Software For Predicting Wellbore Corrosion Rate In CO₂ Flooding Oil Wells Based On Machine Learning

Carbon dioxide flooding oil recovery is a technique commonly used in domestic oilfields in recent years to improve recovery efficiency.However,the resulting CO₂corrosion problems have restricted the development of CO₂ flooding processes.For wellbores where corrosion has occurred,life prediction is required,that is,corrosion rate needs to be predicted.At present,there are not few researches on corrosion rate prediction models,but most of them have problems such as single models and weak generalization.This article is based on machine learning.The accuracy of the self-learning corrosion rate prediction model with higher accuracy and higher applicability is of great significance for the monitoring of on-site corrosion rate and the future application of artificial intelligence in oil fields.In this paper,using python language,based on data mining methods,the effects of Ca²⁺concentration,Mg²⁺concentration,HCO₃^-concentration,CO₃^2-concentration,Cl^-concentration,p H,temperature,material and other parameters on wellbore corrosion were studied,and the correlation coefficient matrix was used to study the independence between the influencing factors and the degree of influence on the corrosion of the wellbore,based on feature engineering methods such as feature encoding and standardization of data,ridge regression,Lasso,elastic network,support vector machine,and gradient lifting were established,XGBoost and other machine learning regression models,through k-fold cross-validation to optimize the model hyperparameters.Finally,a two-layer multi-algorithm fusion learner is built based on the optimized model.The results show that it has higher accuracy,stronger stability and generalization ability than a single model such as support vector machine.On the basis of theoretical research,combined with python language,Scikit-learn module and interface application toolkit Py Qt5,the self-learning corrosion rate prediction software was compiled and applied in the field.The software is easy to operate,easy to use,and has certain promotion Value.