Sequential Quadratic Programming for Solving Constrained Production Optimization--Case Study From Brugge Field

TL;DR: The results demonstrate that ensemble-based optimization work flows are able to produce improved robust recovery strategies for realistic field-sector models against acceptable computational cost.

TL;DR: GE-S VR is roughly an order of magnitude more computationally efficient than LS-SVR but also provides a better approximation of a complex cost-function surface so that it is possible to locate multiple optima in cases where LS- SVR fails to identify themultiple optima.

TL;DR: A modified formulation of the ensemble gradient is applied which results in improved performance compared to earlier formulations and multi-dimensional scaling is applied to aid in understanding the complex nature of the objective function surface and the performance of the optimization algorithm.

TL;DR: An alternative procedure for handling nonlinear state constraints is developed, which avoids explicit enforcement of nonlinear constraints for each reservoir model yet yields results where any constraint violation for any model is extremely small.

TL;DR: Practical Optimization: Algorithms and Engineering Applications provides a hands-on treatment of the subject of optimization suitable for use in one or two semesters of a first-year graduate course or an advanced undergraduate course.

TL;DR: A view of the progress and understanding that has been achieved during the last eight years about Lagrangian functions and its relevance to practical algorithms is given.

TL;DR: In preparation for the SPE Applied Technology Workshop (ATW) held in Brugge in June 2008, a unique benchmark project was organized to test the combined use of waterflooding-optimization and history-matching methods in a closed-loop workflow.