A Numerical Comparison and Uncertainty Analysis of Two Transient Models for Kick Management in a Backpressure MPD System

摘要

The objective is to compare two different transient models for evaluating kick management in backpressure managed pressure drilling systems and to analyse numerical uncertainties and impact on simulation results. Two different numerical methods will be compared with respect to how accurate they describe the maximum surface rates occurring during a kick scenario with water based mud. The importance of being aware of the uncertainty in results due to numerical diffusion is demonstrated. In addition, different techniques for reducing the numerical diffusion will be discussed and the impact on the predicted rates will be demonstrated. This will include a study of grid refinement and application of front tracking or slope limiter techniques. In addition, a comparison of a kick in oil based mud vs. water based mud in a HPHT MPD scenario will also be shown to highlight the main difference between these systems. A backpressure managed pressure drilling system makes it possible to manage small pressure margins since bottomhole pressure can be controlled by choke pressure adjustments. However, when operating close to pore pressure, small kicks can be taken. These influxes can be circulated to surface if the surface equipment can handle the pressure and the mud gas separator have sufficient capacity. How large kicks one can handle is a decision that can be supported by transient simulations. Here it will be demonstrated that it is important to be aware of and reduce the numerical diffusion to improve prediction of the maximum rates that will occur. It will be shown that by increasing number of boxes in the discretization and introducing methods for reducing numerical diffusion, a more accurate prediction of the maximum rates occurring can be obtained. The main contribution of this work will be to make engineers aware of that there can be uncertainties involved in the simulation tools they are using and to share knowledge about how one can reduce those uncertainties.