Uncertainty evaluation of wellbore stability model predictions

Abstract There is an increasing concern in the oil and gas industry regarding wellbore stability problems. The need to improve well operations becomes more imperative as the operators move towards more challenging and harsher environments such as ultra-deep waters and high-pressure and high-temperature (HPHT) fields. Different inconsistencies affect many previous wellbore stability analyses, resulting in incorrect results, or results that cannot be extended to other well configurations by well planners. Typical wellbore fracture and collapse models provide single point estimates of the geopressures. The model input data may be uncertain. Failure to capture these uncertainties has led to poor predictions. The purpose of this work is to investigate typical fracture and collapse models with respect to in accuracies in the input data with a stochastic method. Uncertainties in the input data, which include in-situ stresses, rock strength data, and pore pressure will be evaluated, to show how these contribute to the cumulative uncertainties in the model predictions. In this approach, the input parameters are assigned appropriate probability distributions. The distributions are then applied in the wellbore stability models. By means of Monte Carlo simulations, the uncertainties are propagated and the histograms of the outputs are generated. Two types of distributions – triangular and uniform – are applied, to see how the types of input-parameter distributions that are assumed influence the model predictions. Sensitivity analysis is also conducted. This is to ascertain the most significant input factors, which are largely responsible for the cumulative uncertainties or variabilities in the critical fracturing and collapse pressures. The proposed methodology can help in reducing many drilling problems such as circulation loss, stuck pipe, and well collapse. As a result, the industry may save much non-productive time. In addition, well planners will have improved information to make critical decisions.