Skip to main content

Advertisement

SpringerLink
Log in

Simultaneous Estimation of Multiple Hydrate Core Characteristics from a Production Time-Series Using Coupled ANN–GA Methodology

  • Original Paper
  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

Experimental retrieval of hydrate-core properties is limited due to scarcity of samples, difficult operating conditions, and the requirement of several empirical studies, which make core characterization an expensive and highly time-consuming process. In a first attempt, this study employed a coupled ANN–GA framework to estimate multiple parameters simultaneously from a production time-series obtained from thermally stimulated depressurization-based gas recovery. Five intrinsic hydrate-core properties (i.e., porosity, permeability, saturation of hydrate and aqueous phases, and effective permeability constant) and one process-dependent parameter (heat transfer coefficient) were retrieved in this study. A representative case study and sensitivity analysis were performed first to highlight the physical aspects of thermally stimulated depressurization and therein involved the key parameters. Next, the details of ANN development, validation, and inverse methodology were presented and discussed. The study concludes that ANN is a robust surrogate for simulating thermally stimulated gas production from hydrate cores. In addition, ANN–GA coupled methodology can simultaneously retrieve multiple hydrate-core characteristics from a production time-series with reasonable accuracy. All five intrinsic properties were retrieved with the highest accuracy, with below 5% error. However, the retrieved value of the heat transfer coefficient was less accurate than others, primarily it being a process-dependent parameter.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13

Similar content being viewed by others

References

  • Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717–727.

    Article  Google Scholar 

  • Azizi, S., Awad, M. M., & Ahmadloo, E. (2016). Prediction of water holdup in vertical and inclined oil–water two-phase flow using artificial neural network. International Journal of Multiphase Flow, 80, 181–187.

    Article  Google Scholar 

  • Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3–31.

    Article  Google Scholar 

  • Boswell, R., Myshakin, E., Moridis, G., Konno, Y., Collett, T. S., Reagan, M., Ajayi, T., & Seol, Y. (2019a). India National Gas Hydrate Program Expedition 02 summary of scientific results: Numerical simulation of reservoir response to depressurization. Marine and Petroleum Geology, 108, 154–166.

    Article  Google Scholar 

  • Boswell, R., Yoneda, J., & Waite, W. F. (2019b). India National Gas Hydrate Program Expedition 02 summary of scientific results: Evaluation of natural gas-hydrate-bearing pressure cores. Marine and Petroleum Geology, 108, 143–153.

    Article  Google Scholar 

  • Chanda, S., Muralidhar, K., & Nimdeo, Y. M. (2018). Joint estimation of thermal and mass diffusivities of a solute-solvent system using ANN-GA based inverse framework. International Journal of Thermal Sciences, 123, 27–41.

    Article  Google Scholar 

  • Chanda, S., & Singh, R. P. (2019). Prediction of gas production potential and hydrological properties of a methane hydrate reservoir using ANN-GA based framework. Thermal Science and Engineering Progress, 11, 380–391.

    Article  Google Scholar 

  • Chong, Z. R., Yang, S. H. B., Babu, P., Linga, P., & Li, X.-S. (2016). Review of natural gas hydrates as an energy resource: Prospects and challenges. Applied Energy, 162, 1633–1652.

    Article  Google Scholar 

  • Civan, F. (2011). Porous media transport phenomena. Wiley. https://doi.org/10.1002/9781118086810

    Book  Google Scholar 

  • Class, H., Helmig, R., & Bastian, P. (2002). Numerical simulation of non-isothermal multiphase multicomponent processes in porous media: 1. An efficient solution technique. Advances in Water Resources, 25, 533–550.

    Article  Google Scholar 

  • Collett, T. S. (2002). Energy resource potential of natural gas hydrates. AAPG Bulletin, 86, 1971–1992.

    Google Scholar 

  • Collett, T. S., & Ladd, J. (2000). Detection of gas hydrate with downhole logs and assessment of gas hydrate concentrations (saturations) and gas volumes on the Blake Ridge with electrically resistivity log data. (proceedings of the Ocean Drilling Program: Scientific Results), USGS Publications Warehouse, 164, 179–191.

    Google Scholar 

  • Cook, A. E., & Waite, W. F. (2018). Archie’s saturation exponent for natural gas hydrate in coarse-grained reservoirs. Journal of Geophysical Research: Solid Earth, 123, 2069–2089.

