查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting from Sylhet, B angladesh, by NewsRx journalists, research stated, “Permeability is the most imp ortant petrophysical characteristic for determining how fluids pass through rese rvoir rocks. This study aims to develop and assess intelligent computer-based mo dels for predicting permeability.” Our news correspondents obtained a quote from the research from Shahjalal Univer sity of Science and Technology: “The research focuses on three novel models-Deci sion Tree, Bagging Tree, and Extra Trees-while also investigating previously app lied techniques such as random forest, support vector regressor (SVR), and multi ple variable regression (MVR). The primary dataset consists of 197 data points f rom a heterogeneous petroleum reservoir in the Jeanne d’Arc Basin, including lab oratory-derived permeability (K), oil saturation (SO), water saturation (SW), gr ain density (rgr), porosity (ph), and depth. The most effective machine learning models are identified by a thorough analysis that makes use of a variety of sta tistical metrics, such as the coefficient of the determinant (R2), mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), mean abso lute percentage error (MAPE), maximum error (maxE), and minimum error (minE). Ad ditionally, core features are ranked based on their importance in permeability m odeling. This study deviates from conventional approaches by proposing an effici ent means of forecasting permeability, reducing reliance on labor-intensive and time-consuming laboratory work. The findings reveal that MVR is unsuitable for p ermeability prediction, with all developed models outperforming it. Extra Trees emerges as the most accurate model, with an R2 of 0.976, while random forest and bagging tree exhibit slightly lower R2 values of 0.961 and 0.964, respectively. The ranking of these algorithms based on performance criteria is as follows: ex tra trees, bagging tree, random forest, SVR, decision tree, and MVR. The study a lso presents a detailed analysis of the impact of input parameters, highlighting porosity (ph) and water saturation (SW) as the most influential, while grain de nsity (rgr), oil saturation (SO), and depth are considered less important.”
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