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<p>This paper proposes a new equation for predicting the residual collapse of worn casing in oil and gas operations. Casing tubulars, usually installed in complex environments, experience significant load variations throughout their lifecycle. Mechanical interactions, especially during drilling, cause wear in the casing wall, creating crescent-shaped grooves&#xa0;and reducing collapse strength. A parametric study was conducted to evaluate the influence of geometric and physical variables on the residual collapse, such as wear depth, wall thickness, outer and tool joint diameter, and yield stress, also enabling the development of optimized datasets. The proposed model adapts the Klever-Tamano equation, proposing derating factors to consider the collapse strength loss, due to casing wear, including variables of the parametric study. The parameters of the model are calibrated using nonlinear 2D plane strain Finite Element Analysis (FEA). A new objective function was introduced to minimize model uncertainty. Datasets were created considering the usual oil and gas industry parameters from API&#xa0;TR 5C3. A comprehensive comparison of the proposed model to other models from literature was performed, for the FEA testing and experimental datasets. The proposed model provided better accuracy on the normalized collapse pressures than the other models for the FEA testing dataset. For the experimental dataset analyses, the proposed equation led to relative improvements up to 10.57% and 30.74% (mean and standard deviation), close to the experimental reference values and the ones obtained by FEA simulations. For this last comparison, it was observed a quite low computational cost by the proposed model, which is suitable for larger applications. The model was also applied to a hypothetical case study of a well that is investigated to be converted to work on injections, in the context of Carbon Capture, Utilization, and Storage (CCUS). It was observed that the proposed model led to safety factors higher than the ones obtained by other models of literature. </p>

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Prediction model of collapse strength for worn casing: a comprehensive 2D nonlinear approach with CCUS applications

  • 期刊论文

  • 发表日期：

    2025年8月2日

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  • Rafael Nunes da Cunha,
  • Gustavo Teixeira da Silva,
  • Lucas Pereira de Gouveia,
  • Eduardo Toledo de Lima Junior,
  • João Paulo Lima Santos,
  • William Wagner Matos Lira,
  • Charlton Okama de Souza

摘要

This paper proposes a new equation for predicting the residual collapse of worn casing in oil and gas operations. Casing tubulars, usually installed in complex environments, experience significant load variations throughout their lifecycle. Mechanical interactions, especially during drilling, cause wear in the casing wall, creating crescent-shaped grooves and reducing collapse strength. A parametric study was conducted to evaluate the influence of geometric and physical variables on the residual collapse, such as wear depth, wall thickness, outer and tool joint diameter, and yield stress, also enabling the development of optimized datasets. The proposed model adapts the Klever-Tamano equation, proposing derating factors to consider the collapse strength loss, due to casing wear, including variables of the parametric study. The parameters of the model are calibrated using nonlinear 2D plane strain Finite Element Analysis (FEA). A new objective function was introduced to minimize model uncertainty. Datasets were created considering the usual oil and gas industry parameters from API TR 5C3. A comprehensive comparison of the proposed model to other models from literature was performed, for the FEA testing and experimental datasets. The proposed model provided better accuracy on the normalized collapse pressures than the other models for the FEA testing dataset. For the experimental dataset analyses, the proposed equation led to relative improvements up to 10.57% and 30.74% (mean and standard deviation), close to the experimental reference values and the ones obtained by FEA simulations. For this last comparison, it was observed a quite low computational cost by the proposed model, which is suitable for larger applications. The model was also applied to a hypothetical case study of a well that is investigated to be converted to work on injections, in the context of Carbon Capture, Utilization, and Storage (CCUS). It was observed that the proposed model led to safety factors higher than the ones obtained by other models of literature.