Machine-learning application to assess occurrence and saturations of methane hydrate in marine deposits offshore India
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Published:2023-12-15
Issue:1
Volume:12
Page:T63-T75
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ISSN:2324-8858
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Container-title:Interpretation
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language:en
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Short-container-title:Interpretation
Author:
Chong Leebyn1ORCID, Collett Timothy S.2ORCID, Creason C. Gabriel3ORCID, Seol Yongkoo4ORCID, Myshakin Evgeniy M.5
Affiliation:
1. National Energy Technology Laboratory, Pittsburgh, Pennsylvania, USA and NETL Support Contractor, Pittsburgh, Pennsylvania, USA. 2. U.S. Geological Survey, Denver Federal Center, Denver, Colorado, USA. 3. National Energy Technology Laboratory, Albany, Oregon, USA. 4. National Energy Technology Laboratory, Morgantown, West Virginia, USA. 5. National Energy Technology Laboratory, Pittsburgh, Pennsylvania, USA and NETL Support Contractor, Pittsburgh, Pennsylvania, USA. (corresponding author)
Abstract
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. 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.
Funder
National Energy Technology Laboratory
Publisher
Society of Exploration Geophysicists
Subject
Geology,Geophysics
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