Machine Learning Application to Assess Occurrence and Saturations of Methane Hydrate in Marine Deposits Offshore India

Chong, Leebyn; Collett, Timothy S.; Creason, Christopher Gabriel; Seol, Yongkoo; Myshakin, Evgeniy M.

doi:10.1190/int-2023-0056.1

Title: Machine Learning Application to Assess Occurrence and Saturations of Methane Hydrate in Marine Deposits Offshore India

Journal Article · 03 October 2023 · Interpretation

DOI: https://doi.org/10.1190/int-2023-0056.1 · OSTI ID:2059620

Chong, Leebyn ^[1]; Collett, Timothy S. ^[2]; Creason, Christopher Gabriel ^[3]; Seol, Yongkoo ^[4]; Myshakin, Evgeniy M. ^[1]

National Energy Technology Laboratory (NETL), Pittsburgh, PA (United States); NETL Support Contractor, Pittsburgh, PA (United States)
US Geological Survey, Denver, CO (United States)
National Energy Technology Laboratory (NETL), Albany, OR (United States)
National Energy Technology Laboratory (NETL), Morgantown, WV (United States)

Artificial Neural Networks (ANN) were used to assess methane hydrate occurrence and saturation in marine sediments offshore India. The ANN analysis classifies the gas hydrate occurrence into three types: methane hydrate in pore space, methane hydrate in fractures, or no methane hydrate. Further, predicted saturation characterizes the volume of gas hydrate with respect to the available void volume. Log data collected at six wells, which were drilled during the India National Gas Hydrate Program Expedition 02 (NGHP-02), provided a combination of well log measurements that were used as input for machine learning (ML) models. Well log measurements included density, porosity, electrical resistivity, natural gamma radiation, and acoustic wave velocity. Combinations of well logs used in the ML models provide good overall balanced accuracy (0.79 to 0.86) for the prediction of the gas hydrate occurrence and good accuracy (0.68 to 0.92) for methane hydrate saturation prediction in the marine accumulations against reference data. The accuracy scores indicate that the ML models can successfully predict reservoir characteristics for marine methane hydrate deposits. In conclusion, the results indicate that the ML models can either augment physics-driven methods for assessing the occurrence and saturation of methane hydrate deposits or serve as an independent predictive tool for those characteristics.

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Research Organization:: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)

Sponsoring Organization:: USDOE Office of Fossil Energy and Carbon Management (FECM)

OSTI ID:: 2059620

Journal Information:: Interpretation, Journal Name: Interpretation Journal Issue: 1 Vol. 12; ISSN 2324-8858

Publisher:: Society of Exploration GeophysicistsCopyright Statement

Country of Publication:: United States

Language:: English

References (39)

