Predicting the Reservoir Response to Intelligent Wells

Abstract

Recent developments in completion design have seen the evolution of the intelligent well. The ability of this well to both measure and control production, and/or injection, within the subsurface is considered a major step forward in field management. It is therefore crucial to be able to model and understand the impact on the field of the technology prior to installation. By modelling the entire system the ideal completion design and field location are evaluated, whilst the reservoir response to the variable choke is predicted for the life of the well. The economic value of the technology can now be understood. Previous attempts at modelling intelligent wells have crudely adjusted either perforation skin, or near wellbore permeability, to mimic the choking effect. However, the challenge is to firstly, develop a model which correctly predicts the fluid and pressure distribution throughout the completion as well as across the reservoir for each choke orifice, and then to apply the model as a reservoir management tool. This paper describes a method of executing the challenge by incorporating the surface network and variable choke model of a reservoir simulator within the subsurface. The model simulates the multiphase flow and pressure distribution within the annulus and tubing for any choke orifice. Production is allocated to each choked section and perforation accordingly. Simple adjustments, or the incorporation of new components, within the network, readily permits simulation of the field response to various completion designs. Performance sensitivity of the model is evaluated in simple homogeneous and heterogeneous environments. Intelligent well models are then developed for two conceptual field developments. In each, field performance is compared with traditional completion designs. Introduction Since the development and installation of the first intelligent well, several authors1,2,3 have presented and discussed the concept of real-time reservoir management at the sandface. The intelligent well advanced this cause by incorporating a) downhole sensors, able to measure temperature, pressure and flow rates, and b) downhole control devices, able to control production from specific reservoir intervals. This combination of real-time downhole measurement and control is a major attraction to anyone involved in reservoir management. As the intelligent well is able to have such a profound influence on the fluid distribution within the reservoir, it is essential to evaluate the impact of the technology on the entire field prior to any development. In doing this, both the subsurface and surface infrastructure requirements and performance must also be considered. In addition, as controlling production from specific intervals helps mitigate the effects of reservoir heterogeneity, it is likely that the technology is a candidate for more complex reservoirs. There is therefore a need to model an intelligent well with a full field reservoir simulator. This paper develops an intelligent well completion model utilising existing and recent software developments within a simulator. The study objectives are to test the model performance in simple homogeneous and heterogeneous environments and understand how both the choke orifice and completion design influence the pressure and flow distribution within the well and the reservoir. This is achieved by creating a node network able to output node pressures and flow rates at specific times for specific chokes. The model is adapted and introduced into two field development scenarios. The first develops a layered heterogeneous reservoir with a vertical intelligent well, whilst the second develops a faulted compartmentalised reservoir with a dual branch multilateral, incorporating a variable choke. In each case pressure support is maintained by water injection. A comparison of field performance with conventionally completed wells is undertaken in both cases.

Advanced Well Simulation in a Multilayered Reservoir

“…However, only very few examples are given in the literature on real or potential applications of intelligent completions. 1,5,6,15,16 Downhole permanent gauges. Most downhole pressure and temperature sensors are electronic systems.…”