    Article  Google Scholar 

  • Dandekar, A. Y. (2013). Petroleum reservoir rock and fluid properties. CRC Press.

    Book  Google Scholar 

  • Fujii, T., Suzuki, K., Takayama, T., Tamaki, M., Komatsu, Y., Konno, Y., Yoneda, J., Yamamoto, K., & Nagao, J. (2015). 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. Marine and Petroleum Geology, 66, 310–322.

    Article  Google Scholar 

  • Gamwo, I. K., & Liu, Y. (2010). Mathematical modeling and numerical simulation of methane production in a hydrate reservoir. Industrial & Engineering Chemistry Research, 49, 5231–5245.

    Article  Google Scholar 

  • Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning (1st ed.). Addison-Wesley Longman Publishing Co.

    Google Scholar 

  • Hagan, M. T., Demuth, H. B., Beale, M. H., & De Jesús, O. (1996). Neural network design. PWS Publication.

    Google Scholar 

  • Holland, M. E., Schultheiss, P. J., & Roberts, J. A. (2019). Gas hydrate saturation and morphology from analysis of pressure cores acquired in the Bay of Bengal during expedition NGHP-02, offshore India. Marine and Petroleum Geology, 108, 407–423.

    Article  Google Scholar 

  • Hsu, K., Gupta, H. V., & Sorooshian, S. (1995). Artificial neural network modeling of the rainfall-runoff process. Water Resources Research., 31(10), 2517–2530.

    Article  Google Scholar 

  • Joshi, A. K., Sain, K., & Pandey, L. (2019). Gas hydrate saturation and reservoir characterization at sites NGHP-02-17 and NGHP-02-19, Krishna Godavari Basin, eastern margin of India. Marine and Petroleum. Geology, 108, 595–608.

    Article  Google Scholar 

  • Khetan, A., Das, M. K., & Muralidhar, K. (2013). Analysis of methane production from a porous reservoir via simultaneous depressurization and CO2 sequestration. Special Topics and Reviews in Porous Media - an International Journal, 4, 237–252.

    Article  Google Scholar 

  • Kim, Y., Jang, H., Kim, J., & Lee, J. (2017). Prediction of storage efficiency on CO2 sequestration in deep saline aquifers using artificial neural network. Applied Energy, 185(1), 916–928.

    Article  Google Scholar 

  • Kondori, J., Zendehboudi, S., & Hossain, M. E. (2017). A review on simulation of methane production from gas hydrate reservoirs: Molecular dynamics prospective. Journal of Petroleum Science and Engineering, 159, 754–772.

    Article  Google Scholar 

  • Konno, Y., Masuda, Y., Akamine, K., Naiki, M., & Nagao, J. (2016). Sustainable gas production from methane hydrate reservoirs by the cyclic depressurization method. Energy Conversion and Management, 108(15), 439–445.

    Article  Google Scholar 

  • Lee, M. W., & Collett, T. S. (2009). Gas hydrate saturations estimated from fractured reservoir at Site NGHP-01-10, Krishna-Godavari Basin, India. Journal of Geophysical Research: Solid Earth, 114, 1–13.

    Google Scholar 

  • Masuda, Y. (1999). Modeling and experimental studies on dissociation of methane gas hydrates in Berea sandstone cores. In Proceedings of the third international gas hydrate conference. Salt Lake City, USA.

  • McPhee, J., & Yeh, W.W.-G. (2008). Groundwater management using model reduction via empirical orthogonal functions. Journal of Water Resources Planning and Management, 134, 161–170.

    Article  Google Scholar 

  • Mohaghegh, S., Amini, S., Gholami, V., Gaskari, R., & Bromhal, G. S. (2012). Grid-based surrogate reservoir modeling (SRM) for fast track analysis of numerical reservoir simulation models at the gridblock level. In: SPE Western North American regional meeting, Bakersfield, California, USA.

  • Mohaghegh, S. (2000). Virtual-intelligence applications in petroleum engineering: Part 1: Artificial neural networks. Journal of Petroleum Technology, 52, 64–73.

    Article  Google Scholar 

  • Mohaghegh, S., Modavi, C. A., & Hafez, H. H. (2009). Development of surrogate reservoir models (SRM) for fast track analysis of complex reservoirs. International Journal of Oil, Gas and Coal Technology, 2, 2.