India National Gas Hydrate Program Expedition 02 summary of scientific results: Numerical simulation of reservoir response to depressurization Boswell, Ray; Myshakin, Evgeniy; Moridis, George Marine and Petroleum Geology, Vol. 108 https://doi.org/10.1016/j.marpetgeo.2018.09.026	journal	October 2019
Gas hydrate saturation and morphology from analysis of pressure cores acquired in the Bay of Bengal during expedition NGHP-02, offshore India Holland, M. E.; Schultheiss, P. J.; Roberts, J. A. Marine and Petroleum Geology, Vol. 108 https://doi.org/10.1016/j.marpetgeo.2018.07.018	journal	October 2019
Key Findings of the World’s First Offshore Methane Hydrate Production Test off the Coast of Japan: Toward Future Commercial Production Konno, Yoshihiro; Fujii, Tetsuya; Sato, Akihiko Energy & Fuels, Vol. 31, Issue 3 https://doi.org/10.1021/acs.energyfuels.6b03143	journal	February 2017
Seismic velocities for hydrate-bearing sediments using weighted equation Lee, M. W.; Hutchinson, D. R.; Collett, T. S. Journal of Geophysical Research: Solid Earth, Vol. 101, Issue B9 https://doi.org/10.1029/96JB01886	journal	September 1996
Downhole physical property-based description of a gas hydrate petroleum system in NGHP-02 Area C: A channel, levee, fan complex in the Krishna-Godavari Basin offshore eastern India Waite, W. F.; Jang, J.; Collett, T. S. Marine and Petroleum Geology, Vol. 108 https://doi.org/10.1016/j.marpetgeo.2018.05.021	journal	October 2019
Automated Well-Log Processing and Lithology Classification by Identifying Optimal Features Through Unsupervised and Supervised Machine-Learning Algorithms Singh, Harpreet; Seol, Yongkoo; Myshakin, Evgeniy M. SPE Journal, Vol. 25, Issue 05 https://doi.org/10.2118/202477-PA	journal	August 2020
Development of New Permeability Formulation From Well Log Data Using Artificial Intelligence Approaches Moussa, Tamer; Elkatatny, Salaheldin; Mahmoud, Mohamed Journal of Energy Resources Technology, Vol. 140, Issue 7 https://doi.org/10.1115/1.4039270	journal	March 2018
Comparison of kriging, machine learning algorithms and classical thermodynamics for correlating the formation conditions for CO2 gas hydrates and semi-clathrates Ahmadi, Mohammadali; Chen, Zhangxin; Clarke, Matthew Journal of Natural Gas Science and Engineering, Vol. 84 https://doi.org/10.1016/j.jngse.2020.103659	journal	December 2020
Application of machine learning to characterize gas hydrate reservoirs in Mackenzie Delta (Canada) and on the Alaska north slope (USA) Chong, Leebyn; Singh, Harpreet; Creason, C. Gabriel Computational Geosciences, Vol. 26, Issue 5 https://doi.org/10.1007/s10596-022-10151-9	journal	May 2022
Gas hydrate saturations estimated from fractured reservoir at Site NGHP-01-10, Krishna-Godavari Basin, India Lee, M. W.; Collett, T. S. Journal of Geophysical Research, Vol. 114, Issue B7 https://doi.org/10.1029/2008JB006237	journal	January 2009
Lithofacies characterization of gas hydrate prospects discovered during the National Gas Hydrate Program expedition 02, offshore Krishna-Godavari Basin, India Nanda, Jagannath; Shukla, K. M.; Lall, M. V. Marine and Petroleum Geology, Vol. 108 https://doi.org/10.1016/j.marpetgeo.2019.03.032	journal	October 2019
Regional stratigraphic framework and gas hydrate occurrence offshore eastern India: Core-log-seismic integration of National Gas Hydrate Program Expedition 02 (NGHP-02) Area-B drill sites Hsiung, Kan-Hsi; Saito, Saneatsu; Kanamatsu, Toshiya Marine and Petroleum Geology, Vol. 108 https://doi.org/10.1016/j.marpetgeo.2018.06.021	journal	October 2019
Local subspace-based outlier detection using global neighbourhoods van Stein, Bas; van Leeuwen, Matthijs; Back, Thomas 2016 IEEE International Conference on Big Data (Big Data) https://doi.org/10.1109/BigData.2016.7840717	conference	December 2016
The Electrical Resistivity Log as an Aid in Determining Some Reservoir Characteristics Archie, G. E. Transactions of the AIME, Vol. 146, Issue 01 https://doi.org/10.2118/942054-G	journal	December 1942
Impact of Borehole Washout on Acoustic Logs and Well-to-Seismic Ties Ugborugbo, Omuvwie; Rao, Tummala All Days https://doi.org/10.2118/128346-MS	conference	August 2009
Bottom-simulating reflector dynamics at Arctic thermogenic gas provinces: An example from Vestnesa Ridge, offshore west Svalbard: Arctic Gas Hydrate Stability Dynamics Plaza-Faverola, A.; Vadakkepuliyambatta, S.; Hong, W. -L. Journal of Geophysical Research: Solid Earth, Vol. 122, Issue 6 https://doi.org/10.1002/2016jb013761	journal	June 2017
Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network Mahmoud, Ahmed Abdulhamid A.; Elkatatny, Salaheldin; Mahmoud, Mohamed International Journal of Coal Geology, Vol. 179 https://doi.org/10.1016/j.coal.2017.05.012	journal	June 2017
Classification assessment methods Tharwat, Alaa Applied Computing and Informatics https://doi.org/10.1016/j.aci.2018.08.003	journal	August 2018
Prediction of gas hydrate saturation using machine learning and optimal set of well-logs Singh, Harpreet; Seol, Yongkoo; Myshakin, Evgeniy M. Computational Geosciences, Vol. 25, Issue 1 https://doi.org/10.1007/s10596-020-10004-3	journal	October 2020
