强大的神经网络的应用,模拟原油粘度降低使用纳米颗粒的模型
1Exploration and Development Research Institute, Shengli Oilfield Company SINOPEC, Dongying, Shandong Province 257015, China.
ACS omega
|December 1, 2025
概括
纳米颗粒有效降低原油粘度,以提高石油回收率 (EOR). 一个级联前进的神经网络 (CFNN-LM) 模型准确地预测了粘度的减少,识别了关键影响因素,如温度和纳米粒子度.
科学领域:
- 石油工程和纳米技术
- 专注于纳米材料的应用,以优化石油开采过程.
背景情况:
- 原油粘度是一个关键参数,影响了增强石油回收 (EOR) 的流动行为和回收效率.
- 纳米粒子 (NPs) 已经成为减少油脂粘度和提高EOR性能的有前途的药物.
研究的目的:
- 开发和评估人工智能模型,用于预测用纳米颗粒处理的原油粘度的降低.
- 确定影响原油纳米粒子诱导粘度降低的关键因素.
主要方法:
- 使用646个实验点的数据集来建模粘度降低.
- 应用并比较了四种神经网络架构:多层感知器 (MLP),级联前进神经网络 (CFNN),辐射基函数网络 (RBF) 和通用回归神经网络 (GRNN).
- 采用莱文伯格-马奎特算法用于训练神经网络,皮尔森和斯皮尔曼灵敏度分析,以及用于模型验证的杆方法.
主要成果:
- 使用莱文伯格-马奎特算法 (CFNN-LM) 训练的CFNN实现了最高的准确性,平均绝对相对误差为1.83%,R2为0.9851.
- 粘度降低与温度,纳米颗粒度和剪切率正相关,并且通过更小的纳米颗粒大小来增强.
- 灵敏度分析确定了初始油粘度,纳米粒子密度/大小 (线性) 和纳米粒子度/温度 (非线性) 作为主要因素.
结论:
- CFNN-LM模型提供了对原油中纳米粒子诱导的粘度减少的强大而准确的预测.
- 原油的内在特性和纳米粒子特性是粘度降低的主要驱动因素,度和温度显示出显著的非线性影响.
- 这项研究验证了数据集的可靠性和针对EOR应用开发的CFNN-LM模型的预测能力.
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