Optimization of Commingled Production Using Infinitely Variable Inflow Control Valves
- Marc Naus (Shell International Ltd.) | Norbert Dolle (Shell International Ltd.) | Jan-Dirk Jansen (Delft University of Technology)
- Document ID
- Society of Petroleum Engineers
- SPE Production & Operations
- Publication Date
- May 2006
- Document Type
- Journal Paper
- 293 - 301
- 2006. Society of Petroleum Engineers
- 4.1.2 Separation and Treating, 5.3.2 Multiphase Flow, 2 Well Completion, 5.7.2 Recovery Factors, 2.3 Completion Monitoring Systems/Intelligent Wells, 5.5.2 Construction of Static Models, 5.6.11 Reservoir monitoring with permanent sensors, 5.2.1 Phase Behavior and PVT Measurements, 5.5 Reservoir Simulation, 3 Production and Well Operations, 2.2.2 Perforating, 4.1.5 Processing Equipment, 4.3.4 Scale, 5.1 Reservoir Characterisation, 4.4 Measurement and Control, 5.4.1 Waterflooding, 5.4.2 Gas Injection Methods, 5.6.4 Drillstem/Well Testing
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We developed an operational strategy for commingled production with infinitely variable inflow control valves (ICVs) using sequential linear programming (SLP). The optimization algorithm requires instantaneous and derivative information. We propose a workflow in which the production engineer relies on measurements to determine the flow rate and pressure values and on models to determine the derivative information (i.e., the changes in flow rates as a result of a change in an ICV setting). Such a model typically would be a steady-state wellbore simulator including choke models to represent the ICVs and inflow models to represent the near-well reservoir flow in the various zones. The parameters of the model need to be updated regularly using real-time measurements and production tests, and we discuss the impact of different smart-well instrumentation levels on the updating process.
We simulated the performance of this production-optimization strategy in a reservoir simulator. Some numerical aspects of the algorithm and problems encountered during implementation are discussed. The performance of the algorithm was tested in two reservoir settings. In both cases, the optimization resulted in accelerated oil production compared to conventional, surface-controlled production. However, accelerated production did not always result in higher ultimate recovery compared to the conventional case. In such situations, the benefits of either short-term production optimization (accelerating production) or long-term reservoir management (maximizing recovery) should be weighed.
Smart Wells. The introduction of smart completions in the oil industry has significantly increased the scope for control of commingled production. ICVs allow for the adjustment of inflow in each individual zone; see Fig. 1. Efficient use of ICVs requires the capability to measure the inflow from each zone. Using downhole instrumentation, this can be done directly, with downhole flowmeters, or more indirectly, through "soft sensing?? (i.e., through interpretation of pressure and temperature data from surface and downhole sensors in combination with models for pressure and temperature drop over the wellbore and the valves). All these measurements require occasional calibration based on surface production tests, where ideally the flow rates of each individual layer should be tested.
In addition to measurement and control hardware, smart-well operations require a control strategy. Present operation of smart wells is based mostly on a "reactive?? control strategy, in which valves are closed in reaction to the breakthrough of water or gas. The present paper proposes a more "proactive?? strategy to continuously optimize the oil production of a well, using measured data while honoring constraints on water and gas production.
Optimization Methods. Optimization with the objective to improve the economics of oil or gas production can, in general, be considered on two different time scales: (1) reservoir management, which involves the long-term saturation response of the reservoir (e.g., optimization of sweep efficiency in waterflooding), and (2) production optimization, which involves the pressure and short-term saturation responses (such as water breakthrough) (Rossi et al. 2000). Short-term production optimization can be performed with simulation models for wellbore flow and near-wellbore reservoir response. The objective is to maximize production at a specific moment in time, which leads to the use of optimization techniques such as SLP (Handley-Schachler et al. 2000) or sequential quadratic programming (SQP) (Wang et al. 2002; Davidson and Beckner 2003). In reservoir management, however, the objective is to maximize recovery or present value (PV) over a long time period. This requires the use of a reservoir simulator in combination with a gradient-based optimization technique (Brouwer and Jansen 2002; Asheim 1988; Sudaryanto and Yortsos 2000; Yeten et al. 2002, 2004) or a nonclassical optimization technique such as a genetic algorithm (Palke and Horne 1997; Yang et al. 2003) to optimize multiple control variables at multiple points in time. The present study is restricted to short-term production optimization.
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