IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i20p5369-d428337.html
   My bibliography  Save this article

A Novel Data-Driven Method to Estimate Methane Adsorption Isotherm on Coals Using the Gradient Boosting Decision Tree: A Case Study in the Qinshui Basin, China

Author

Listed:
  • Jiyuan Zhang

    (Key Laboratory of Unconventional Oil & Gas Development, China University of Petroleum (East China), Qingdao 266580, China
    School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Qihong Feng

    (Key Laboratory of Unconventional Oil & Gas Development, China University of Petroleum (East China), Qingdao 266580, China
    School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Xianmin Zhang

    (Key Laboratory of Unconventional Oil & Gas Development, China University of Petroleum (East China), Qingdao 266580, China
    School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Qiujia Hu

    (PetroChina Huabei Oilfield Co., Renqiu 062552, China)

  • Jiaosheng Yang

    (PetroChina Research Institute of Petroleum Exploration and Development, Langfang Branch, Langfang 065007, China)

  • Ning Wang

    (PetroChina Huabei Oilfield Co., Renqiu 062552, China)

Abstract

The accurate determination of methane adsorption isotherms in coals is crucial for both the evaluation of underground coalbed methane (CBM) reserves and design of development strategies for enhancing CBM recovery. However, the experimental measurement of high-pressure methane adsorption isotherms is extremely tedious and time-consuming. This paper proposed the use of an ensemble machine learning (ML) method, namely the gradient boosting decision tree (GBDT), in order to accurately estimate methane adsorption isotherms based on coal properties in the Qinshui basin, China. The GBDT method was trained to correlate the adsorption amount with coal properties (ash, fixed carbon, moisture, vitrinite, and vitrinite reflectance) and experimental conditions (pressure, equilibrium moisture, and temperature). The results show that the estimated adsorption amounts agree well with the experimental ones, which prove the accuracy and robustness of the GBDT method. A comparison of the GBDT with two commonly used ML methods, namely the artificial neural network (ANN) and support vector machine (SVM), confirms the superiority of GBDT in terms of generalization capability and robustness. Furthermore, relative importance scanning and univariate analysis based on the constructed GBDT model were conducted, which showed that the fixed carbon and ash contents are primary factors that significantly affect the adsorption isotherms for the coal samples in this study.

