School of Energy Resources, China University of Geosciences, Beijing 100083, China
Department of Petroleum and Geosystems Engineering, University of Texas at Austin, TX 78712, USA
Petroleum Engineering College, Yangtze University, Wuhan 430100, China
: Received: 1 August 2016 / Approved: 2 August 2016 / Online: 2 August 2016 (05:42:07 CEST)
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.
How to cite:
Liu, C.; Chen, Y.; Jia, L.; Ma, D.; Li, K. Static Formation Temperature Prediction Based on Bottom Hole Temperature. Preprints2016, 2016080010 (doi: 10.20944/preprints201608.0010.v1).
Liu, C.; Chen, Y.; Jia, L.; Ma, D.; Li, K. Static Formation Temperature Prediction Based on Bottom Hole Temperature. Preprints 2016, 2016080010 (doi: 10.20944/preprints201608.0010.v1).
The static formation temperature (SFT) is required to determine the thermophysical properties and production parameters in geothermal and oil reservoirs. However, the SFT is not easy to be obtained 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 was proposed. The unknown coefficients of the model were derived from least squares fit by Particle Swarm Optimization (PSO) algorithm. Besides, the ability to predict SFT based on a few BHT data (such as first 3, 4, or 5 ones of a data set) was evaluated. The accuracy of the proposed method to predict SFT was testified with a deviation percentage less than ±4% and high values of regression coefficient R2 (>0.98). The proposed method could be used as a practical tool to predict SFT in both geothermal and oil wells.
static formation temperature; shut-in time; least squares; PSO