Volume 1, Issue 1, October 2016, Page: 20-25
An Improved Model for Predicting Fluid Temperature in Deep Wells
Boyun Guo, Petroleum Engineering Department, University of Louisiana at Lafayette, Louisiana, USA
Jinze Song, Petroleum Engineering Department, University of Louisiana at Lafayette, Louisiana, USA
Received: Jul. 17, 2016;       Accepted: Oct. 14, 2016;       Published: Oct. 21, 2016
DOI: 10.11648/j.mma.20160101.14      View  3293      Downloads  142
The objective of this study was to develop an improved method to predict fluid temperature profiles in high-temperature wells for designing production string in deep-water development. The method was developed on the basis of heat transfer involves heat convection and conduction inside the production string and in the annular space. The governing equations were solved using the method of characteristics, resulting in two simple closed-form equations. The method was coded in a spreadsheet for easy applications. Data from three wells were employed to check the accuracy of the new method. Comparisons of results from Hasan's method, Gilbertson et al.'s method, and the new method with temperature data measured in two gas-lift wells show that the new method best predicts well temperatures in trend. A comparison of results given by Mao's method and the new method with temperatures observed in a deep-water gas well testing indicates that the new method better predicts well temperatures with errors less than 4%. This work provides petroleum engineers a simple and accurate method for predicting temperature profiles in oil and gas production operations, especially deep-water operations. It eliminates the need for sophisticated analytical and numerical models in fluid temperature analysis.
Fluid Temperature, Deep Wells, Gas-Lift Wells, Heat Transfer
To cite this article
Boyun Guo, Jinze Song, An Improved Model for Predicting Fluid Temperature in Deep Wells, Mathematical Modelling and Applications. Vol. 1, No. 1, 2016, pp. 20-25. doi: 10.11648/j.mma.20160101.14
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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