Research And Development Of Tubing And Casing Quality Evaluation System In Intelligent Manufacturing Environment

After many revolutionary changes in the world’s industry,it has entered the era of intelligent industry 4.0.With the rapid development of industrial level,the demand for oil is also increasing.The use of tubing and casing in oil production is very large,and its manufacturing quality will directly affect the safety of petroleum exploitation.In the process of oil exploitation,even if the qualified tubing and casing are used,there will still be safety accidents.After analysis,this is due to the random connection of multi-stage tubing and casing,but it does not take into account the impact of the test data in the qualified interval.For the processing of tubing and casing detection data,the traditional method simply compares the detection data of a certain parameter,but lacks a comprehensive and in-depth analysis of the data,which will produce a large number of data isolated islands.In order to make a new research on the quality evaluation system of tubing and casing,the API formula is first analyzed to find the key parameters affecting the performance of the casing.The specific work of the project is divided into three parts:first,the wireless data module is introduced into the detection system of oil and bushing to realize the transmission of test data.Secondly,on the basis of C#language,using WPF framework,Linq language and DevExpress set,a set of perfect computer software is developed,including user information management,detection scheme establishment,report printing,and detection data analysis and monitoring modules.Finally,according to the analysis results of API formula,6 parameters such as diameter,wall thickness and pitch of tubing and casing are selected in accordance with the actual situation.As the key factor affecting the comprehensive quality of tubing and casing,the evaluation model of BP neural network for this subject is established.Tlhrough the analysis of many parameters of tubing and casing,and effectively utilizing the data isolated islands,this is a very important development direction in intelligent manufacturing.At the end of the paper,the tubing and casing detection data provided by the company are preprocessed to compare the different optimization algorithms of BP neural network.At the same time,through repeated experiments,the most suitable hidden layer nodes are determined to be 7,and the structure of the LM-BP neural network model is determined,so that the failure of the tubing and casing may be predicted.The experimental results verify that the wireless data acquisition system designed in this subject can run normally,and the host computer software meets the requirements.Historical data are used to test it,which indicates that the evaluation model established in this thesis can predict the failure of tubing and casing.Oil field can be used as a reference for the use of tubing and casing or to adjust the later inspection and maintenance scheme.