Abstract
A robust and efficient approach for computing optimal injection and production rates is proposed to manage the waterflood front and maximize sweep efficiency. The work relies on equalizing the arrival times of the waterfront at all producers within selected sub-regions of a water flood project. The optimization approach proceeds smoothly toward the solution even if the initial conditions are far away because the arrival time optimization has favorable quasi-linear properties. The proposed approach also accounts for geologic uncertainty via a stochastic optimization framework using multiple realizations. Analytical forms for gradients and Hessian of the objective functions are derived, making the optimization computationally efficient for large-scale applications. In addition, optimization is performed under operational and facility constraints using a sequential quadratic programming approach.
The applicability of the approach is demonstrated using three field-scale examples. The first one is a benchmark case called ‘Brugge’ field, based on a North Sea Brent-type field. The production rate is optimized for this field as part of a closed-loop reservoir management process where the production history is matched prior to the production optimization. The optimization is performed over multiple realizations to account for geologic uncertainty. The second example is a CO2 pilot project area in the Goldsmith San Andres Unit (GSAU) in west Texas. The production rates are optimized for 31 wells while accounting for changing field condition such as shut-in and infill drilling. The third example is a super-giant middle-eastern field. The optimization is performed for a sub-region of this field which contains a large number of wells nearly 300 wells consisting of conventional vertical and horizontal wells and smart horizontal wells. The results clearly demonstrate the robustness and the efficiency of the proposed approach with considerable increase in cumulative oil production and a substantial decrease in the associated water production.
