首页|University of Houston Researcher Focuses on Machine Learning (A Comparative Anal ysis of the Prediction of Gas Condensate Dew Point Pressure Using Advanced Machi ne Learning Algorithms)
University of Houston Researcher Focuses on Machine Learning (A Comparative Anal ysis of the Prediction of Gas Condensate Dew Point Pressure Using Advanced Machi ne Learning Algorithms)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on artificial intelligence are present ed in a new report. According to news reporting originating from Houston, Texas, by NewsRx correspondents, research stated, "Dew point pressure (DPP) emerges as a pivotal factor crucial for forecasting reservoir dynamics regarding condensat e-to-gas ratio and addressing production/completion hurdles, alongside calibrati ng EOS models for integrated simulation." Our news journalists obtained a quote from the research from University of Houst on: "However, DPP presents challenges in terms of predictability. Acknowledging these complexities, we introduce a state-of-the-art approach for DPP estimation utilizing advanced machine learning (ML) techniques. Our methodology is juxtapos ed against published empirical correlation-based methods on two datasets with li mited sizes and diverse inputs. With superior performance over correlation-based estimators, our ML approach demonstrates adaptability and resilience even with restricted training datasets, spanning various fluid classifications. We acquire d condensate PVT data from publicly available sources and GeoMark RFDBASE, encom passing dew point pressure (the target variable), as well as compositional data (mole percentages of each component), temperature, molecular weight (MW), and sp ecific gravity (SG) of heptane plus, which served as input variables. Before ini tiating the study, thorough assessments of measurement quality and results using statistical methods were conducted leveraging domain expertise."
University of HoustonHoustonTexasU nited StatesNorth and Central AmericaAlgorithmsCyborgsEmerging Technolog iesMachine Learning
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