Application of Loess Procedure in Modelling Geothermal Well Discharge Data from Menengai Geothermal Wells in Kenya
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 5, September 2016, Pages: 260-269
Received: Jul. 15, 2016;
Accepted: Jul. 22, 2016;
Published: Aug. 6, 2016
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Madegwa James Etyang, Department of Mathematics, Kenyatta University, Nairobi, Kenya
Edward Gachangi Njenga, Department of Mathematics, Kenyatta University, Nairobi, Kenya
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To measure the output of a geothermal well, also known as amount of megawatts of a well, discharge tests are done between two to four months after drilling of the well to collect the relevant types of data which includes wellhead pressure, lip pressure and the weir height. After collection of these data,  formula is applied in determining the well output. These data exhibits skewness and excess kurtosis also known as heavy – tailedness, an attempt to fit ordinary least squares (OLS) model to such data leads to model misspecification. Therefore, in this study, robust non-parametric estimation has been used to fit these data as applied by . The model is known to be robust to outliers which characterize the wells data, robustness signifies insensitivity to deviations from the strict model assumptions. A comparison between the robust method used and OLS method has also been made with graphical illustrations. The results show that locally weighted regression (loess) method used with a smoothing parameter of 0.07 and a polynomial of order 2 fits the geothermal well discharge data. It was confirmed that geothermal well discharge data is characterized by outliers which may affect the ultimate determination of the value of a well output and therefore there is need for further statistical data processing to remove the errors before Russel James method is applied.
Locally Weighted Regression, Wellhead Pressure, Lip Pressure, Weir Height, Geothermal Well Output
To cite this article
Madegwa James Etyang,
Edward Gachangi Njenga,
Application of Loess Procedure in Modelling Geothermal Well Discharge Data from Menengai Geothermal Wells in Kenya, American Journal of Theoretical and Applied Statistics.
Vol. 5, No. 5,
2016, pp. 260-269.
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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