首页|New Machine Learning Findings from University of Texas Austin Reported (Real-tim e Prediction of Bottom-hole Circulating Temperature In Geothermal Wells Using Ma chine Learning Models)
New Machine Learning Findings from University of Texas Austin Reported (Real-tim e Prediction of Bottom-hole Circulating Temperature In Geothermal Wells Using Ma chine Learning Models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting originating from Austin, Te xas, by NewsRx correspondents, research stated, “Drilling high-temperature geoth ermal wells presents technical and economic challenges. Real-time and precise es timation of bottom-hole circulating temperature (BHCT) during geothermal drillin g is crucial for maximizing the working life of drill bits and temperature-limit ed bottom-hole assembly (BHA) components, thereby avoiding unplanned and unneces sary bit/BHA trips.” Funders for this research include University of Texas at Austin, Bureau of Econo mic Geology at the University of Texas at Ausitn.
AustinTexasUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Texas Austin
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