Cementing Quality Prediction And Technical Measures Decision-making Method Based On Big Data Modeling

In the era of big data information,there are numerous technological gaps in drilling and completion engineering technology that can be combined with big data science.Especially in the field of cementing engineering technology,there are many factors that affect the quality of cementing,and the mutual influence between these factors is nonlinear,time-varying,and uncertain,which increases the complexity and difficulty of implementing cementing engineering design and cementing quality analysis based on data and experience.Sometimes,it may even cause deviations in design or analysis results,affecting the timeliness and accuracy of formulating cementing technical measures.Large data modeling and intelligent calculation can effectively process,analyze and evaluate a large number of complex cementing quality influence parameters and their validity,and can build a systematic technical platform for intelligent analysis of cementing quality and intelligent decision-making of cementing engineering technical measures,and effectively solve the analysis timeliness and accuracy of complex problems such as the formulation of cementing technical measures,control of key factors,and analysis and evaluation of cementing quality.Therefore,establishing a method and system for intelligent analysis of cementing quality and decision-making of technical measures based on big data mining and intelligent computing is of great significance for deepening the research and development of cementing engineering technology.Based on the complex and diverse data formats of oil and gas well cementing quality and its influencing factors collected on site,research was conducted on basic data processing methods,cementing quality prediction and analysis models,key influencing factor extraction methods,and cementing technical measures decision-making models.Model experiments were conducted to analyze the model accuracy.The following research results were obtained through the research of the paper: a data processing method and SQL basic database were established for the factors affecting the quality of cementing,which solved the problem of raw data being unable to be directly substituted into model modeling and calculation;Established the XGboost algorithm big data model,solved the problem of imbalanced datasets affecting cementing quality parameters,evaluated the applicability of the XGboost algorithm big data model,and optimized and adjusted the built-in parameters of the model through example experiments,improving the prediction accuracy from 87% to around 89%.Initially,a method based on the XGboost algorithm big data algorithm was established for predicting cementing quality under given cementing conditions and evaluating the weight of key influencing factors;By introducing particle swarm optimization(PSO)algorithm to optimize the original feature data,an improved prediction model based on PSO-XGboost algorithm was established.The weight of key factors affecting cementing quality under given conditions in the oilfield field was calculated,and the prediction accuracy of the model was improved to around 92%;A decision model for cementing construction technical measures was established based on similarity algorithm,and a decision method was established to calculate the key influencing factors of cementing quality based on objective conditions such as oilfield blocks and wellbore.This provides a foundation for the design of cementing construction technical measures for oil and gas wells.