A New Analytical Model to Evaluate Uncertainty of Wellbore Collapse Pressure Based on Advantageous Synergies of Different Strength Criteria

Considering the uncertainties of rock mechanical parameters, formation pressure and in situ stresses, the uncertainty of the wellbore collapse pressure should be evaluated. Before the uncertainty of evaluation, the collapse pressure model needs to be selected reasonably. In this paper, a new model w...

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Bibliographic Details
Published in:Rock mechanics and rock engineering 2019-08, Vol.52 (8), p.2649-2664
Main Authors: Zhang, Lisong, Bian, Yinghui, Zhang, Shiyan, Yan, Yifei
Format: Article
Language:English
Subjects:
Analytic hierarchy process
Analytical methods
Civil Engineering
Coefficients
Collapse
Computer simulation
Criteria
Data processing
Distribution
Earth and Environmental Science
Earth Sciences
Evaluation
Geophysics/Geodesy
Mathematical models
Mechanical properties
Monte Carlo simulation
Original Paper
Parameter uncertainty
Parameters
Pressure
Probability distribution
Probability theory
Simulation
Statistical methods
Stress concentration
Uncertainty
Weight
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Summary:Considering the uncertainties of rock mechanical parameters, formation pressure and in situ stresses, the uncertainty of the wellbore collapse pressure should be evaluated. Before the uncertainty of evaluation, the collapse pressure model needs to be selected reasonably. In this paper, a new model was proposed to evaluate the collapse pressure, considering the advantageous synergies of different strength criteria. Especially, weight coefficients were introduced to represent the effect of different strength criteria on the collapse pressure, and were calculated by analytic hierarchy process. Then, an analytical method was proposed to address the uncertainty of the collapse pressure based on improved Rosenbluthe method, considering the new collapse pressure model. By means of the analytical method, the collapse pressure was obtained as the probability distribution under the condition that the uncertainties of input parameters were quantified based on well log data. More importantly, the analytical method was validated by Monte Carlo simulation. The results show that the probability distribution agrees very well between the analytical method and Monte Carlo simulation. Note that, the new collapse pressure model has the best matching for the probability distribution desired, which can be treated as the advantageous synergies of the new collapse pressure model.
ISSN:0723-2632
1434-453X
DOI:10.1007/s00603-018-1729-1