The Design of Annular Flow Isolation in Advanced Wells under Reservoir Uncertainty

Advanced well completions are proven and effective solutions to mitigate water/gas breakthrough problems in horizontal and multilateral wells. An important parameter adversely affecting the (Autonomous) Inflow Control Devices [(A)ICD] completion’s performance is the annular flow. The flow impacts can be minimised or eliminated by installation of a sufficient number of Annular Flow Isolations (AFIs). This number is in its turn constrained by the completion string costs and risks of its installation in a long wellbore of often-complex geometry. In addition, most industry, AFI design, workflows have been based on the evaluation of a static well-reservoir model or static parameters. (MoradiDowlatabad et al., 2014) developed a novel AFI design methodology to optimise number and location of a limited number of AFIs based on the lifetime well performance. The workflow delivers significant improvement in the total oil production for the wells deployed new AFI design comparing with the wells designed by the traditional workflows. However, the workflow relies on the calculations of numerical reservoir/well models to identify the optimal AFI design, yet the reservoir simulation models are always uncertain to some extent. A robust approach is proposed to include impacts of the reservoir uncertainties into the AFI design decision workflow

A New Correlation for Prediction of Critical Two-phase Flow through Wellhead Chokes

The first scope of this study is to develop a new accurate empirical Gilbert type critical flow correlation based on 361 actual production tests data from Middle Eastern oil fields by means of non-linear regression analysis. The second scope is to study the impact of temperature on Gilbert type critical flow correlation for these data sets. In order to modify the Gilbert type critical flow correlation for these data sets, correlations are tuned based on available field data points using nonlinear regression method. in this study, generalized reduced gradient (GRG) algorithm of iteration was used to find the correlation coefficients based on available field data and the convergence criteria is to minimize the value of the squared sum, SS, of the difference between the real data and the estimated one. Based on error analysis, for the oil fields, liquid flow rate prediction is improved when new approaches (including/excluding temperature) are used. It is also concluded that the accuracy of new approach to predict production rate is not expressively improved by including the temperature in choke performance correlation for this data set compared to the case without temperature

Investigation into the effect of capillary number on productivity of a lean gas condensate reservoir

The main objective of this study is to provide a comprehensive investigation into the effect of Capillary number (NCa) on production from the lean gas condensate reservoirs and to show the importance of NCa in these types of reservoirs. In the current study a gas condensate reservoir has been simulated and validated using field and PVT data. The effect of NCa on the parameters such as producing gas flow rate, bottom-hole pressure, average reservoir pressure, the amount of condensate in the reservoir and the condensate saturation versus the distance from the production well has been investigated. It was found that NCa can increase the relative permeability of the gas followed by the reduction in condensate formation and pressure drop in the vicinity of wellbore. Furthermore, the results stated that NCa exists in about 10ft away from the well and affects mainly the well productivity parameters such as bottom-hole pressure and gas flow rate. However, it does not affect the average reservoir pressure and condensate throughout the reservoir as much as the near wellbore parameters