基于神经网络算法的CO₂驱注采井管柱腐蚀速率的建模分析

Study on Internal Corrosion of CO₂ Injection Wellbore Based on ECOSSA-AMLP

摘要: 为了预防注采管损坏引发安全事故，对CO₂驱注采管腐蚀速率进行预测。在传统的多层感知机(MLP)框架上增加注意力机制模块进行特征提取，采用樽海鞘群优化算法(SSA)对模型参数寻优，为了进一步提高模型泛化能力，引入Tent映射、精英质心和反向学习策略改进原始樽海鞘群算法，构建ECOSSA-AMLP CO₂驱注采管腐蚀速率预测模型。以杏河油藏为实例进行验证，结果表明：ECOSSA-AMLP模型的预测结果与实际值的拟合度更高，能够精确预测CO₂驱条件下注采管的腐蚀状态，为CO₂驱注采井安全运营提供技术支撑和决策依据。

Abstract: In order to prevent safety accidents caused by damaged injection and extraction tubes. the corro-sion rate of CO₂ flooding injection-production pipe is predicted. The attention mechanism module is added to the traditional MLP framework for feature extraction, and the model parameters are optimized by the sea squirt group optimization algorithm (SSA). In order to further improve the generalization ability of the model, the original sea squirt group algorithm is improved by intro-ducing Tent mapping, elite centroid and reverse learning strategy, and the corrosion rate predic-tion model of the injection-production pipe in ECOSSA-AMLP CO₂ flooding is constructed. Using the Xinghe Reservoir as an example, the results shows that the ECOSSA-AMLP model fits the actual values and can accurately predict the corrosion status of the injection and extraction tubes under CO₂ drive conditions. It provides technical support and decision basis for the safe operation of CO₂ flooding, injection and mining wells.