We developed an operational strategy for commingled production with infinitely variable Inflow Control Valves (ICVs) using sequential linear programming. The optimization algorithm requires instantaneous and derivative information. We propose a workflow where 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 would typically be a steady-state well bore 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.
Introduction
Smart Wells
The introduction of smart completions in the oil industry has significantly increased the scope for control of commingled production. Inflow Control Valves (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 down hole instrumentation, this can be done directly, with down hole flow meters, or more indirectly through ‘soft sensing’, i.e. through interpretation of pressure and temperature data from surface and down hole sensors in combination with models for pressure and temperature drop over the well bore and the valves. All these measurements require occasional calibration versus 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 mostly based on a "reactive" control strategy, where 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 times scales:reservoir management, which involves the long-term saturation response of the reservoir (e.g. optimization of sweep efficiency in waterflooding) andproduction optimization, which involves the pressure and short-term saturation responses (such as water breakthrough)1. Short-term production optimization can be performed using simulation models for well bore flow and near-well bore reservoir response. The objective is to maximize production at a specific moment in time, which leads to the use of optimization techniques such as sequential linear programming (SLP)2, or sequential quadratic programming (SQP)3,4. 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 technique5–9, or a ‘non-classical’ optimization technique such as a genetic algorithm10,11, to optimize multiple control variables at multiple points in time. The present study is restricted to short-term production optimization.
