钻井液与完井液2025，Vol.42Issue(3)：359-367,9.DOI:10.12358/j.issn.1001-5620.2025.03.012
数字孪生环境下基于生成对抗网络的钻井液流变性能预测方法
Method of Predicting Drilling Fluid Rheology Based on Generative Adversarial Networks in Digital Twin Environment
摘要
Abstract
A method of predicting drilling fluid rheology based on genitive adversarial network in digital twin environment has been developed to deal with problems in laboratory measurement of drilling fluid rheology manually,such as low efficiency,high cost and poor stability etc.First,a twin model for drilling fluid formulation and measurement system is constructed in accordance with digital twin five-dimensional model.Information collectors such as sensors in the physical measurement system can collect the live measured data of drilling fluid properties,and the composition of the drilling fluid and the measured drilling fluid properties are integrated and sent to a virtual space to construct a database for drilling fluid property prediction.Second,using the improved generative adversarial network algorithm,a drilling fluid rheology prediction model is constructed.Historical twin data of the drilling fluid are then extracted from the database and are used as a dataset to train the model,and a best-fitting model is thus obtained.The prediction ability of the model is verified through the prediction experiment of drilling fluid rheology.Use of the model shows that the correlation coefficient R between the predicted values and the true values exceeds 0.96,and the mean absolute percentage error(AAPE)is not higher than 4.1%,indicating that the model has higher prediction accuracy,and is able to accomplish the predciiton of drilling fluid rheology.关键词
数字孪生/生成对抗网络/钻井液/性能预测Key words
Digital twin/Generative adversarial network/Drilling fluid/Property prediction分类
能源科技引用本文复制引用
郭亮,徐行,刘开勇,姚如钢,唐赛宇,向渝..数字孪生环境下基于生成对抗网络的钻井液流变性能预测方法[J].钻井液与完井液,2025,42(3):359-367,9.基金项目
中国石油长城钻探钻井液公司"钻完井用自动化配置与黏度检测装置检测分析服务项目"(GWDC/DF/202206/003). （GWDC/DF/202206/003）
