微型电脑应用2025，Vol.41Issue(11)：6-11,6.
基于GRU的地层液离子浓度趋势预测方法研究
Research on Trend Predictive Method of Formation Fluid Ion-concentration Based on GRU
摘要
Abstract
The changing trend of formation fluid ion-concentration is an important basis for predicting scale formation in oil pro-duction wells.Due to the characteristics of complex types,high dimensions and weak regularity of ion-concentration data,tra-ditional prediction methods have poor prediction effects and low accuracy.To this end,combined with the theory of deep learn-ing,a method for predicting the ion-concentration of formation fluid based on recurrent neural networks is proposed.Based on an in-depth analysis of the types and characteristics of ion-concentration data,the data decomposition method is applied to rea-sonably increase the number of samples.Design a gated recurrent unit(GRU)network model to learn the variation pattern of ion-concentration data and obtain the predicted value of ion-concentration.In real scenarios,comparative experiments are de-signed to test the prediction accuracy and stability of the method.The experimental results show that the proposed method has high prediction accuracy and good stability.关键词
采油井结垢/深度学习/循环神经网络/离子浓度趋势预测Key words
scale formation of oil production well/deep learning/recurrent neural network/ion-concentration trend predictive分类
信息技术与安全科学引用本文复制引用
张岩,张可佳,刘涛..基于GRU的地层液离子浓度趋势预测方法研究[J].微型电脑应用,2025,41(11):6-11,6.基金项目
国家自然科学基金面上项目(42172161) （42172161）
国家青年科学基金项目(42102173) （42102173）
中国石油科技创新基金(2020D-5007-0102) （2020D-5007-0102）
黑龙江省优秀青年科学基金(YQ2020D001) （YQ2020D001）
黑龙江省自然科学基金(LH2020F003) （LH2020F003）
黑龙江省创新型科研人才培养计划(UNPYSCT-2020144) （UNPYSCT-2020144）
东北石油大学引导性创新基金项目(15071202202) （15071202202）
东北石油大学2021年高等教育教学改革项目 （）
