Probabilistic Assessment of Cement Sheath Integrity in Oil and Gas Wells

Autores

  • Thiago Barbosa da Silva Laboratory of Scientific Computing and Visualization, Federal University of Alagoas
  • Eduardo Toledo de Lima Junior Laboratory of Scientific Computing and Visualization, Federal University of Alagoas
  • Charlton Okama de Souza Leopoldo Américo Miguez de Mello Research and Development Center - CENPES/Petrobras

Palavras-chave:

Cement sheath, Wellbore integrity, Probabilistic model

Resumo

Cementing is one of the most important stages of oil and gas well construction. It consists of displacing cement paste to the annular space between the casing and wellbore, after laying the casing string of each drilled phase. The objective is to guarantee hydraulic isolation in permeable zones and ensure the borehole structural stability. Given its importance and complexity, the cement sheath demands a robust integrity assessment, considering that if it is poorly designed or executed, it causes operational problems such as unwanted influx, so-called kick, or it can even lead to a critical event like a blowout. This work proposes a probabilistic analysis of the analytical models that quantify the interaction of the casing-cement-formation system, to evaluate the displacements and stresses acting on the interfaces between these components. The classical Mohr-Coulomb criterion is applied, which pro- vides the limiting stress for shear failure in the cement sheath. The probability of failure is estimated using the First Order Reliability Method (FORM). Some design variables such as material and geometrical parameters of tubulars, cement and formation are randomly described, and their influence on the probabilistic response is investigated. Case studies are presented to illustrate the application of the proposed methodology in the reliability-based analysis of the cement sheath integrity, contributing to the decision-making process in well structure design.

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Publicado

2024-04-29

Edição

Seção

M16 Structural Reliability Methods and Design Optimization Under Uncertainties