Image Reconstruction for Invasive ERT in Vertical Oil Well Logging

周海力; 徐立军; 曹章; 胡金海; 刘兴斌

doi:10.1016/S1004-9541(12)60394-2

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Image Reconstruction for Invasive ERT in Vertical Oil Well Logging

SELECTED PAPERS FROM THE 6TH WORLD CONGRESS ON INDUSTRIAL PROCESS TOMOGRAPHY (WCIPT6) | Updated：2026-01-06

- Image Reconstruction for Invasive ERT in Vertical Oil Well Logging
- Image Reconstruction for Invasive ERT in Vertical Oil Well Logging
- 中国化学工程学报（英文版） 2012年20卷第2期页码：319-328
- Affiliations：
  
  1. Key Laboratory of Precision Opto-mechatronics Technology, Ministry of Education, School of Instrument Science and Opto-electronic Engineering, Beihang University,Beijing,China,100191
  2. Production Logging Institute, Daqing Logging & Testing Services Company,Daqing,China,163453
- Author bio：
- Funds：
  
  Supported by the National Natural Science Foundation of China (61001135);the Fundamental Research Funds for the Central Universities (YWF-11-03-Q-072)
- DOI：10.1016/S1004-9541(12)60394-2
  中图分类号：
- 纸质出版：2012
- Accepted：
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周海力, 徐立军, 曹章, 等. Image Reconstruction for Invasive ERT in Vertical Oil Well Logging[J]. 中国化学工程学报（英文版）, 2012,20(2):319-328.

ZHOU Haili, XU Lijun, CAO Zhang, HU Jinhai, LIU Xingbin. Image Reconstruction for Invasive ERT in Vertical Oil Well Logging[J]. , 2012, 20(2): 319-328.
周海力, 徐立军, 曹章, 等. Image Reconstruction for Invasive ERT in Vertical Oil Well Logging[J]. 中国化学工程学报（英文版）, 2012,20(2):319-328. DOI： 10.1016/S1004-9541(12)60394-2.

ZHOU Haili, XU Lijun, CAO Zhang, HU Jinhai, LIU Xingbin. Image Reconstruction for Invasive ERT in Vertical Oil Well Logging[J]. , 2012, 20(2): 319-328. DOI： 10.1016/S1004-9541(12)60394-2.

摘要

Abstract

An invasive electrical resistance tomographic sensor was proposed for production logging in vertical oil well.The sensor consists of 24 electrodes that are fixed to the logging tool

which can move in the pipeline to acquire data on the conductivity distribution of oil/water mixture flow at different depths.A sensitivity-based algorithm was introduced to reconstruct the cross-sectional images.Analysis on the sensitivity of the sensor to the distribution of oil/water mixture flow was carried out to optimize the position of the imaging cross-section.The imaging results obtained using various boundary conditions at the pipe wall and the logging tool were compared.Eight typical models with various conductivity distributions were created and the measurement data were obtained by solving the forward problem of the sensor system.Image reconstruction was then implemented by using the simulation data for each model.Comparisons between the models and the reconstructed images show that the number and spatial distribution of the oil bubbles can be clearly identified.

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