使用基于分类的约束建模优化高度受约束的石油回收过程
Zahir Aghayev1,2, Dimitrios Voulanas3,4, Eduardo Gildin3
1Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, Connecticut 06269, United States.
Industrial & engineering chemistry research
|April 21, 2025
概括
这项研究优化了复杂的洪水处理操作,使用替代模型和分类来处理众多约束,大大降低了计算成本,以获得最大的经济收益.
科学领域:
- 石油工程是石油工程中的一个.
- 计算科学 计算科学
- 优化理论 优化理论
背景情况:
- 实际操作,特别是石油工程,经常遇到复杂的,高维度的问题与众多的约束.
- 优化这些流程,如水泛滥,由于大量的操作限制,在计算上是昂贵和具有挑战性的.
研究的目的:
- 开发一个高效和可行的优化方法,用于高度限制的水库模拟.
- 为了减少与优化复杂的洪水流程相关的计算负担.
主要方法:
- 使用Feedforward神经网络 (FFNN) 作为客观量化 (净当前价值 - NPV) 的替代模型.
- 实施了基于分类的约束处理技术,将众多约束转化为二进制可行/不可行的问题.
- 在线训练模型使用水库模拟数据 (Egg和UNISIM模型) 并在线阶段使用基于模型和基于搜索的优化器.
主要成果:
- 通过20,000个底孔压力 (BHP) 设置和NPV输出,实现了替代模型 (R2>0.98) 的高预测精度.
- 基于分类的约束处理有效地区分了可行和不可行的储设置.
- 综合方法显著降低了计算负载,同时确保了优化和可行的解决方案.
结论:
- 拟议的方法提供了一个计算效率高和有效的解决方案,以优化高度受限制的洪水运行.
- 将准确的代用模型与基于分类的约束处理集成,与确定性方法相比,可以获得更好的结果,最大限度地提高经济收益.
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