Summary
Reservoir simulation is a valuable tool for performance prediction, production optimization, and field-development decision making. In recent years, significant progress has been made in developing automated workflows for optimization of production and field development by combining reservoir simulation with numerical optimization schemes. Although optimization under geologic uncertainty has received considerable attention, the uncertainty associated with future development activities has not yet been considered in field-development optimization. In practice, reservoirs undergo extensive development activities throughout their life cycle. Disregarding the possibility of future developments can lead to field-performance predictions and optimization results that might be far from optimal. This paper presents a stochastic optimization formulation to account for the uncertainty in future development activities while optimizing current decision variables (e.g., well controls and locations). A motivating example is presented first to demonstrate the significance of including the uncertainty in future drilling plans in oilfield-development optimization. Because future decisions might not be implemented as planned, a stochastic optimization framework is developed to incorporate future drilling activities as uncertain (random) variables. A multistage stochastic programming framework is introduced, in which the decision maker selects an optimal strategy for the current stage decisions while accounting for the uncertainty in future development activities. For optimization, a sequential approach is adopted whereby well locations and controls are repeatedly optimized until improvements in the objective function fall below a threshold. Case studies are presented to demonstrate the advantages of treating future field-development activities as uncertain events in the optimization of current decision variables. In developing real fields, where various unpredictable external factors can cast uncertainty regarding future drilling activities, the proposed approach provides solutions that are more robust and can hedge against changes/uncertainty in future development plans better than conventional workflows.