    Article  Google Scholar 

  • Moridis, G. J. (2004). Numerical studies of gas production from Class 2 and Class 3 hydrate accumulations at the Mallik Site, Mackenzie Delta, Canada. SPE Reservoir Evaluation & Engineering, 7, 175–183.

    Article  Google Scholar 

  • Moridis, G. J., Reagan, M. T., Queiruga, A. F., & Boswell, R. (2019). 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. Marine and Petroleum Geology, 108, 660–696.

    Article  Google Scholar 

  • Myshakin, E. M., Seol, Y., Lin, J. S., Uchida, S., Collett, T. S., & Boswell, R. (2019). Numerical simulations of depressurization-induced gas production from an interbedded turbidite gas hydrate-bearing sedimentary section in the offshore India: Site NGHP-02-16 (Area-B). Marine and Petroleum Geology, 108, 619–638.

    Article  Google Scholar 

  • Priest, J. A., Hayley, J. L., Smith, W. E., Schultheiss, P., & Roberts, J. (2019). PCATS triaxial testing: Geomechanical properties of sediments from pressure cores recovered from the Bay of Bengal during expedition NGHP-02. Marine and Petroleum Geology, 108, 424–438.

    Article  Google Scholar 

  • Reagan, M., Moridis, G. J., Collett, T., Boswell, R., Kurihara, M., Reagan, M. T., Koh, C., & Sloan, E. D. (2008). Toward production from gas hydrates: Current status, assessment of resources, and simulation-based evaluation of technology and potential. SPE Reservoir Evaluation & Engineering, 12(05), 745–771.

    Google Scholar 

  • Riedel, M., Collett, T. S., Kim, H.-S., Bahk, J.-J., Kim, J.-H., Ryu, B.-J., & Kim, G. Y. (2013). Large-scale depositional characteristics of the Ulleung Basin and its impact on electrical resistivity and Archie-parameters for gas hydrate saturation estimates. Marine and Petroleum Geology, 47, 222–235.

    Article  Google Scholar 

  • Robinson, T. D., Eldred, M. S., Willcox, K. E., & Haimes, R. (2008). Surrogate-based optimization using multifidelity models with variable parameterization and corrected space mapping. AIAA Journal, 46, 2814–2822.

    Article  Google Scholar 

  • Ruan, X., Song, Y., Liang, H., Yang, M., & Dou, B. (2012). Numerical simulation of the gas production behavior of hydrate dissociation by depressurization in hydrate-bearing porous medium. Energy and Fuels, 26, 1681–1694.

    Article  Google Scholar 

  • Seol, J., & Lee, H. (2013). Natural gas hydrate as a potential energy resource: From occurrence to production. Korean Journal of Chemical Engineering, 30, 771–786.

    Article  Google Scholar 

  • Shahkarami, A., Mohaghegh, S., Gholami, V., Haghighat, A., & Moreno, D. (2014). Modeling pressure and saturation distribution in a CO2 storage project using a Surrogate Reservoir Model (SRM). Greenhouse Gases: Science and Technology, 4, 289–315.

    Article  Google Scholar 

  • Singh, R. P., & Chanda, S. (2017). Prediction of production potential of a hydrate reservoir using. In: Asian symposium on computational heat transfer and fluid flow (ASCHT 2017), Chennai, India.

  • Singh, R. P., Lall, D., & Vishal, V. (2022). Prospects and challenges in unlocking natural-gas-hydrate energy in India: Recent advancements. Marine and Petroleum Geology, 135, 105397.

    Article  Google Scholar 

  • Singh, R. P., Parashar, S., Muralidhar, K., & Das, M. K. (2020a). Recovery of methane from a gas hydrate reservoir using depressurization and N2 injection. Special Topics and Reviews in Porous Media - an International Journal, 12(1), 53–71.

    Article  Google Scholar 

  • Singh, R. P., Shekhawat, K. S., Das, M. K., & Muralidhar, K. (2020b). Geological sequestration of CO2 in a water-bearing reservoir in hydrate-forming conditions. Oil & Gas Science and Technology - Revue d’IFP Energies Nouvelles, 75, 51.

    Article  Google Scholar 

  • Singh, R. P., Yadav, R., Muralidhar, K., & Das, M. K. (2021). Effect of confined boundary and mud-layers on depressurization-based gas recovery and land subsidence in hydrate reservoirs. Marine Georesources and Geotechnology, 40(1), 78–95.

