Total organic carbon (TOC) quantification using artificial neural networks: Improved prediction by leveraging XRF data
AuthorsChan, Septriandi A.
Hassan, Amjed M.
Humphrey, John D.
Online Publication Date2021-07-29
Print Publication Date2021-07
Permanent link to this recordhttp://hdl.handle.net/10754/670723
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AbstractThis 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 data used in the study include conventional well logs, redox-sensitive elements from XRF, and TOC values measured in lab for a total of 150 core samples obtained from five wells. Selected well logs including gamma ray (GR), bulk density (RHOB), uranium (URAN), and XRF-derived elements, including molybdenum (Mo), copper (Cu), and nickel (Ni), were used to train and develop the ANN model to predict and generate continuous high-resolution TOC log profiles for the five wells. TOC data were classified into two groups based on geological descriptions and well locations. Statistical analyses were performed to establish the range of data used for each group and to evaluate relationships among the TOC and input parameters. The developed ANN model showed a high performance in providing a continuous profile of TOC. The difference between absolute average is less than 0.50 and the correlation coefficient (R-value) is greater than 0.70. Empirical correlations were extracted from the best performing ANN model, which will allow easy and quick estimation for TOC values. The developed correlations outperform available methods for determining TOC and reduce the estimation error by 42 %.
CitationChan, S. A., Hassan, A. M., Usman, M., Humphrey, J. D., Alzayer, Y., & Duque, F. (2021). Total organic carbon (TOC) quantification using artificial neural networks: Improved prediction by leveraging XRF data. Journal of Petroleum Science and Engineering, 109302. doi:10.1016/j.petrol.2021.109302
SponsorsThis project was funded by Saudi Aramco and King Fahd University of Petroleum and Minerals (KFUPM) through the Center of Integrative Petroleum Research (CIPR), project number CIPR2318. This work was supported by the College of Petroleum Engineering and Geosciences, KFUPM, to whom we are most grateful. Abduljamiu Amao and Ignatius Argadestya are thanked for their assistance with graphics. The manuscript was greatly improved by the thorough treatment by the anonymous reviewers and the Journal editorial board.
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