The impact of near-wellbore refinement on modelling advanced and smart well completions in reservoir simulation

Abstract

Advanced or smart completion wells are different from conventional wells by being equipped with downhole flow control devices such as Interval Control Valves (ICV) and Inflow Control Devices (ICD) to offer improved reservoir management and control and thus maximise hydrocarbon production and recovery. In order to justify their implementation and increase their economic return, a high degree of robustness in modelling, prediction and optimisation of their performance is required. To improve the robustness of forecasting production from advanced or smart wells using reservoir simulation, high-level details in rock and fluid flow properties are needed in the near-wellbore region to accurately capture the flow dynamics. The paper presents an improved approach that enables us to robustly predict the performance of advanced or smart wells in reservoir simulation and highlights the importance of representing the near-wellbore region when optimizing smart well completions. Performances of advanced or smart well completions are very dependent on changes in flow rate, pressure, and saturations, which mainly occur in areas around the wells. The paper demonstrates the use of local grid refinement (LGR) in the near-wellbore region to enhance the accuracy level of simulation predictions. In the study, an objective function based ICV optimization strategy was used to identify the optimum settings for every time step during the simulation run. We also demonstrate how to correlate ICV settings to Passive Inflow Control Device (PICD) or Autonomous Inflow Control Device (AICD) strengths if a requirement arises to impose the use of ICVs. Using a well-established synthetic reservoir model, we demonstrate how the representation of the near-wellbore region impacts reservoir performance predictions and influences the way ICVs and ICDs are optimized. We observe that by applying this approach, the predicted NPV and recovery factor change by 6.6% and 6.1%, respectively. In addition, this study also quantifies the impact of near-wellbore representation on four completion types; Openhole, ICV, PICD and AICD completions. The novelty of this paper is that it presents an approach to improve production forecasts that supports decision making during field development planning to maximize profit and minimize risk.