油气田开发

和声搜索优化算法在油藏工程辅助历史拟合中的应用

  • SHAMS Mohamed ,
  • EL-BANBI Ahmed ,
  • SAYYOUH Helmy
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  • 1. Dana Gas, Plot 188, City Center, 5th Settlement, New Cairo, 11835 Egypt;
    2. Petroleum Engineering Department, Cairo University, Giza, 12613 Egypt
SHAMS Mohamed(1986-),男,埃及人,高级油藏工程师,主要从事油藏工程和油藏模拟方面的研究。地址:Plot 188, City Center, 5th Settlement, New Cairo, 11835 Egypt。E-mail: mohamed.shams@danagas.com

收稿日期: 2019-02-16

  网络出版日期: 2020-01-17

Harmony search optimization applied to reservoir engineering assisted history matching

  • SHAMS Mohamed ,
  • EL-BANBI Ahmed ,
  • SAYYOUH Helmy
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  • 1. Dana Gas, Plot 188, City Center, 5th Settlement, New Cairo, 11835 Egypt;
    2. Petroleum Engineering Department, Cairo University, Giza, 12613 Egypt

Received date: 2019-02-16

  Online published: 2020-01-17

摘要

基于对和声搜索优化算法(HSO)特点及其优越性的分析,将其应用于埃及苏伊士湾Amal油田Kareem油藏油藏工程辅助历史拟合中。HSO算法具有如下优越性:对解空间探索和开发能力之间的良好平衡使得算法具有鲁棒性和高效性;生成解的多样性由两个组件有效控制,更适用于油藏工程历史拟合这样的高度非线性问题;和声记忆库取值、微调和随机化3个组件之间的配合有助于找到无偏性解;算法实现过程简单。将HSO算法与油藏工程辅助历史拟合中2种常用的优化技术(遗传算法和粒子群算法)应用于3个不同复杂程度的油藏工程历史拟合问题——2个不同尺度的物质平衡历史拟合和1个油藏历史拟合,通过对比3种算法拟合效果验证了HSO算法的正确性和优越性。Kareem油藏历史拟合结果表明,在辅助历史拟合工作流中采用HSO算法作为优化方法可以显著提高拟合质量,缩短求解时间。图7表2参44

本文引用格式

SHAMS Mohamed , EL-BANBI Ahmed , SAYYOUH Helmy . 和声搜索优化算法在油藏工程辅助历史拟合中的应用[J]. 石油勘探与开发, 2020 , 47(1) : 148 -154 . DOI: 10.11698/PED.2020.01.14

Abstract

Based on the analysis of characteristics and advantages of HSO (harmony search optimization) algorithm, HSO was used in reservoir engineering assisted history matching of Kareem reservoir in Amal field in the Gulf of Suez, Egypt. HSO algorithm has the following advantages: (1) The good balance between exploration and exploitation techniques during searching for optimal solutions makes the HSO algorithm robust and efficient. (2) The diversity of generated solutions is more effectively controlled by two components, making it suitable for highly non-linear problems in reservoir engineering history matching. (3) The integration between the three components (harmony memory values, pitch adjusting and randomization) of the HSO helps in finding unbiased solutions. (4) The implementation process of the HSO algorithm is much easier. The HSO algorithm and two other commonly used algorithms (genetic and particle swarm optimization algorithms) were used in three reservoir engineering history match questions of different complex degrees, which are two material balance history matches of different scales and one reservoir history matching. The results were compared, which proves the superiority and validity of HSO. The results of Kareem reservoir history matching show that using the HSO algorithm as the optimization method in the assisted history matching workflow improves the simulation quality and saves solution time significantly.

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