Uncertainty-Based Approach for Predicting the Operating Window in UBO Well Design

Abstract

Summary Underbalanced operations (UBO) are carried out to bypass drilling challenges that could be difficult to resolve by use of conventional drilling techniques. Steady-state multiphase-flow models are used to construct underbalanced-drilling operational windows. These advanced software models are deterministically formulated. It is known that some of the model input parameters, such as the multiphase-flow parameters, friction factors, and reservoir productivity, are subject to uncertainties. Failures to capture these variabilities may introduce some error in the model prediction, resulting in poor well planning and implementation. The purpose of this work is to implement probabilistic modeling of underbalanced drilling by use of a simple steady-state two-phase model. Both predefined uncertain and fixed factors serve as inputs to a pre-existing deterministic model. By applying Monte Carlo simulations, the model predicts outputs that follow a statistical distribution. A sensitivity analysis is conducted to determine the input factor that is most responsible for the uncertainty in the predicted bottomhole pressure (BHP). The results demonstrate that uncertainty modeling can improve underbalanced-drilling design and operations. A more realistic operational window is obtained, ensuring that underbalanced condition is maintained throughout the target section. With a better understanding of uncertainties and the corresponding impacts, well planners can make better decisions regarding well design criteria and safe operational conditions, and avoid huge economic consequences.