Simultaneous and sequential approaches to joint optimization of well placement and control

Determining optimal well placement and control is essential to maximizing production from an oil field. Most academic literature to date has treated optimal placement and control as two separate problems; well placement problems, in particular, are often solved assuming some fixed flow rate or botto...

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Bibliographic Details
Published in:Computational geosciences 2014-08, Vol.18 (3-4), p.433-448
Main Authors: Humphries, Thomas D., Haynes, Ronald D., James, Lesley A.
Format: Article
Language:English
Subjects:
Algorithms
Decoupling
Earth and Environmental Science
Earth Sciences
Flow rates
Geoengineering
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Mathematical Modeling and Industrial Mathematics
Oil and gas fields
Optimization
Optimization algorithms
Original Paper
Placement
Searching
Soil Science & Conservation
Strategy
Swarm intelligence
Wells
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Summary:Determining optimal well placement and control is essential to maximizing production from an oil field. Most academic literature to date has treated optimal placement and control as two separate problems; well placement problems, in particular, are often solved assuming some fixed flow rate or bottom-hole pressure at injection and production wells. Optimal placement of wells, however, does depend on the control strategy being employed. Determining a truly optimal configuration of wells thus requires that the control parameters be allowed to vary as well. This presents a challenging optimization problem, since well location and control parameters have different properties from one another. In this paper, we address the placement and control optimization problem jointly using approaches that combine a global search strategy (particle swarm optimization, or PSO) with a local generalized pattern search (GPS) strategy. Using PSO promotes a full, semi-random exploration of the search space, while GPS allows us to locally optimize parameters in a systematic way. We focus primarily on two approaches combining these two algorithms. The first is to hybridize them into a single algorithm that acts on all variables simultaneously, while the second is to apply them sequentially to decoupled well placement and well control problems. We find that although the best method for a given problem is context-specific, decoupling the problem may provide benefits over a fully simultaneous approach.
ISSN:1420-0597
1573-1499
DOI:10.1007/s10596-013-9375-x