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Transportation and production collaborative scheduling optimization with multi-layer coding genetic algorithm for

Qiushi Li1, Yuze Li2, Haitong Sun3

  • 1Digital and Intelligent Business Unit, PetroChina Changqing Oil Field Branch, Xian, 710018, China.

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Summary
This summary is machine-generated.

This study introduces a genetic algorithm to optimize oil tanker scheduling for marginal oil wells, significantly reducing transportation costs and improving production efficiency in low-permeability fields. The new method quickly finds feasible solutions, especially for large-scale operations.

Keywords:
Genetic algorithmMILPMinimize the driving distanceQuick solution

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Area of Science:

  • Petroleum Engineering
  • Operations Research
  • Artificial Intelligence

Background:

  • Marginal wells in low-permeability oil fields present unique challenges including small storage, scattered distribution, and intermittent production.
  • Current manual scheduling for oil well production and crude oil transportation is inefficient, hindering capacity release and increasing costs.
  • Existing mixed-integer linear programming (MILP) models are computationally intensive, with long solution times due to large variable scales.

Purpose of the Study:

  • To develop an efficient algorithm for optimizing crude oil transportation scheduling for marginal oil wells.
  • To address the limitations of manual scheduling and computationally expensive MILP models.
  • To minimize transportation costs and maximize oil well production capacity through improved scheduling.

Main Methods:

  • A novel multi-layer coded genetic algorithm (GA) was designed for scheduling oil tanker routes and loading/unloading operations.
  • The GA's first layer encodes the tanker driving path, while the second layer handles crude oil quantities and timing.
  • The algorithm employs code manipulation techniques like flipping, exchanging, and shifting to expand the search domain.

Main Results:

  • The proposed genetic algorithm was validated against an exact algorithm on various scales (5, 10, 30, 200 wells).
  • The GA demonstrated significantly faster computation times, particularly for large-scale scenarios (e.g., 200 wells).
  • For a 200-well case, the GA found a feasible scheme with a total driving distance of 11,280 km in 1062.3 seconds.

Conclusions:

  • The developed genetic algorithm provides a rapid and effective solution for crude oil tanker dispatching in non-pipeline oil fields.
  • This approach enhances operational efficiency by enabling quick formulation of dispatching plans and minimizing travel distances.
  • The study offers practical guidance for oilfields to optimize logistics and reduce operational costs in marginal well exploitation.