Risk-Controlled Wellbore Stability Criterion Based on a Machine-Learning-Assisted Finite-Element Model

Abstract

In certain drilling scenarios, the mud weight required to completely prevent wellbore enlargement can be impractically high. In such cases, what is known as risk-controlled wellbore stability criterion is introduced. This criterion allows for a certain and manageable level of wellbore enlargements to take place. Conventionally, the allowable level of wellbore enlargements in this type of model has always been based on the magnitude of the breakout angle. However, wellbore enlargements, as seen in caliper and image logs, can be highly irregular in terms of their distribution around the wellbore. This means that risk controlling wellbore stability through the breakout angle parameter can be insufficient. Instead, the total volume of cavings is introduced as the risk-control parameter. Unlike the breakout angle, the total volume of cavings can be coupled with a suitable hydraulics model to determine the threshold of manageable enlargement. The volume of cavings is determined using a machine-learning (ML)-assisted 3D elastoplastic finite-element model (FEM). The model implementation is first validated through experimental data. Next, a full data set from offset wells is used to populate and train the model. The trained model is then used to produce estimations of risk-controlled stability mud weights for different drilling scenarios. The model results are compared against those produced by conventional methods. Finally, both the FEM-ML model and the conventional method's results are compared against the drilling experience of the offset wells. The results illustrate how this methodology provides a more comprehensive and new solution to risk controlling wellbore stability.