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• W3105322534 abstract "Abstract Vitrinite reflectance (VR) is considered the most used maturity indicator of source rocks. Although vitrinite reflectance is an acceptable parameter for maturity and is widely used, it is sometimes difficult to measure. Furthermore, Rock-Eval pyrolysis is a current technique for geochemical investigations and evaluating source rock by their quality and quantity of organic matter, which provide low cost, quick, and valid information. Predicting vitrinite reflectance by using a quick and straightforward method like Rock-Eval pyrolysis results in determining accurate and reliable values of VR with consuming low cost and time. Previous studies used empirical equations for vitrinite reflectance prediction by the T max data, which was accompanied by poor results. Therefore, finding a way for precise vitrinite reflectance prediction by Rock-Eval data seems useful. For this aim, vitrinite reflectance values are predicted by 15 distinct machine learning models of the decision tree, random forest, support vector machine, group method of data handling, radial basis function, multilayer perceptron, adaptive neuro-fuzzy inference system, and multilayer perceptron and adaptive neuro-fuzzy inference system, which are coupled with evolutionary optimization methods such as grasshopper optimization algorithm, bat algorithm, particle swarm optimization, and genetic algorithm, with four inputs of Rock-Eval pyrolysis parameters of T max , S 1 /TOC, HI, and depth for the first time. Statistical evaluations indicate that the decision tree is the most precise model for VR prediction, which can estimate vitrinite reflectance precisely. The comparison between the decision tree and previous proposed empirical equations indicates that the machine learning method performs much more accurately." @default.
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• W3105322534 date "2020-11-18" @default.
• W3105322534 modified "2023-10-17" @default.
• W3105322534 title "Prediction of vitrinite reflectance values using machine learning techniques: a new approach" @default.
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• W3105322534 doi "https://doi.org/10.1007/s13202-020-01043-8" @default.
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