Total organic carbon (TOC) quantification using artificial neural networks: Improved prediction by leveraging XRF data

نویسندگان

چکیده

This study develops a new artificial neural network (ANN) model for predicting the total organic carbon (TOC) of an organic-rich carbonate mudstone formation using conventional well log data and X-ray fluorescence spectroscopy (XRF) analysis. The used in include logs, redox-sensitive elements from XRF, TOC values measured lab 150 core samples obtained five wells. Selected logs including gamma ray (GR), bulk density (RHOB), uranium (URAN), XRF-derived elements, molybdenum (Mo), copper (Cu), nickel (Ni), were to train develop ANN predict generate continuous high-resolution profiles classified into two groups based on geological descriptions locations. Statistical analyses performed establish range each group evaluate relationships among input parameters. developed showed high performance providing profile TOC. difference between absolute average is less than 0.50 correlation coefficient (R-value) greater 0.70. Empirical correlations extracted best performing model, which will allow easy quick estimation values. outperform available methods determining reduce error by 42 %.

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ژورنال

عنوان ژورنال: Journal of Petroleum Science and Engineering

سال: 2022

ISSN: ['0920-4105', '1873-4715']

DOI: https://doi.org/10.1016/j.petrol.2021.109302