Suggested Citation

  • Jiyuan Zhang & Qihong Feng & Xianmin Zhang & Qiujia Hu & Jiaosheng Yang & Ning Wang, 2020. "A Novel Data-Driven Method to Estimate Methane Adsorption Isotherm on Coals Using the Gradient Boosting Decision Tree: A Case Study in the Qinshui Basin, China," Energies, MDPI, vol. 13(20), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5369-:d:428337
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/20/5369/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/20/5369/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhikun Luo & Zhifeng Sun & Fengli Ma & Yihan Qin & Shihao Ma, 2020. "Power Optimization for Wind Turbines Based on Stacking Model and Pitch Angle Adjustment," Energies, MDPI, vol. 13(16), pages 1-15, August.
    2. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    3. Zhigao Peng & Shenggui Liu & Yingjun Li & Zongwei Deng & Haoxiong Feng, 2020. "Pore-Scale Lattice Boltzmann Simulation of Gas Diffusion–Adsorption Kinetics Considering Adsorption-Induced Diffusivity Change," Energies, MDPI, vol. 13(18), pages 1-18, September.
    4. Mehdi Jadidi & Stevan Kostic & Leonardo Zimmer & Seth B. Dworkin, 2020. "An Artificial Neural Network for the Low-Cost Prediction of Soot Emissions," Energies, MDPI, vol. 13(18), pages 1-27, September.
    5. Nilesh Dixit & Paul McColgan & Kimberly Kusler, 2020. "Machine Learning-Based Probabilistic Lithofacies Prediction from Conventional Well Logs: A Case from the Umiat Oil Field of Alaska," Energies, MDPI, vol. 13(18), pages 1-15, September.
    6. Donghyeon Kim & Youngjin Seo & Juhyun Kim & Jeongmin Han & Youngsoo Lee, 2019. "Experimental and Simulation Studies on Adsorption and Diffusion Characteristics of Coalbed Methane," Energies, MDPI, vol. 12(18), pages 1-16, September.
    7. Prince Waqas Khan & Yung-Cheol Byun & Sang-Joon Lee & Dong-Ho Kang & Jin-Young Kang & Hae-Su Park, 2020. "Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources," Energies, MDPI, vol. 13(18), pages 1-16, September.
    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. Zehao Xie & Qihong Feng & Jiyuan Zhang & Xiaoxuan Shao & Xianmin Zhang & Zenglin Wang, 2021. "Prediction of Conformance Control Performance for Cyclic-Steam-Stimulated Horizontal Well Using the XGBoost: A Case Study in the Chunfeng Heavy Oil Reservoir," Energies, MDPI, vol. 14(23), pages 1-22, December.
    2. Qingzhong Zhu & Yanhui Yang & Xueying Zhang & Sanshuai Wang & Jinzhao Yang & Jiyuan Zhang, 2022. "Pore-Scale Simulation of Gas and Water Two-Phase Flow in Rough-Walled Fractures Using the Volume of Fluid Method," Energies, MDPI, vol. 15(24), pages 1-15, December.
    3. Reza Rezaee, 2022. "Editorial on Special Issues of Development of Unconventional Reservoirs," Energies, MDPI, vol. 15(7), pages 1-9, April.
    4. Jiyuan Zhang & Bin Zhang & Shiqian Xu & Qihong Feng & Xianmin Zhang & Derek Elsworth, 2021. "Interpretation of Gas/Water Relative Permeability of Coal Using the Hybrid Bayesian-Assisted History Matching: New Insights," Energies, MDPI, vol. 14(3), pages 1-19, January.

    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. Reza Rezaee, 2022. "Editorial on Special Issues of Development of Unconventional Reservoirs," Energies, MDPI, vol. 15(7), pages 1-9, April.
    2. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
    3. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    4. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    5. Nahushananda Chakravarthy H G & Karthik M Seenappa & Sujay Raghavendra Naganna & Dayananda Pruthviraja, 2023. "Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    6. Tim Voigt & Martin Kohlhase & Oliver Nelles, 2021. "Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
    7. Wen, Shaoting & Buyukada, Musa & Evrendilek, Fatih & Liu, Jingyong, 2020. "Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models," Renewable Energy, Elsevier, vol. 151(C), pages 463-474.
    8. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    9. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    10. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
    11. Smyl, Slawek & Hua, N. Grace, 2019. "Machine learning methods for GEFCom2017 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1424-1431.
    12. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
    13. Eike Emrich & Christian Pierdzioch, 2016. "Volunteering, Match Quality, and Internet Use," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 136(2), pages 199-226.
    14. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    15. Zhu, Siying & Zhu, Feng, 2019. "Cycling comfort evaluation with instrumented probe bicycle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 217-231.
    16. Catherine Ikae & Jacques Savoy, 2022. "Gender identification on Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(1), pages 58-69, January.
    17. Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
    18. Martijn Kagie & Michiel Van Wezel, 2007. "Hedonic price models and indices based on boosting applied to the Dutch housing market," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(3‐4), pages 85-106, July.
    19. Matthias Bogaert & Michel Ballings & Dirk Van den Poel, 2018. "Evaluating the importance of different communication types in romantic tie prediction on social media," Annals of Operations Research, Springer, vol. 263(1), pages 501-527, April.
    20. Dursun Delen & Hamed M. Zolbanin & Durand Crosby & David Wright, 2021. "To imprison or not to imprison: an analytics model for drug courts," Annals of Operations Research, Springer, vol. 303(1), pages 101-124, August.

    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:20:p:5369-:d:428337. 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.