Abstract
Resistivity and acoustic logs are widely used to estimate gas hydrate saturation in various sedimentary systems using one of the two popular methods ((1) acoustic velocity and (2) electrical resistivity), but the limitations of these two methods are often overlooked, which include (i) well-specific calibration of empirical exponents in the electrical resistivity method, (ii) assumption of known pore morphology for gas hydrates in the acoustic velocity method, and (iii) presence of unknown mineralogy and bulk modulus terms in the acoustic velocity method. NMR-density porosity-derived gas hydrate saturation based on the analysis of the transverse magnetization relaxation time (T2) is considered the most precise method, but acquisition of NMR-based logs is limited at relatively recent drilled sites; additionally, its use in conventional oil and gas reservoirs is not that common due to higher cost and operational deployment limitations associated with acquiring NMR well-logs. This study proposes a new method that predicts gas hydrate saturation (Sh) for any well using porosity, bulk density, and compressional wave (P wave) velocity well-logs with neural network (or stochastic gradient descent regression) without any well-specific calibration and/or other aforementioned shortcomings of the existing methods. The method is developed by examining the underlying dependency between Sh and different combinations of well-logs, chosen from 6 routine logs, with 12 different machine learning (ML) algorithms. The accuracy of the proposed method in predicting Sh is ~ 84%, which is better than the accuracy of seismic and electrical resistivity methods (≤ 75%) per the results reported by three different studies. The robustness of the method in the specific case of permafrost-associated gas hydrates is demonstrated with well-log data from two wells drilled on the Alaska North Slope.
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Data availability
Well-log data used in this study can be downloaded from the following link: https://edx.netl.doe.gov/dataset/answelllogs. Other data generated or used in this study are included in this paper.
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Acknowledgments
This research was performed in support of the National Energy Technology Laboratory’s ongoing research on numerical simulations support for reservoir characterization and production performance prediction of gas hydrate reservoirs. This research was supported in part by an appointment to the National Energy Technology Laboratory Research Participation Program, sponsored by the US Department of Energy and administered by the Oak Ridge Institute for Science and Education. Research performed by Leidos Research Support Team (LRST) staff was conducted under the RSS contract 89243318CFE000003. Thanks to Ray Boswell (US DOE) and Tim S. Collett (USGS) for their comments that were helpful in improving the manuscript.
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The code for this study was written in Python using scikit-learn and Tensorflow as the machine learning packages.
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This research was supported in part by an appointment to the National Energy Technology Laboratory Research Participation Program, sponsored by the US Department of Energy and administered by the Oak Ridge Institute for Science and Education. This work was funded by the Department of Energy, National Energy Technology Laboratory, an agency of the US Government, through a support contract with LRST.
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HS conceived the research idea, designed the workflow, and wrote the code. H.S., E.M.M., and Y.S. analyzed the results. H.S. wrote the manuscript, E.M.M. and Y.S. edited it. All authors have reviewed and approved the final version of the manuscript.
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Singh, H., Seol, Y. & Myshakin, E.M. Prediction of gas hydrate saturation using machine learning and optimal set of well-logs. Comput Geosci 25, 267–283 (2021). https://doi.org/10.1007/s10596-020-10004-3
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DOI: https://doi.org/10.1007/s10596-020-10004-3