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Related Experiment Video

Updated: May 26, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Optimum oil production planning using infeasibility driven evolutionary algorithm.

Hemant Kumar Singh1, Tapabrata Ray, Ruhul Sarker

  • 1School of Engineering and IT, UNSW@ADFA, Canberra ACT, 2600, Australia. h.singh@adfa.edu.au

Evolutionary Computation
|December 17, 2011
PubMed
Summary
This summary is machine-generated.

This study optimizes gas injection for enhanced oil recovery (EOR) to maximize oil production under daily gas availability constraints. An evolutionary algorithm efficiently solved a 56-well problem, with multi-objective and profit-maximization formulations also explored.

Related Experiment Videos

Last Updated: May 26, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

  • Petroleum Engineering
  • Optimization Theory
  • Computational Science

Background:

  • Enhanced Oil Recovery (EOR) using gas injection is crucial for wells with low reservoir pressure.
  • Daily gas availability presents a significant constraint in optimizing oil extraction.
  • Maximizing oil production is a key objective for major oil exploration companies.

Purpose of the Study:

  • To develop and apply an optimization strategy for gas injection in oil production.
  • To address the practical challenge of maximizing oil extraction under gas supply limitations.
  • To explore alternative formulations for improved economic outcomes in oil recovery.

Main Methods:

  • An infeasibility-driven evolutionary algorithm was employed to solve the constrained optimization problem.
  • A multi-objective formulation was developed and solved using various algorithms.
  • A modified single-objective formulation focused on profit maximization was proposed.

Main Results:

  • The evolutionary algorithm demonstrated efficiency in solving a 56-well reservoir optimization problem.
  • Multi-objective formulations provided flexibility, reducing the need for frequent single-objective problem re-solving.
  • The profit-maximization approach showed potential for greater economic benefits, even with reduced oil output.

Conclusions:

  • Optimization of gas injection is vital for efficient enhanced oil recovery.
  • Evolutionary and multi-objective algorithms offer effective solutions for complex oil production planning.
  • Shifting focus from oil quantity to profit maximization can yield superior financial returns in EOR operations.