd inary Least Square)是遥感影像配准常用的校正估计模型,令人遗憾的是,在控制点存在误差的情况下,它的估计是有偏的,并且不能够正确传递和估计校正影像的误差大小。引入一致校正最小二乘方法CALS(ConsistentAd justed Least Squares),在此基础上提出的一个改进的方法,称之为松弛一致校正最小二乘方法RCALS(Relaxed ConsistentAd justed Least Squares)。这类回归模型具有改正控制点(解释变量)中的误差和跟踪回归模型中的误差传递的能力。为了验证CALS和RCALS模型的有效性,本文利用模拟影像进行分析。这里着重分析OLS,CALS和RCALS模型在几何校正过程中的比较。结果表明,RCALS和CALS的结果优于OLS估计结果。展开更多
Special core analysis(SCAL)measurements play a noteworthy role in reservoir engineering.Due to the time-consuming and costly character of these measurements,routine core analysis(RCAL)data should be inspected thorough...Special core analysis(SCAL)measurements play a noteworthy role in reservoir engineering.Due to the time-consuming and costly character of these measurements,routine core analysis(RCAL)data should be inspected thoroughly to select a representative subset of samples for SCAL.There are no comprehensive guidelines on how representative samples should be selected.In this study,a new framework is presented for selection of representative samples for SCAL.The foundation of this framework is using methods of PSRTI,FZI*(FZI-star)and TEM-function for the early estimation of petrophysical static,dynamic,and pseudo-static rock types at RCAL stage.The global hydraulic element(GHE)approach is benefitted and a FZI*-based GHE method(i.e.,GHE*)is presented for partitioning data.The framework takes into consideration different laboratory,reservoir engineering,geological,petrophysical and statistical factors.A carbonate reservoir case is presented to support our methodology.We also show that the current forms of Lorenz and Stratigraphic Modified Lorenz Plots in reservoir engineering are not appropriate,and present new forms of them.展开更多
文摘在遥感影像配准过程中,通常假设控制点是“完美的”。然而,在实际情况中,由于控制点本身不可避免的带有一定的误差导致这种假设在一定情况下并不成立,并且将会影响遥感影像几何校正的精度。普通最小二乘方法OLS(O rd inary Least Square)是遥感影像配准常用的校正估计模型,令人遗憾的是,在控制点存在误差的情况下,它的估计是有偏的,并且不能够正确传递和估计校正影像的误差大小。引入一致校正最小二乘方法CALS(ConsistentAd justed Least Squares),在此基础上提出的一个改进的方法,称之为松弛一致校正最小二乘方法RCALS(Relaxed ConsistentAd justed Least Squares)。这类回归模型具有改正控制点(解释变量)中的误差和跟踪回归模型中的误差传递的能力。为了验证CALS和RCALS模型的有效性,本文利用模拟影像进行分析。这里着重分析OLS,CALS和RCALS模型在几何校正过程中的比较。结果表明,RCALS和CALS的结果优于OLS估计结果。
文摘Special core analysis(SCAL)measurements play a noteworthy role in reservoir engineering.Due to the time-consuming and costly character of these measurements,routine core analysis(RCAL)data should be inspected thoroughly to select a representative subset of samples for SCAL.There are no comprehensive guidelines on how representative samples should be selected.In this study,a new framework is presented for selection of representative samples for SCAL.The foundation of this framework is using methods of PSRTI,FZI*(FZI-star)and TEM-function for the early estimation of petrophysical static,dynamic,and pseudo-static rock types at RCAL stage.The global hydraulic element(GHE)approach is benefitted and a FZI*-based GHE method(i.e.,GHE*)is presented for partitioning data.The framework takes into consideration different laboratory,reservoir engineering,geological,petrophysical and statistical factors.A carbonate reservoir case is presented to support our methodology.We also show that the current forms of Lorenz and Stratigraphic Modified Lorenz Plots in reservoir engineering are not appropriate,and present new forms of them.
