NDLI: Comprehensive framework for gradient-based optimization in closed-loop reservoir management

Content Provider	Springer Nature Link
Author	Bukshtyv, Vladislav Volkov, Oleg Durlofsky, Louis J. Aziz, Khalid
Copyright Year	2015
Abstract	An efficient, robust, and flexible adjoint-based computational framework for performing closed-loop reservoir management is developed and applied. The methodology includes gradient-based production optimization and data assimilation (history matching). Flexibility is achieved through the use of automatic differentiation (AD) within the reservoir simulation, production optimization, and history matching modules. The use of AD will also facilitate the application of closed-loop reservoir management to physical models of higher complexity. A fast sequential convex programming (SCP) solver based on the method of moving asymptotes (MMA) is applied for the production optimization component of the closed-loop. This technique is shown to outperform the sequential quadratic programming (SQP) method, which is commonly used for production optimization computations. The history matching component of the workflow integrates both production data and proxy seismic measurements into a unified adjoint-based data assimilation framework. The effect of noisy data, and data of different types, on the accuracy of the history matching component is assessed. The overall closed-loop reservoir management methodology is tested using the well-documented Brugge model. Results demonstrate the efficient performance of the individual closed-loop components and the improvement in net present value that is achieved using these procedures.
Starting Page	877
Ending Page	897
Page Count	21
File Format	PDF
ISSN	14200597
Journal	Computational Geosciences
Volume Number	19
Issue Number	4
e-ISSN	15731499
Language	English
Publisher	Springer International Publishing
Publisher Date	2015-05-26
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Closed-loop reservoir management Production optimization History matching Seismic data Noisy data Gradient-based optimization Adjoint formulation Automatic differentiation PCA-based parameterization Earth Sciences Geotechnical Engineering & Applied Earth Sciences Hydrogeology Mathematical Modeling and Industrial Mathematics 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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