Efficient workflow for optimizing well controls

Asadollahi, Masoud; Nævdal, Gunnar; Shafieirad, Ali

doi:10.1016/j.petrol.2011.12.023

Cited by 9 publications

(1 citation statement)

References 17 publications

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“…Other studies which directly incorporate details of the reservoir's conditions in their model apply both gradient-based and derivative-free methods for optimising water flooding operations, especially in the context of closed-loop reservoir management (Chen et al, 2010;Asadollahi et al, 2012;Epelle and Gerogiorgis, 2020). However, the focus herein is the fast-paced dynamics of the wells, manifolds, pipelines and separator components of the production system.…”

Section: Relevant Literaturementioning

confidence: 99%

A computational performance comparison of MILP vs. MINLP formulations for oil production optimisation

Epelle

Gerogiorgis

2020

Computers & Chemical Engineering

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Model-based oil production systems optimisation under pressure and facility routing constraints is a testing challenge, especially in presence of complex downhole wellbore phenomena (water coning, slugging, phase separation). Nonlinearities and nonconvexities from underlying physics and binary decisions exacerbate model complexity, yielding Mixed Integer Nonlinear Programs (MINLP). To guarantee solvability of optimisation formulations and reduce MINLP complexity, piecewise linearisation techniques based on Special Ordered Sets of type 2 (SOS2) constraints are developed towards approximating nonlinear functions and transforming models to Mixed Integer Linear Programs (MILP). Nevertheless, computational analyses of MILP vs. MINLP formulations for oil production optimisation are scarce. This study explores the benefits of an MILP reformulation applied to three case studies of varying complexity. We compare MILP model results to original MINLP formulation solutions with multiple solvers, evaluating the impact of the number of linearisation breakpoints used on solution time, accuracy, robustness, model development effort and ease of automation.

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Section: Relevant Literaturementioning

confidence: 99%