首页|Data on Machine Learning Described by Researchers at SouthwestPetroleum Univers ity (Hybrid Machine Learning Model Based OnGwo and Pso Optimization for Predict ion of Oilwell Cement Compressive Strength Under Acidic Corrosion)
Data on Machine Learning Described by Researchers at SouthwestPetroleum Univers ity (Hybrid Machine Learning Model Based OnGwo and Pso Optimization for Predict ion of Oilwell Cement Compressive Strength Under Acidic Corrosion)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsreporting from Chengdu, People’s Republ ic of China, by NewsRx journalists, research stated, “It is difficultto solve t he problem that the cement sheath of oil and gas wells is corroded by acid gas, and the change incompressive strength (CS) of the cement sheath after corrosion is the key to affecting the sealing capacityof the cement sheath. In this stud y, we used four traditional machine learning (ML) algorithms-artificialneural n etwork (ANN), support vector machine regression (SVR), extreme learning machine (ELM), andrandom forest (RF)-to establish a model for predicting the CS of corr oded cement stone.”
ChengduPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningSouthwest Petroleum Univers ity
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