Static Formation Temperature Prediction Based on Bottom Hole Temperature

Changwei Liu, Kewen Li, Youguang Chen, Lin Jia and Dong Ma
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Changwei Liu: School of Energy Resources, China University of Geosciences, Beijing 100083, China
Kewen Li: School of Energy Resources, China University of Geosciences, Beijing 100083, China
Youguang Chen: Department of Petroleum and Geosystems Engineering, University of Texas at Austin, Austin, TX 78712, USA
Lin Jia: School of Energy Resources, China University of Geosciences, Beijing 100083, China
Dong Ma: Petroleum Engineering College, Yangtze University, Wuhan 430100, China

Energies, 2016, vol. 9, issue 8, 1-14

Abstract: 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 R 2 (>0.98). The proposed method could be used as a practical tool to predict SFT in both geothermal and oil wells.

Keywords: static formation temperature; shut-in time; least squares; particle swarm optimization (PSO) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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