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Application of machine learning to characterize gas hydrate reservoirs in Mackenzie Delta (Canada) and on the Alaska north slope (USA)

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Abstract

Artificial neural network-trained models were used to predict gas hydrate saturation distributions in permafrost-associated deposits in the Eileen Gas Hydrate Trend on the Alaska North Slope (ANS), USA and at the Mallik research site in the Beaufort-Mackenzie Basin, Northwest Territories, Canada. The database of Logging-While-Drilling (LWD) and wireline logs collected at five wells (Mount Elbert, Iġnik Sikumi, and Kuparuk 7–11–12 wells at ANS, plus 2L-38 and 5L-38 wells at the Mallik research site) includes more than 10,000 depth points, which were used for training, validation, and testing the machine learning (ML) models. Data used in training the ML models include the well logs of density, porosity, electrical resistivity, gamma radiation, and acoustic wave velocity measurements. Combinations of two or three out of these five well logs were found to reliably predict the gas hydrate saturation with accuracy varying between 80 and 90% when compared to the gas hydrate saturations derived from Nuclear Magnetic Resonance (NMR)-based technique. The ML models trained on data from three ANS wells achieved high fidelity predictions of gas hydrate saturation at the Mallik site. The results obtained in this study indicate that ML models trained on data from one geological basin can successfully predict key reservoir parameters for permafrost-associated gas hydrate accumulations within another basin. A generalized approach for selecting a well log combination that can improve model accuracy is discussed. Overall, the study outcome supports earlier work demonstrating that ML models trained on non-NMR well logs are a viable alternative to physics-driven methods for predicting gas hydrate saturations.

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Acknowledgements

This work was performed in support of the U.S. Department of Energy’s Fossil Energy Crosscutting Technology Research Program. The research was executed through the NETL Research and Innovation Center’s Hydrate Research Field Work Proposal. This research was supported in part by an appointment to the National Energy Technology Laboratory Research Participation Program, sponsored by the U.S. Department of Energy. The authors would like to express their sincere appreciation to the U.S. DOE-NETL, and the Japanese Ministry of Economy, Trade and Industry for providing the permission to disclose this research. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. or Japanese Government.

Disclaimer

This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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Authors and Affiliations

  1. National Energy Technology Laboratory, 626 Cochrans Mill Road, P.O. Box 10940, Pittsburgh, PA, 15236, USA

    Leebyn Chong & Evgeniy M. Myshakin

  2. NETL Support Contractor, 626 Cochrans Mill Road, P.O. Box 10940, Pittsburgh, PA, 15236, USA

    Leebyn Chong & Evgeniy M. Myshakin

  3. National Energy Technology Laboratory, 3610 Collins Ferry Road, Morgantown, WV, 26507, USA

    Harpreet Singh & Yongkoo Seol

  4. NETL Support Contractor, 3610 Collins Ferry Road, Morgantown, WV, 26507, USA

    Harpreet Singh

  5. National Energy Technology Laboratory, 1450 Queen Avenue SW, Albany, OR, 97321, USA

    C. Gabriel Creason

  6. NETL Support Contractor, 1450 Queen Avenue SW, Albany, OR, 97321, USA

    C. Gabriel Creason

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Chong, L., Singh, H., Creason, C.G. et al. Application of machine learning to characterize gas hydrate reservoirs in Mackenzie Delta (Canada) and on the Alaska north slope (USA). Comput Geosci 26, 1151–1165 (2022). https://doi.org/10.1007/s10596-022-10151-9

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