Evaluation of drilling parameters in gas hydrate exploration wells Merey, Şükrü Journal of Petroleum Science and Engineering, Vol. 172 https://doi.org/10.1016/j.petrol.2018.08.079	journal	January 2019
Analysis of the effect of particle size on permeability in hydrate-bearing porous media using pore network models combined with CT Wang, Jiaqi; Zhao, Jiafei; Zhang, Yi Fuel, Vol. 163 https://doi.org/10.1016/j.fuel.2015.09.044	journal	January 2016
Evaluation of the performance of the oceanic hydrate accumulation at site NGHP-02-09 in the Krishna-Godavari Basin during a production test and during single and multi-well production scenarios Moridis, George J.; Reagan, Matthew T.; Queiruga, Alejandro F. Marine and Petroleum Geology, Vol. 108 https://doi.org/10.1016/j.marpetgeo.2018.12.001	journal	October 2019
Assessment of gas hydrate accumulations using velocities derived from vertical seismic profiles and acoustic log data in Krishna-Godavari Basin, India Yadav, U. S.; Shukla, K. M.; Ojha, Maheswar Marine and Petroleum Geology, Vol. 108 https://doi.org/10.1016/j.marpetgeo.2019.02.001	journal	October 2019
Economic Characteristics of Deepwater Natural Gas Hydrate Max, Michael D.; Johnson, Arthur H. Exploration and Production of Oceanic Natural Gas Hydrate https://doi.org/10.1007/978-3-030-00401-9_2	book	October 2018
Drilling of gas hydrate reservoirs Merey, Şükrü Journal of Natural Gas Science and Engineering, Vol. 35 https://doi.org/10.1016/j.jngse.2016.09.058	journal	September 2016
Estimation of gas hydrate and free gas saturation, concentration, and distribution from seismic data Lu, Shaoming; McMechan, George A. GEOPHYSICS, Vol. 67, Issue 2 https://doi.org/10.1190/1.1468619	journal	March 2002
Operational overview of the first offshore production test of methane hydrates in the Eastern Nankai Trough Yamamoto, Koji; Terao, Yoshihiro; Fujii, Tesuya Offshore Technology Conference https://doi.org/10.4043/25243-MS	conference	May 2014
Comprehensive Review on Exploration and Drilling Techniques for Natural Gas Hydrate Reservoirs Sahu, Chandan; Kumar, Rajnish; Sangwai, Jitendra S. Energy & Fuels, Vol. 34, Issue 10 https://doi.org/10.1021/acs.energyfuels.0c02202	journal	August 2020
Geological setting and characterization of a methane hydrate reservoir distributed at the first offshore production test site on the Daini-Atsumi Knoll in the eastern Nankai Trough, Japan Fujii, Tetsuya; Suzuki, Kiyofumi; Takayama, Tokujiro Marine and Petroleum Geology, Vol. 66 https://doi.org/10.1016/j.marpetgeo.2015.02.037	journal	September 2015
Permafrost-associated natural gas hydrate occurrences on the Alaska North Slope Collett, Timothy S.; Lee, Myung W.; Agena, Warren F. Marine and Petroleum Geology, Vol. 28, Issue 2 https://doi.org/10.1016/j.marpetgeo.2009.12.001	journal	February 2011
Effect of Perforation Interval Design on Gas Production from the Validated Hydrate-Bearing Deposits with Layered Heterogeneity by Depressurization Xia, Yingli; Xu, Tianfu; Yuan, Yilong Geofluids, Vol. 2020 https://doi.org/10.1155/2020/8833884	journal	June 2020
India National Gas Hydrate Program Expedition 02 Summary of Scientific Results: Gas hydrate systems along the eastern continental margin of India Collett, Timothy S.; Boswell, Ray; Waite, William F. Marine and Petroleum Geology, Vol. 108 https://doi.org/10.1016/j.marpetgeo.2019.05.023	journal	October 2019
New technique for accurate porosity estimation from logging-while-drilling nuclear magnetic resonance data, NGHP-02 expedition, offshore, India Jain, Vikas; Saumya, Sachit; Vij, Jitesh Marine and Petroleum Geology, Vol. 108 https://doi.org/10.1016/j.marpetgeo.2018.11.001	journal	October 2019
Multiple physical properties of gas hydrate-bearing sediments recovered from Alaska North Slope 2018 Hydrate-01 Stratigraphic Test Well Yoneda, Jun; Jin, Yusuke; Muraoka, Michihiro Marine and Petroleum Geology, Vol. 123 https://doi.org/10.1016/j.marpetgeo.2020.104748	journal	January 2021
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization Li, Lisha; Jamieson, Kevin; DeSalvo, Giulia arXiv https://doi.org/10.48550/arXiv.1603.06560	text	January 2016
The interaction of climate change and methane hydrates: Climate-Hydrates Interactions Ruppel, Carolyn D.; Kessler, John D. Reviews of Geophysics, Vol. 55, Issue 1 https://doi.org/10.1002/2016RG000534	journal	February 2017
Effects of underlying gas on formation and gas production of methane hydrate in muddy low-permeability cores Zhao, Jie; Zheng, Jia-nan; Wang, Xinru Fuel, Vol. 309 https://doi.org/10.1016/j.fuel.2021.122128	journal	February 2022
Introduction to special section: Exploration and characterization of gas hydrates Boswell, Ray; Bünz, Stefan; Collett, Timothy S. Interpretation, Vol. 4, Issue 1 https://doi.org/10.1190/INT-2016-0103-SPSEINTRO.1	journal	February 2016
Comparison of machine learning methods for estimating permeability and porosity of oil reservoirs via petro-physical logs Ahmadi, Mohammad Ali; Chen, Zhangxing Petroleum, Vol. 5, Issue 3 https://doi.org/10.1016/j.petlm.2018.06.002	journal	September 2019

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Title: Machine Learning Application to Assess Occurrence and Saturations of Methane Hydrate in Marine Deposits Offshore India

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