Evaluation of Source Rock Potentiality and Prediction of Total Organic Carbon Using Well Log Data and Integrated Methods of Multivariate Analysis, Machine Learning, and Geochemical Analysis

• Published in: Natural resources research (New York, N.Y.) Vol. 31; no. 1; pp. 619 - 641
• Main Authors: Nyakilla, Edwin E., Silingi, Selemani N., Shen, Chuanbo, Jun, Gu, Mulashani, Alvin K., Chibura, Patrick E.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2022; Springer Nature B.V
• Subjects: Chemistry and Earth Sciences; Computer Science; Core analysis; Earth and Environmental Science; Earth Sciences; Economic importance; Fossil Fuels (incl. Carbon Capture); Gaussian process; Geochemistry; Geography; Hydrocarbons; Kerogen; Learning algorithms; Machine learning; Mathematical Modeling and Industrial Mathematics; Mineral Resources; Multivariate analysis; Oil exploration; Organic carbon; Original Paper; Physics; Predictions; Pyrolysis; Rocks; Statistical analysis; Statistics; Statistics for Engineering; Support vector machines; Sustainable Development; Total organic carbon; Triassic; Cluster analysis; Factor analysis; Machine learning; Geochemical analysis; ); Pearson's correlation coefficient; Source rock

In this study, integrated approaches based on multivariate analysis (MVA), machine learning (ML), and geochemical analysis are proposed to investigate the potential of hydrocarbon reserves and total organic carbon (TOC) prediction. These approaches employed the MVA technique as a future selection me...