IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i15p3903-d392375.html
   My bibliography  Save this article

A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning

Author

Listed:
  • Zhixue Sun

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Baosheng Jiang

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Xiangling Li

    (PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China)

  • Jikang Li

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Kang Xiao

    (PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China)

Abstract

The identification of underground formation lithology can serve as a basis for petroleum exploration and development. This study integrates Extreme Gradient Boosting (XGBoost) with Bayesian Optimization (BO) for formation lithology identification and comprehensively evaluated the performance of the proposed classifier based on the metrics of the confusion matrix, precision, recall, F1-score and the area under the receiver operating characteristic curve (AUC). The data of this study are derived from Daniudui gas field and the Hangjinqi gas field, which includes 2153 samples with known lithology facies class with each sample having seven measured properties (well log curves), and corresponding depth. The results show that BO significantly improves parameter optimization efficiency. The AUC values of the test sets of the two gas fields are 0.968 and 0.987, respectively, indicating that the proposed method has very high generalization performance. Additionally, we compare the proposed algorithm with Gradient Tree Boosting-Differential Evolution (GTB-DE) using the same dataset. The results demonstrated that the average of precision, recall and F1 score of the proposed method are respectively 4.85%, 5.7%, 3.25% greater than GTB-ED. The proposed XGBoost-BO ensemble model can automate the procedure of lithology identification, and it may also be used in the prediction of other reservoir properties.

Suggested Citation

  • Zhixue Sun & Baosheng Jiang & Xiangling Li & Jikang Li & Kang Xiao, 2020. "A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning," Energies, MDPI, vol. 13(15), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3903-:d:392375
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/15/3903/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/15/3903/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gang Chen & Mian Chen & Guobin Hong & Yunhu Lu & Bo Zhou & Yanfang Gao, 2020. "A New Method of Lithology Classification Based on Convolutional Neural Network Algorithm by Utilizing Drilling String Vibration Data," Energies, MDPI, vol. 13(4), pages 1-24, February.
    2. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
    3. Chuanbo Shen & Solomon Asante-Okyere & Yao Yevenyo Ziggah & Liang Wang & Xiangfeng Zhu, 2019. "Group Method of Data Handling (GMDH) Lithology Identification Based on Wavelet Analysis and Dimensionality Reduction as Well Log Data Pre-Processing Techniques," Energies, MDPI, vol. 12(8), pages 1-16, April.
    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. Junlong Zhang & Youbin He & Yuan Zhang & Weifeng Li & Junjie Zhang, 2022. "Well-Logging-Based Lithology Classification Using Machine Learning Methods for High-Quality Reservoir Identification: A Case Study of Baikouquan Formation in Mahu Area of Junggar Basin, NW China," Energies, MDPI, vol. 15(10), pages 1-15, May.
    2. Cenk Temizel & Uchenna Odi & Karthik Balaji & Hakki Aydin & Javier E. Santos, 2022. "Classifying Facies in 3D Digital Rock Images Using Supervised and Unsupervised Approaches," Energies, MDPI, vol. 15(20), pages 1-15, October.
    3. Timur Merembayev & Darkhan Kurmangaliyev & Bakhbergen Bekbauov & Yerlan Amanbek, 2021. "A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan," Energies, MDPI, vol. 14(7), pages 1-16, March.

    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. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "State Space Approach to Adaptive Artificial Intelligence Modeling: Application to Financial Portfolio with Fuzzy System," CARF F-Series CARF-F-422, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    2. Wilson, Christopher & van der Velden, Maja, 2022. "Sustainable AI: An integrated model to guide public sector decision-making," Technology in Society, Elsevier, vol. 68(C).
    3. Yi, Jing & Canning, Patrick N. & Ge, Houtian & Rehkamp, Sarah & Gomez, Miguel I., 2023. "A national database of highly perishable fresh produce production with temporal and spatial resolution," 2023 Annual Meeting, July 23-25, Washington D.C. 335590, Agricultural and Applied Economics Association.
    4. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    5. 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.
    6. Kim, Sung Wook & Oh, Ki-Yong & Lee, Seungchul, 2022. "Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries," Applied Energy, Elsevier, vol. 315(C).
    7. Xuequn Wang & Xiaolin Lin & Bin Shao, 2023. "Artificial intelligence changes the way we work: A close look at innovating with chatbots," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(3), pages 339-353, March.
    8. Himanshu Sharma & Anu G. Aggarwal, 2022. "Segmenting Reviewers Based on Reviewer and Review Characteristics," International Journal of Business Analytics (IJBAN), IGI Global, vol. 9(1), pages 1-20, January.
    9. Fekadu Agmas Wassie & László Péter Lakatos, 2024. "Artificial intelligence and the future of the internal audit function," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    10. 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).
    11. Joaquim AP Braga & António R Andrade, 2019. "Optimizing maintenance decisions in railway wheelsets: A Markov decision process approach," Journal of Risk and Reliability, , vol. 233(2), pages 285-300, April.
    12. Manis, K.T. & Madhavaram, Sreedhar, 2023. "AI-Enabled marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues," Journal of Business Research, Elsevier, vol. 157(C).
    13. Changhao Zhang & Mengyu Ren, 2023. "Customer service robot model based on e-commerce dual-channel channel supply coordination and compensation strategy in the perspective of big data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(2), pages 591-601, April.
    14. Chen, Xia & Geyer, Philipp, 2022. "Machine assistance in energy-efficient building design: A predictive framework toward dynamic interaction with human decision-making under uncertainty," Applied Energy, Elsevier, vol. 307(C).
    15. Moll, Jodie & Yigitbasioglu, Ogan, 2019. "The role of internet-related technologies in shaping the work of accountants: New directions for accounting research," The British Accounting Review, Elsevier, vol. 51(6).
    16. Martin Obschonka & David B. Audretsch, 2020. "Artificial intelligence and big data in entrepreneurship: a new era has begun," Small Business Economics, Springer, vol. 55(3), pages 529-539, October.
    17. Efrat Taig & Ohad Ben-Shahar, 2019. "Gradient Surfing: A New Deterministic Approach for Low-Dimensional Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 855-878, March.
    18. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
    19. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "State Space Approach to Adaptive Fuzzy Modeling: Application to Financial Investment," CIRJE F-Series CIRJE-F-1067, CIRJE, Faculty of Economics, University of Tokyo.
    20. Gianluca Gabrielli & Alice Medioli & Paolo Andrei, 2022. "Accounting and Big Data: Trends, opportunities and direction for practitioners and researchers," FINANCIAL REPORTING, FrancoAngeli Editore, vol. 2022(2), pages 89-112.

    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:13:y:2020:i:15:p:3903-:d:392375. 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.