首页|University of Oklahoma Researcher Broadens Understanding of Machine Learning (Pr edicting Gas Separation Efficiency of a Downhole Separator Using Machine Learnin g)
University of Oklahoma Researcher Broadens Understanding of Machine Learning (Pr edicting Gas Separation Efficiency of a Downhole Separator Using Machine Learnin g)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented. According to news reporting originating from Norman, Okl ahoma, by NewsRx correspondents, research stated, "Artificial lift systems, such as electrical submersible pumps and sucker rod pumps, frequently encounter oper ational challenges due to high gas-oil ratios, leading to premature tool failure and increased downtime. Effective upstream gas separation is critical to mainta in continuous operation." The news reporters obtained a quote from the research from University of Oklahom a: "This study aims to predict the efficiency of downhole gas separator using ma chine learning models trained on data from a centrifugal separator and tested on data from a gravity separator (blind test). A comprehensive experimental setup included a multiphase flow system with horizontal (31 ft. (9.4 m)) and vertical (27 ft. (8.2 m)) sections to facilitate the tests. Seven regression models-multi linear regression, random forest, support vector machine, ridge, lasso, k-neares t neighbor, and XGBoost-were evaluated using performance metrics like RMSE, MAPE , and R-squared. In-depth exploratory data analysis and data preprocessing ident ified inlet liquid and gas volume flows as key predictors for gas volume flow pe r minute at the outlet (GVFO)."
University of OklahomaNormanOklahomaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMach ine Learning