NDLI: A derivative-free methodology with local and global search for the constrained joint optimization of well locations and controls

Content Provider	Springer Nature Link
Author	Isebor, Obiajulu J. Durlofsky, Louis J. Echeverría Ciaurri, David
Copyright Year	2013
Abstract	In oil field development, the optimal location for a new well depends on how it is to be operated. Thus, it is generally suboptimal to treat the well location and well control optimization problems separately. Rather, they should be considered simultaneously as a joint problem. In this work, we present noninvasive, derivative-free, easily parallelizable procedures to solve this joint optimization problem. Specifically, we consider Particle Swarm Optimization (PSO), a global stochastic search algorithm; Mesh Adaptive Direct Search (MADS), a local search procedure; and a hybrid PSO–MADS technique that combines the advantages of both methods. Nonlinear constraints are handled through use of filter-based treatments that seek to minimize both the objective function and constraint violation. We also introduce a formulation to determine the optimal number of wells, in addition to their locations and controls, by associating a binary variable (drill/do not drill) with each well. Example cases of varying complexity, which include bound constraints, nonlinear constraints, and the determination of the number of wells, are presented. The PSO–MADS hybrid procedure is shown to consistently outperform both stand-alone PSO and MADS when solving the joint problem. The joint approach is also observed to provide superior performance relative to a sequential procedure.
Ending Page	482
Page Count	20
Starting Page	463
File Format	PDF
ISSN	14200597
e-ISSN	15731499
Journal	Computational Geosciences
Issue Number	3-4
Volume Number	18
Language	English
Publisher	Springer International Publishing
Publisher Date	2013-11-27
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Field development optimization Production optimization Computational methods Earth Sciences Applications of mathematical programming Mixed integer programming Geotechnical Engineering & Applied Earth Sciences Derivative-free optimization Derivative-free methods and methods using generalized derivatives Hydrogeology Nonconvex programming, global optimization Well placement Nonlinear programming Mathematical Modeling and Industrial Mathematics Reservoir simulation-based optimization Soil Science & Conservation
Content Type	Text
Resource Type	Article
Subject	Computers in Earth Sciences Computational Theory and Mathematics Computer Science Applications Computational Mathematics

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