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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.
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. 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.