    Article  Google Scholar 

  • Sloan, E. D., Jr., & Koh, C. (2007). Clathrate hydrates of natural gases. CRC Press.

    Book  Google Scholar 

  • Sun, X., & Mohanty, K. K. (2006). Kinetic simulation of methane hydrate formation and dissociation in porous media. Chemical Engineering Science, 61, 3476–3495.

    Article  Google Scholar 

  • Sun, X., Nanchary, N., & Mohanty, K. K. (2005). 1-D modeling of hydrate depressurization in porous media. Transport in Porous Media, 58, 315–338.

    Article  Google Scholar 

  • Vishal, V., Lall, D., Sarna, S., Sharma, A., & Ranjith, P. G. (2020). Sensitivity analysis of methane hydrate bearing Class 3 reservoirs during thermal injection. Journal of Petroleum Science and Engineering, 195, 107575.

    Article  Google Scholar 

  • Wang, C. Y., & Cheng, P. (1996). A multiphase mixture model for multiphase, multicomponent transport in capillary porous media—I. Model development. International Journal of Heat Mass Transfer, 39, 3607–3618.

    Article  Google Scholar 

  • Wen, Y. G., Chen, Q. X., Chen, Y. W., & Fan, S. S. (2013). Research progress on hydrate self-preservation effect applied to storage and transportation of natural gas. Advanced Materials Research, 772, 795–801.

    Article  Google Scholar 

  • Wilder, J. W., Moridis, G. J., Wilson, S. J., Kurihara, M., White, M. D., Masuda, Y., Anderson, B. J., Collett, T. S., Hunter, R. B., & Narita, H. (2008). An international effort to compare gas hydrate reservoir simulators. In: Proceedings of 6th international conference on gas hydrates (ICGH 2008), Vancouver, Canada.

  • Yoneda, J., Oshima, M., Kida, M., Kato, A., Konno, Y., Jin, Y., Jang, J., Waite, W. F., Kumar, P., & Tenma, N. (2019a). Pressure core based onshore laboratory analysis on mechanical properties of hydrate-bearing sediments recovered during India’s National Gas Hydrate Program Expedition (NGHP) 02. Marine and Petroleum Geology, 108, 482–501.

    Article  Google Scholar 

  • Yoneda, J., Oshima, M., Kida, M., Kato, A., Konno, Y., Jin, Y., Jang, J., Waite, W. F., Kumar, P., & Tenma, N. (2019b). Permeability variation and anisotropy of gas hydrate-bearing pressure-core sediments recovered from the Krishna-Godavari Basin, offshore India. Marine and Petroleum Geology, 108, 524–536.

    Article  Google Scholar 

  • Zhang, Z., & Agarwal, R. (2013). Numerical simulation and optimization of CO2 sequestration in saline aquifers. Computers & Fluids, 80, 79–87.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the help of the Gas Hydrate Research and Technology Centre (GHRTC) of the Oil and Natural Gas Corporation (ONGC) for providing the necessary support for this study via a Memorandum of Understanding (Agreement No. TV116268 dated November 08, 2018) with the Indian Institute of Technology Bombay, Mumbai, India.

Author information

Authors and Affiliations

  1. Interdisciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Mumbai, 400076, India

    Raghvendra Pratap Singh & Vikram Vishal

  2. Computational and Experimental Geomechanics Laboratory, Department of Earth Sciences, Indian Institute of Technology (IIT) Bombay, Powai, Mumbai, 400076, India

    Raghvendra Pratap Singh & Vikram Vishal

Authors
  1. Raghvendra Pratap Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vikram Vishal
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RPS was involved in conceptualization, data curation, formal analysis, investigation, software, validation, visualization, writing—original draft. VV contributed to methodology, software, writing—review and editing, supervision.

Corresponding author

Correspondence to Raghvendra Pratap Singh.

Ethics declarations

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, R.P., Vishal, V. Simultaneous Estimation of Multiple Hydrate Core Characteristics from a Production Time-Series Using Coupled ANN–GA Methodology. Nat Resour Res 31, 1539–1558 (2022). https://doi.org/10.1007/s11053-022-10045-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-022-10045-8

Keywords

Search

Navigation