Study On Scale Analysis Method Of ASP Flooding Production Well

Currently,the widespread implementation of ternary composite drive oil recovery technology has resulted in a growing concern regarding well fouling.This issue not only significantly diminishes the economic and social benefits of oilfield enterprises but also poses substantial challenges to crude oil extraction in oilfields.An investigation and research conducted at an oil production plant in Daqing revealed that the existing system for analyzing fouling characteristics in ternary composite drive recovery wells remains in the hands of experts who manually select and analyze fouling characteristics.This approach exhibits low levels of informatization,requires extensive data calculations,and involves a complex analysis process.Moreover,the process of feature selection and analysis overly relies on experts’ experiences,rendering it highly subjective and affecting the accuracy of fouling prediction in recovery wells.Consequently,the establishment of a fouling analysis model for ternary composite drive recovery wells and the development of a prediction system have emerged as urgent needs within the field of oilfield crude oil extraction.Furthermore,these initiatives serve as effective means to mitigate the trend of recovery well fouling.To address the aforementioned challenges,this study conducts an in-depth analysis of the business process associated with recovery well fouling characterization.It designs a fouling characterization model specifically tailored to ternary composite drive recovery wells and analyzes and implements the model components.Firstly,the study thoroughly examines the process and criteria for fouling analysis in recovery wells and devises a comprehensive framework for the fouling analysis model.Secondly,it investigates the source data pertaining to fouling and establishes a data model,followed by preprocessing operations such as handling missing values,identifying and addressing outliers,performing normalization operations,and initially screening fouling features.Subsequently,the data resulting from the initial screening of fouling features undergo further analysis.The Lasso feature selection algorithm is employed to refine the data,while the correlation analysis method is utilized to explore the relationship between scaling features and temporal regularity.Next,commonly used classification algorithms including support vector machine,random forest,and decision tree are employed to evaluate the feature selection method.Additionally,various classification prediction models such as BO-CNN-BiGRU,LSTM,GRU,and RNN are designed to assess the performance of multiple evaluation methods in terms of accuracy,recall,and other indicators.Ultimately,BO-CNN-BiGRU,which exhibits superior indicators,is selected as the final classification prediction model.Finally,leveraging the fouling analysis business background of recovery wells and the fouling feature analysis model specific to ternary composite drive recovery wells,a prediction system for fouling analysis in ternary composite drive recovery wells is designed and implemented.The research findings indicate that the ternary composite drive recovery well fouling characterization prediction model effectively addresses the current challenges associated with scale characterization.The system successfully predicts the scale trend in recovery wells over time,significantly enhancing the efficiency and accuracy of field scale characterization and demonstrating considerable practical value.