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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

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Abstract

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 method in source rock evaluation. We used geochemical data from 30 core samples taken equally from wells SS-5 and SS-7. Geochemical parameters, namely TOC, free hydrocarbon, thermal pyrolysis hydrocarbon, hydrogen index, production index, and oxygen index, were determined for statistical evaluation. IBM SPSS statistical software and MATLAB (R2020a) were used for MVA and ML, respectively. The performance of the models built using MVA and ML were evaluated by, among others, coefficient of determination (R2) and mean square error (MSE). Findings revealed that fair through good to excellent source rock with TOC ranging from 0.85 to 2.95 wt% are hosted in the Triassic beds of Tanga. A high 1.61% Ro at a mature peak of 463 °C predominates with the existence of type III/II kerogen that can produce both oil and gas. Considering TOC prediction from conventional well log data, optimized Gaussian process regression showed the best performance followed by MVA and support vector machine, giving the MSEs of 0.5629, 0.6172, and 0.7023, respectively. In terms of prediction accuracy, their R2 values of 0.952, 0.9346, and 0.835, respectively, were in good agreement with the geochemical results. The concurrence of geochemical analysis, ML, and MVA revealed that the Tanga basin has great hydrocarbon potential of great economic importance. The study revealed that combining MVA and other methods can be applied to assess the hydrocarbon resource potential of other prospects around the globe.

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modified from Kapilima (2003) and Said et al. (2015)

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Acknowledgments

This work was supported by the Major National Science and Technology Programs in the “Thirteenth Five-Year” Plan period (No. 2017ZX05032-002-004), National Natural Science Foundation China (grant Nos.41972326 and 51774258), and the Fundamental Research Fund for the Central Universities, China University of Geosciences (Wuhan, No. CUGCJ1820). Department of Petroleum Engineering and International Education College at the China University of Geosciences provided helps for the success of this experimental study.

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Authors and Affiliations

  1. Department of Petroleum Engineering, School of Earth Resources, China University of Geosciences, Wuhan, 430074, China

    Edwin E. Nyakilla, Selemani N. Silingi, Chuanbo Shen, Gu Jun, Alvin K. Mulashani & Patrick E. Chibura

  2. Department of Petroleum Geology School of Earth Resources, China University of Geosciences, Wuhan, 430074, China

    Chuanbo Shen

  3. Department of Geology, Earth Sciences Institute of Shinyanga, (ESIS, P.O.Box 1016, Shinyanga, Tanzania

    Selemani N. Silingi

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  1. Edwin E. Nyakilla
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  3. Chuanbo Shen
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Correspondence to Chuanbo Shen or Gu Jun.

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Nyakilla, E.E., Silingi, S.N., Shen, C. et al. 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. Nat Resour Res 31, 619–641 (2022). https://doi.org/10.1007/s11053-021-09988-1

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