Research On Intelligent Recommendation System For The Quality Of 3D Induction Logging

With the use of the 3D induction logging tool independently developed by China Petroleum Group Logging Co.,Ltd.,when the logging data is abnormal during the logging process,it is necessary to check the cause of the failure one by one based on the experience of oilfield experts.This thesis takes 3D induction logging quality faults as the research object,uses common logging quality faults and corresponding fault causes to build a knowledge base,combines recommendation algorithms from the field of fault experts,and develops a personalized recommendation system to help operators quickly Find the cause of quality problems,ensure safe construction and production,and improve the efficiency of 3D induction logging.In this context,this thesis investigates,collects,and sorts out the operation knowledge information of 3D induction logging,builds a 3D induction logging quality fault-cause knowledge base,and uses the collaborative filtering algorithm based on the SVD model to recommend the best possible fault causes,and designed and developed a3 D induction logging quality intelligent recommendation system,the specific work is as follows:First of all,this thesis studies the abnormality of logging curves and the fault characteristics of logging instruments in 3D induction logging,so as to summarize the common fault types and corresponding fault causes,and establish a fault knowledge base.Secondly,carry out score quantitative analysis on the cause of the failure,use the K-means algorithm of machine learning,combine the elbow method and the contour coefficient method to determine the cluster to which the cause of the failure belongs,and use the KNN algorithm to perform correlation analysis and prediction on the scoring data of the cause of the failure.Next,the collaborative filtering algorithm based on the SVD model is used to recommend the most probable fault causes to the fault symptoms.Then,through comparative experiments with the other two recommendation algorithms,the experimental results prove that the collaborative filtering recommendation algorithm based on the SVD model can be applied to the 3D induction logging quality intelligence system.Finally,the 3D induction logging intelligent recommendation system is designed and implemented,and the corresponding fault causes are recommended for the fault symptoms.The core functions such as fault management,fault cause,fault recommendation,and user management are designed and implemented in sequence,and each module is displayed through the web interface.So as to prove the feasibility and practicality of the recommendation system.The system intelligently seeks solutions to 3D induction logging quality problems through recommendation algorithms,avoiding the impact of logging quality problems on logging results,thereby improving the accuracy and precision of 3D induction logging.