Well control optimization considering formation damage caused by suspended particles in injected water

TL;DR: This study provides a refined theoretical discussion on why StoSAG is generally superior to EnOpt and to provide a reasonable example (Brugge field) where StoSAg generates estimates of optimal well operating conditions that give a life-cycle NPV significantly higher than the NPV obtained from EnOpt.

TL;DR: In this article, the authors investigated the nanoparticles presence in injected supercritical gas effect on the oil recovery and found that up to 3.5vol % of nanoparticles, oil recovery increment slope tends to zero, and after 4 volumetric % of the nanoparticle oil recovery factor indicates reduction.

Abstract: Proper well placement can improve the oil recovery and economic benefits during oilfield development. Due to the nonlinear and complex properties of well placement optimization, an effective optimization algorithm is required. In this paper, cat swarm optimization (CSO) algorithm is applied to optimize well placement for maximum net present value (NPV). CSO algorithm, a heuristic algorithm that mimics the behavior of a swarm of cats, has characteristics of flexibility, fast convergence, and high robustness. Oilfield development constraints are taken into account during well placement optimization process. Rejection method, repair method, static penalization method, dynamic penalization method and adapt penalization method are, respectively, applied to handle well placement constraints and then the optimal constraint handling method is obtained. Besides, we compare the CSO algorithm optimization performance with genetic algorithm (GA) and differential evolution (DE) algorithm. With the selected constraint handling method, CSO, GA, and DE algorithms are applied to solve well placement optimization problem for a two-dimensional (2D) conceptual model and a three-dimensional (3D) semisynthetic reservoir. Results demonstrate that CSO algorithm outperforms GA and DE algorithm. The proposed CSO algorithm can effectively solve the constrained well placement optimization problem with adapt penalization method.

TL;DR: This work proposes a meta-optimized hybrid cat swarm mesh adaptive direct search (O-CSMADS) algorithm for well placement optimization that outperforms stand-alone CSO, MADS, and CSMADS and shows great potential for other petroleum engineering optimization problems.

TL;DR: In this paper, the global and local convergence properties of a class of augmented Lagrangian methods for solving nonlinear programming problems are considered. And the stopping rules for the inner minimization algorithm have this in mind.

TL;DR: In this paper the performance of the CMA evolution strategy with rank-μ-update and weighted recombination is empirically investigated on eight multimodal test functions and the effect of the population size λ on the performance is investigated.

TL;DR: In this article, the performance of the CMA evolution strategy with rank-μ-update and weighted recombination is empirically investigated on eight multimodal test functions, and the effect of the population size A on the performance is investigated.