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    A decision support model for differential sticking avoidance

    190329_FernandesLourenco2012.pdf (2.492Mb)
    Access Status
    Open access
    Authors
    Fernandes Lourenço, Affonso Marcelo
    Date
    2012
    Supervisor
    Dr. Jorge H. B. Sampaio Jr
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    School
    School of Science and Engineering, Department of Petroleum Engineering
    URI
    http://hdl.handle.net/20.500.11937/700
    Collection
    • Curtin Theses
    Abstract

    An innovative theoretical model to quantify the risk of differential sticking is presented. The proposed risk assessment is based on the concept of likelihood versus consequence. The likelihood of the problem’s occurrence in a given wellbore segment (case) is evaluated from a knowledge-based model and translated by a similarity measure of relevant operational conditions between the target case and historical cases with known outcomes. The stand alone module performed satisfactorily and predicts the likelihood of occurrence by more than a chance probability, demonstrated by a rate of sixty eight percent (68%) correct predictions against field data from forty four wells drilled by different operators in several fields.The consequence assessment is performed through an unidimensional mechanistic model that predicts the downhole overpull (differential sticking force) and performed well while estimating reported overpulls from known field instances of the problem. Together, the models serve as a risk assessment tool able to correctly describe risk operational trends while designing or drilling wells, with critical situations being defined as high likelihood plus high potential overpulls. Both models utilizes unique experimental data about mechanical properties of drilling fluids filtercakes (hardness, torque resistance and adhesion-cohesion strength) under simulated downhole conditions, raised through the HTHP Mudcake Characterization Equipment developed during the course of this research work. Moreover, the study contributes towards the development of modern predictive models aiming at combining large amount of available operational drilling data (LWD, PWD, mudlogging, survey, drilling reports, etc), expert’s knowledge, laboratory data and phenomenological models in order to optimize drilling operations.

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