Preprint Article Version 1 NOT YET PEER-REVIEWED

Static Formation Temperature Prediction Based on Bottom Hole Temperature

  1. School of Energy Resources, China University of Geosciences, Beijing 100083, China
  2. Department of Petroleum and Geosystems Engineering, University of Texas at Austin, TX 78712, USA
  3. Petroleum Engineering College, Yangtze University, Wuhan 430100, China
Version 1 : Received: 1 August 2016 / Approved: 2 August 2016 / Online: 2 August 2016 (05:42:07 CEST)

A peer-reviewed article of this Preprint also exists.

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

Journal reference: Energies 2016, 9, 646
DOI: 10.3390/en9080646

Abstract

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.

Subject Areas

static formation temperature; shut-in time; least squares; PSO

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