IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1464-d751276.html
   My bibliography  Save this article

Use of Cluster Analysis to Group Organic Shale Gas Rocks by Hydrocarbon Generation Zones

Author

Listed:
  • Tadeusz Kwilosz

    (Oil and Gas Institute-National Research Institute, 25A Lubicz Str., 31-503 Krakow, Poland)

  • Bogdan Filar

    (Oil and Gas Institute-National Research Institute, 25A Lubicz Str., 31-503 Krakow, Poland)

  • Mariusz Miziołek

    (Oil and Gas Institute-National Research Institute, 25A Lubicz Str., 31-503 Krakow, Poland)

Abstract

In the last decade, exploration for unconventional hydrocarbon (shale gas) reservoirs has been carried out in Poland. The drilling of wells in prospective shale gas areas supplies numerous physicochemical measurements from rock and reservoir fluid samples. The objective of this paper is to present the method that has been developed for finding similarities between individual geological structures in terms of their hydrocarbon generation properties and hydrocarbon resources. The measurements and geochemical investigations of six wells located in the Ordovician, Silurian, and Cambrian formations of the Polish part of the East European Platform are used. Cluster analysis is used to compare and classify objects described by multiple attributes. The focus is on the issue of generating clusters that group samples within the gas, condensate, and oil windows. The vitrinite reflectance value (R o ) is adopted as the criterion for classifying individual samples into the respective windows. An additional issue was determining other characteristic geochemical properties of the samples classified into the selected clusters. Two variants of cluster analysis are applied—the furthest neighbor method and Ward’s method—which resulted in 10 and 11 clusters, respectively. Particular attention was paid to the mean Ro values (within each cluster), allowing the classification of samples from a given cluster into one of the windows (gas, condensate, or oil). Using these methods, the samples were effectively classified into individual windows, and their percentage share within the Silurian, Ordovician, and Cambrian units is determined.

Suggested Citation

  • Tadeusz Kwilosz & Bogdan Filar & Mariusz Miziołek, 2022. "Use of Cluster Analysis to Group Organic Shale Gas Rocks by Hydrocarbon Generation Zones," Energies, MDPI, vol. 15(4), pages 1-14, February.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1464-:d:751276
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1464/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/4/1464/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhifeng Zhang & Yongjian Huang & Bo Ran & Wei Liu & Xiang Li & Chengshan Wang, 2021. "Chemostratigraphic Analysis of Wufeng and Longmaxi Formation in Changning, Sichuan, China: Achieved by Principal Component and Constrained Clustering Analysis," Energies, MDPI, vol. 14(21), pages 1-21, October.
    2. Sebastian Waszkiewicz & Paulina I. Krakowska-Madejska, 2021. "Vitrinite Equivalent Reflectance Estimation from Improved Maturity Indicator and Well Logs Based on Statistical Methods," Energies, MDPI, vol. 14(19), pages 1-16, September.
    3. Partha Pratim Mandal & Reza Rezaee & Irina Emelyanova, 2021. "Ensemble Learning for Predicting TOC from Well-Logs of the Unconventional Goldwyer Shale," Energies, MDPI, vol. 15(1), pages 1-30, December.
    4. Edyta Puskarczyk, 2020. "Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits," Energies, MDPI, vol. 13(7), pages 1-18, March.
    5. Ahmed Abdulhamid Mahmoud & Salaheldin Elkatatny & Abdulwahab Z. Ali & Mohamed Abouelresh & Abdulazeez Abdulraheem, 2019. "Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques," Sustainability, MDPI, vol. 11(20), pages 1-15, October.
    6. Pan Wang & Suping Peng, 2018. "A New Scheme to Improve the Performance of Artificial Intelligence Techniques for Estimating Total Organic Carbon from Well Logs," Energies, MDPI, vol. 11(4), pages 1-24, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marcin Kremieniewski, 2022. "Improving the Efficiency of Oil Recovery in Research and Development," Energies, MDPI, vol. 15(12), pages 1-7, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcin Kremieniewski, 2022. "Improving the Efficiency of Oil Recovery in Research and Development," Energies, MDPI, vol. 15(12), pages 1-7, June.
    2. Jiangtao Sun & Wei Dang & Fengqin Wang & Haikuan Nie & Xiaoliang Wei & Pei Li & Shaohua Zhang & Yubo Feng & Fei Li, 2023. "Prediction of TOC Content in Organic-Rich Shale Using Machine Learning Algorithms: Comparative Study of Random Forest, Support Vector Machine, and XGBoost," Energies, MDPI, vol. 16(10), pages 1-26, May.
    3. Miltiadis D. Lytras & Anna Visvizi, 2021. "Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making," Sustainability, MDPI, vol. 13(7), pages 1-3, March.
    4. Mkono, Christopher N. & Chuanbo, Shen & Mulashani, Alvin K. & Mwakipunda, Grant Charles, 2023. "Deep learning integrated approach for hydrocarbon source rock evaluation and geochemical indicators prediction in the Jurassic - Paleogene of the Mandawa basin, SE Tanzania," Energy, Elsevier, vol. 284(C).
    5. Guangjuan Fan & Ting Dong & Yuejun Zhao & Yalou Zhou & Wentong Zhao & Jie Wang & Yilong Wang, 2023. "Establishment and Application of a Pattern for Identifying Sedimentary Microfacies of a Single Horizontal Well: An Example from the Eastern Transition Block in the Daqing Oilfield, Songliao Basin, Chi," Energies, MDPI, vol. 16(20), pages 1-19, October.
    6. Ahmed Abdulhamid Mahmoud & Salaheldin Elkatatny & Dhafer Al Shehri, 2020. "Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
    7. Liang Sun & Suping Peng & Dengke He, 2018. "A Novel Static Correction Approach for Eliminating the Effect of Geophones—A Case Study in Coal Reservoirs, Ordos Basin, China," Energies, MDPI, vol. 11(12), pages 1-12, November.
    8. Ewa Krzeszowska, 2024. "Chemostratigraphic Approach to the Study of Resources’ Deposit in the Upper Silesian Coal Basin (Poland)," Energies, MDPI, vol. 17(3), pages 1-21, January.
    9. Sebastian Waszkiewicz & Paulina I. Krakowska-Madejska, 2021. "Vitrinite Equivalent Reflectance Estimation from Improved Maturity Indicator and Well Logs Based on Statistical Methods," Energies, MDPI, vol. 14(19), pages 1-16, September.
    10. Stanisław Baudzis & Joanna Karłowska-Pik & Edyta Puskarczyk, 2021. "Electrofacies as a Tool for the Prediction of True Resistivity Using Advanced Statistical Methods—Case Study," Energies, MDPI, vol. 14(19), pages 1-18, September.
    11. Ahmad Al-AbdulJabbar & Salaheldin Elkatatny & Ahmed Abdulhamid Mahmoud & Tamer Moussa & Dhafer Al-Shehri & Mahmoud Abughaban & Abdullah Al-Yami, 2020. "Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique," Sustainability, MDPI, vol. 12(4), pages 1-19, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1464-:d:751276. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.