In response to the problems of low accuracy in manual interpretation of logging data and easy omission of important information in the traditional multi arm caliper logging casing damage detection process,we propose a multi arm caliper logging casing damage detection method based on Bayesian neural network,combined with multi arm caliper logging data from a certain work area in Daqing Oilfield.This method can form a multi arm wellbore diameter dataset based on correcting the orientation of the original logging curve,filling in missing values,and summarizing curve data for common casing damage categories.At the same time,the dataset is normalized and used as training data for casing damage detection experiments.Comparison shows that in the detection of casing damage in multi arm caliper logging,the Bayesian neural network trained using the MC Dropout variational inference method performs better in performance and robustness compared to BP neural network,random forest,the Bayesian neural network trained using BayesByBackprop and SGLD variational inference methods.The experiment shows that the proposed method is more effective in detecting casing damage in multi arm logging,with an average accuracy of 95.67％,which is significantly improved compared to traditional manual interpretation methods.It can also provide better interpretability for the uncertainty of classification results,greatly improving the credibility of measuring detection results.
关键词
多臂井径/套损检测/贝叶斯神经网络/变分推理/不确定性
Key words
multi-arm well diameter/casing damage detection/Bayesian neural network/variational inference/uncertainty
维普
万方数据