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Energies 2016, 9(8), 646;

Static Formation Temperature Prediction Based on Bottom Hole Temperature

School of Energy Resources, China University of Geosciences, Beijing 100083, China
Department of Petroleum and Geosystems Engineering, University of Texas at Austin, Austin, TX 78712, USA
Petroleum Engineering College, Yangtze University, Wuhan 430100, China
Author to whom correspondence should be addressed.
Academic Editor: Jacek Majorowicz
Received: 4 June 2016 / Revised: 29 July 2016 / Accepted: 2 August 2016 / Published: 17 August 2016
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Static formation temperature (SFT) is required to determine the thermophysical properties and production parameters in geothermal and oil reservoirs. However, it is not easy to determine SFT by both experimental and physical methods. In this paper, a mathematical approach to predicting SFT, based on a new model describing the relationship between bottom hole temperature (BHT) and shut-in time, has been proposed. The unknown coefficients of the model were derived from the least squares fit by the particle swarm optimization (PSO) algorithm. Additionally, the ability to predict SFT using a few BHT data points (such as the first three, four, or five points of a data set) was evaluated. The accuracy of the proposed method to predict SFT was confirmed by a deviation percentage less than ±4% and a high regression coefficient R2 (>0.98). The proposed method could be used as a practical tool to predict SFT in both geothermal and oil wells. View Full-Text
Keywords: static formation temperature; shut-in time; least squares; particle swarm optimization (PSO) static formation temperature; shut-in time; least squares; particle swarm optimization (PSO)

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Liu, C.; Li, K.; Chen, Y.; Jia, L.; Ma, D. Static Formation Temperature Prediction Based on Bottom Hole Temperature. Energies 2016, 9, 646.

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