Journal of Southwest Petroleum University(Science & Technology Edition) ›› 2021, Vol. 43 ›› Issue (4): 191-198.DOI: 10.11885/j.issn.16745086.2021.04.29.03

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Prediction of CO2 Corrosion of Cement Paste Based on Solid Calcium Content

YUAN Bin1, YUAN Kunfeng1, XU Bihua1, YAN Shuang2   

  1. 1. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, Sichuan 610500, China;
    2. Northeast Sichuan Gas Field, Southwest Oil & Gas Field Company, PetroChina, Dazhou, Sichuan 635000, China
  • Received:2021-04-29 Published:2021-08-06

Abstract: CO2 will corrode the cement sheath of oil well, leading to its strength decline, make it lose the function of protecting casing and sealing off oil, gas and water. Thus shorten the life of oil and gas well and cause huge economic loss. If the corrosion depth and corrosion laws of CO2 downhole can be predicted, the life of the oil well can be predicted, and the corrosion resistance of the cement ring can be improved. However, most of the current CO2 corrosion depth models are semi empirical models based on experimental data fitting, which are not universally applicable. To solve this problem, a prediction model of CO2 corrosion depth was established based on the law of mass conservation, diffusion convection equation and calcium precipitation rate. The model takes into account many factors, especially the diffusion of CO2 and the precipitation of calcium ions, so it has strong applicability. The reliability of the model is verified by CO2 corrosion test, and the change law of cement matrix change after corrosion is analyzed by using the model. The results show that with the increase of corrosion time, the porosity and permeability of cement sheath increase, and the tortuosity decreases, which leads to the acceleration of material convection diffusion and corrosion rate; the closer to the corrosion end, the greater the porosity and permeability, and the smaller the tortuosity.

Key words: oil well cement, CO2, calcium ion, corrosion depth, prediction model

CLC Number: